http://srmtoday.makes.news/SRM Today Gen AI RSS feedhttp://www.rssboard.org/rss-specificationenThu, 16 Apr 2026 12:56:02 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/14/microsoft-highlights-the-hurdles-and-solutions-in-scaling-ai-for-manufacturing<p>While AI shows promise in manufacturing, many projects stall before full deployment, prompting Microsoft to emphasise integrated data architectures, interoperability, and strategic governance to turn pilot success into reliable production systems.</p><p>Manufacturers are finding that the promise of artificial intelligence is easier to demonstrate than to deploy. Pressure from labour shortages, higher costs, volatile supply chains and rising demand has pushed AI to the top of the agenda, but Microsoft argues that many projects still stall before they reach day-to-day operations.</p> <p>In a customer session on Microsoft Marketplace, the company said the central challenge is no longer whether AI can work in manufacturing, but how it can be embedded safely, economically and at scale. That difficulty is showing up across the sector: fragmented operational data, complicated links between factory systems and cloud platforms, and a gap between ambition and the maturity needed to industrialise new tools. Microsoft says more than half of manufacturers remain in pilot phase.</p> <p>The underlying problem, according to Microsoft’s manufacturing material, is not usually model quality. It is the architecture around the model. If data remains trapped in separate systems, even a successful proof of concept can stay isolated from the realities of production. Microsoft’s broader manufacturing strategy has therefore focused on interoperability, responsible AI and secure scaling, with the aim of turning data into a usable asset across engineering, operations and the supply chain.</p> <p>A recurring theme is that AI only becomes valuable when it is connected to a broad operational picture. That means bringing together ERP platforms, manufacturing execution systems, maintenance records, IoT sensors, historians, logs and frontline expertise. With that foundation in place, AI agents and analytics can move beyond reporting and start supporting decisions in context. Microsoft says this is the difference between producing insights and enabling action.</p> <p>The same logic applies to the divide between edge and cloud. In manufacturing, the two are not alternatives but partners. Edge computing is suited to low-latency inference near machines, where milliseconds matter. Cloud resources, by contrast, are better for heavier analytics, cross-site comparison, digital twins and larger-scale optimisation. Microsoft’s view is that a governed data layer linking the two can help manufacturers improve operations without disrupting production.</p> <p>The company points to several common use cases where AI is already proving its worth. Predictive maintenance is one, because it draws on telemetry, failure histories, work orders, inspection notes and the accumulated knowledge of experienced staff. When that information is combined, manufacturers can cut unplanned downtime without replacing existing maintenance systems. Production optimisation is another, using process data and AI reasoning to spot bottlenecks, yield loss and throughput constraints. Frontline enablement is a third, with agents and assistants helping workers access instructions and operational know-how at the point of need.</p> <p>Microsoft’s manufacturing materials also stress that there is no single route to adoption. Some companies will build their own systems to preserve differentiation. Others will buy ready-made solutions to move faster. Many will choose a blend, keeping proprietary logic in-house while using partner products for common capabilities. Microsoft says Marketplace is designed to support all three approaches by offering vetted applications, models, agents and connectors that can be deployed into Azure with governance and cost controls in place.</p> <p>That emphasis on procurement and governance reflects a wider message from Microsoft’s manufacturing guidance: pilots often fail not because the technology is unusable, but because the path to production is too cumbersome. According to the company, organisations should start with a high-value use case, ensure the data is connected and governed across IT and OT systems, and make an explicit decision about what to build, buy or blend.</p> <p>The message is consistent across Microsoft’s manufacturing content. AI is being presented less as a standalone tool than as part of a broader industrial operating model, one that links data, security, deployment discipline and partner ecosystems. For manufacturers under pressure to improve resilience and productivity, the winning edge may come not from launching more experiments, but from turning the right ones into reliable production systems.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69ddc404c08864238791d49fTue, 14 Apr 2026 22:42:47 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/14/microsofts-new-dynamics-365-sales-agents-shift-from-supportive-ai-to-active-workflow-partners<p>Microsoft enhances its Dynamics 365 Sales platform with new AI agents capable of autonomous research, qualification, and closing support, signalling a move towards more proactive sales automation.</p><p>Microsoft’s latest Dynamics 365 Sales agents mark a sharper move away from AI as a helpful side panel and towards AI as an active part of the sales process. Where Copilot features have mostly supported sellers by drafting emails, summarising accounts and suggesting next steps, the newer agents are designed to do more of the work inside the workflow itself.</p> <p>According to Microsoft’s documentation, the AI agents in Dynamics 365 Sales can autonomously research leads and opportunities, help qualify prospects and support closing activity. The company has positioned the product as part of a broader shift from systems that store information to systems that take action, with sales tools increasingly expected to analyse signals, recommend priorities and reduce the manual work that slows reps down.</p> <p>The most visible additions are the Sales Qualification Agent and the Sales Close Agent. Microsoft says the qualification tool can research prospects using CRM data and approved sources, assess fit and engagement, and draft outreach. The aim is to speed up first contact with promising leads while reducing time wasted on poor-fit accounts.</p> <p>The close-stage agent is built to help sellers manage live opportunities more effectively. Microsoft describes it as offering research, engagement and conversation support, including natural-language chat over sales data and customer history. The system is also designed to highlight changes since the last interaction, surface likely risks and suggest next actions across Dynamics 365 and Outlook.</p> <p>That matters because much of sales work is still lost to administration. Microsoft’s sales and marketing materials stress automated data capture, opportunity scoring and workflow integration as a way to let teams spend more time with buyers and less time hunting through records. In practice, the promise is not simply faster selling, but a more disciplined process: cleaner data, clearer priorities and fewer missed follow-ups.</p> <p>Other Microsoft materials point to a wider set of agents beyond qualification and closing, including research-focused tools and conversational interfaces inside Microsoft 365 Copilot. The company has also been expanding what it calls agentic AI across Dynamics 365, signalling that sales automation is likely to become more embedded rather than more visible.</p> <p>For Microsoft partners working around the platform, the technology is only part of the story. The success of these agents still depends on the quality of the underlying CRM setup, the reliability of the data and whether sellers trust the recommendations they receive. That means adoption, governance and process design remain central, even as the software becomes more autonomous.</p> <p>The result is a change in emphasis. Dynamics 365 Sales is no longer being presented only as a tool that helps reps work faster. Microsoft is now pitching it as a system that can help decide what deserves attention in the first place.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69ddc404c08864238791d48fTue, 14 Apr 2026 22:42:45 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/12/box-ceo-aaron-levie-advocates-embracing-high-ai-token-usage-as-a-sign-of-innovation<p>Box CEO Aaron Levie highlights the shift in Silicon Valley's attitude towards AI token consumption, viewing high usage as a marker of ambition and experimentation, amid industry discussions on infrastructure and governance challenges.</p><p>Box chief executive Aaron Levie is not losing sleep over how many AI tokens his engineers are burning through, arguing that a high bill can be a sign the company is pushing into new territory rather than wasting money.</p> <p>Speaking on a recent episode of the a16z Show, Levie said: "We should probably waste a lot of tokens because that means that we're trying new things." The comment reflects a broader mood in Silicon Valley, where some executives are increasingly treating heavy token usage as a proxy for ambition, experimentation and speed.</p> <p>That attitude is not universal, but it is gaining ground. Nvidia chief executive Jensen Huang recently said he would be "deeply alarmed" if an engineer on a $500,000 salary were not using the equivalent of $250,000 in tokens, while companies such as Meta and OpenAI have reportedly encouraged more aggressive usage by displaying internal leaderboards for heavy users.</p> <p>Levie's stance fits with how he has framed AI more generally over the past year. In a September 2025 interview, he said there is "no free lunch right now in AI", stressing that the value of agents depends heavily on access to the right context, especially when they are working across unstructured enterprise data. He has also argued that AI agents are more likely to sit on top of existing software systems than replace them outright.</p> <p>At Box, that means the debate is not just about engineering teams or model costs. Levie said the same questions are now spreading across the business, with legal, sales and other departments also drawing on AI tools and driving up usage. In his telling, the shift is forcing companies to rethink their spending assumptions as AI becomes embedded in everyday operations.</p> <p>That, in turn, raises operational problems that go well beyond the price of inference. Levie said companies are having to decide whether a task should run as a long prompt or as a longer-lived agent, whether work should be parallelised, and how much inefficiency they are willing to tolerate in exchange for discovery and automation.</p> <p>He also pointed to a more basic constraint: capacity. In his view, many of these questions will remain unresolved until the industry can build far more data centre infrastructure. Only then, he suggested, will AI providers be in a position to ease pricing pressure and stop treating tokens as such a scarce resource.</p> <p>The issue is becoming more urgent as agentic AI spreads through enterprise software. Box's own roadmap has increasingly centred on AI-enabled workflows, including a multi-year collaboration with Amazon Web Services announced in late 2025 to deepen its use of AI infrastructure and tools such as Bedrock and Anthropic's Claude. The company has said that work is aimed at automating workflows, creating FAQ-style assistance and improving content analysis inside its platform.</p> <p>For Levie, the bigger worry is not token consumption itself but the governance problems that come with agents acting at scale. He said finance chiefs and technology leaders are scrambling to determine whether their current IT and integration controls are fit for an environment in which systems may be hit thousands of times an hour by autonomous software.</p> <p>The practical concern, he suggested, is less about speed than about coordination: making sure one agent does not move a file while another is writing to it, or delete something while a third process is still depending on it. In that sense, the token debate may be a proxy for a larger transition. The question for companies is no longer simply how much AI they can afford, but how much autonomy they are willing to let it have.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69db1b05c09e034c872b3dfcSun, 12 Apr 2026 17:22:42 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/12/openreach-enhances-broadband-upgrades-with-proactive-ai-boosting-customer-satisfaction-and-operational-efficiency<p>Openreach utilises NiCE Cognigy’s proactive AI agents to transform its £15 billion Full Fibre programme, reducing missed appointments and increasing Trustpilot scores amid UK's largest broadband upgrade.</p><p>Openreach has turned to proactive AI agents from NiCE Cognigy as it seeks to streamline one of the UK's largest broadband upgrade programmes, using the technology across 15 million customer journeys.</p> <p>The BT Group-owned network operator said the system marks a shift away from the traditional reactive model, with automated messages now sent by text, email and voice to update customers, answer questions and carry out routine tasks before problems escalate. The aim is to make the upgrade process clearer and less disruptive for households while reducing the burden on contact-centre teams.</p> <p>According to Openreach, the results have been material. The company said missed appointments and inbound contact volumes have each fallen by around a third, while repeat calls have also dropped, freeing staff to deal with more complex issues. Openreach also said customer sentiment has improved sharply, pointing to a rise in its Trustpilot score from 2.0 to 4.7 out of 5 following the rollout.</p> <p>Chris Herbert, Openreach's director of customer service, said the deployment was delivering "tens of millions in financial benefits" for the company and its customers, adding that the move to proactive engagement had improved appointment success and given people more clarity during a major national upgrade.</p> <p>Jeff Comstock, president of CX Product and Technology at NiCE, said the project showed how agentic AI could be used to automate complex customer interactions while maintaining trust, inclusivity and control.</p> <p>The deployment comes as Openreach continues a £15 billion investment in its Full Fibre network, with a target of reaching 25 million UK premises by the end of 2026 and up to 30 million by 2030. The scale of that build-out has made customer communications a significant operational challenge, particularly where appointments, engineering visits and service changes need to be managed across millions of homes and businesses.</p> <p>Industry reports covering the rollout said the system is also intended to support Openreach's partners, including broadband providers such as Vodafone and Sky, by reducing friction in the upgrade process. For Openreach, the broader significance lies in showing how AI is being used not just to cut costs, but to handle high-volume customer engagement in a way that is more predictable and, if the company's figures are accurate, more effective.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69db1b05c09e034c872b3df8Sun, 12 Apr 2026 17:22:32 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/12/ge-aerospace-advances-ai-integration-with-tangible-business-gains-and-indian-talent-hub-expansion<p>GE Aerospace is moving AI from experimental to essential, reporting significant efficiency improvements and expanding its Indian workforce to drive innovation in engine monitoring and software development.</p><p>GE Aerospace is pushing artificial intelligence deeper into day-to-day operations, with the company saying it is now seeing clear business benefits rather than just experimental promise.</p> <p>Dinakar Deshmukh, the firm’s executive director for data science and AI, said machine learning tools used to monitor engines have cut false positives by more than half and reduced lead times by over 60 per cent. He said the systems are able to identify unusual patterns that would be hard for people to spot, improving the way the company tracks commercial aircraft engines.</p> <p>Generative AI is also moving into use, though Deshmukh said the technology remains a work in progress. Even so, he said some applications are already in production and are delivering measurable gains, including productivity improvements of 20 to 25 per cent in software development.</p> <p>India is becoming increasingly central to that push. GE Aerospace has about 2,500 employees in India, and more than half of its AI team is based in Bengaluru. That reflects the company’s broader reliance on the country, where it has operated for more than four decades and runs major engineering and manufacturing capabilities, including its technology centre in Bengaluru and a facility in Pune that makes components for several engine platforms.</p> <p>The company is also building out its supplier network in India and has separately expanded its Next Engineers programme in Bengaluru to help develop future talent. Deshmukh said the group is being selective about where it applies AI, focusing on high-value operational areas rather than spreading the technology too broadly. He also said the hardest task is taking a tool from prototype to full-scale deployment, and that GE’s investment in AI has risen by 2.5 to 3 times over the past two and a half years.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69db89d8c09e034c872b491aSun, 12 Apr 2026 17:22:03 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/12/tata-consultancy-services-accelerates-ai-driven-staffing-and-talent-development<p>TCS shifts nearly half of its internal job allocations to an AI-powered platform, signalling a major disruption in how large firms manage talent and project staffing amidst a broad AI integration strategy.</p><p>Tata Consultancy Services has said that almost half of its internal job allocations are now being handled by an AI-powered talent marketplace, marking a sharp shift away from the traditional reliance on managers and staffing teams to move employees between projects.</p> <p>According to NDTV Profit, the system uses machine-generated recommendations to match staff with open assignments, helping the company align available skills with project demand more quickly. TCS has presented the change as part of a wider effort to embed artificial intelligence across its operations, including recruitment, learning and human resources support.</p> <p>The update comes as the company appears to be broadening its own AI capabilities. Job listings on The Ladders show TCS recruiting for a range of roles tied to AI engineering, solution architecture and agentic software development in locations including Minnesota, New York and Bengaluru. Those postings suggest the Indian IT giant is not only using AI to manage staffing internally but is also building the engineering capacity needed to deliver AI-led products and services for clients.</p> <p>Separately, a review by The AI Market Pulse found dozens of active AI and machine learning vacancies at TCS, with salaries advertised across a wide range and skills sought in areas such as Python, prompt engineering, AWS, LangChain, Azure, GCP, PyTorch, TensorFlow and Kubernetes. Taken together, the hiring push indicates that TCS is deepening its commitment to artificial intelligence at both the operational and delivery levels.</p> <p>For an industry long dependent on large-scale human resource planning, the move underlines how quickly AI is being folded into core management functions. At TCS, that now appears to include deciding where people go next.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69dbc303ecb84ee51cfe234cSun, 12 Apr 2026 17:21:46 +0000http://srmtoday.makes.news/gen-ai/2026/04/12/why-most-enterprise-ai-initiatives-miss-out-on-boosting-profitability-and-how-to-fix-it<p>Despite widespread adoption, many organisations struggle to translate AI investments into real profit gains, highlighting a need for strategic shift towards margin-focused insights demonstrated by innovative solutions like Suppeco’s SuppEQ.</p><p>In the rapidly evolving landscape of enterprise AI, few leaders are brave enough to acknowledge when initiatives fall flat. Behind glossy dashboards, utilisation metrics, and impressive efficiency gains showcased in PowerPoint presentations, there often lies a troubling disconnect: the real impact on profitability remains elusive.</p> <p>Most organisational AI programmes are built on a foundation of what might be called "polite fiction." The technology works, and the business case appears credible, at least on paper. But the underlying premise? That’s usually a mile off the mark.</p> <p>In traditional business case development, ‘EBIT’ , earnings before interest and taxes , is rarely spelled out explicitly. Instead, executives pitch savings in cost and efficiency, FTE reductions, and faster processes as proxy metrics, all of which contribute ostensibly to increased margins. But these metrics are often just assumptions, sitting at the heart of the narrative without proper measurement or ownership. As a result, they tend to be overlooked or forgotten altogether , never translating into tangible EBITDA improvements.</p> <p>A revealing insight comes from McKinsey &amp; Company’s survey of 2,000 global executives across 105 countries. While 88% of these leaders are deploying AI, only 39% explicitly seek to impact EBIT, and a mere 6% are actually making a meaningful difference. These numbers suggest a stark reality: organisations are investing heavily in AI, yet most are missing the core opportunity to drive real profitability.</p> <p>Procurement, in particular, exemplifies this challenge. The function has spent years deliberating over commercial value, often investment-driven by tools designed around optimisation rather than direct margin enhancement. Over the last decade, procurement teams have adopted smarter sourcing workflows, automated approvals, and elaborate supplier portals , tools that are loaded with features, yet rarely tied cleanly to margin improvement.</p> <p>The issue is not capabilities; it’s strategic direction. Most procurement AI solutions answer a simple question: how can we do what we already do, but faster? This is a logical approach, but it’s fundamentally flawed when it comes to safeguarding margins. Because the true margin leakage isn’t occurring within process inefficiencies; it’s emanating from the very bedrock of commercial relationships , the static versus dynamic drift, slipping commitments, and supplier dynamics that quarterly scorecards cannot detect in real time.</p> <p>Enter the concept of Dynamic Margin Erosion: the invisible, relentless leak of value that demands a different approach. Waiting for a quarterly review or relying on hindsight won’t cut it anymore.</p> <p>Suppeco’s SuppEQ is built to address this challenge head-on, without detours or intermediate proxies. It integrates directly into contractual and operational signals that underpin supplier relationships, identifying the telltale signs before significant damage occurs. It reduces friction between strategic intelligence and its real-world financial impact, making margin protection immediate and actionable.</p> <p>The disparity between the 6% of organisations winning with AI and the overwhelming majority that are not ultimately boils down to a mindset shift. The winning leaders stop asking, “How fast can we do this?” and instead ask, “Where are the critical fault lines in our commercial relationships? Where is the value leaving, and why?”</p> <p>If you lead procurement and cannot confidently answer those questions today, then the chances are good that the answer is already embedded somewhere within your existing data , you’ve simply yet to uncover it.</p> <p>Visit Suppeco. Say hello. Discover how we’re redefining margin preservation and profit optimisation in the age of enterprise AI.</p>69dbd43ce1f6b5a3a65c3da2Sun, 12 Apr 2026 17:20:32 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/10/the-shift-from-ai-adoption-to-systemic-reorganisation-in-finance-automation<p>While AI tools for finance have advanced, many organisations are realising that achieving true automation requires a fundamental redesign of workflows, data structures, and processes to enable end-to-end, autonomous finance operations.</p><p>For years, finance software makers sold chief financial officers a seductive idea: add smarter automation and the back office would begin to run itself. In practice, many companies have discovered that better models do not automatically produce better outcomes. The problem, increasingly, is not whether artificial intelligence can read invoices, spot anomalies or classify transactions, but whether the wider finance operation is organised in a way that lets those tools work across the whole process.</p> <p>That shift in thinking is now at the centre of a broader push toward what some vendors and consultants call touchless finance or autonomous finance. Deloitte has described the concept as “Lights Out Finance”, a model in which AI and machine learning handle routine work end-to-end, leaving people to focus on exceptions, strategy and relationships. Oracle has likewise argued that CFOs are moving toward a more automated operating model, while Workday has pointed to rising use of AI for invoice processing, account reconciliation and data entry.</p> <p>But the reality inside many enterprises remains messier. PYMNTS reported that while invoice capture accuracy and data extraction have improved, many finance teams have simply bolted AI on top of older, fragmented workflows. The result is a process that may begin more efficiently, yet still breaks down when documents move between systems, teams and approval chains that do not share consistent data or rules.</p> <p>Michael Younkie, vice president of product management at Billtrust, told PYMNTS that finance teams continue to wrestle with “inconsistent and incomplete data structures, bad data, dirty data” as well as legacy ERP systems with limited accounts receivable API capabilities. That matters because even strong AI performance at the front end cannot fix what happens downstream if the underlying architecture is disjointed.</p> <p>Research cited by PYMNTS Intelligence suggests many executives now understand this distinction. In its Enterprise AI Benchmark Report, 71% of leaders at companies with annual revenue of $1 billion or more said organisational readiness, rather than AI itself, was the main constraint on performance. Only 11% pointed to the technology as the key barrier. PYMNTS Intelligence also found in December that 66% of accounts payable teams had seen manual workloads increase over the previous year, underlining how stubborn the operational burden remains.</p> <p>The implication is that the next phase of finance automation is less about adding more intelligence at isolated points and more about redesigning the workflow itself. Instead of treating invoice capture, validation, approval and payment as separate events, the emerging model sees them as connected states within a single system. In accounts receivable, the same logic applies to cash application, collections and dispute resolution.</p> <p>That is why the most ambitious finance leaders are now asking a different question: not whether a tool has AI features, but whether the entire invoice or cash cycle can run autonomously from start to finish. The answer, for many organisations, still depends on standardising data, simplifying hand-offs and rethinking processes that were built for a manual era.</p> <p>The destination, at least in theory, is touchless finance. In that model, machines handle the routine work while humans step in only where judgement, negotiation or oversight is needed. The promise is not merely faster processing, but a finance function that is less reactive, less fragmented and more strategic. What is changing now is not the aspiration, but the recognition that reaching it will require rewiring the system, not just upgrading the software.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69d877d6da7219bf2fe261cfFri, 10 Apr 2026 06:55:15 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/10/visa-s-new-ai-commerce-platform-aims-to-build-trust-in-agent-led-shopping<p>Visa introduces Intelligent Commerce Connect, a unified platform designed to facilitate AI-initiated transactions and surface product info within AI shopping agents, as consumer trust and technological readiness remain key challenges for mainstream adoption.</p><p>Visa’s latest move into AI-powered shopping suggests that agentic commerce is moving from concept to infrastructure. The payments company has unveiled Intelligent Commerce Connect, a single integration designed to let businesses accept transactions initiated by AI agents, surface product catalogues inside AI platforms and handle payment, tokenisation, spend controls and authentication without forcing merchants to build separate connections for each channel.</p> <p>According to Visa’s announcement, the service is intended to work across Visa and non-Visa card networks and to support major agent protocols. It is currently in pilot with partners including Aldar, AWS, Diddo, Highnote, Mesh, Payabli and Sumvin. Visa has framed the offer as a network-, protocol- and token-vault-agnostic on-ramp for companies preparing for a future in which AI agents do more of the work normally done by human shoppers.</p> <p>The company is not starting from zero. In earlier announcements, Visa said it had already completed hundreds of secure, agent-initiated transactions with partners and was working with more than 100 collaborators globally on the plumbing required for AI commerce. It has also positioned its broader Intelligent Commerce push alongside partnerships with major technology firms including Anthropic, IBM, Microsoft, Mistral AI, OpenAI, Perplexity, Samsung and Stripe.</p> <p>That technical readiness, however, is only part of the story. The harder problem may be consumer trust. Research cited by CXM World suggests that while AI usage is rising, willingness to let it complete purchases still lags. Bain found that 72% of consumers have used AI tools in some form, but only 24% currently feel comfortable letting AI finish a purchase, and just 10% have actually done so, mostly for low-cost, routine goods. Forrester’s Consumer Pulse data found that 54% of US online adults are not comfortable sharing personal information with generative AI tools, while a Radial survey showed 19% of consumers saying they would never share payment details with an AI agent.</p> <p>Visa’s own B2AI research points to a more nuanced picture. It found that 36% of respondents trust bank-backed AI systems and 35% trust payment-network-enabled AI, compared with 28% for independent AI agents. Confidence is notably stronger among younger shoppers, with 48% of Gen Z respondents saying they trust payment-network-enabled AI, versus 20% of Boomers.</p> <p>That split matters for customer experience leaders. If agentic commerce becomes a mainstream buying route, brands will need more than functional integration; they will need clear proof that AI-led purchasing is secure, explainable and well governed. The risk, as the CXM World analysis argues, is not that the technology arrives too soon, but that companies fail to build the trust architecture required to make it usable at scale.</p> <p>Visa’s pitch is that it can help solve that gap by using the trust associated with its payment network to make AI transactions feel safer. Whether consumers accept that proposition will determine how quickly agentic commerce moves from pilot projects to everyday retail behaviour. For now, the industry appears to be laying tracks ahead of demand, even as many shoppers remain hesitant to hand the checkout over to an algorithm.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69d877d6da7219bf2fe261d3Fri, 10 Apr 2026 06:55:13 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/10/openais-enterprise-revenue-surpasses-40-as-ai-agents-become-integral-to-business-workflows<p>OpenAI reports its enterprise products now account for over 40% of its revenue, signalling a rapid shift towards AI-enabled business operations and multi-agent systems, with the company aiming for substantial growth and potential public listing.</p><p>OpenAI says enterprise products now account for more than 40% of its revenue, a sign that the company’s push into business software is gathering pace as it leans harder into AI agents and automated workflows.</p> <p>Denise Dresser, OpenAI’s chief revenue officer, said in a company note on Wednesday that the shift has been striking. She said the firms moving fastest have gone far beyond using AI for routine tasks such as drafting emails or condensing documents and are instead deploying coordinated groups of agents that can preserve context, work across sessions and act inside business systems with limited human supervision.</p> <p>OpenAI said its annualised revenue reached $25 billion in February, up from $20 billion at the end of 2025, and it expects enterprise revenue to draw level with consumer revenue by the end of 2026. That trajectory suggests the company sees business adoption, rather than mass-market subscriptions alone, as central to its next phase of growth.</p> <p>The strategy is increasingly built around agents as a default interface for companies. OpenAI has launched tools aimed at helping businesses create and manage these systems, including Frontier, a platform for building and governing enterprise agents. The company also rolled out ChatGPT Agent, which it says can handle tasks such as trip planning, hotel bookings, competitor research, slide creation and online purchasing without direct intervention.</p> <p>Dresser argued that many organisations are still missing a simple way to deploy agents as effective teammates without having to redesign their operations from scratch. OpenAI’s pitch is that its platform can bridge that gap.</p> <p>The company’s internal figures underline how quickly the market is developing. Dresser said Codex, OpenAI’s coding agent, has passed 3 million users, having been close to zero at the start of the quarter. Paying business users reached 9 million in February, up from 5 million in August, while weekly active users across OpenAI’s products climbed to 910 million.</p> <p>OpenAI’s own guidance on agents suggests the company is still encouraging businesses to start small. In a practical guide published on its website, it recommends first maximising what a single agent can do before splitting work across several systems. It describes two common designs: a central manager agent coordinating specialist agents, and a decentralised setup in which agents operate as peers and hand work off according to their strengths.</p> <p>That technical framing fits a broader industry move towards multi-agent systems. Analysts and executives have increasingly portrayed them as the next stage of enterprise AI, with specialised systems breaking complex work into smaller tasks. OpenAI chief executive Sam Altman has positioned such architectures at the heart of the company’s future product roadmap.</p> <p>The company’s hiring also points in the same direction. It recently brought in Peter Steinberger, founder of the open-source agent platform OpenClaw, to help lead its efforts in personal AI agents, indicating that OpenAI’s ambitions extend beyond corporate customers.</p> <p>The business momentum comes as OpenAI is also preparing for a possible public listing. Chief financial officer Sarah Friar confirmed this week that retail investors will be included in the share allocation. OpenAI has said it is targeting $85 billion in revenue by 2030, a scale that would depend heavily on agents becoming embedded in everyday business operations rather than remaining an experimental add-on to chat-based tools.</p> <p>For now, the message from OpenAI is clear: the company believes the enterprise market is moving from curiosity to infrastructure, and that the real test is no longer whether businesses will use AI, but how deeply they will wire agents into the way they work.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69d877d6da7219bf2fe261c7Fri, 10 Apr 2026 06:55:00 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/10/oracle-introduces-ai-powered-agentic-applications-to-transform-customer-experience-management<p>Oracle launches five Fusion Agentic Applications to automate routine tasks, enhance decision-making, and embed AI-driven agents within its customer experience platform, signalling a significant shift in enterprise AI adoption.</p><p>Oracle has added a new layer of automation to its customer experience software, unveiling five Fusion Agentic Applications designed to let AI agents carry out routine work, flag exceptions and support decisions across sales, service and marketing.</p> <p>The launch, announced on 9 April at Oracle’s AI World Tour in New York, extends Oracle’s Fusion Cloud Applications with what the company describes as coordinated teams of specialised agents operating on Oracle Cloud Infrastructure and large language models. According to Oracle, the applications can work with unified enterprise data, workflows, policy rules, approval chains and transactional context, all while remaining inside the platform’s existing security controls.</p> <p>The five applications cover a broad stretch of customer operations. Oracle says the Contract Compliance Workspace is intended to scan agreements for policy deviations and recommend next steps, while the Cross-Sell Program Workspace is aimed at spotting expansion opportunities. The Marketing Command Center is designed to help teams identify revenue opportunities from connected enterprise signals, the Sales Command Center focuses on pipeline progression and churn reduction, and the Service Manager Workspace is built to surface escalations and service risks before they appear in standard dashboards.</p> <p>Oracle is also pairing the CX release with an Agentic Applications Builder inside Oracle AI Agent Studio. That tool is meant to let organisations create and run automation using reusable Oracle, partner and external agents without conventional software development. In practice, that positions Oracle as one of the latest major enterprise vendors trying to make agentic AI a built-in part of everyday business software rather than a separate add-on.</p> <p>Chris Leone, Oracle’s executive vice president of Applications Development, said in the company’s announcement that customer expectations and operational complexity had moved beyond traditional systems, creating demand for software that does more than assist staff. Oracle also says the new applications include observability, return-on-investment measurement and safety controls, a sign that governance is being pitched as a core feature rather than an afterthought.</p> <p>The CX announcement follows a wider push by Oracle to embed agentic capabilities across its cloud portfolio. The company’s AI World Tour in New York showcased developments across applications, infrastructure and database technology, while Oracle has separately unveiled agentic applications for finance, supply chain and HR at other recent events. That broader rollout suggests the company is not treating customer experience as a standalone experiment, but as part of a wider enterprise strategy.</p> <p>Oracle did not disclose pricing or release timing for the CX applications. Even so, the announcement underscores how quickly the market for agentic AI in customer-facing software is maturing, with vendors now competing not just on model access, but on whether AI can safely execute work inside complex enterprise processes.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69d877d6da7219bf2fe261cbFri, 10 Apr 2026 06:54:58 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/10/project44-s-acquisition-of-lunapath-ai-signals-a-move-towards-autonomous-logistics-automation<p>Chicago-based project44 deepens its AI ambitions with the acquisition of LunaPath.ai, aiming to develop a self-executing supply chain platform that acts on operational issues rather than just monitoring freight.</p><p>project44 is deepening its push into artificial intelligence with the acquisition of LunaPath.ai, a move that underlines chief executive Jett McCandless’s ambition to build a supply chain platform that can do more than monitor freight. The Chicago-based company said on 9 April that it had bought the logistics orchestration start-up in an all-cash deal, adding a layer of agentic AI designed to act on operational problems rather than simply flag them.</p> <p>The deal fits neatly into project44’s broader strategy. Over the past decade, the company has built what it describes as a vast real-time logistics data graph, tying together information from ERP, TMS, yard management and visibility systems to create a live picture of freight movement. That foundation has become central to its AI plans, because the company argues that intelligent automation only works properly when it is grounded in accurate operational context.</p> <p>LunaPath’s software is built for exactly that environment. Its AI agents are designed to handle repetitive logistics work such as status checks, proof-of-delivery requests, appointment confirmation, claims initiation and carrier follow-up. Rather than operating as isolated tools, they are meant to coordinate across systems and workflows, helping teams resolve exceptions faster and with less manual intervention.</p> <p>McCandless has been vocal about his preference for AI built into the core of the product rather than layered on top afterwards. He said project44 has spent years testing different platforms and concluded that LunaPath performed best for logistics execution, in part because it was trained specifically for those workflows. The company also says its scale gives it an advantage: project44 now processes 4.6 million shipments a day, a volume it believes improves the quality of automation as more transactions flow through the platform.</p> <p>The acquisition follows a series of AI-related product moves. On 8 April, project44 unveiled an AI agent portfolio at its decision44 event, covering use cases such as freight procurement, disruption response and carrier onboarding. In October 2025, it introduced Multi-Agent Orchestration for its Decision Intelligence Platform, a framework intended to coordinate specialised agents and move operations from reactive problem-solving towards more autonomous execution.</p> <p>project44 has also been building through acquisitions before. Its purchase of Ocean Insights in 2021 expanded its ocean freight intelligence business and helped strengthen its end-to-end visibility offering. The LunaPath deal suggests the company now wants to extend that model from seeing what is happening in the supply chain to actively deciding and doing something about it.</p> <p>For McCandless, the underlying bet is that logistics AI must be native to the workflow if it is to matter at scale. In that sense, the acquisition is less a bolt-on and more a sign of where project44 thinks the market is heading: from data and alerts to systems that can carry out the work themselves.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69d877d6da7219bf2fe261b9Fri, 10 Apr 2026 06:54:04 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/06/from-experimentation-to-enterprise-how-agentic-ai-is-transforming-business-operations-at-scale<p>The shift from pilot projects to operational AI agents signifies a new era of digital workforce integration, but success hinges on governance, data readiness, and strategic alignment to avoid fragmentation and ensure measurable value.</p><p>For much of the last decade organisations treated artificial intelligence as an experimental capability to be trialled cautiously: small pilots, contained budgets and narrow use cases that allowed teams to learn while keeping disruption to a minimum. That pattern is breaking. What was once a tentative exploration has become an operational imperative as businesses move from proof-of-concept demonstrations to deploying task‑oriented AI agents across everyday workflows.</p> <p>According to a 2025 study from MIT, the spread of generative AI has been rapid, yet only a small minority of firms translate experimentation into sustained outcomes when automation is not embedded in core processes. TechRadar Pro’s reporting finds those dynamics shifting: benchmarking now shows a large share of agentic automation projects are delivering measurable value, and many organisations are already treating autonomous systems as part of the workforce rather than peripheral pilots.</p> <p>The manifest change is the rise of agentic systems that can analyse information, trigger processes and take limited decisions with minimal human oversight. Industry briefings indicate companies typically run dozens of such agents today, with plans to expand further. Researchers and vendors alike describe this as the emergence of a digital workforce: software agents taking on repetitive, time‑consuming tasks so human staff can concentrate on strategic work, creative problem‑solving and higher‑value interactions.</p> <p>That promise, however, sits beside persistent obstacles. Multiple guides and analyses warn that a majority of pilots still fail to progress into production because of data shortcomings, governance gaps and weak executive alignment. Archool’s 2025 adoption guide noted that more than 60% of pilots stumble at the handover to operational systems. CDW’s guidance stresses that moving to scale requires treating AI as an operating‑model change rather than a one‑off project, with investment in data foundations, security and infrastructure.</p> <p>As deployments multiply, companies face an organisational challenge commonly labelled “automation sprawl”. Uncoordinated rollouts by separate teams can create duplicated effort, conflicting processes and opaque reporting. TechTarget’s coverage highlights that scaling AI successfully depends on disciplined architecture, enforced governance and defined accountability so that agents complement existing systems rather than fragment them.</p> <p>Trust has become the dominant barrier to widescale adoption. Budget constraints have receded as the primary concern; instead, leaders increasingly ask whether agentic systems are safe, auditable and controllable. Vendors sometimes overstate capabilities, and analysts caution that without robust risk controls and clear performance thresholds many projects will underdeliver. Gartner and other market watchers warn of significant failure rates unless organisations prioritise explainability, monitoring and intervention mechanisms.</p> <p>The strategic rationale for the shift is speed. A growing number of companies see agentic automation as a lever to accelerate product development and time to market by removing bottlenecks in routine processes. TechRadar’s reporting and concurrent industry analyses argue that when agents are woven into end‑to‑end workflows they not only save staff hours but also enable faster iteration, testing and deployment of new services.</p> <p>Architecturally, multi‑agent orchestration is emerging as the dominant pattern in larger enterprises. Recent market reporting shows many Global 2000 firms have moved beyond pilots into multi‑agent production environments, and analysts expect the agentic AI market to expand considerably in the coming decade. Yet the differentiator between success and costly disappointment will be the extent to which organisations enforce interoperability, shared data models and central oversight.</p> <p>Practical next steps for leaders aiming to scale include establishing clear governance frameworks, defining measurable business‑level outcomes, investing in data readiness and treating orchestration as a platform problem rather than a collection of point solutions. CDW and TechTarget both emphasise cross‑functional sponsorship and enterprise architecture alignment as prerequisites for turning pilots into mission‑critical capabilities.</p> <p>The cumulative lesson is straightforward: the question for business leaders has shifted from whether to experiment with agentic AI to how to adopt it responsibly, coherently and at pace. Those that align technology, processes and accountability stand to gain a durable productivity advantage; those that do not risk fragmentation, wasted investment and unmet expectations.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69d332397eeff949e5c2ffdeMon, 06 Apr 2026 09:47:18 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/06/europes-ai-spending-set-for-sharp-rise-as-generative-models-dominate-enterprise-investments<p>European expenditure on AI is projected to soar to $290 billion by 2029, driven by a surge in software and generative AI adoption amid evolving regulations and sectoral growth.</p><p>European expenditure on artificial intelligence in the coming years is set to accelerate sharply, with IDC forecasting the region will spend $290 billion on AI by 2029. According to IDC’s Worldwide AI and Generative AI Spending Guide, that figure represents a compound annual growth rate of 33.7% between 2025 and 2029, driven by substantial commitments from banking, retail and software and information services, and a rapidly expanding appetite for AI in healthcare.</p> <p>Industry spending patterns point to software as the dominant force in the market. IDC expects software to constitute the largest slice of AI budgets in 2026 and to be the fastest-growing technology segment through 2029, as platforms and agentic systems become central to enterprise deployments. The research house anticipates generative AI offerings will make up almost 54% of the market by the end of the forecast period, reflecting a shift toward models that can create and automate at scale.</p> <p>Financial services remain the single largest vertical, with banking accounting for roughly 12.5% of AI spending in 2026. Banks are expanding use cases from fraud detection and threat intelligence to contact-centre automation and multi-agent workflows, while also prioritising FinOps, sovereign cloud options and governance frameworks. Retail follows closely, allocating budgets to digital commerce, personalisation, pricing optimisation and supply-chain efficiency. Healthcare is the fastest-growing industry segment, IDC projects, as clinical workflow automation and resource optimisation gain traction across major European markets. Media and entertainment, professional services, utilities and life sciences are also posting above-average growth, supported by generative capabilities for content creation and audience targeting.</p> <p>“Despite geopolitical tensions and supply chain disruptions, the AI market remains dynamic and is rapidly transitioning from experimental to operational and strategic for enterprises,” said Carla La Croce, Research Manager for Data and Analytics at IDC. “Organizations are no longer treating AI as a standalone tool , they are repositioning it as a strategic asset to transform their business models. The emergence of agentic AI tools has made this transformation more urgent and more profound than many anticipated,” she added.</p> <p>Market momentum is being reinforced by measurable returns on investment in cost reduction, improved customer experience and enhanced risk controls, prompting firms to reallocate funds from traditional IT toward AI initiatives. IDC’s data shows AI platforms within the software category expanding particularly rapidly, as organisations shift from pilots to mission-critical, multi-agent systems and cloud-native development.</p> <p>External research underlines this broader dynamic. Gartner projects Europe’s overall IT spending will rise in 2026, noting that GenAI model expenditure could surge by more than three quarters that year, while the consultancy estimates global AI spending will reach $2.52 trillion in 2026 with AI infrastructure contributing significantly to that rise. IDC itself has recorded record levels of investment in AI infrastructure, reporting $86 billion spent globally in Q3 2025 and signalling a sustained build-out of the foundations needed to host agentic workloads.</p> <p>Yet obstacles remain that could alter the pace and geography of adoption. IDC warns that regulatory fragmentation tied to the EU AI Act may influence where and how companies invest, creating uneven compliance burdens across member states. Talent shortages in AI skills and mounting pressure to control cloud expenditure are additional constraints that industry observers say will shape vendor strategies and customer demand. Those pressures are also driving uptake of governance, compliance and assurance services, particularly within regulated sectors.</p> <p>Vendors and service providers are responding by offering platform and infrastructure services tailored to enterprise needs, while buyers increasingly demand assurances around risk, cost-efficiency and sovereignty. According to IDC, these market forces are likely to sustain double-digit growth in European AI spend through 2029, even as firms weigh regulatory, economic and supply-chain uncertainties.</p> <p>The combined picture presented by IDC and corroborating industry analysis suggests Europe is moving beyond experimentation: AI is being embedded into core business models and operations, but the trajectory of that transition will be shaped by regulatory choices, skills availability and the economics of cloud and infrastructure.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69d3477c7eeff949e5c3067dMon, 06 Apr 2026 09:46:16 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/06/publicis-accelerates-sports-expansion-amid-limited-ad-tech-transparency-shifts<p>Despite increased calls for transparency from demand-side platforms, advertisers remain primarily driven by campaign effectiveness, prompting Publicis to bolster its sports and culture capabilities through a major acquisition and strategic expansion.</p><p>Demand-side platforms have been loudly touting greater transparency in the wake of Publicis Groupe’s decision last month to stop recommending The Trade Desk, but agency buyers and brand marketers appear unmoved.</p> <p>According to AdExchanger, several rival DSPs rushed to court The Trade Desk’s clients, pitching clearer reporting and openness to independent audits as reasons to shift spend. Viant, industry sources told AdExchanger, has been among the most proactive. Other ad-tech firms including StackAdapt, Tatari, Ilumin and Quantcast have been targeting marketers on LinkedIn to draw distinctions with The Trade Desk’s operations. Yet Digiday reporting and comments from multiple media buyers indicate these overtures have produced little commercial movement: advertisers remain focused on campaign outcomes rather than the internal mechanics of their partners’ platforms.</p> <p>“CMOs don’t really care how their tech partners run their businesses,” Roy Geva Olmert, SVP of client services at RTBHouse, told Digiday, underscoring a pragmatic, results-first view that appears to dominate buying decisions. Justin Scarborough, head of programmatic at Crossmedia, described the flurry of activity around Publicis’ announcement as a conversation many in trading desks see as momentary rather than transformational. Industry insiders note that the largest shares of ad budgets still flow through major technology platforms whose workings are routinely described as opaque, dampening the appetite for swapping one black box for another.</p> <p>Publicis itself has used the episode to accelerate a different strategic play: a major expansion of its sports and culture capabilities. The group has struck a definitive agreement to buy 160over90 from WME Group, a deal widely reported to be valued at more than $500 million and one that will fold about 670 employees into Publicis Sports. Publicis’ CEO Arthur Sadoun described the transaction as part of a broader investment in sports, following prior acquisitions including Adopt and Bespoke Sports &amp; Entertainment, and said the company sees sports marketing as a significant growth engine for client work, according to SportsBusiness Journal.</p> <p>Publicis plans to fold 160over90’s full-service roster , spanning PR, experiential programmes, branded content and sponsorship valuation , into its existing capabilities and to link those services to data-driven advertising. Suzy Deering, CEO of Publicis Sports, told O’Dwyer’s the firm will integrate 160over90 into the sports unit and report to her. The group intends to build a consolidated “fan graph” on top of Epsilon’s identity assets to connect audiences, sponsorships, media and athlete partnerships and to measure outcomes across those touchpoints. Sadoun has also flagged generative AI as a tool to join data and creative at scale, though he acknowledged that practical applications are still emerging.</p> <p>The industry’s shifting payment rules added another wrinkle for smaller advertisers. Platforms have been moving away from accepting credit-card payments for ad buys, closing what some described as a useful loophole for direct-to-consumer founders and boutique social shops that relied on cards to capture loyalty and cashback benefits. AdExchanger’s coverage traces a wave of policy changes: Google phased out advertiser credit-card payments last year, Meta signalled a delayed deadline for eliminating card billing, and Amazon Ads told customers that, from April 15, manual credit-card payments will no longer be accepted, requiring merchants to link bank accounts. Some advertisers say Amazon offered ad credits to ease the transition.</p> <p>Beyond these headline moves, the ad-tech and media landscape continues to evolve. AdExchanger highlights a number of recent developments: Nota shut down an AI-generated local-news network after plagiarism was exposed, several publishers reported double-digit CPM gains tied to The Trade Desk’s OpenPath product, OpenAI acquired tech podcast TBPN, Harlequin entered a multiyear deal with AI entertainment firm Dashverse to produce AI-driven microdramas from its properties, and OpenAI executive Fidji Simo began a medical leave as the company prepares for an expected IPO.</p> <p>Taken together, the episodes underline two concurrent trends in the ad ecosystem. On one hand, transparency and platform governance have become louder talking points among agencies and tech vendors; on the other, commercial behaviour remains driven by effectiveness, scale and the practicalities of campaign delivery. Publicis’ sports acquisitions illustrate how global agencies are seeking differentiated, vertically integrated offerings that marry creative, commerce and data , while many advertisers continue to prioritise measurable returns over the internal operating models of the tools they use.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69d355b5311288ec419ddb6eMon, 06 Apr 2026 09:46:16 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/06/allsaints-adopts-ai-first-platform-to-accelerate-fashion-retail-decision-making<p>AllSaints is overhauling its buying and merchandising operations with an AI-native platform from Impact Analytics, aiming to speed decision-making and reduce manual data work in its fashion retail model.</p><p>AllSaints is overhauling its buying and merchandising stack with an AI-first platform designed to speed decision-making and free merchant teams from repetitive data work.</p> <p>The London-based fashion chain has chosen Impact Analytics to replace a legacy environment built on spreadsheets and manual processes with a single, AI-native planning suite. According to the company in a statement, the rollout will be phased and will cover CortexEye for business intelligence, allocation and replenishment, markdown and promotional pricing optimisation, merchandise financial planning, and assortment and range planning.</p> <p>The shift centres on shortening the retailer’s weekly trading cycle. Currently, preparing AllSaints’ Monday trading packs requires teams to begin compiling data as early as Sunday to reach clarity by Monday afternoon. Under the new arrangement, the full Monday trading pack will be available by 8:00 a.m. on Monday, segmented by department and team, enabling faster, more focused action across markets.</p> <p>“One of the four pillars of our transformation is to become data-driven and powered by AI,” said Alfie Meekings, Chief Transformation and Technology Officer at AllSaints. “We have a great team, but they have to spend so much time manually pulling and analysing data. This partnership allows us to eliminate repetitive, low-value tasks so our merchandisers can focus on understanding what is truly happening in our brand and make quicker, higher-quality decisions to get the right products in the right places for our customers.”</p> <p>Impact Analytics describes CortexEye as a decision-intelligence tool that lets retail leaders query enterprise data in natural language and receive explainable answers about what is driving performance. According to the vendor, the platform fuses signals from merchandising, marketing, pricing, supply chain, store operations and external factors in real time, delivers high conversational AI accuracy and can cut diagnostic investigation times substantially. The company said in a press release the technology achieves 97% conversational accuracy and can reduce investigative workflow times by up to 50%.</p> <p>“Brands like AllSaints are at an inflexion point where speed, clarity, and confidence in decision-making are critical,” said Prashant Agrawal, Founder and CEO at Impact Analytics. “This partnership is designed to replace fragmented spreadsheets with a unified, AI native platform and business intelligence solution that amplifies merchant expertise, accelerates planning cycles, and drives measurable impact across buying, pricing, and inventory decisions.”</p> <p>Industry coverage of the deal notes that retailers increasingly seek automated, insight-led planning to improve forecast precision and to reallocate staff time from data preparation to interpretation and strategy. Reports from trade outlets indicate AllSaints’ move mirrors a wider push in fashion retail toward integrated platforms that combine financial planning, replenishment and promotional optimisation.</p> <p>AllSaints frames the project as part of a broader transformation programme; the company intends the platform to surface the most material signals to merchants so that weekly meetings start with clear priorities rather than manual report building. For its part, Impact Analytics positions the work as a transition away from fragmented spreadsheets toward an end-to-end merchandise planning lifecycle.</p> <p>While vendors’ performance claims are compelling, retail executives typically weigh such promises against integration complexity and data governance needs when replacing entrenched systems. Analysts say success will hinge on how quickly the platform can be populated with clean, connected data and how effectively teams adopt the new workflows.</p> <p>AllSaints will phase the deployment across merchandising functions, with the stated aim of accelerating planning cadences and improving the speed and quality of commercial decisions.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69d36ae50c26e23ddd29edc2Mon, 06 Apr 2026 09:46:04 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/03/ai-investment-continues-despite-challenges-as-leaders-emphasise-governance-and-transformation<p>A new KPMG study reveals that while most business leaders prioritise AI spending, only a small percentage are extracting significant value, highlighting the importance of governance, workforce development, and strategic transformation amidst rising deployment risks.</p><p>Seventy-four percent of business leaders worldwide still rank artificial intelligence as a strategic spending priority even as companies wrestle with proving concrete returns, according to a new KPMG study. The research warns that allocating budget to AI is not the same as capturing durable value and that many organisations must reframe AI as a full-scale transformation rather than a peripheral add-on.</p> <p>KPMG found that 64% of firms report AI is delivering meaningful business value, yet three quarters remain worried about data security and privacy. The consultancy noted a clear shift from experimental generative models to more autonomous, agentic systems: 32% of respondents said they have deployed agentic AI at scale and 27% are operating multiple AI agents. That transition, KPMG argues, raises fresh demands for governance and operational discipline.</p> <p>Only a small proportion of organisations qualify as AI leaders. KPMG places just 11% in that category, but those leaders are far more likely to extract value: 82% of them see significant benefits from AI compared with 62% of non-leaders. Confidence in risk management also varies sharply by maturity; roughly one in five early-stage firms feel able to manage AI risks, versus nearly half of companies identified as leaders. According to KPMG, firms that invest in workforce development are almost four times more likely to realise AI’s potential.</p> <p>“There is no agentic future without trust and no trust without governance that keeps pace,” said Steve Chase, Global Head of AI and Digital Innovation, emphasising the need for sustained spending on people, training and change management if organisations are to scale AI responsibly.</p> <p>The KPMG findings sit alongside broader market signals that suggest both promise and caution. Morgan Stanley’s 2026 analysis frames AI as a force reshaping macroeconomic investment patterns, projecting almost $3 trillion in global data centre building by 2028 and reporting that adopters among large-cap companies have seen outsized margin gains. The bank’s outlook predicts that productivity improvements tied to AI could drive a sizeable share of near-term global growth as firms move from deployment to concrete application in areas from autonomous vehicles to drug discovery.</p> <p>Yet funding patterns have become more discriminating. S&amp;P Global’s review of generative-AI financing shows investors shifting focus toward infrastructure and silicon suppliers, which have delivered steadier returns, while some enterprise software names face tougher scrutiny as customers demand clearer revenue and ROI prospects. Industry research cited by a market report similarly highlights the emergence of edge AI and robotics as areas of episodic leadership, even as hyperscalers provide more predictable performance.</p> <p>Analysts and industry observers are increasingly attentive to execution risks. Axios noted that 2026 has seen a move from engineering spectacular prototypes toward demonstrating reliable, monetisable outcomes; leaders are racing to pair powerful models with deterministic systems and robust change programmes so AI can operate within complex business processes. The scale of geopolitical competition and the need for secure domestic infrastructure further complicate the landscape, according to Morgan Stanley.</p> <p>Policy and capital flows also matter. A market intelligence report on AI spending trends points to vigorous corporate investment, active venture capital and targeted government support as shaping regional trajectories. That backdrop reinforces KPMG’s contention that data quality, governance, compliance and security must be addressed early if organisations hope to convert pilot success into enterprise-wide transformation.</p> <p>The practical takeaway for companies is clear: continued capital deployment into models and compute will not automatically produce superior outcomes. Firms that pair technology investment with governance frameworks, targeted talent hiring, comprehensive training programmes and deliberate human–AI collaboration have the best chance of turning promise into performance.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69cf3f500c26e23ddd296398Fri, 03 Apr 2026 22:19:51 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/03/autonomous-ai-shopping-agents-are-disrupting-retail-loyalty-and-personalisation<p>The rise of agentic AI systems like Woolworths’ Olive and Loblaw’s ChatGPT integration marks a transformative shift in how retailers implementation personalisation and loyalty rewards, prioritising real-time capabilities to meet evolving consumer expectations.</p><p>In a quiet acceleration of retail’s digital evolution, autonomous AI shopping agents are beginning to interpose themselves between consumers and the point of purchase, changing how loyalty and personalisation operate. Major grocery groups have already moved from experimental chatbots to agentic systems that can plan meals, interpret images, assemble baskets and adjudicate offers on a shopper’s behalf.</p> <p>According to RetailBiz, Woolworths in January became the first Australian retailer to deploy Google Cloud’s Gemini Enterprise for Customer Experience, upgrading its Olive assistant from a service bot into a proactive shopping agent able to process text, voice and images and preserve conversational context across browsing, buying and aftercare. Industry reporting shows Olive will be able to, for example, identify when a cart falls short of a threshold for a promotion and recommend a small add-on to trigger a discount. Woolworths is now among international retailers experimenting with agentic AI to streamline digital commerce; ComputerWeekly and Marketing-Interactive noted the move was unveiled at NRF 2026 and framed as a step toward a more intuitive, task-capable assistant.</p> <p>A parallel initiative in Canada underlines the trend. Loblaw has launched a PC Express shopping app inside ChatGPT, enabling conversational meal discovery and ingredient curation, with product suggestions drawn from local store assortments and the option to add selected items to a cart for fulfilment, according to Loblaw’s announcement reported by GlobeNewswire and GroceryBusiness. The integration is presented as a way to meet shoppers within the conversational interfaces they already use and to make grocery planning more efficient and personalised.</p> <p>These early deployments illustrate a fundamental operational challenge for loyalty schemes. Research cited by RetailBiz shows PayPal Australia found 48 per cent of Australians have used AI assistants to search for products and 78 per cent expect AI shopping tools to become commonplace. Adobe data cited in the same analysis reported e-commerce traffic from generative AI platforms jumped 693 per cent in November–December 2025 versus the prior year. When an automated agent evaluates whether to redeem points, apply a member-only price or switch to a competitor mid-transaction, the speed, clarity and machine-readability of a loyalty system determine whether a programme remains visible and valuable.</p> <p>Historically, loyalty relied on human memory and intermittent engagement, card scans, app visits, manual offer activations, creating friction that protected underlying programme economics through unused benefits. Agents remove that friction. They do not forget or overlook offers; they compute the best financial outcome in real time. If a loyalty engine cannot respond to an agent’s query during the decision window, the agent will simply optimise for the next-best price or offer, effectively rendering slower programmes invisible.</p> <p>That shift exposes a shortfall in many systems marketed as “personalisation”. As RetailBiz argues, a great deal of contemporary personalisation is driven by batch segmentation, refreshed daily or weekly, an approach adequate for human shoppers but insufficient for agentic commerce. Google’s roadmap for agentic consent, which would allow customers to grant agents ongoing access to member pricing and personalised offers, heightens the stakes: API responsiveness and unambiguous, machine-readable rules become competitive levers.</p> <p>Providers of loyalty and personalisation technologies must therefore meet exacting requirements. They need rule sets that are explicit and consumable by machines, not buried in prose. They must deliver sub-second responses at scale to support decision-making during peak loads. They must verify balances, tier status and offer eligibility in real time so agents can trust the outcomes they return.</p> <p>Eagle Eye, the vendor represented by Jonathan Reeve in the lead article, positions its platform around those capabilities. According to the company, its Google Cloud-powered architecture supports issuance and redemption of highly personalised offers in real time across channels, including in-store, and can validate and apply bespoke discounts with sub-250 millisecond response targets at heavy load. The firm frames real-time personalisation not as an add-on but as foundational infrastructure that both humans and machine agents will query continuously.</p> <p>For retailers reworking their loyalty stacks, two practical assessments emerge from this context. First, systems should be able to detect a shopper’s immediate context, location, local conditions, items already in a digital basket, and generate a single-customer personalised offer in the instant that context changes. Second, platforms must validate and apply constrained or conditional discounts, for example, a $5-off-$50 offer available only to specific members, within a fraction of a second while enduring multiple-times-peak traffic. Failure on either front risks ceding transactions to faster, more machine-friendly rivals.</p> <p>The broader implication is clear: visibility to autonomous agents will become a new battleground. Programmes that remain reliant on delayed synchronisation or human-triggered processes are at risk of irrelevance in an environment where agents routinely make purchase choices on behalf of consumers. As retail architectures adjust, real-time personalisation will be the backbone that enables offers to be both meaningful to shoppers and actionable for machines.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69cf3f4f0c26e23ddd296386Fri, 03 Apr 2026 22:19:12 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/03/revolution-in-accounts-payable-as-automation-and-ai-reshape-financial-operations<p>Ottimate’s latest report reveals a shift towards integrated and intelligent AP operations, with finance teams leveraging automation and AI to enhance speed, accuracy, and strategic value, marking a significant evolution from fragmented processes.</p><p>Accounts payable is moving beyond firefighting and fragmented processes toward integrated, intelligent operations, according to Ottimate’s State of AP Maturity Report. The study finds a growing cohort of finance teams embracing automation and AI to accelerate invoice throughput, tighten controls and gain near-real-time insight into cash positions and supplier risk. Ottimate argues these changes are enabling organisations to reduce mistakes, speed approvals and strengthen governance while repositioning AP as a strategic partner to the wider business.</p> <p>Industry observers describe a similar transition from partial digitisation to fully linked AP workflows. MHC Automation says the next phase of evolution centres on end-to-end automation: unified invoice intake, AI-driven data capture and seamless routing that minimise manual touchpoints and improve fraud detection. Quadient highlights complementary trends, including touchless invoice processing, the rise of digital payments and demand for real-time reporting and stronger compliance controls as priorities for 2026.</p> <p>Experts chart an automation maturity curve that begins with basic OCR and workflow routing and advances toward intelligent, autonomous capabilities. ChatFin.ai maps that progression and forecasts a future in which autonomous finance agents handle routine transactions while human staff focus on exception management, supplier relationships and strategic cash optimisation. Fintask’s guide stresses measurable returns from these investments, noting that careful vendor selection and phased implementation are crucial to achieving tangible ROI.</p> <p>Data points from vendors and analysts underscore the momentum. e42.ai reports more than 60% of finance professionals expected full AP invoice automation by 2025, and estimates AI can cut human error in invoice processing by up to 40%. Zahara and other providers emphasise gains in speed, auditability and visibility when purchase orders, invoices and payments are connected, enabling finance teams to close books faster and provide timely cash-flow updates to stakeholders.</p> <p>Adoption is not uniform. Ottimate’s report distinguishes high performers, organisations that combine AI, process redesign and integrated platforms, from laggards still reliant on manual entry and siloed systems. The gap often reflects differences in executive sponsorship, supplier onboarding and willingness to reengineer legacy workflows rather than simply layering new tools on top of old processes.</p> <p>Security and fraud prevention remain central concerns as automation proliferates. Several industry commentaries warn that stronger authentication, end-to-end encryption and built-in compliance controls must accompany automation to mitigate emerging threats. At the same time, embedded payments and automated supplier payouts are accelerating, creating new opportunities to reduce days-payable-outstanding and strengthen vendor relationships when implemented alongside robust controls.</p> <p>For finance leaders, the practical path forward combines technology with governance and change management. Vendors and analysts recommend starting with quick-win automations, invoice capture and matching, while planning for scalable, AI-enabled capabilities and supplier adoption programmes. When executed well, the shift delivers lower operational cost, improved accuracy and a clearer line of sight into cash and risk, allowing AP to evolve from a transactional function into a proactive driver of financial performance.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69cf3f4f0c26e23ddd29638aFri, 03 Apr 2026 22:12:23 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/03/ikeas-ai-driven-role-transformation-boosts-revenue-and-retains-jobs<p>IKEA's strategic shift from traditional customer support to AI-enhanced roles has unlocked significant new revenue while re-skilling staff, demonstrating a model for responsible automation.</p><p>IKEA has shifted its response to routine customer enquiries from headcount reduction to role transformation, a strategy the company and industry observers say has unlocked substantial new revenue while preserving jobs.</p> <p>According to a report by StartupTalky, the Swedish retailer introduced an AI chatbot called Billy to take on basic tasks such as order-status checks and product information. StartupTalky said the automation freed up large volumes of customer-service capacity and allowed the business to focus human effort where judgement and creativity are required. In an interview cited by StartupTalky, digital futurist Brian Solis of Info-Tech Research Group described the change as significantly easing the operational burden on support teams.</p> <p>Published accounts differ slightly on the scale of Billy’s take-up: NewsBytes reported the bot now handles more than half of customer interactions, while other summaries put the figure between about 47% and 57%. LinkedIn News noted IKEA began retraining staff in 2021, and TwinLadder’s case study said some 8,500 contact-centre employees were reskilled as interior-design advisers. Industry analysts point out that such variance is common when companies aggregate metrics from different channels and time periods.</p> <p>Rather than moving displaced staff to unemployment, IKEA redeployed many former frontline agents into paid advisory roles offering personalised home-design guidance. According to StartupTalky and PeopleMatters, this shift created a paid consulting model; multiple sources estimate the initiative generated roughly €1 billion in additional revenue in its first year. TwinLadder’s research suggested a comparable uplift, citing about $1.4 billion in incremental sales tied to the reskilling programme.</p> <p>The approach contrasts with firms that have relied on automation primarily to cut labour costs. Some technology-driven customer-service rollouts, such as those reported at fintech firms, initially reduced headcount but later encountered quality issues that prompted a partial return to human agents. IKEA’s model, by contrast, is being held up by commentators as an example of automation augmenting rather than replacing human capability: the bot delivers speed and scale, while trained advisers handle consultative, higher-value work.</p> <p>Company statements emphasise investment in learning and mobility. As Info-Tech Research Group and PeopleMatters reported, IKEA frames its steps as part of a broader commitment to continuous development and internal career progression. Observers say the result is a dual benefit for the retailer: operational efficiencies from automation alongside new service revenues underpinned by human expertise.</p> <p>The IKEA case illustrates a wider debate about how businesses deploy AI. According to industry commentary, the outcome appears to depend less on the technology itself and more on corporate choices about workforce design, training and the creation of monetisable services. Where companies treat automation as an opportunity to upgrade roles and offer new products, the evidence from IKEA’s reported figures suggests it can expand the business while limiting job losses.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69cf3f4f0c26e23ddd296394Fri, 03 Apr 2026 22:11:47 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/01/alibaba-launches-large-scale-autonomous-ai-digital-employees-for-e-commerce-merchants<p>Alibaba has begun equipping millions of Taobao and Tmall sellers with autonomous AI 'digital employees', signalling a major shift towards agentic AI in online retail and commerce operations, with potential to revolutionise merchant practices and customer interactions.</p><p>Alibaba has begun equipping millions of Taobao and Tmall sellers with autonomous AI "digital employees", marking one of the largest commercial rollouts of agentic artificial intelligence in e-commerce to date. According to Tech Wire Asia, the new capability extends the company’s merchant tools into a continuously operating layer that can respond to customers, distribute vouchers and adjust prices without direct human prompts.</p> <p>The productisation follows a company restructure in mid‑March that brought together Alibaba’s Tongyi Laboratory, the Qwen AI team and its Wukong enterprise platform under a single business group, a move Alibaba says is intended to accelerate what it calls an "agentic era". The Wukong platform, introduced shortly after the reorganisation, is described by the company as an AI‑native environment able to coordinate multiple agents to complete complex tasks within a unified interface. According to Tech Wire Asia, Alibaba plans to supply its merchant AI services with one trillion tokens to enable proactive, transaction‑oriented behaviour rather than simple reactive chat.</p> <p>Executives framed the shift as the transition from AI as an assistive tool to AI as an operating partner. Xu Haipeng, who oversees the two marketplaces, told merchants at Tmall’s TopTalk summit that autonomous, execution‑oriented systems are now operational and that, over the next one to two years, e‑commerce practices will increasingly involve collaboration between humans and "digital employees". That language signals Alibaba’s intent to position these agents as continuous, accountable actors in merchants’ operations rather than occasional productivity aids.</p> <p>Alibaba’s merchant ML offerings have already seen broad uptake. Tech Wire Asia reports that over the past year roughly five million merchants used the platforms’ AI tools, which Alibaba estimates have cut costs by about 100 billion yuan. Its customer service agent, Dianxiaomi, has interacted with some 300 million customers and is credited with boosting transaction conversion rates by roughly 30%, figures that Alibaba and reporting outlets cite as evidence of meaningful commercial impact.</p> <p>The deployment also reflects a strategic advantage grounded in systems integration. Analysts and industry coverage note that Alibaba can route customers from discovery to payment and logistics within its own ecosystem , Taobao, Tmall, Alipay and Alibaba Cloud are being exposed as modular skills to its agents , enabling end‑to‑end workflows without third‑party handoffs. That stack contrasts with many Western enterprise deployments, which often remain fragmented across vendors and integration agreements, and is central to Alibaba’s claim that agents can carry out full transactions autonomously.</p> <p>The consumer side of Alibaba’s AI has been developed in parallel. In January, Alibaba upgraded its Qwen chatbot with agentic features that let it perform tasks such as ordering food and booking travel across the group’s services, according to Digital Commerce 360 and payments coverage in PYMNTS. Those reports describe added payments and service integrations that allow in‑chat purchases and bookings, underscoring how the company is knitting conversational agents into transactional flows.</p> <p>Market forecasts underline the commercial stakes. Industry projections cited by reporting outlets suggest China’s AI agent market could expand from under US$1 billion in 2024 to more than US$30 billion by 2028, with enterprise applications expected to attract the majority of investment. Gartner has estimated a rapid uptake of task‑specific agents in enterprise software in the near term, a trend that Alibaba’s merchant rollout appears designed to exploit.</p> <p>Regulatory and trust challenges remain part of the backdrop. Taobao has previously moved to curb the misuse of AI‑generated product imagery, an issue highlighted by the South China Morning Post, and Alibaba’s earlier tests of generative tools for merchant marketing are ongoing. Observers say policing content authenticity and ensuring transparent agent behaviour will be critical as autonomous functions take on customer‑facing roles and transactional authority.</p> <p>For enterprise teams watching the market, Alibaba’s move provides a practical demonstration of how quickly pilots can scale when models, payment rails and fulfilment systems are already integrated. Company executives have publicly argued that a small number of people, aided by agentic AI, could manage very large businesses; the Taobao and Tmall merchant deployment offers the first substantive illustration of that thesis in operation, even as questions about oversight, content trust and cross‑border applicability persist.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69cc9ac1ae9471fe4c45da71Wed, 01 Apr 2026 08:38:27 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/01/mark-anthony-group-leverages-snowflakes-ai-driven-data-integration-to-transform-decision-making<p>Beverage producer Mark Anthony Group is implementing a company-wide data transformation, harnessing Snowflake’s cloud platform and AI tools to streamline operations, enhance insights, and reduce costs across its brands like White Claw and Mike’s Hard Lemonade.</p><p>Beverage producer Mark Anthony Group has embarked on a company-wide data overhaul that its technology partner Snowflake says is intended to break down long-standing information silos, speed decision-making and lower overall IT costs.</p> <p>According to a Snowflake blog post, the project began by consolidating information previously scattered across regional and functional systems into a single cloud data platform. The move replaced irregular flat‑file exchanges with direct, near real‑time integrations to trading partners and suppliers, a shift Snowflake says has improved reliability, reduced manual handoffs and made it easier to surface and correct data errors at their origin. That, in turn, has given teams quicker access to consistent figures for inventory, demand and customer activity, areas management regards as critical when overseeing brands such as White Claw and Mike’s Hard Lemonade at scale.</p> <p>A central plank of the work has been a semantic layer that enforces shared terminology and aligns definitions across the enterprise. By linking MAG’s business glossary and data catalogue to the platform, the company aims to ensure queries produce comparable results regardless of local phrasing or the user’s technical skill. Snowflake describes that foundation as a precondition for reliable enterprise AI, echoing the argument that advanced analytics deliver value only after organisations have created a single source of truth.</p> <p>On top of this basis, MAG is piloting a tailored implementation of Snowflake Intelligence: a global application that wraps the vendor’s intelligence engine with a simplified, business‑facing interface. The pilot reportedly exposes text‑to‑SQL capabilities , and in some cases voice queries , so commercial teams can interrogate data in plain English without needing to write queries. Features highlighted include dataset explainability, contextual descriptions of underlying logic, mobile access and integration into existing collaboration tools so insights can be surfaced inside workflows rather than through separate portals.</p> <p>The company has also begun to automate responses to operational signals, applying machine learning models to accelerate actions such as inventory adjustments and supply‑chain remediation. Snowflake says this reduces manual intervention and shortens the time between insight and execution, while internal users predict the approach will uncover new revenue opportunities and surface operational inefficiencies more quickly. “I now have quicker access to all my data, which will help me across so many different initiatives,” Wong said. “What are some new revenue opportunities? What are some operational inefficiencies we can target? How do I improve product quality now that I have greater insights into it? It’s going to trigger a new utilization of data that we haven’t had before,” he added. “That’s going to fundamentally change our business processes and workflows, bringing to life a vision of agentic enterprise.”</p> <p>Snowflake’s account of MAG’s programme sits within a broader industry push to marry cloud data platforms with generative AI and operational tooling. Snowflake has promoted its Intelligence offering as a retail and consumer‑goods solution, and the vendor’s multi‑year relationship with Amazon Web Services is framed as a means of delivering AI‑ready infrastructure. According to Snowflake, partnerships such as its strategic tie‑up with Accenture are directed at helping enterprises scale AI and extract business value more rapidly, while a separate integration with Palantir aims to simplify the pipeline from raw data to deployable analytics by improving interoperability and cutting data management overheads.</p> <p>Industry events and webinars hosted by Snowflake reinforce the message that fragmented, duplicated or untrusted datasets are the main barrier to enterprise AI adoption and that a unified data layer is essential for broader AI initiatives to succeed. Organisers point to early adopters that are moving beyond point‑use cases towards agentic workflows that stitch together data, models and automation to generate measurable ROI.</p> <p>For MAG, the immediate priorities appear to be expanding the Intelligence pilot, rolling out the semantic standards globally and embedding automated decisioning into routine processes. If the promised reductions in total cost of ownership and faster insight‑to‑action cycles are realised, the project could provide a blueprint for other consumer goods firms wrestling with complex supply chains and dispersed IT estates. However, observers caution that realising the benefits of such programmes requires sustained governance, clear data ownership and ongoing investment to keep definitions and integrations up to date as products, channels and partners evolve.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69cc9ac1ae9471fe4c45da63Wed, 01 Apr 2026 08:37:20 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/01/irish-firms-face-challenges-in-scaling-ai-for-transformational-growth<p>Despite significant investment, Irish companies struggle to convert AI pilots into meaningful financial gains, highlighting a need for wider deployment and strategic reinvention, says PwC Ireland.</p><p>‘The real power of AI lies in using it to tackle the complex challenges rather than more simple use cases and reinventing how we do things’ , Jonathan Hayes, Director, AI, PwC Ireland.</p> <p>Despite heavy investment and widespread experimentation, Irish firms have so far struggled to convert AI programmes into clear financial gains, according to a series of PwC surveys and reports. The PwC 2026 CEO Survey finds that just 17% of Irish chief executives said AI delivered increased revenues in the previous 12 months, well below the global figure of 29%. Cost reductions were also modest: 23% of Irish CEOs reported savings from AI, compared with 26% internationally. Only 9% of Irish leaders report broad application of AI across multiple business functions. According to the report by PwC Ireland, these shortfalls point to a widening gap between pilots and scaled deployment.</p> <p>PwC’s analysis emphasises that companies which have embedded AI across products, services and customer experiences see measurable financial rewards. A global PwC study cited by the firm found that organisations applying AI widely achieved nearly four percentage points higher profit margins than peers that had not. Yet the local data suggests many Irish organisations remain at the early stages of that journey.</p> <p>Agentic AI , autonomous, decision-capable systems , is central to the potential step-change. PwC Ireland’s AI Agent Survey 2025 shows substantial optimism about agentic technologies: 54% of business leaders expect agents to deliver a significant competitive advantage in the year ahead, and 70% plan to increase AI-related budgets. However, actual transformational use remains limited. Only 16% of Irish respondents are developing new agentic products and services, and just 11% are redesigning processes around agents. Fewer than 9% report broad adoption of AI agents, a stark contrast with 52% reported in the United States.</p> <p>The surveys point to a pattern: firms that restrict AI to isolated efficiency projects rarely capture the broader value available from reinvention. PwC Ireland’s report Turning AI into Real Returns highlights the importance of building robust foundations , integrated technology environments, responsible AI frameworks and enterprise governance , if organisations are to turn AI experiments into sustained revenue and margin improvements. The 2026 global survey similarly finds that only a small minority of CEOs report both cost and revenue benefits from AI, underscoring that scale, not experimentation alone, drives returns.</p> <p>Practically, AI is proving most potent where it enables personalised offerings and accelerates customer journeys rather than replacing human relationships. PwC’s findings suggest AI can cut routine friction from sales processes so customers reach the right expert faster, enabling firms to convert engagement into higher-value interactions. When applied across product development, delivery and customer experience, AI also opens the door to smarter subscription models and platform ecosystems that diversify and stabilise revenue streams.</p> <p>Yet the shift required is cultural as much as technical. Jonathan Hayes at PwC Ireland argues that the real value lies in deploying AI to address complex problems and reimagine business models, not merely automating simple tasks. The surveys echo that view: as long as most activity remains in pilots or narrow use cases, the economy-wide impact will be muted.</p> <p>The broader economic outlook adds pressure. The PwC 2026 Global CEO Survey reports falling confidence in near-term revenue growth, with only 30% of CEOs confident about revenue prospects over the next 12 months, down from 38% in 2025. Geopolitical uncertainty and cyber threats also feature as rising concerns, factors that make effective, well-governed use of AI both more urgent and more challenging.</p> <p>For Irish businesses, the data suggests a clear pathway: move beyond isolated proofs of concept, invest in enterprise-grade infrastructure and governance, and redesign operating models to capture agentic AI’s strategic potential. Firms that make that transition stand to gain not only incremental efficiencies but fundamentally different ways of creating value , from tailored customer experiences to new product-led revenue streams. Until those changes become widespread, however, the promise of AI in Ireland will remain only partially realised.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69cc9ac1ae9471fe4c45da53Wed, 01 Apr 2026 08:36:42 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/04/01/gloat-launches-agentic-hr-platform-focused-on-real-time-context-aware-workforce-decisions<p>Gloat unveils the Agentic HR Platform, a groundbreaking AI-driven solution that embeds nine years of enterprise-specific workforce context to enable proactive talent management within existing collaboration tools and HR systems.</p><p>Gloat has unveiled what it describes as a new class of workforce technology: the Agentic HR Platform, which the company says puts context at the centre of AI-driven talent management rather than layering intelligence on top of existing HR systems. According to the announcement, the platform’s agents carry nine years of enterprise-specific workforce context and are designed to identify and act on talent opportunities and risks in real time, operating inside the collaboration tools employees already use.</p> <p>Ben Reuveni, Co-Founder and CEO of Gloat, framed the distinction between ordinary automation and what the company calls “agentic” HR sharply: "Everyone has agents now. Very few have context. An agent without context is just a chatbot that clicks buttons. What makes a workforce agent intelligent isn’t the model – it’s whether it understands your people, your policies, your org structure, your business logic, your approval chains. That’s what Loomra provides. Nine years of enterprise-specific workforce context that you cannot prompt-engineer into existence. That’s the difference between an agent that acts and an agent that knows what it’s doing."</p> <p>Gloat’s offering centres on Loomra, described as a workforce context engine that fuses a knowledge graph, semantic embeddings, skills inference, career-trajectory modelling and enterprise-scale matching. The company says this underlayer allows agents to do more than fetch records: they can reason about complex workforce decisions and recommend or initiate actions such as redeployment, reskilling or personalised retention interventions.</p> <p>The platform also includes an Agent Builder for creating and orchestrating agents. Gloat highlights prebuilt templates for common HR workflows , Workforce Redeployment, Career Development, Internal Talent Sourcing, Succession Planning and Learning &amp; Reskilling , and a no-code Agent Studio intended to enable HR teams to assemble bespoke agents without software engineering support. Gloat emphasises that agents work “in the flow of work”, surfacing recommendations inside Microsoft Copilot, Teams, Slack, Google Chat and other collaboration tools, while integrating with HCM systems such as Workday, SAP SuccessFactors and Oracle without replacing them.</p> <p>Gloat positions the product as a corrective to what it describes as an industry tendency to treat HR innovation as transactional automation. In the company’s view, many current agent initiatives simply reduce administrative friction rather than proactively shaping workforce supply to meet shifting business demand. Josh Bersin, the HR analyst, endorsed Gloat’s approach to layering context above HCM workflows: “Gloat is embarking on a category-creating mission. Rather than build agents on top of existing systems and data structures, Gloat has built a context layer which in turn talks to the typical workflows of HCM. This infrastructure, along with Gloat’s tools to automate agent building into Teams, Slack, MS Copilot, and other existing products, could bring Agentic HR solutions to market with speed and flexibility we have not seen before.”</p> <p>The company’s own materials and related product pages outline specific agent use cases that illustrate the shift from insight to action. According to Gloat’s blog and product documentation, agentic capabilities include autonomous internal talent sourcing, dynamic matching of employees to roles and learning opportunities, predictive identification of flight risk, and automated, personalised outreach to retain or redeploy people. The Talent Insight Agent, for example, is presented as a tool that synthesises disparate HR signals into concise manager-facing briefs on team health, skill gaps and attrition risk, while enforcing policy and approval logic.</p> <p>Gloat has been building toward this moment for several years. The firm’s customer and industry pages point to global deployments across more than 120 countries, adoption by major enterprises and recognition for its Workforce Intelligence suite, which won a Human Resource Executive Top HR Product award in 2022 for its ability to harmonise datasets from multiple HR systems and external market sources. According to Gloat’s case materials, those capabilities underpin the contextual models the company now surfaces through agents.</p> <p>Industry guidance from Gloat also reflects a practical stance on adoption. The company offers training and an Agentic HR Academy that provides a glossary, competitive comparisons and a roadmap for organisations assessing readiness and planning implementation. Gloat’s own best-practice advice, echoed in its thought leadership, stresses governance, alignment with business goals and careful orchestration of workflow changes rather than wholesale replacement of HCM platforms.</p> <p>Critically, the announcement leaves several operational and governance questions for buyers. While Gloat describes tight policy enforcement and approval chains as native features, organisations will still need to evaluate data integration depth, privacy and compliance controls, and the human oversight mechanisms that govern autonomous interventions. The broader market is already seeing a proliferation of vendor claims about agentic capabilities; independent verification of outcomes such as improved internal mobility, reduced attrition or faster redeployment will be important for HR leaders considering the technology.</p> <p>Gloat argues the pace of change in roles and skills demands more continuous workforce adjustment than legacy HR tools were designed to provide. In its framing, agentic HR is less a product category and more an operational shift: moving from episodic programmes to continuously operating capability that aligns people to business priorities as they change. Whether that promise translates into measurable improvements across diverse enterprises will depend on implementation, data quality and the maturity of organisational workflows.</p> <p>According to Gloat, the platform is intended to complement rather than displace existing HCM systems, providing a semantic reasoning layer that can bridge organisational data silos and act where conventional HR software cannot. The company claims this approach will allow enterprises to respond faster to shifting priorities, surface internal opportunities earlier and intervene on retention risks before they crystallise.</p> <p>As organisations weigh a new generation of AI-powered HR tools, the debate is likely to centre on two linked questions: can agentic systems reliably make and execute workforce decisions at scale, and can companies build the governance to ensure those decisions align with policy, fairness and business strategy. Gloat’s launch brings one particular answer to market , a context-rich agent architecture that the vendor says is designed to close the gap between insight and action , but the broader proof points will emerge as customers deploy the agents in live, complex environments.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69cc9ac1ae9471fe4c45da4fWed, 01 Apr 2026 08:36:21 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/26/agentic-ai-revolutionising-procurement-with-proactive-autonomous-capabilities-by-2026<p>The rise of agentic AI is set to transform procurement into a proactive, strategic function, automating complex workflows and driving innovation amid global uncertainties, with widespread adoption expected by 2026.</p><p>Artificial intelligence is rapidly reshaping procurement, with the emergence of agentic AI marking a significant leap beyond traditional generative models. Unlike reactive generative AI, which depends on human prompts to produce content or provide answers, agentic AI operates proactively, autonomously reasoning, planning, and executing complex workflows with minimal human oversight. This transformation positions agentic AI as a critical enabler for procurement functions striving to enhance efficiency, resilience, and strategic value in an increasingly complex global environment.</p> <p>Procurement leaders today grapple with mounting pressures to extract greater value across wider operational areas despite constrained resources. Agentic AI presents a compelling solution by automating time-consuming, tactical tasks traditionally handled by procurement teams. This automation frees personnel to concentrate on higher-level strategy, such as supplier innovation, risk mitigation, and sustainability initiatives. Industry analyses underscore that agentic systems can manage entire procurement cycles, from sourcing to contracting and supplier management, independently, accelerating decision-making while reducing manual workloads.</p> <p>According to insights from McKinsey, procurement’s evolving landscape, with its geopolitical uncertainties, regulatory complexities, and overwhelming data flows, demands smarter, more agile approaches that move beyond transactional workflows. Agentic AI addresses these challenges by enabling proactive insights and seamless cross-system integrations that support dynamic planning and execution. Procurement leaders must therefore transform their operating models to fully harness agentic AI’s potential, positioning the function as a strategic driver for growth, sustainability, and organisational resilience.</p> <p>Echoing this perspective, research and thought leadership published by procurement technology providers highlight the foundational distinction between conventional AI agents and agentic AI. While standard AI agents assist with discrete tasks, agentic AI independently sets objectives, formulates and adapts plans, and carries out processes end to end. This shift translates into procurement operations becoming more predictive and intelligent, capable of autonomously identifying supplier opportunities, undertaking negotiations, and foreseeing risks before they materialise.</p> <p>Forecasts and industry roadmaps suggest that by 2026, agentic AI will transition from an emerging technology to an indispensable tool embedded in procurement strategies. Procurement functions leveraging agentic AI are expected to outpace competitors in agility and innovation, shifting from reactive operations to proactive value creation. This evolution is supported by expanding investments in education and capability-building; for example, academic institutions such as the University of Texas at Austin’s McCombs School of Business are introducing specialist courses designed to equip procurement professionals with the skills needed to integrate agentic AI into strategic sourcing and supply chain management.</p> <p>Implementing agentic AI also promises to transform procurement’s data landscape, enabling the management and analysis of vast real-time datasets with enhanced precision, as noted by technology firms like IBM. This capability is critical in today’s volatile supply chains where rapid response to disruptions and nuanced supplier insights can spell the difference between success and failure. By automating complex decision-making processes, agentic AI positions procurement as a competitive advantage, moving beyond operational efficiency toward driving innovation and long-term resilience.</p> <p>In conclusion, while procurement teams have begun exploring AI’s possibilities, the emergence of agentic AI represents a strategic inflection point. Organisations that embrace these technologies stand to unlock unprecedented value by delegating routine workflows to intelligent agents, fostering a shift from labour-intensive operations to strategic leadership. The procurement function’s agentic future promises not just incremental improvements, but a fundamental reinvention of how value is generated and sustained in global supply chains.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69c4bffe58cfa3564cf8d02cThu, 26 Mar 2026 22:01:51 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/26/marks-spencer-deploys-ai-to-transform-store-management-and-boost-customer-focus<p>Marks &amp; Spencer is rolling out generative AI tools across 11,000 employees to streamline operations, enhance customer service, and redefine store management in the digital age.</p><p>Marks &amp; Spencer (M&amp;S) is advancing its digital transformation with the rollout of artificial intelligence tools designed to optimise daily operations for store managers and support centre staff. The retailer announced it will equip around 11,000 employees with generative and agentic AI technologies intended to streamline labour-intensive tasks, thereby enabling frontline managers to focus more on customer service and team support.</p> <p>The new AI capabilities are set to assist with a range of functions such as analysing sales data, summarising meeting notes, and automating shift scheduling and handovers. By harnessing these tools, M&amp;S aims to reduce the administrative burdens that have traditionally constrained store managers, freeing them up to enhance stock availability and customer experience. Chief Executive Stuart Machin highlighted the integration of AI as a strategic pillar in the company’s broader technology overhaul. “Scaling the use of AI is central to our technology transformation and today’s announcement is just one of the steps forward on that journey,” Machin stated.</p> <p>M&amp;S's plans are supported by a significant investment in Microsoft’s AI ecosystem, with the acquisition of 11,000 Microsoft 365 Copilot licenses to embed AI directly into the workflows of its workforce. This partnership reflects a growing trend among large enterprises to adopt AI solutions that deliver actionable insights rapidly, consolidating information from disparate sources to empower decision-making at various organisational levels.</p> <p>The deployment of AI tools for store managers echoes developments in sales and operational intelligence platforms like Goran and Jamy, which showcase how AI listens to sales interactions, identifies effective dialogue patterns, and produces enriched meeting summaries. Goran, for example, transforms sales call data into actionable coaching playbooks, enabling leadership to base training on proven best practices backed by data rather than intuition. Similarly, Jamy’s AI meeting assistant captures and transcribes conversations across multiple virtual platforms, generating structured summaries and extracting key action points, with capabilities extending to real-time translation to support international teams.</p> <p>In the retail and hospitality sectors, centralised management tools such as Syrve supplement such AI workflows by offering workforce scheduling, performance analytics, and campaign management across multiple locations from a single dashboard. This integration of AI-driven insights with operational platforms is crucial for organisations aiming to maintain agility, optimise labour allocation, and continually enhance customer-centric services.</p> <p>The adoption of conversation intelligence (CI) technologies is set to accelerate across industries. CI software automatically records and analyses sales and service calls to identify objection patterns, key messaging strategies, and critical moments that influence deal outcomes. Industry analysts project that conversational AI and generative tools will transition from pilot phases into widespread operational deployments by 2025, with Gartner predicting that 85% of customer service leaders will explore or trial such technologies this year.</p> <p>Furthermore, AI meeting assistants such as Supernormal and meeting lifecycle platforms like WeconnectU are redefining how organisations handle internal collaboration. These tools offer fully automated transcription, agenda management, minute-taking, and task assignment, transforming meetings from administrative necessities into strategic instruments that improve alignment and accountability.</p> <p>As M&amp;S embraces AI to enhance its internal processes, the company exemplifies how retailers are increasingly tapping into cutting-edge technology not only for external marketing and inventory management but also to empower their workforce directly. By simplifying data complexity and automating routine tasks, AI promises to boost operational efficiency and elevate the role of store managers into more customer-focused leaders, an essential evolution as retailers navigate a highly competitive and digitally driven marketplace.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69c4bffd58cfa3564cf8d01eThu, 26 Mar 2026 22:00:13 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/26/how-solo-founders-are-accelerating-growth-with-generative-ai<p>Two solo entrepreneurs reveal how generative AI is transforming their ability to scale operations, attract clients, and make strategic hires by codifying tacit knowledge and enhancing discoverability.</p><p>For solo founders accustomed to handling every aspect of their businesses, generative artificial intelligence has begun to change the arithmetic of growth. Two small-business owners in different sectors say AI has helped them codify tacit knowledge, attract clients and make their first hires , moves that, they contend, have materially expanded capacity without diluting service.</p> <p>Katherine Pomerantz, who runs Money Storyteller, told Business Insider that after nearly a decade of operating alone she delayed hiring because she found it difficult to translate her strategic, voice-driven client work into repeatable tasks. "Being able to turn it into a step-by-step workflow for someone else, I just don't think that way," she said. After adopting ChatGPT, she used the tool to analyse call recordings, emails and brainstorming notes and to surface recurring patterns in how she advises clients. According to the Business Insider report, Pomerantz then converted those insights into procedural documents and decision guides that an administrator can use to prepare responses or triage client queries before she intervenes.</p> <p>The result, she says, was operational leverage. Pomerantz hired an administrator in the United States and a bookkeeper in the Philippines; she told Business Insider the new team helps produce roughly twice the output she managed solo. She also uses AI to generate preliminary briefings when clients ask technical questions about financial choices, allowing her to focus her time on high-value decisions while clients receive prompt, detailed information. "We want them to feel like they get a lot more of my time than they do," she said.</p> <p>In New York, Danielle Nazinitsky of Decode Real Estate has taken a different but complementary approach. Speaking to Business Insider, she described ChatGPT as "a referral engine" after discovering prospective clients were being led to her name via generative-AI results. That prompted a deliberate investment in search visibility and brand content: she engaged a GEO firm, increased blog output and encouraged reviews to strengthen her presence on Google and in AI-driven discovery tools. According to reporting by Side, that focus on SEO played a role in winning a $12 million listing pitch, illustrating how digital discoverability can translate into high-value opportunities.</p> <p>Nazinitksy also deploys AI for marketing and for enhancing property presentations. Reporting in SFGate notes she has used AI-powered staging and listing-copy tools to make older or modest properties more attractive, while cautioning that automated approaches lack the nuance of human judgement and can render content impersonal if overused. Nazinitsky told Business Insider she is careful to reserve specialist work , press releases, branding and transactional oversight , for paid professionals, arguing that outsourcing to true experts often yields better results than relying on AI alone. "When I spend the money on an actual expert, I get a much better result," she said.</p> <p>The two founders’ stories show a recurring theme: AI is most valuable where it externalises routine elements of expertise, turning implicit processes into templates that less experienced staff or contractors can follow. Industry examples back that up; profiles and interviews with Pomerantz and Nazinitsky, including a podcast in which Pomerantz outlines her Money Storyteller Method, emphasise storytelling and process design as central to scaling professional services. Customer reviews on platforms such as Zillow reflect the role of reputation and client experience in generating repeat business and referrals, reinforcing the need to combine digital tactics with credible, human-led service.</p> <p>At the same time, both founders illustrate the bounds of automation. They use generative models to augment, not replace, human judgement: Pomerantz checks and signs off on final decisions; Nazinitsky hires specialists for high-stakes work. For solo founders considering their first hires, these examples suggest a hybrid path , use AI to extract and organise know-how, then invest selectively in people who can apply that scaffolded expertise with care and commercial judgement.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69c4bffd58cfa3564cf8d00eThu, 26 Mar 2026 21:58:59 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/24/agentic-ai-transforms-online-retail-with-proactive-multimodal-shopping-assistants<p>The next phase of online retail is driven by 'agentic' AI platforms that autonomously guide shoppers through purchase journeys, promising to enhance conversion rates and reduce support costs amid rising adoption across the sector.</p><p>The next phase of online retail is being shaped less by product pages and more by conversational engines: AI agents that guide, persuade and support shoppers across the entire customer journey. Retailers and startups are now deploying “agentic” commerce platforms that act autonomously, surfacing choices, completing multi-step purchases and handling post-sale service, aiming to replicate the informed, proactive assistance shoppers once received only in premium stores.</p> <p>According to a ranking published by RetailBoss, Crescendo.ai tops the list of current offerings. The company markets an enterprise-grade, multimodal assistant that operates via voice and chat in dozens of languages and, the firm claims, delivers near-perfect response accuracy. Crescendo’s own materials assert a 99.8% accuracy rate and dramatic uplifts in conversion metrics; the company also highlights chat-to-order conversion figures far above conventional benchmarks and is demonstrating the product at NRF 2026. Those performance claims, presented by the vendor, position the assistant as a tool that can convert conversational support from a cost centre into a direct revenue channel.</p> <p>Fashion-focused platforms are among the clearest examples of how agentic AI is being tailored to category needs. Daydream, profiled in the RetailBoss list and covered by Vogue Business and Forbes, has built a chat-first agent expressly for apparel. The company says its system interprets nuanced style signals, colour, cut and occasion, and combines frontier models such as OpenAI and Google Gemini with proprietary and open source components. Daydream also reports a large partner network at launch and has attracted significant investor backing, signalling strong market interest in specialist, discovery-led experiences.</p> <p>Not all successful deployments target discovery alone. Shop.app, the Shopify-led consumer app, is notable for stitching together multi-store discovery with unified checkout and real-time order tracking, offering continuity for shoppers already embedded in that ecosystem. Other vendors highlighted by RetailBoss show a range of strategic emphases: proactive engagement and intent detection from Rep AI; visual styling and catalogue automation from Vue.ai; knowledge-base driven instant Q&amp;A from eesel AI; and customer-experience automation from Gorgias AI that blends sentiment-aware responses with omnichannel ticketing.</p> <p>Some platforms aim at merchants focused on scale and efficiency rather than consumer-facing novelty. Ringly.io emphasises CRM integration and automated, personalised outreach to maintain loyalty without proportionate headcount growth, while Magicdoor.ai targets the informed buyer with multi-model research tools for complex purchase decisions. Across the field, vendors frame these agents not only as conversion engines but as mechanisms to reduce support costs and free human agents for higher-value tasks.</p> <p>Industry claims about accuracy and conversion gains should be read with editorial caution. Several of the strongest performance statistics in circulation come from company-released data or marketing materials; independent, category-wide benchmarks remain limited. That caveat notwithstanding, market observers and trade reporting indicate a clear pattern: retailers investing in agentic interfaces can expect a mix of improved discovery, fewer abandonment events and richer post-sale engagement when systems are properly integrated with product data and fulfilment workflows.</p> <p>As these technologies proliferate, the practical test will be whether agents sustain trust and relevance at scale, accurately reflecting inventory, pricing and delivery realities while handling exceptions gracefully. For now, the vendors leading the conversation are those combining deep vertical knowledge, multimodal model stacks and tight operational integration with merchants’ existing systems, a combination that increasingly defines competitive advantage in the conversational layer of commerce.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69c21e66d85cb0be34fdc3a2Tue, 24 Mar 2026 15:02:34 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/24/agentic-ai-ushers-in-new-era-of-autonomous-business-processes<p>As enterprises transition from rule-based systems to agentic AI, they face the challenge of integrating autonomous decision-making into existing operations, promising significant efficiency gains but demanding robust governance and strategic adoption.</p><p>Enterprises that have spent the past decade layering robotic process automation, workflow engines and machine learning are now confronting a distinct inflection point: moving from rule-bound automation to agentic AI capable of planning, deciding and executing multi-step processes with minimal human hand-holding. According to the lead analysis provided, this next wave of automation seeks to shift responsibility for end-to-end business flows from human supervisors and discrete bots to autonomous AI agents that coordinate across applications and teams.</p> <p>The promise is substantial. Industry estimates cited in the lead piece indicate AI-driven automation can lift operational efficiency by 40–60% and trim costs by as much as 30%. Such figures are echoed in specialist coverage noting how agentic systems can transform functions from IT service management to customer support and finance by automating ticket routing, powering intelligent virtual agents, surfacing proactive insights and orchestrating downstream workflows. According to TechRadar Pro, these capabilities reduce manual workloads while improving resolution speed and accuracy.</p> <p>Realising those gains requires more than model performance; it demands integration with existing enterprise architecture. TechRadar Pro’s examination of operationalisation stresses that agentic AI must be embedded into current applications and human processes rather than run as isolated experiments. Low-code platforms are highlighted as an accelerant, enabling faster, repeatable deployment of agents through reusable components and visual orchestration, and allowing business teams to contribute without deep engineering effort.</p> <p>Practical deployment also raises governance and transparency challenges. Sources emphasise that enterprises must retain control over compliance, auditability and data lineage as agents take on decision-making. Industry vendors describe intelligent workflow platforms that support dynamic routing, real-time adjustments and continuous learning from outcomes so decisions remain traceable. According to NICE, these platforms enable workflows that adapt to context and coordinate steps across systems and teams while providing mechanisms for oversight and improvement.</p> <p>Sector use cases point to where agentic AI can create competitive advantage. TechRadar Pro and Informatica coverage illustrate applications across healthcare, retail, insurance and finance: optimising care plans, anticipating inventory needs, accelerating claims handling and detecting fraud. Informatica’s analysis further argues that robust data integration and governance are prerequisites for reliable agent behaviour, enabling faster, smarter decisions while limiting operational risk.</p> <p>Organisational readiness therefore becomes as much about platforms and data as about models. Successful adopters will combine enterprise-grade integration, low-code orchestration, clear accountability frameworks and continuous monitoring to move from pilots into production. Forecasts suggest the pace of adoption will quicken: analysts expect embedded AI agents and democratised creation tools to make agentic capabilities mainstream in corporate workflows by the mid-2020s, particularly in finance, HR and supply chain operations, according to TechRadar Pro commentary.</p> <p>For executives planning a transition, the evidence points to a phased approach: catalogue high-value, repeatable processes; secure reliable data pipelines and governance; adopt platforms that offer composable, observable agents; and build human-in-the-loop controls for edge cases and compliance. When these elements are combined, organisations can convert the theoretical efficiency gains cited in the lead report into measurable operational improvements while retaining necessary oversight.</p> <p>Agentic AI promises to change how work is organised by moving beyond isolated task automation to orchestrated, outcome-focused processes. But the shift will only deliver sustainably if enterprises pair advanced agent capabilities with pragmatic integration, governance and a clear roadmap from pilot to production.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69c21e66d85cb0be34fdc38eTue, 24 Mar 2026 15:02:18 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/24/indias-quiet-ai-revolution-transforms-farming-with-data-driven-insights<p>India is gradually integrating AI and digital technologies into farming practices, enhancing productivity and sustainability through innovative tools and government-backed initiatives, despite facing data, infrastructure, and trust challenges.</p><p>For generations, Indian farming relied on local knowledge and close reading of weather and livestock. In recent years a quieter transformation has begun: digital sensors, satellite imagery and machine learning are being introduced into fields and herds, initially in clustered projects around states such as Maharashtra and Madhya Pradesh.</p> <p>Practical applications of artificial intelligence on farms are already diverse. Algorithms trained on satellite and drone imagery are being used to flag nutrient shortfalls in soils, and to spot early signs of pests and disease. Collar-mounted devices on cattle track activity, fertility cycles and health indicators in real time. Robotics, still at an experimental stage, promise targeted mechanical weeding as an alternative to manual labour or blanket herbicide spraying. “AI-enabled robots can be sent into farms to do weeding, as an alternative to herbicides, which have hazardous effects on human health,” noted Dr Bharat Kakade, President and Managing Trustee, BAIF Development Research Foundation, Pune.</p> <p>Partnerships between research institutions, non-governmental groups and technology firms have produced some measurable gains. According to reporting on pilot projects in western India, an initiative involving the Agriculture Development Trust Baramati, Microsoft and Oxford University has driven sugarcane yields up by roughly a quarter. Those same systems are being retooled for vegetable crops such as tomato and brinjal, with the aim of delivering crop- and farm-specific advisories rather than broad prescriptions. “What we are doing in sugarcane – soil health management, soil moisture, soil nutrients, crop health, crop nutrition, and crop protection – similar things will be done for vegetables,” Dr Kakade said.</p> <p>Central government programmes are attempting to scale these capabilities. The Digital Agriculture Mission, approved with an allocation of Rs 2,817 crore in 2024, is building a foundation of verified datasets on landholdings, crops and livestock intended to feed AI tools. AgriStack, the digital identity layer at the heart of the plan, had produced over 7.63 crore Farmer IDs by November 2025, including 1.93 crore for women farmers, industry reporting and government releases show. The Press Information Bureau says the mission has surveyed some 23.5 crore crop plots and that new systems such as the National Pest Surveillance System cover dozens of crops and hundreds of pest types while supplying real‑time advisories to extension workers.</p> <p>Several national platforms already reach millions. According to the Press Information Bureau, the Kisan e‑Mitra chatbot has fielded more than 9.3 million queries in 11 regional languages, and an AI pilot offering local monsoon‑onset forecasts for Kharif 2025 communicated by SMS reached 3.88 crore farmers across 13 states; substantial proportions of those surveyed reported changing sowing decisions on the basis of the forecasts. Government and industry analyses also point to technology-driven improvements in crop insurance processes: YES‑TECH, a yield estimation system using remote sensing and AI, has been taken up by nine states and enabled a move away from exhaustive ground‑based crop-cutting experiments in places such as Madhya Pradesh. The CROPIC tool allows farmers to upload geotagged, time‑stamped photos to support faster damage assessments under the Pradhan Mantri Fasal Bima Yojana.</p> <p>Field-level reporting illustrates both benefits and limits. Farmers in Maharashtra who subscribe to AI‑enabled advisory apps receive frequent updates on soil moisture, nutrient status and disease risk; they report savings on inputs and higher productivity. Yet practitioners and researchers emphasise that current systems are not yet autonomous. Data gaps remain, and many AI outputs require verification by agronomists or extension officers before they can be acted on safely. “Unless you have sufficient data collected, gathered, analysed, and until accuracy reaches a certain level, you can’t rely on solutions. As data increases, accuracy and reliability improve,” Dr Kakade said.</p> <p>The government’s own AI Playbook for Agriculture, produced with the World Economic Forum, acknowledges systemic obstacles: fragmented data ecosystems, uneven digital infrastructure, affordability constraints and the challenges of last‑mile delivery. Skilled human resources who understand both agricultural science and data science are in short supply, and early‑stage funding is needed to shepherd pilots to commercially sustainable models, analysts warn.</p> <p>Beyond technical hurdles, adoption hinges on trust and usability. Voice‑first, multilingual interfaces such as the proposed Bharat‑VISTAAR platform aim to make advisory services accessible to smallholders who are uncomfortable with text‑based apps. Yet privacy, data governance and the concentration of agricultural data into large digital stacks remain subjects of policy debate, and analysts caution that farmers must retain control over the information that underpins decision‑making.</p> <p>For now, India’s agricultural landscape is being reshaped incrementally. AI is shifting some decisions from gut feeling to data‑driven signals, improving timeliness and, in specific pilots, raising yields and reducing costs. But scaling those gains will depend on expanding reliable datasets, strengthening extension networks to validate machine outputs, and designing affordable, locally appropriate solutions that farmers trust and can use day to day.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69c21e66d85cb0be34fdc39aTue, 24 Mar 2026 15:02:18 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/24/elsewhere-systems-launches-ai-procurement-pilot-to-shape-trust-networks-and-decision-automation<p>Elsewhere Systems has begun a closed trial of its AI Procurement Pilot Programme, aiming to define trust architectures that influence how AI systems select and reuse suppliers, potentially reshaping digital discovery and decision-making in supply chains.</p><p>Elsewhere Systems has begun a closed trial of what it calls an AI Procurement Pilot Programme and published a working definition of "Trust Networks" as the organising mechanism it says will govern decision-making in AI-mediated discovery.</p> <p>The firm said the pilot is intended to move the debate from concept to practice by working with a small set of partner organisations to map how automated systems currently choose suppliers, identify recurring high-confidence pathways, and redesign signals, content and operations so organisations can be repeatedly selected by those systems. According to the announcement, the programme will test whether AI-driven selection happens upstream of human comparison and whether repeated reuse, rather than reach or awareness, drives concentration of demand.</p> <p>Elsewhere Systems frames the problem as a change in the role of discovery. Where digital strategy for decades focused on winning visibility, higher search ranking, wider platform presence, being included in comparison sets, AI systems, the company argues, are shifting toward acting as decision engines that interpret intent and resolve towards a small number of reliable pathways. Those stabilised patterns of selection, the company says, crystallise into Trust Networks: interconnected sets of organisations and delivery pathways that are favoured because they reduce uncertainty and deliver consistent outcomes. The visible effect of such networks, Elsewhere Systems adds, is the formation of defaults that accelerate execution and narrow apparent choice.</p> <p>The company's analysis also draws a distinction between operators and marketplaces. Elsewhere Systems claims operators, which control end-to-end delivery, have an advantage because they reduce coordination risk and produce more predictable outcomes; by contrast, marketplaces fragment accountability and increase variability, making it harder for AI systems to identify dependable patterns. From this perspective, the strategic aim for suppliers shifts from maximising exposure to demonstrating low-variance performance that can be safely reused across contexts.</p> <p>Independent consultancy work on AI and procurement offers overlapping but broader context. Recent industry analyses describe procurement being transformed by autonomous and agent-like systems that can execute sourcing decisions and optimise supply chains with minimal human intervention. Those reports stress that AI can move procurement from a transactional, cost-centred function into a strategic capability that improves resilience, supplier relationships and value capture. Practical deployments cited in the sector literature point to significant efficiency gains where analytics, automation and generative tools are used to streamline sourcing and contract management.</p> <p>However, industry advisers also warn of trade-offs. Turning discovery into pre-resolved selection can concentrate demand and raise barriers for new entrants unless design choices explicitly preserve diversity and contestability. The same reviews that highlight savings and strategic upside from AI-driven procurement also underscore the need for governance, transparency and measures to manage supplier risk and avoid unintended market consolidation.</p> <p>Elsewhere Systems positions its pilot as an operational response to that landscape: an effort to define an "AI Trust Architecture", the structures and signals organisations must deploy to be repeatedly chosen by AI systems. The company said participating organisations will be supported to transition from visibility-led tactics to approaches focused on selection, reuse and default formation.</p> <p>The announcement offers no public list of pilot partners or detailed success metrics. Elsewhere Systems said further results will emerge from live fieldwork and system testing with the selected cohort. The firm provided a contact email and its website for media enquiries.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69c21e66d85cb0be34fdc39eTue, 24 Mar 2026 15:02:11 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/20/aws-ai-league-revolutionises-practical-ai-training-with-competitive-model-fine-tuning<p>The AWS AI League, a pioneering hands-on programme in collaboration with Atos, is transforming enterprise AI skill development by using competitions and practical projects to build confidence and operational expertise in generative AI model fine-tuning, offering a new blueprint for scalable, cost-effective AI adoption.</p><p>Organisations that want to scale AI capability are discovering that conventional training alone rarely moves teams from theoretical knowledge to confident, production-ready practice. According to an AWS blog co-authored with Atos, certification pathways and e-learning establish foundations but frequently leave a gap: learners earn credentials without the hands-on experience needed to apply generative AI to complex business problems. To bridge that divide, Atos and AWS piloted a competitive, experiential programme, the AWS AI League, aimed at turning learning into tangible model-building skills across a broad employee base.</p> <p>The League combines instructor-led workshops, guided low-code tooling and a structured competition to accelerate practical skills. Participants used Amazon SageMaker Studio and SageMaker JumpStart to fine-tune pre-trained foundation models, focusing on transfer learning approaches that adapt large language models to narrow, domain-specific tasks rather than training models from scratch. According to AWS documentation, the initiative was introduced in mid-2025 to help enterprises and individual developers build skills in fine-tuning, model customisation and prompt engineering, with AWS offering credits and a championship prize pool to incentivise participation.</p> <p>Atos chose an underwriting assistant as the competition’s exemplar: an Intelligent Insurance Underwriter trained to assess risk, recommend policy conditions and explain its reasoning in industry-appropriate language. Built on a cost-conscious stack, fine-tuned open-source models managed in SageMaker, with data stored in Amazon S3 and tooling for dataset creation, the project aimed to show how specialist knowledge can be embedded into smaller models for faster, cheaper inference. The AWS blog reports that Atos staff now hold over 5,800 AWS certifications and 11 Golden Jackets, and that the company is working toward a goal of 100% AI fluency across its workforce by 2026.</p> <p>The League’s three-phase format, an initial immersive workshop, an intensive development period where teams iterated on datasets and hyperparameters, and a live finale judged by experts, audience voting and an automated LLM evaluator, was designed to maintain momentum and surface measurable outcomes. Gamification proved pivotal: Atos recorded 409 active participants who produced more than 4,100 fine-tuned models during the two-week virtual stage, according to Atos’ account of the event. The highest-performing submissions illustrated a central point of the programme: domain-specific fine-tuning can allow a relatively compact model to rival much larger baselines. The AWS blog notes that some 3 billion-parameter fine-tuned models achieved win rates above 93% against a 90 billion-parameter reference model on the competition’s unseen-question benchmark.</p> <p>The contest also exposed common practical pitfalls. Overfitting emerged when teams trained models too tightly on their datasets, producing repetitive or irrelevant answers on novel prompts. Participants used evaluation loss and perplexity metrics to monitor generalisation and adjusted hyperparameters such as epochs, learning rate, batch size and Low-Rank Adaptation settings to strike a balance between underfitting and memorisation. The organisers supplied tools to simplify dataset creation, in Atos’s case a PartyRock application that output JSONL-formatted instruction–response pairs, and some teams augmented or remodelled that output to increase variety and coverage. Atos reported that iterative dataset refinement and disciplined hyperparameter sweeps were among the most important levers for success.</p> <p>From an operational standpoint, the exercise highlighted cost-efficiency gains from specialisation. The AWS blog states that fine-tuned 3B models ran effectively on ml.g5.4xlarge instances, while much larger base models required ml.g5.48xlarge hardware, implying substantial savings for inference at scale. Post-event surveys cited by Atos indicated an 85% increase in participant confidence when discussing and implementing generative AI with customers, suggesting the short, hands-on format compressed what might otherwise take months of conventional training into a matter of weeks.</p> <p>The AWS AI League has been rolled out beyond private partner events. According to AWS announcements and press releases, the programme launched publicly in July and August 2025 with regional events, such as a Jakarta edition, feeding into global finals at AWS re:Invent, where prize incentives and credits were used to drive engagement. AWS materials position the League as a repeatable model for enterprises to host internal tournaments, while individual developers can use the format at AWS Summits and other live events to sharpen practical skills.</p> <p>The Atos experience sits inside a wider partnership with AWS. In October 2024 the two companies opened a GenAI Innovation Studio in Pune to co-develop industry-focused generative AI solutions, and in July 2025 Atos listed its Polaris AI Platform in the new AI Agents and Tools category of the AWS Marketplace, signalling a strategic push toward deployable agentic and generative products. Together these moves reflect a shift from teaching concepts to operationalising specialised models and agentic architectures across business workflows.</p> <p>For organisations designing AI enablement programmes, the Atos–AWS pilot surfaces several actionable lessons. Structured, hands-on exercises that abstract infrastructure complexity but preserve model mechanics enable cross-role participation; gamified competition increases sustained engagement; careful dataset design and methodical hyperparameter tuning are more important than raw dataset size; and domain-specific fine-tuning can make smaller models both performant and cost-effective components within larger agentic systems.</p> <p>The AWS AI League case shows how an experiential, measurement-driven learning format can convert foundational training into deployable skills and demonstrable business value, while also revealing the technical and operational choices that determine whether fine-tuning yields robust, generalisable models rather than short-lived gains on a narrow benchmark. According to Atos and AWS, the pilot’s combination of tooling, competitive structure and real-world use cases has helped accelerate the journey from certification to confident, customer-ready AI practice.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69ba34bf4391726718842599Fri, 20 Mar 2026 01:21:40 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/20/salesforce-introduces-agentforce-sales-to-revolutionise-high-volume-sales-automation<p>Salesforce has launched Agentforce Sales, a suite of AI-driven agents integrated within Sales Cloud and Slack, aimed at transforming high-volume sales tasks and enabling 'agentic' selling that blends outreach with continuous service.</p><p>Salesforce has unveiled Agentforce Sales, a set of AI agents designed to shoulder high-volume sales tasks such as prospecting, nurturing leads, preparing meetings, updating pipelines and generating quotes, signalling a shift toward what the company calls "agentic" selling that blurs the line between sales outreach and continuous service.</p> <p>According to Salesforce, these agents operate inside Sales Cloud and within Slack, drawing on Customer 360 context and connected enterprise data to carry out routine work and surface recommended actions for sellers. The vendor says pre-built agents cover common sales motions: identifying and prioritising prospects, autonomously nurturing leads and booking meetings, producing account briefs and meeting prep materials, updating CRM fields and suggesting next steps, and generating compliant quotes within governed workflows. A partner-focused agent is also intended to deliver 24/7 enablement for co-selling activities.</p> <p>“By providing every rep with a team of agents to manage high-volume tasks, we are eliminating the administrative ‘tax’ on sales teams. This ensures every lead is nurtured and every rep can focus on the high-value relationships that drive revenue,” Kris Billmaier, EVP and GM, Agentforce Sales at Salesforce, said in the announcement.</p> <p>Salesforce frames the move as a productivity lever: the company claims Agentforce Sales can recover as much as 25 hours per seller each week, converting that time into broader coverage and faster follow-up. Customer examples cited by Salesforce are intended to illustrate that promise. “Agentforce can now engage with our prospects 24/7 and respond immediately, with all of the context needed to answer questions clearly, thereby improving our customer experience,” Eswar Veluri, CTO at Equinox, is quoted as saying. Internally, Salesforce’s sales organisation reported agents contacting 130,000 previously untouched leads and creating 3,200 opportunities in four months; Adam Alfano, President of Sales at Salesforce, said he expects those figures to grow tenfold next year.</p> <p>Slack is central to the delivery model. Salesforce’s Customer Zero initiative and the Agentforce integration for Slack show agents embedded into conversational workflows so employees receive context-aware assistance without leaving the messaging environment. Features such as Channel Expert and Slack Agent Templates are intended to make it easier to field FAQs, surface resources and automate actions within channels. Salesforce has since declared Slackbot, an AI personal agent for work, generally available, positioning it as a native conduit for agents to answer questions, organise tasks and take actions using enterprise data.</p> <p>Independent metrics provided by Salesforce further portray the commercial scale of agent-driven interactions. The company reported that AI and agents accounted for a substantial share of activity during Cyber Week 2025, and that retailers using Agentforce 360 experienced faster sales growth than peers. In later corporate disclosures and earnings commentary, Salesforce said Agentforce in Slack helped save hundreds of thousands of employee hours, processed trillions of AI tokens, and handled hundreds of thousands of leads and millions of service requests, underscoring the platform’s rapid adoption within its own customers and workforce.</p> <p>For customer experience and revenue operations leaders, the practical implications are twofold. On the upside, agentic selling can dramatically shorten response times and ensure continuous, scaled engagement that would be impossible to sustain with humans alone. On the downside, automation can equally amplify poor interactions if governance, context controls and quality safeguards are not established. If automated outreach, follow-ups and scheduling run at scale, organisations must make decisions about consistent voice, escalation paths, permissioning, audit trails and accuracy checks so buyers do not encounter conflicting messages across sales and service.</p> <p>The challenge ahead is not simply implementing agents, but integrating them into coherent experience design. Industry observers and practitioners will watch who owns the “voice” of automated outreach, how teams validate the context agents use from Customer 360 and third-party models, and where seamless handoffs to humans occur. Salesforce positions Agentforce as an extension of Customer 360 and an enabler of an "Agentic Enterprise" that connects agents across sales, marketing and service; the firms that gain the most, Salesforce suggests, will treat deployment as a cross-functional CX transformation rather than a narrow sales automation project.</p> <p>As enterprises adopt agentic workflows inside the flow of work, governance choices about approvals, auditability and escalation will need to live where decisions are made, often inside tools such as Slack, so that leaders can measure experience consistency as well as efficiency. The coming months will test whether agentic selling can maintain coherent, contextual customer journeys at scale or simply accelerate inconsistency across channels.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69ba34bf439172671884259bFri, 20 Mar 2026 01:21:35 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/20/nvidia-expands-enterprise-ai-ambitions-with-new-partnerships-and-production-focused-platforms<p>At GTC 2026, NVIDIA unveiled a series of collaborations and product initiatives aimed at transforming artificial intelligence from pilot projects into foundational elements of everyday business operations, signalling a shift towards scalable, governed enterprise AI platforms.</p><p>Technology company NVIDIA used its GTC 2026 showcase to push itself beyond the role of a component vendor and into the centre of enterprise AI, unveiling a series of collaborations and product initiatives designed to move artificial intelligence from pilot projects into routine business operations.</p> <p>At the event NVIDIA outlined expanded alliances with Salesforce, Amazon Web Services and NTT DATA aimed at creating an integrated stack for production-grade AI. According to CX Today, those deals focus on three linked priorities: inference that powers real-time chat and voice agents, “AI factories” that combine data, models and infrastructure into repeatable deployment pipelines, and agent platforms that can act inside business systems rather than merely answer questions.</p> <p>NVIDIA signalled its ambition to be the common underlying layer for enterprise AI by emphasising low-latency inference and operational tooling. The company introduced an AI factory platform intended to orchestrate continuous model production, deployment and lifecycle management across infrastructure and applications, and said its agentic frameworks OpenClaw and NemoClaw are being hardened with enterprise-grade security so organisations can deploy autonomous agents within governed environments. At GTC CEO Jensen Huang argued that as software and hardware iterate together, computing costs will fall: “As all this happens, and we continue to update our software, computing costs go down,” he said during his keynote.</p> <p>The Salesforce tie-up is positioned to connect AI agents directly to CRM data and regulated workflows. According to CX Today, Salesforce will incorporate NVIDIA Nemotron models into its Agentforce environment so agents can access long customer histories, trigger processes and update records while running under on‑premises or private‑cloud controls, capabilities aimed at sectors such as financial services and healthcare where privacy and compliance have constrained automation.</p> <p>AWS will bring scale to that vision. A NVIDIA blog post recounting recent moves shows the cloud provider has deepened technical integration with NVIDIA, including embedding NVLink Fusion into custom silicon such as the Tranium4 family and extending support for NVIDIA architectures across EC2 instances. CX Today reports AWS plans to deploy more than one million NVIDIA GPUs across cloud regions and to offer next‑generation architectures so customers can run training and inference without building their own datacentres.</p> <p>Service integrators are already packaging those building blocks into what they call enterprise AI factories. NTT DATA, a GTC sponsor, showcased offerings that bundle NVIDIA’s full‑stack platform with data, models, governance and deployment templates to accelerate production rollouts, according to NTT DATA event materials. John Fanelli, Vice President, Enterprise Software, NVIDIA, described the market drive toward scalable production platforms: “Enterprises are now seeking robust, scalable platforms that can successfully transition their AI initiatives from pilot projects to full-scale production,” he said. “NTT DATA’s AI factory offerings, built on the NVIDIA full-stack platform, provide clients with the domain-specific solutions needed to confidently achieve production-grade enterprise AI at scale.”</p> <p>Consultancies and systems integrators are further amplifying the message. Accenture, a platinum sponsor at GTC, demonstrated joint work on Physical AI and AI factories highlighting how curated deployments can deliver measurable outcomes, according to Accenture’s event summary. Capgemini, making its conference debut as a sponsor, displayed industry-ready demos across finance, telecommunications and automotive built on NVIDIA tech and AWS collaboration. Earlier partnerships announced at GTC and in 2025, including an initiative with Nokia to explore AI‑native radio access networks, underscore the broader effort to make accelerated computing the backbone of next‑generation infrastructure, DataCenterFrontier reported.</p> <p>NVIDIA also reiterated an aggressive market forecast: as enterprises embed AI into day‑to‑day processes demand for its accelerators should rise sharply, with the company projecting chip revenue could reach at least $1 trillion by 2027 as AI becomes an operational layer across industries, according to CX Today. That projection, coupled with Huang’s comments about falling costs from repeated software and hardware optimisation, frames NVIDIA’s strategy: supply the compute and tools while partnering with cloud providers, software vendors and systems integrators to lower the barriers to production.</p> <p>Taken together, the announcements point to a shift in how organisations are expected to adopt AI: from isolated models and experiments to governed, repeatable platforms that combine high‑performance hardware, cloud scale, prebuilt workflows and vendor services. Industry participants at GTC argued those elements are essential if enterprises want agents that can reason, act and automate across systems in real time while satisfying regulatory and privacy requirements.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69ba34bf439172671884259dFri, 20 Mar 2026 01:21:24 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/20/gartner-predicts-autonomous-supply-chains-will-handle-60-of-disruptions-by-2031<p>Gartner forecasts a major shift towards autonomous supply chain management by 2031, with AI expected to resolve the majority of disruptions, transforming resilience and operational models amid increasing geopolitical uncertainties.</p><p>Gartner forecasts that by 2031 a majority of supply chain interruptions will be handled autonomously, with 60% resolved without human intervention as artificial intelligence grows more capable of sensing and acting in real time. The prediction, published by the research firm, frames autonomy as the next stage in supply‑chain resilience as geopolitical tensions and trade uncertainty increase the frequency and complexity of shocks.</p> <p>“As more frequent and complex disruptions continue to test response capacity, organizations are moving toward AI that can sense and act in real time to improve the consistency and speed of decisions,” said Julia von Massow, Director Analyst in Gartner’s Supply Chain practice. Gartner’s own research, including a survey of 509 supply‑chain leaders conducted in October 2025, shows many chief supply‑chain officers either already experimenting with agentic AI or planning deployments within two years.</p> <p>Gartner advises executives to broaden autonomy deliberately, beginning with low‑risk decisions while strengthening data quality, governance and contingency protocols. The firm stresses that higher‑stakes choices should, for the foreseeable future, remain human‑in‑the‑loop to avoid unacceptable exposure, allowing organisations to build the foundations needed for wider automation over time.</p> <p>The forecast sits alongside other Gartner projections that together sketch a rapid shift in how supply chains operate. According to Gartner, half of cross‑functional supply‑chain management solutions will include agentic AI by 2030, creating software capable of assuming end‑to‑end tasks across planning, execution and exception handling. The firm also predicts that 70% of large organisations will adopt AI‑based forecasting for demand prediction by 2030, enabling “touchless forecasting” that reduces routine manual inputs.</p> <p>But Gartner warns that technology alone will not guarantee value. The company projects that 60% of digital supply‑chain adoption efforts will fall short of promised outcomes by 2028 unless firms protect learning and development budgets and adopt agile approaches to skills development. That caution is echoed by commentary in Forbes which argues that successful AI transformation hinges on keeping humans in control, integrating systems tightly and upskilling teams to work alongside intelligent tools.</p> <p>The move to automation will reshape operating models and talent mixes. Gartner predicts that by 2030 one in 20 supply‑chain managers will be responsible for robots rather than people, reflecting investments in robotics to counteract labour shortages and rising costs. Industry analysis published by Velocity3PL highlights how agentic AI can shift decision‑making from reactive to proactive, with some vendors claiming autonomous systems can handle a large share of routine decisions and improve resilience against geopolitical risk.</p> <p>Gartner recommends CSCOs own an enterprise‑wide AI roadmap aligned to disruption management, invest in data governance, budget for change management and create rapid escalation plans for autonomous decision failures. The firm also urges ongoing measurement of autonomy’s emotional and performance impacts on staff to guide responsible scaling.</p> <p>Taken together, these forecasts portray a near future in which supply chains are more intelligent and more automated, yet dependent on substantial investments in governance, talent and contingency planning if the promised resilience and cost benefits are to materialise.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69bb8529b0f511ed0d544de6Fri, 20 Mar 2026 01:21:07 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/20/generative-ai-transforms-water-utilities-from-threat-to-trusted-assistant<p>Generative AI is increasingly being adopted by water and wastewater utilities not to replace staff, but to enhance operational knowledge, improve fault resolution, and optimise resources amidst aging infrastructure and staffing challenges.</p><p>The prospect of artificial intelligence displacing utility staff has been a persistent fear among water and wastewater professionals. Yet practitioners deploying generative AI argue the technology is more likely to augment operators’ capabilities than to render them redundant. Olivier Terrien, head of business development at TeamSolve, says generative AI “can make an operator’s workday easier.” He argues it helps capture and surface institutional knowledge that is at risk as experienced staff retire or move on, serving as a persistent digital memory for utilities.</p> <p>TeamSolve’s practical approach centres on a domain-specific platform the company calls Knowledge Twin. The system ingests sources such as CMMS records, SCADA telemetry, standard operating procedures and field reports to give operators, engineers and maintenance crews near‑real‑time access to context and historic insight. Terrien says early pilots show staff resolve faults more quickly, locate the information they need with less friction and preserve expertise that otherwise would have been lost through turnover.</p> <p>The potential operational gains mirror wider industry findings. According to Autodesk, AI-driven optimisation of energy use, predictive maintenance and demand forecasting can cut operating expenditure by roughly 20–30%. WaterOnline and other sector commentators have documented examples of AI and IoT working together to reduce leaks, trim pumping energy and improve scheduling, sometimes delivering energy savings in the order of 20–25%.</p> <p>Beyond cost and energy metrics, generative AI can change how unusual or emergency conditions are handled. Terrien describes an instance where an operator confronted with an unfamiliar transient was able to retrieve an automated operational history indicating the event was previously recorded and non‑critical, preventing an unnecessary escalation. Other industry pieces note generative models’ strength at synthesising large volumes of data into plain‑language guidance or suggested actions, while also supporting anomaly detection that flags deviations from expected patterns.</p> <p>Adoption has accelerated but remains uneven. A 2024 survey reported by a sector publication found 78% of organisations were using AI, up markedly from the prior year, yet many utilities still cite hurdles such as fragmented legacy systems, data quality challenges and limited budgets. PwC and ASUG both emphasise that while data modernisation helps, organisations do not always need a perfect data estate to begin extracting value, clean, well‑organised subsets of data can be leveraged to build useful pilot projects and virtual assistants.</p> <p>Practical deployments underscore these points. One East Coast utility that previously tracked maintenance events by hand moved to digitised collection and a consolidated knowledge base; the utility then used generative tooling to automate regular reporting, substantially reducing manual effort. International examples show operators using AI recommendations to determine chemical dosing or next steps during incidents, and predictive analytics have been used to forecast breaks or supply interruptions so interventions can be prioritised before failures occur.</p> <p>Industry advisers stress governance and staged adoption. Terrien and TeamSolve advocate integrating generative AI where data maturity allows and prioritising safe, scalable rollouts that involve operators from the start. Commentary from consultancies highlights the need for clear audit trails, human‑in‑the‑loop controls and continuous validation so recommendations remain reliable and accountable.</p> <p>As utilities face ageing infrastructure, shrinking workforces and growing operational complexity, vendors and practitioners present generative AI as a tool to raise efficiency, protect institutional knowledge and strengthen frontline decision‑making. According to TeamSolve’s recent work and wider industry reporting, the most effective implementations are those that treat AI as an assistant to experienced staff rather than as a substitute, pairing automated insight with operator judgement and established procedures.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69bb8528b0f511ed0d544de4Fri, 20 Mar 2026 01:20:53 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/20/servicenow-warns-australian-businesses-risk-falling-behind-in-ai-integration<p>ServiceNow urges Australian companies to embed AI across workflows rather than relying on point solutions, highlighting the risks of complacency and the benefits of integrated, governed AI implementations for customer and employee experience.</p><p>Many Australian businesses that hoped to lag briefly behind innovators in artificial intelligence risk being overtaken, ServiceNow executives warn, as the company presses the case for embedding AI across workflows rather than bolting it onto single systems.</p> <p>According to ServiceNow’s 2026 Customer Experience Report, Australians spent 113.5 million hours on hold in 2025, a fall of 10 million hours from the prior year, yet the study exposes a sharp mismatch between expectations and reality: almost half of consumers say a poor encounter would drive them to a competitor and 67% worry AI may not understand their problem. The research further finds three-quarters of customers prefer self-service and around half have seen benefits from AI-driven tools such as 24/7 support and more personalised interactions, but only 39% of executives rate AI as at least moderately important for improving customer experience. ServiceNow frames the shortfall as both a customer and employee experience challenge, because friction for staff translates into worse service for customers. </p> <p>“Everyone’s got to be acting at pace,” said Pete Andrew, group vice-president for Australia and New Zealand at ServiceNow, urging organisations to move beyond “fast follower” strategies and redesign how work is done. He and his colleagues point to the employee domain as fertile ground for rapid returns: freeing HR teams from routine administration allows them to focus on complex human-centred work that improves retention and productivity. “Get the experience right – an important part of which is making it seamless – and you’ll do OK,” Andrew added.</p> <p>ServiceNow highlights a string of product moves intended to deliver that seamlessness. The firm’s recent integration of Moveworks into its platform created EmployeeWorks, a conversational AI layer that converts natural-language requests into end-to-end execution across multiple systems. The company says the capability underpins its broader Autonomous Workforce initiative, which it describes as an “AI specialist” able to act with the scope, authority and governance required for enterprise tasks. ServiceNow also unveiled AI Control Tower, a governance-centric product that not only monitors but can enact policies and controls across models and applications.</p> <p>Industry users describe practical payoffs and the governance tensions organisations must manage. Mining services group Orica adopted EmployeeWorks to unify multiple HR systems and support multilingual, accessible interactions through Microsoft Teams. Leo Luk, Orica’s global process strategy and enablement manager, said the tool went live in North America and produced immediate results, such as enabling a regional HR team to create a new position overnight. Irene Klymenko, Orica’s senior manager of AI portfolio and delivery, cautioned that while innovation could not be unduly delayed for risk reasons, “We can’t scale what we can’t see”, the company insisted on governance from the outset and moved away from tracking projects in spreadsheets.</p> <p>Third-party analysis and previous ServiceNow research underline the scale of the opportunity and the risk of complacency in Australia. A 2025 ServiceNow readiness study scored the country 36 out of 100 for AI preparedness, noting that although around 82% of organisations planned greater AI investment, only a minority had clear visions, suitable skills or formalised data governance. Independent reporting earlier highlighted that employees spend significant portions of work time on administrative tasks, waiting for answers or chasing internal information, while only a small share of organisations currently deploy AI chatbots despite customer appetite for self-service.</p> <p>Consultants and board-level sponsors who spoke at ServiceNow events urged a dual focus on measurable value and trustworthy controls. Deloitte representatives said governance tools such as AI Control Tower help with risk assessment, asset visibility and tying AI investments to business outcomes, while senior practitioners stressed the need to measure the value of AI agents and to stop projects that fail to move the needle. One warning was particularly stark: failure to weave AI into existing workflows risks a recurrence of shadow IT, fast, local solutions that create fragmented spending, security gaps and inconsistent outcomes across an organisation.</p> <p>The public sector is also a vector for accelerated adoption. ServiceNow has promoted versions of its EmployeeWorks and Autonomous Workforce offerings tailored for government environments, arguing that mission-critical functions require enterprise-grade trust, scale and compliance. Separately, industry announcements indicate a surge of major cloud and AI deals in Australia, with both private groups and agencies pursuing multi-year partnerships to deploy AI across functions from retail to banking.</p> <p>Taken together, the evidence advanced by ServiceNow and its users points to a simple prescription for firms that want to avoid being left behind: invest in AI that orchestrates across systems, pair rollout with early governance and measurement, and shift attention from point tools to workflow automation that delivers demonstrable outcomes for employees and customers.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69bb8528b0f511ed0d544dd0Fri, 20 Mar 2026 01:20:20 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/20/accenture-and-databricks-launch-specialised-ai-deployment-group-to-accelerate-enterprise-adoption<p>Accenture and Databricks have established a dedicated business group aimed at helping organisations transition from AI pilots to large-scale, enterprise-ready deployments, addressing industry-specific challenges and promoting multi-cloud strategies.</p><p>Accenture and Databricks have created a specialised business group to help organisations move beyond pilot projects and deploy large-scale artificial intelligence and agent-based systems on enterprise data platforms.</p> <p>According to Accenture, the Accenture Databricks Business Group will guide clients through the shift from experimentation to production using the Databricks Data Intelligence Platform as a central technology. The initiative is intended to tackle persistent barriers to scale, chiefly fragmented data environments and legacy IT that restrict access to governed, enterprise-ready data. Accenture said the unit will support data migration, modernisation and multi‑cloud strategies while delivering industry-specific implementations across financial services, retail, life sciences and the public sector.</p> <p>The new group is backed by a workforce of more than 25,000 Databricks‑trained professionals who will deploy solutions tailored for enterprise AI workloads, including database infrastructure optimised for model training and inference, conversational interfaces that let staff query corporate data, and toolchains for building AI agents trained on internal datasets. Accenture highlighted existing customer use cases that pair its services with Databricks: Albertsons is using the platform to combine historical and predictive analytics for pricing, BASF has rolled out a digital assistant for finance and controlling, and Kyowa Kirin International is modernising its data estate to improve access to trusted information.</p> <p>“By combining our deep mission expertise with Databricks’ Data Intelligence Platform, we’re helping critical agencies move from experimentation to production faster. Our work together on US Federal mission projects demonstrates how data-driven innovation can accelerate outcomes,” Accenture Federal Services said on a LinkedIn post.</p> <p>The move fits into a broader pattern of Accenture forming dedicated alliances to industrialise AI. In December 2025 Accenture launched the Accenture Anthropic Business Group, with roughly 30,000 staff trained to work with Anthropic’s Claude models and a suite of offerings aimed at regulated industries, according to Accenture’s newsroom. At the same time, Accenture and Palantir created the Accenture Palantir Business Group to accelerate data-driven decision-making in government, energy and oil and gas, with forward‑deployed engineers and over 2,000 Palantir‑skilled Accenture professionals supporting deployments. Accenture’s expanded collaboration with Snowflake established an Accenture Snowflake Business Group to combine cloud, AI and data capabilities, backed by more than 5,000 SnowPro‑certified practitioners and a global Centre of Excellence. Earlier, Accenture’s partnership with NVIDIA formed an NVIDIA Business Group and an AI Refinery engineering network to support agentic architectures and foundation model development across tens of thousands of practitioners.</p> <p>Those parallel efforts underscore a strategic approach: marry vertical and mission expertise with platform partners to shorten time to value and reduce the engineering lift required to operationalise models at scale. Industry observers say that combining deep implementation teams with platform tooling helps organisations confront common operational hurdles, data governance, lineage, reproducibility and cloud portability, while providing prebuilt patterns for productionising models and agents.</p> <p>Databricks, for its part, has been attracting a range of services partners seeking to accelerate customers’ AI journeys. In June 2025 Kyndryl announced a global strategic alliance with Databricks to help clients modernise IT estates and adopt the Databricks platform for scalable analytics and AI, signalling growing demand for partner ecosystems that deliver both platform expertise and systems‑integration services.</p> <p>For clients, the promise of these business groups is pragmatic: reduce friction in creating an “AI‑ready” data estate, embed governance and security, and enable business functions to extract operational value from models and agents. Accenture’s description of the Databricks group emphasises practical outcomes, migrating data, standing up AI‑optimised infrastructure and building conversational and agentic experiences tuned to internal datasets, rather than purely experimental work.</p> <p>Yet the strategy also raises questions for organisations about vendor concentration and interoperability. Industry executives note that while tightly coupled partnerships can speed delivery, they require careful attention to long‑term portability, contractual terms around models and data, and the balance between customised solutions and standardised architectures.</p> <p>Accenture and Databricks will be positioning the new business group to meet that balance: provide accelerators and skilled practitioners to shorten deployment timelines while supporting multi‑cloud architectures and industry‑specific needs. If the group achieves the scale claimed by its backers, it will join a roster of Accenture platform alliances designed to move enterprises from experimentation into sustained, governed AI operations.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69bb8528b0f511ed0d544dc6Fri, 20 Mar 2026 01:20:11 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/20/procurement-automation-evolves-from-cost-cutting-tool-to-strategic-resilience-enabler<p>As procurement teams face increased scrutiny and faster cycles, automation is shifting from optional to essential, offering real-time insights, risk mitigation, and strategic advantages beyond cost savings.</p><p>Procurement leaders facing tighter teams, faster cycles and sharper regulatory scrutiny increasingly see automation not as optional but as foundational to modern sourcing and supply‑chain management. The technology’s promise extends beyond replacing paper with pixels: when properly deployed it can knit together fragmented data, detect emerging risks, cut transactional costs and shorten the time needed to respond to disruption. Achieving those gains, however, depends on how organisations prepare and which capabilities they choose to deploy.</p> <p>Greater clarity over where money is going remains the most immediate payoff. Automation can ingest and harmonise spend records from supplier portals, ERP systems and ad hoc repositories, producing a single view of purchasing activity. According to Velocity Procurement, the sector is moving from periodic, manually assembled reports to continuous, real‑time spend analytics that combine supplier feeds and market indexes; when augmented with predictive and prescriptive models, that visibility not only exposes maverick buying but also suggests how to rebalance sourcing to reduce costs.</p> <p>Continuous monitoring of supplier and market risk is another area where automation shifts procurement from reactive to proactive. Industry research and vendor roadmaps show that automated tools can watch for supplier financial stress, geo‑political events, shipping interruptions and compliance gaps, flagging issues as they emerge rather than waiting for quarterly reviews. Market intelligence firms note that real‑time signals and pricing guidance speed decision making and provide early warnings that buy procurement teams time to act.</p> <p>The strongest financial case for automation is often cited in invoice processing and accounts payable. Organisations that automate end‑to‑end payment workflows typically lower per‑invoice handling costs, reduce errors that trigger duplicate or late payments, and become eligible for early‑payment discounts that manual processes frequently miss. SAP recommends combining consolidated spend classification with AI‑driven analytics to surface persistent savings opportunities and make cost reductions more predictable; at the same time consultants warn that the technology’s return depends heavily on process redesign and staff readiness.</p> <p>Supplier relationship management gains are a frequently overlooked element of the ROI story. Analysis by SRM vendors suggests that automating tasks such as onboarding, contract renewal and performance tracking shortens procurement cycles, reduces supplier risk and improves supplier performance over time. Autonomous AI agents, as explored by several industry providers, can further take on routine communications, continuously monitor supplier metrics and propose corrective actions, freeing procurement professionals to focus on negotiation and strategic collaboration.</p> <p>Resilience is the strategic argument that resonates most strongly with chief supply‑chain officers. Platforms that fuse live telemetry, contractual data and approved vendor lists can model the impact of a logistics outage, a factory failure or a region‑level political event and identify viable alternate sources within hours rather than days. Academic work on AI‑driven disruption monitoring describes frameworks that detect, assess and recommend responses across extended networks, shortening recovery windows and informing board‑level decisions about supply‑chain design.</p> <p>Despite the capabilities on offer, successful outcomes are not automatic. Multiple sources stress the centrality of organisational readiness: clean, well‑structured data; defined processes; and governance that assigns decision rights and remediation workflows. Vendors and analysts alike caution that automation projects launched without these foundations often deliver uneven results, with pockets of efficiency rather than enterprise‑wide transformation.</p> <p>For procurement leaders the practical imperative is therefore twofold: pilot technologies that demonstrate measurable value in spend control, payment efficiency or supplier performance, while investing in data hygiene, change management and cross‑functional pathways so gains can scale. When those elements come together, automation becomes not merely a cost saver but a source of strategic insight and resilience for the wider business.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69bb8528b0f511ed0d544dceFri, 20 Mar 2026 01:19:22 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/17/procurement-2026-how-ai-agents-will-redefine-strategic-decision-making-and-efficiency<p>A webinar forecasts that by 2026, intelligent systems will shoulder routine procurement decisions, enabling professionals to focus on strategy, risk, and innovation amid growing reliance on agentic AI and hybrid human-machine models.</p><p>Ardent Partners’ recent webinar, Procurement 2026: BIG Trends and Predictions, set out a view of procurement’s next chapter in which intelligent systems move from assisting teams to shouldering routine decisions. Featuring Andrew Bartolini and Vishal Patel, the session argued that 2026 will see procurement organisations increasingly rely on agentic AI to interpret context, recommend actions and execute predefined responses within governed boundaries, freeing professionals to concentrate on strategic work.</p> <p>This shift is not about sidelining people. According to the webinar, the emergent model pairs human judgement with software agents that can monitor markets, surface risks and act on agreed rules. That hybrid arrangement mirrors analysis from McKinsey, which forecasts that AI agents will automate repetitive activities while raising overall procurement efficiency by as much as 25–40 per cent, provided organisations redesign operating models and develop new capabilities to coordinate humans and digital counterparts.</p> <p>The technical capabilities of these agents are already becoming practical for tactical domains. Vendor management, invoice processing, compliance monitoring and standard sourcing activities are cited repeatedly as early applications where systems can detect anomalies, apply policy and trigger workflows without continuous human prompting. SourceReady similarly highlights a move from insight dashboards to systems that autonomously manage inventory, prescreen suppliers and optimise logistics , while stressing the need for explainability so decisions remain auditable and trusted.</p> <p>A corollary is a transformation in how category work is organised. Industry commentators describe a transition from static plans to living “category intelligence” that ingests supplier data, price signals and external indicators to inform ongoing sourcing choices. DragonSourcing’s overview of 2026 trends lists category intelligence alongside predictive analytics and real‑time supplier risk as defining features of next‑generation procurement. FluentaOne emphasises that this approach requires centralised, high‑quality data to enable reliable autonomous decisioning.</p> <p>Organisations that accelerate this evolution will need robust governance and clear boundaries for agent behaviour. Several voices warn against uncritical automation: AI should enhance decision speed and reach while remaining transparent and controllable. 2A Magazine and SAP both underline the importance of human oversight for strategic and high‑risk decisions, and recommend systems that can simulate impacts across functions so procurement actions are coordinated with finance, operations and supply‑chain partners.</p> <p>Beyond internal efficiency, the emerging capability set reframes procurement as a source of enterprise insight. When platforms aggregate supplier, demand and market signals continuously, procurement data informs resilience, growth and sustainability choices rather than merely measuring cost. This aligns with broader market commentary that positions procurement technology as part of an enterprise orchestration layer, one that helps organisations respond to disruptions and pursue nearshoring, ESG and other strategic objectives in real time.</p> <p>Realising these benefits will require investment beyond software: revised processes, skills development, data consolidation and cultural change. The consensus among analysts is that controlled autonomy , systems empowered to act within transparent, audited limits , offers the most realistic pathway. Firms that adopt agentic tools alongside strengthened governance and higher‑quality data will be best placed to convert increased speed and capacity into strategic influence.</p> <p>In short, 2026 looks set to be the year procurement moves from reactive execution toward an operational model in which intelligent systems handle routine choices and people focus on relationships, risk management and innovation. According to the webinar and corroborating industry reports, the prize is not merely greater efficiency but a fundamentally different role for procurement within the enterprise , provided organisations address the technical, governance and talent challenges that come with autonomous capability.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69b8e46da80794ebae632802Tue, 17 Mar 2026 09:34:04 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/17/alibaba-s-new-ai-assistant-platform-aims-to-dominate-china-s-agentic-ai-market<p>Alibaba is launching a service that enables companies to create autonomous AI assistants for digital tasks, positioning itself to lead China's rapidly growing agentic AI sector amid rising enterprise demand and regulatory challenges.</p><p>Alibaba is preparing to roll out a service that will enable companies to build and oversee autonomous AI assistants able to carry out digital tasks with minimal human oversight, a move that positions the group to capitalise on rapid enterprise demand for so‑called agentic AI in China.</p> <p>The offering is intended to let corporate customers create agents that automate activities such as data analysis, scheduling, workflow coordination and customer-facing digital support. According to the Arabian Post report, the initiative complements Alibaba’s existing investment in foundation models, notably its Qwen family, which the company has been using to power a range of generative-AI services and to extend capabilities across mobile and desktop environments.</p> <p>Alibaba’s push comes as domestic appetite for action‑oriented AI has grown sharply after open-source projects such as OpenClaw attracted developer interest by demonstrating how agents can link to applications and perform multi‑step operations automatically. The broader ecosystem is reacting: Tencent and ByteDance have released compatible tools, and several smaller AI firms are racing to niche leadership with specialised models and agentic applications.</p> <p>The company’s enterprise strategy also mirrors internal corporate targets and recent product launches. According to the South China Morning Post, Alibaba.com has set out to have all its merchants using AI tools by the end of 2025, with more than half of its roughly 200,000 merchants already using such tools weekly. Alibaba International has separately announced new AI agents for global merchants, saying features include automated listing generation and creative image tools and that API usage by Chinese merchants reached more than one billion daily calls by July 2025, a dramatic rise from 2023 figures, according to the company statement.</p> <p>Industry recognition supplies further momentum for Alibaba Cloud. Alibaba Cloud was named a market leader in Omdia’s Agentic AI Development Platform in Asia and Oceania, 2026 report, receiving top marks in categories including context engineering and model support, according to Alibaba Cloud’s press release. Market analysts point to that depth of capabilities as an advantage for enterprises seeking to build production‑grade agents that integrate with corporate data and software stacks.</p> <p>Market forecasts anticipate explosive growth for agent platforms. Research published by CIW News projects the Chinese AI agent market could expand roughly 75‑fold by 2028, with infrastructure spending rising from about $3.4 billion to more than $23 billion and the sector scaling to over $30 billion globally. The same analysis notes that nearly all CIOs expect to invest in agents, even as only a minority have mature strategies for deployment, underlining both opportunity and implementation risk.</p> <p>Regulation and security remain significant constraints. Chinese authorities have warned state bodies and some state enterprises against installing certain agent software on official devices because the platforms often require broad permissions to interact with multiple applications and datasets , a concern that regulators say increases the risk of data leakage and external communications. Government guidance illustrates the tightrope Beijing is walking between encouraging technological innovation and preserving information security.</p> <p>Whether enterprise uptake will determine the long‑term winners is a recurring theme among analysts. Firms are trialling agentic tools for functions ranging from automated coding and workflow monitoring to sales and financial reporting, seeking cost savings and productivity gains. But successful commercialisation will depend on providers’ ability to offer scalable, secure integration with business systems and to demonstrate clear return on investment.</p> <p>Alibaba’s planned service, therefore, sits at the intersection of several trends: escalating enterprise demand, competitive pressure from other internet giants and startups, rising market expectations for agentic platforms, and a cautious regulatory environment. The company’s cloud and AI investments, its merchant adoption targets and recent product launches signal an intent to convert research advances into enterprise products; industry observers will be watching whether those offerings can meet corporate needs while satisfying regulators’ security concerns.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69b8e46da80794ebae6327feTue, 17 Mar 2026 09:33:56 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/17/asset-managers-brace-for-ai-driven-transformation-amid-cautious-optimism<p>Senior industry leaders at the Investment Association’s EnTech Global conference highlight rapid AI advancements, opportunities for efficiency gains, and the need for strategic technology deployment to overhaul asset management operations.</p><p>Artificial intelligence is on the verge of reshaping how asset managers operate, senior industry executives told delegates at the Investment Association’s EnTech Global conference in London on 12 March 2026, but realising that potential will require firms to change how they spend on and deploy technology.</p> <p>Ben Lucas, chief executive of Amundi Technology, said recent progress has moved AI beyond incremental task improvements toward outcomes that can affect revenues and margins. Speaking on the panel, Lucas said: "In the last three months, I have seen more material advancement on things that I believe will hit the top or bottom line than I had seen in the last 18 months," adding: "I’m much more confident in what is coming through at the moment." He argued Amundi Technology, as a technology arm of Europe’s largest asset manager, is positioned to offer a European data platform capable of assembling AI services for clients, and reported early signs that these capabilities are beginning to show in the company’s own margin and efficiency. He said: "We are starting to see that from a top-line perspective," and: "We are starting to see the flow through on the other side in terms of our own margin and efficiency."</p> <p>Those commercial opportunities come against a backdrop of heavy and, to date, uneven technology spending across the industry. A consultancy report published in June 2025 found that technology budgets have climbed sharply, with limited correlation to productivity gains, as managers devote most of their IT outlays to maintaining legacy systems rather than financing firmwide transformation. According to McKinsey, the firmwide impact of AI, generative AI and emergent agentic systems could be equivalent to 25% to 40% of an average asset manager’s cost base if deployed effectively, but capturing that upside demands a step-change in how projects are prioritised and scaled.</p> <p>Executives at the conference said practical barriers remain as well as opportunities. Lucas warned that many productivity gains can be unlocked by better adopting existing tools , remote-working platforms, digital signatures and other long-established technologies have yet to be used to their full effect. As he put it: "Everybody is obsessed with the latest AI agent that is going to change the world but there is latent productivity everywhere around us." He added: "If we can transform some of the cultural and human inertia that holds us back and make better use of what we already have, that would be a brilliant starting point."</p> <p>For client-facing wealth businesses, AI promises to address long-standing access and efficiency challenges. Mark Duckworth, group chief executive of Schroders Personal Wealth, highlighted the low rate of financial-advice uptake in the U.K. and the steep fall in adviser numbers since 1990, saying technology can expand reach. "For the first time I am genuinely hopeful for a seismic shift, and the technology that comes along with that is pivotal," he said, describing how voice capture and meeting transcription, introduced as part of a technology-led strategy, now let advisers focus fully on clients. "The next step is to algorithmically move that into a recommendation," Duckworth added, and he said Schroders Personal Wealth plans to deploy agentic AI to clients in the second quarter of 2026. "I am hoping that will be the start of this creation."</p> <p>Industry figures emphasised the need for human oversight and responsible deployment. Sam Alexander, UK country officer and chief operating officer at DWS Group, said innovation should remain anchored in accountable workflows so firms can scale new tools without losing control. "I would like to see AI replace repetitive tasks," she said. "Get rid of those, and we can do more interesting stuff."</p> <p>The commercialisation of AI services is already taking shape through vendor platforms and client rollouts. Amundi Technology has been building cloud-native, modular investment systems used by firms such as Van Lanschot Kempen, which recently adopted Amundi’s Alto Investment platform to consolidate portfolio management, data and middle-office functions. The technology unit was also listed among WealthTech100 companies, reflecting its growing profile in the sector. Amundi Technology appointed Benjamin Lucas as CEO in April 2023 to accelerate such platform-led, data-driven offerings.</p> <p>Consultants urge asset managers to focus AI investment on high-impact, scalable use cases , such as portfolio-manager copilot agents or developer code copilots , that can be implemented relatively quickly while delivering measurable productivity lifts. McKinsey’s analysis suggests prioritising change-the-business initiatives that can be scaled firmwide rather than one-off projects that fail to move the needle.</p> <p>As firms test agentic and generative systems in production environments, the debate will turn to governance, measurable returns and how rapidly scale can be achieved. Executives at the conference voiced cautious optimism: technology and data are largely in place, cloud migration is well advanced, and the next phase will be about execution and change management if the industry is to capture the sizable cost and service gains that consultants and platform vendors say are within reach.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69b8e4a79d48d97c60101925Tue, 17 Mar 2026 09:33:46 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/14/logistics-sector-embraces-rpa-for-transformative-operational-gains<p>As global supply chains face mounting pressures, logistics companies are increasingly adopting Robotic Process Automation to streamline operations, enhance accuracy, and boost scalability, heralding a new era of digital transformation in the sector.</p><p>The logistics sector is undergoing a quiet but significant operational shift as companies move routine digital work away from people and onto software. Where previously staff spent long hours reconciling orders, updating multiple systems and processing paperwork, a growing number of firms are deploying Robotic Process Automation to execute repetitive, rules-based tasks across fragmented IT estates, freeing human teams to handle exceptions and customer-facing work.</p> <p>According to the original report by The Good Men Project, organisations face rising volumes and complexity across transportation, warehousing and fulfilment that make manual workflows increasingly unsustainable. Industry figures cited there indicate widespread adoption: roughly three quarters of organisations have implemented or plan to implement RPA, with more than half launching initiatives during 2025 and 2026 as delivery pressures rose. The article also points to headline benefits recorded by adopters, including large time savings in inventory and order management and sharp increases in shipment throughput and revenue at specific firms.</p> <p>Independent accounts and practical guides corroborate this trajectory and expand the view of where RPA delivers value. According to Impressit, RPA frequently serves as the connective tissue between disparate systems, automating tasks such as order intake, freight-forwarding routines and inter-application data flows; that firm highlights a freight-forwarding case where processing time and costs fell markedly after automation. TechTarget outlines a wider set of supply-chain use cases including predictive maintenance triggers, purchase-order initiation, returns handling and after-sales service, areas where bots can pick up repetitive interactions and reduce error rates.</p> <p>The operational advantages are consistent across multiple sources. Optisol’s analysis emphasises gains in speed, cost reduction, compliance and customer experience, while Moldstud cites sector research suggesting automation can accelerate order processing substantially and halve some processing times in early adopters. These improvements are reflected in the Good Men Project’s examples: one logistics operator saved tens of thousands of working hours by automating warehouse tasks; another reported significant revenue and shipment-volume growth after adopting automation; a third achieved near-perfect invoicing accuracy through automated extraction of shipment data.</p> <p>Beyond efficiency, RPA improves accuracy and visibility. Bots applied to document handling, freight audit and invoice reconciliation can compare billing against contract rates and shipment records at scale, reducing disputed charges and payment errors. Automation that continuously synchronises warehouse, transport management, ERP and CRM systems also creates up-to-date operational dashboards, enabling managers to spot exceptions earlier and reduce response times.</p> <p>The technology landscape for logistics RPA is diverse. A survey of vendor capabilities published by StartUs Insights identifies solutions tailored to tracking, documentation, inventory control and order processing, illustrating how vendors target specific bottlenecks from last-mile coordination to pre-pickup carrier interactions. Combining these tools with intelligent document processing and optical character recognition lets organisations handle unstructured inputs, emails, scanned bills of lading and free-text forms, that historically blocked end-to-end automation.</p> <p>That convergence of RPA and AI is increasingly pivotal. The Good Men Project notes market forecasts that anticipate the RPA market expanding rapidly through the decade and projects many enterprises will pair RPA with AI to enable more advanced, decision-capable automation. Machine-learning models and natural-language techniques allow systems to classify exceptions, extract key fields from documents and route cases to humans only when judgement is required, shifting automation from simple task replay to orchestrated, semi-autonomous workflows.</p> <p>Practical adoption does bring challenges. Data quality, legacy interfaces and governance are recurrent obstacles: bots depend on standardised, accurate inputs; older platforms may require UI-level automation; and access to sensitive commercial data demands robust security policies and monitoring. Change management is also necessary to reposition automation as an enabler of higher-value work rather than a threat to jobs, a theme reinforced across vendor and consultant guidance.</p> <p>For logistics leaders evaluating automation, the evidence points to a staged, risk-managed approach: identify high-volume, well-defined processes for early wins; prioritise data cleanliness and exception-handling rules; invest in secure controls and audit trails; and select tools that enable integration with both modern APIs and legacy screens. Vendor landscapes vary, so mapping specific pain points, freight invoicing, shipment-tracking updates, customs documentation or warehouse synchronisation, to product capabilities yields faster return on investment.</p> <p>As supply chains become more interconnected and customer expectations continue to tighten, RPA is maturing from a niche efficiency lever into a foundational operating capability for logistics. According to multiple industry accounts, when organisations combine automation with intelligent processing and sound governance, they not only cut routine costs but also gain the agility and visibility required to scale operations reliably in an era of greater demand volatility.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69b4ee9a9d48d97c600f9186Sat, 14 Mar 2026 22:51:14 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/14/jeff-bezoss-project-prometheus-aims-to-revolutionise-factory-automation-with-advanced-ai<p>A new venture led by Jeff Bezos, Project Prometheus, seeks to embed adaptive AI into manufacturing systems, promising smarter factories but facing challenges in transparency and implementation readiness.</p><p>Jeff Bezos’s new venture, Project Prometheus, has injected fresh momentum into the long-anticipated effort to bring advanced artificial intelligence into the factory environment, promising to bridge the gap between silicon-era algorithms and the messy realities of the physical world. According to reporting by The Guardian, Forbes, Electronics For You and GeekWire, the secretive startup has secured roughly $6.2 billion in funding, has recruited close to 100 specialists from organisations including OpenAI, DeepMind and Meta, and is being co-led by former Google X executive Vikram “Vik” Bajaj alongside Bezos. The company’s headquarters and technical blueprints remain undisclosed. </p> <p>Prometheus presents itself as an AI research operation aimed specifically at engineering, manufacturing and supply-chain systems. Industry observers say its ambition is to create a machine-level cognitive layer , often described informally as a “brain-layer” , that lets robots and factory systems learn from real-world feedback rather than operate as rigid, pre-programmed automatons. According to Electronics For You and The Print, the initiative spans sectors from computing and automobiles to aerospace and spacecraft systems.</p> <p>The practical gap Prometheus targets is familiar to many manufacturers: automation that excels at repetitive, uniform tasks but struggles when materials, tolerances or environmental conditions deviate from the ideal. Such brittleness forces human intervention, increases downtime and limits the ability of plants to scale adaptive responses. Project Prometheus, if it achieves its stated aims, would put machine learning directly into control and sensing loops, enabling equipment to treat anomalies as training signals rather than fatal errors. That shift could reduce the need for manual troubleshooting, shorten interruptions to production and limit technician exposure to hazardous repairs.</p> <p>A second pillar of the programme emphasises unifying fractured data across the value chain. Manufacturers routinely keep engineering, maintenance, logistics and process-control records in separate systems; tracing the origin of a quality failure often becomes forensic work. Reporting on the startup’s focus suggests Prometheus intends to fuse factory-floor telemetry, supply records and environmental feeds so models can identify root causes , for example linking a surge in defects to a late shipment that forced a raw-material substitution or to humidity swings in a specific zone. Industry data shows that many firms still rely heavily on manual capture methods, and the lead material supplied with this brief notes that a substantial share of companies continue to log operations by hand or in spreadsheets, hampering AI readiness.</p> <p>Third, the project appears to factor in infrastructure resilience. As factories become more automated, their vulnerability to utility volatility , electricity, chilled water, compressed air and the like , grows. By correlating live production metrics with facility-energy and building-management data, advanced models could predict or smooth demand spikes, sequence non-critical tasks during constrained periods and pre-empt equipment stress that presages failure. Observers point out that the startup’s plans for large-scale computing capacity may be intended to support such high-bandwidth, low-latency analysis.</p> <p>While the aspiration is disruptive, the venture’s secrecy invites caution. Reporting from multiple outlets emphasises that many technical details remain private and that the company has not published peer-reviewed results or operational case studies. Accordingly, editorial distance is necessary when assessing claims about capability and timescales. According to The Guardian and Forbes, insiders say the effort has assembled top-tier talent and deep pockets; however, whether those assets will translate quickly into reliable, field-ready systems is uncertain.</p> <p>For manufacturers considering how to respond, the immediate takeaway is not that they must chase bespoke, futuristic hardware but that data hygiene and systems integration will be decisive enablers. Low-code and digital-capture tools, offline-capable apps for remote yards and integration layers that link MES and ERP platforms are practical measures cited by vendors and analysts alike to ready operations for more sophisticated automation. According to sector reporting, years of incremental digitisation , converting paper workflows, synchronising disconnected spreadsheets and ensuring sensor data is reliably captured and timestamped , will materially affect whether a plant can exploit externally developed AI models when they become available.</p> <p>Project Prometheus therefore represents both an engineering bet and a signalling event. With roughly $6.2 billion in capital and a team drawn from leading AI outfits, it could accelerate research into adaptive robotics, end-to-end data linkage and predictive infrastructure management. At the same time, the venture’s opacity means manufacturers and customers should weigh promises against demonstrable performance, and focus short-term investments on the fundamental plumbing , data capture, integration and resilience , that any advanced AI system will require.</p> <p>Whether Prometheus will deliver a generational transformation for the physical economy remains to be proven. For now, its emergence has sharpened the debate about where industry should prioritise spending: on novel algorithms and offline experiments, or on the painstaking work of digitising and connecting the operational backbone that those algorithms will inevitably depend on.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69b4ee9a9d48d97c600f9192Sat, 14 Mar 2026 22:51:11 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/14/ai-revolutionizes-sales-strategies-with-autonomous-agents-and-advanced-tools<p>AI-driven sales tools are transforming revenue operations by automating routine tasks, enhancing personalised outreach, and enabling human sellers to focus on strategic relationship-building. Market growth estimates suggest significant expansion, though deployment challenges remain.</p><p>Artificial intelligence is reshaping how companies generate revenue, moving organisations away from sheer headcount-driven sales models toward technology-augmented revenue operations. Where junior teams once shouldered the repetitive tasks of researching prospects, drafting outreach and managing follow-ups, a new generation of AI systems is taking on those duties so human sellers can concentrate on relationship-building, strategic negotiation and closing complex deals.</p> <p>The shift is already measurable. According to a Salesforce survey, 94% of sales leaders who employ AI agents regard them as essential to meeting business needs, and respondents expect these agents to cut prospect research time by about 34% and email drafting by roughly 36%. The result, Salesforce says, is more time for sellers to prioritise high-value work rather than administrative activity.</p> <p>A range of specialised products now populate the sales stack. Autonomous sales development representatives are one segment gaining traction. Alice, built by 11x, is offered as an independent digital SDR that seeks out target accounts, enriches leads, generates personalised outreach, manages replies and schedules meetings directly into calendars. Vendors present these autonomous agents as engines for running high-volume outbound programmes without proportional increases in headcount.</p> <p>Complementing autonomous prospecting are AI-driven content and conversation tools. Platforms such as Regie.ai automate the crafting of tailored emails, call scripts and multi-step sequences so teams can deploy personalised campaigns at scale. Conversation-intelligence systems like Avoma capture and analyse sales interactions, turning meeting transcripts into searchable knowledge and surfacing recurring objections or tactics associated with wins. Vendors argue this makes coaching and knowledge transfer far more efficient than relying on hand-written notes.</p> <p>Large enterprise suites have also embedded AI features for sellers. Salesforce’s Agentforce layers predictive and workflow automation into its CRM, promising prioritised pipelines and improved forecasting inside a familiar environment. Microsoft’s Copilot integrates with Outlook, Teams and Dynamics to produce meeting summaries, follow-up drafts and action recommendations from existing communications and CRM records. HubSpot’s Sales Hub aims the same functional gains at smaller businesses by offering accessible predictive scoring, automated personalisation and chat automation without heavy technical lift.</p> <p>Data-intelligence players add another dimension. Cognism, for example, applies machine learning to global datasets to supply enriched contact records and buying-intent signals, allowing reps to target companies displaying concrete indicators of purchase consideration rather than relying on generic lists.</p> <p>Industry analyses point to widespread adoption and strong market growth, but they also underline that outcomes vary. Autobound reports that roughly 81% of sales organisations have either adopted or are trialling AI, and finds teams using AI are about 1.3 times more likely to experience revenue growth. Market forecasting agencies estimate the AI-powered sales tool market will expand significantly: Market.us projects a compound annual growth rate around 12.9% with the market approaching $10.2 billion by 2035, while Warmly.ai estimates the broader revenue-AI market reached $8.8 billion in 2025 and could surge toward $63.5 billion by 2032. Separately, Autobound projects the AI SDR segment could attain a $15 billion valuation by 2030.</p> <p>The investment case is tempered by enduring challenges. Warmly.ai notes that although the vast majority of organisations use AI in at least one function, only a minority, about 39%, report a discernible improvement to EBIT, signalling that deployment, integration and change management remain critical hurdles. Pricing, data quality, model governance and alignment with existing sales processes continue to determine whether AI adoption translates into measurable commercial impact.</p> <p>Product comparisons published this year show differentiation in capability and fit. Industry round-ups name tools such as Gong, Clari Copilot and Outreach Kaia among leading assistants, each varying in focus from deal health and forecasting to engagement sequencing and conversational analysis. Commentary from these comparisons highlights that choice of tool often depends on company size, sales complexity and existing technology investments.</p> <p>Taken together, the technologies create an ecosystem in which routine, data-heavy tasks are ceded to machines and human sellers reclaim time for nuance and judgement. For businesses that can integrate these capabilities effectively, aligning signals, workflows and coaching, AI promises faster pipeline generation and tighter forecasting. For those that treat AI as a bolt-on without addressing process, the gains are likely to be modest.</p> <p>As the market matures, the boundary between tools that assist and those that autonomously execute will continue to shift. Vendors and buyers alike will need to balance scalability with oversight, ensuring automated agents operate transparently and that commercial teams retain the contextual intelligence necessary to convert opportunities into sustainable customer relationships.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69b5b833151e4567c8f78437Sat, 14 Mar 2026 22:50:57 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/14/ge-aerospace-expands-ai-driven-logistics-partnership-with-palantir-to-enhance-u-s-air-force-trainer-aircraft-readiness<p>GE Aerospace and Palantir have extended their collaboration to implement advanced agentic artificial intelligence across defence operations, aiming to improve readiness and operational efficiency for U.S. Air Force trainer aircraft through smarter data integration and automation.</p><p>GE Aerospace has broadened its collaboration with Palantir Technologies to deploy more advanced agentic artificial intelligence across the company’s defence sustainment and production operations, with an immediate focus on improving readiness for U.S. Air Force trainer aircraft.</p> <p>According to GE Aerospace, the extended effort will link live operational data from engines in service back into procurement and maintenance workflows, creating a continuous feedback mechanism intended to anticipate component failures, ease supply‑chain bottlenecks and keep aircraft available for training and missions. The companies say Palantir’s Artificial Intelligence Platform is now coordinating functions across fulfilment, sourcing, material allocation and maintenance, repair and overhaul.</p> <p>The partnership’s origins lie in a pilot that targeted the Air Force’s T‑38 trainer fleet and the J85 engine. Industry notices and company statements show GE has since secured a Defense Logistics Agency contract to increase J85 readiness under a digitally enabled TrueChoice Defense arrangement, with GE using AI and analytics to forecast parts demand and expose supply constraints. According to the report by GE Aerospace, the work is intended to speed decision‑making and improve sustainment for the T‑38 training mission.</p> <p>“Meeting today’s readiness demands requires both proven propulsion and smarter use of data,” said Amy Gowder, President and CEO of Defense and Systems for GE Aerospace. “Our collaboration with Palantir is helping customers keep more aircraft available so airmen get the training required to execute their missions.” Mike Gallagher, Head of Defense at Palantir, added, “By pairing GE Aerospace’s deep engineering expertise with Palantir’s AI-enabled software, we are unifying data across the enterprise to keep more aircraft available and more airmen trained.”</p> <p>GE and Palantir portray the arrangement as automating repetitive, manual tasks so engineers can focus on higher‑value work. The companies describe the architecture as agentic AI that not only surfaces insights but can drive actions across a dispersed supplier base, an approach they argue is suited to the accelerating complexity of modern defence logistics.</p> <p>The expansion also sits within a wider industry trend of defence and aviation firms partnering with Palantir to embed data platforms into production and sustainment. In recent years Palantir has announced collaborations with major aerospace and next‑gen aviation players, including Boeing Defense, Space &amp; Security, and Archer Aviation, where its platforms are being used to harmonise data and scale manufacturing and operational software. Separately, GE has been pursuing related autonomy and propulsion projects, teaming with Merlin to develop an autonomy core for crew reduction and uncrewed operations and working with Shield AI on propulsion for experimental unmanned programmes.</p> <p>Taken together, the moves reflect a broader strategy among manufacturers and software firms to fuse engineering expertise with large‑scale data platforms. GE and Palantir say the aim is to ensure digital infrastructure evolves alongside rising operational demands on aircrews, though outside analysts caution that integrating real‑time decisioning across complex supply networks will require sustained data governance, security and supplier adoption to deliver the claimed readiness improvements.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69b5b833151e4567c8f78435Sat, 14 Mar 2026 22:50:45 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/12/adeccos-multi-year-salesforce-deal-aims-for-over-50-ai-powered-revenue-by-2026<p>The Adecco Group has secured a multi-year, unlimited enterprise licence with Salesforce to accelerate agentic AI deployment across its global operations, targeting over 50% of revenue powered by AI by 2026 amid automation and workforce transformation.</p><p>The Adecco Group has agreed a multi‑year, unlimited enterprise licence with Salesforce that the firm says will accelerate deployment of agentic artificial intelligence across its global operations through to 2027. According to the announcement, the arrangement gives Adecco unrestricted access to Agentforce 360, Salesforce’s suite for connecting applications, data and autonomous agents on a single platform.</p> <p>The company said the deal underpins its aim for “over 50% of Adecco revenues to be powered by agentic AI by the end of 2026”, framing the move as a step towards scaling AI‑driven workflows across its Adecco, LHH and Akkodis businesses. The Adecco Group noted that its initial rollout in the UK automated routine recruitment tasks and produced time savings for recruiters, faster placements and improved candidate experience; the firm claims those agentic deployments delivered about 15 percent time savings and reduced cost‑to‑serve.</p> <p>Denis Machuel, chief executive of The Adecco Group, is quoted as saying: "We continue to be the pioneer of human-centric AI implementation. Unlimited access to Agentforce lets us rapidly scale proven agentic AI solutions globally and across our brands. This will improve our service speed, quality and reliability, freeing our people to focus on the human interactions that made them choose this career." The company presents the agreement as part of a broader strategy to combine automation with in‑person, human‑centred services.</p> <p>Salesforce has positioned Agentforce 360 as the backbone for the “agentic enterprise”, and its executives say the platform is designed to let businesses move beyond pilot projects to predictable, always‑on agentic operations. Madhav Thattai of Salesforce is quoted as saying: "By moving beyond experimentation to a full-scale agentic enterprise, the Adecco Group is proving that autonomous agents can deliver the determinism and predictability needed to power a global business. Powered by Agentforce 360, Adecco now has the 'always-on' foundation to connect millions of job seekers with career opportunities using agents to drive 50% of their revenue by the close of 2026."</p> <p>Public material from both parties highlights technical enablers for the roll‑out. Adecco says it has unified data from more than 30 Salesforce instances and other enterprise systems into a single, real‑time candidate profile using Data 360, which the company claims has improved recruiter visibility and fuelled agent‑led workflows. Salesforce’s wider product roadmap , including recent releases that expand agent capabilities, multimodal experiences and prebuilt skills libraries , provides additional context for how such platforms are being marketed to customers seeking rapid scale.</p> <p>Analysts and industry commentary suggest the commercialisation of agentic AI raises questions beyond productivity gains: data governance, integration complexity and the impact on labour models are frequently flagged as risks. Adecco’s statement acknowledges those uncertainties with a forward‑looking disclaimer and emphasises its commitment to responsible, scalable AI and to redeploying staff to higher‑value human interactions rather than simply replacing roles.</p> <p>The next phase of Adecco’s plan, according to its release, will expand agent orchestration in the UK, increase deployments in France and other key markets, and extend agentic capability into nearshore and offshore delivery hubs. The company said this approach is intended to raise competitiveness in smaller markets and to standardise fulfilment quality across its global talent supply chain.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69b267deddee4f0d1237208cThu, 12 Mar 2026 23:07:28 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/12/supply-chains-embrace-agentic-ai-for-rapid-autonomous-decision-making<p>A new IBM study reveals how organisations are shifting from supporting human supply chain decisions to deploying agentic AI models capable of autonomous actions, promising increased resilience and efficiency amidst ongoing disruptions.</p><p>As supply chains confront an accelerating tempo of disruption, a growing number of organisations are shifting from AI that supports human decisions to agentic AI operating models that can act with a high degree of independence. According to the IBM Institute for Business Value study, produced in partnership with Oracle and Accelalpha, these models stitch together ERP records, supply‑chain applications, partner interfaces and external feeds so systems continually optimise procurement, inventory, production, logistics and after‑sales service with minimal human intervention.</p> <p>The defining capability of agentic models is not merely prediction but the capacity to translate insight into immediate action. Where traditional automation follows pre‑set rules, autonomous agents are engineered to interpret real‑time signals and execute changes, rerouting shipments, shifting sourcing strategies, negotiating supplier terms or invoking contingency plans, without waiting for manual approval. The IBM survey found that 62% of supply‑chain leaders believe embedded AI agents speed the move from insight to action, and 76% of chief supply‑chain officers expect process efficiency to rise as agents undertake repetitive, impact‑oriented tasks faster than people can.</p> <p>These agents draw on a far broader range of inputs than legacy systems. Weather patterns, geopolitical developments, market indices and partner data are blended with internal ERP information so agents can simulate scenarios and anticipate shocks before they materialise. That broader data scope underpins what the IBM study describes as ecosystem‑scale resilience: agents exchanging information and coordinating responses across supplier and logistics networks enable faster recovery and more collaborative problem solving. The report highlights dynamic sourcing as an early use case, with agents selecting suppliers based on shifting demand signals, pricing and capacity constraints; similar applications are emerging in inventory optimisation, production‑yield forecasting and transport routing.</p> <p>Organisations that are advancing fastest do not treat agentic AI as a plug‑in technology but redesign operating models around autonomy paired with accountability. According to IBM research on agentic process automation, success increasingly depends on governance, observability and defined human roles, employees will still set objectives, monitor agent performance and calibrate autonomy levels. The IBV study notes significant concerns among executives, 72% flagged data accuracy or bias as a challenge and 63% cited data security and privacy as barriers to wider generative AI deployment, underscoring why oversight is presented as a foundational requirement rather than an afterthought.</p> <p>Broader industry analysis points to rapid adoption across the sector. Gartner predicts that by 2030 half of cross‑functional supply‑chain management solutions will embed intelligent agents capable of autonomously executing decisions across ecosystems, a shift expected to unlock new resource efficiencies and business models. Meanwhile, commercial partnerships are accelerating practical deployments: IBM’s recent collaboration with S&amp;P Global, for example, will layer IBM’s watsonx Orchestrate framework into S&amp;P Global offerings beginning with supply‑chain applications, aiming to combine diverse data domains and agentic orchestration to improve visibility and operational response.</p> <p>For ERP practitioners the implications are profound. Agentic AI shifts enterprise resource planning from a retrospective ledger to an active control plane that intervenes as conditions change, altering how planning, sourcing, logistics and execution are architected. The transition raises integration imperatives as well as governance demands: stronger data exchange with partners improves resilience but also increases the need for transparent decision trails, robust access controls and bias‑mitigation processes. The IBM‑Oracle‑Accelalpha work concludes that long‑term value flows to organisations that pair early investments in visibility and testing with clear accountability structures, ensuring autonomous actions align with commercial objectives and compliance obligations.</p> <p>As firms redesign operating models to harness agentic capabilities, the balance struck between machine autonomy and human oversight will determine whether the next generation of supply chains delivers greater agility without introducing new operational or regulatory risks. Industry studies and vendor initiatives indicate the shift is already moving from concept to practice; the defining task for leaders is to govern that change so autonomy becomes a controlled instrument of resilience rather than an unmonitored source of risk.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69b267deddee4f0d1237207cThu, 12 Mar 2026 23:06:49 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/12/ringcentral-unveils-agentic-voice-ai-to-transform-contact-centres-into-proactive-operational-hubs<p>RingCentral introduces a new class of conversational tools, "agentic voice AI", aiming to shift contact centres from reactive query handlers to proactive, operational partnerships, promising improvements in resolution rates, compliance, and real-time oversight.</p><p>RingCentral is positioning a new class of conversational tools it dubs “agentic voice AI” as a way to shift contact centres from reactive query handlers to proactive operational partners. According to the company, the suite combines autonomous voice agents, real‑time assistant functions and workforce optimisation to accelerate resolution rates, tighten compliance and give supervisors faster situational awareness.</p> <p>At the centre of RingCentral’s offering is AVA, the AI Virtual Assistant, which the vendor says now operates as an active collaborator alongside human agents. The firm describes AVA Agent Assist as moving beyond simple search or script‑lookups to supply contextual, step‑by‑step recommendations during live interactions and to keep track of procedural checklists automatically. RingCentral also says AVA monitors conversational signals and can prompt agents with tone‑ or de‑escalation guidance when it detects customer frustration.</p> <p>RingCentral presents these features as delivering measurable operational benefits, including reduced average handle time, higher first contact resolution and more consistent compliance across agent cohorts. The company’s customer‑facing material frames these outcomes as the direct result of AVA’s ability to surface the right actions and to reflect customers’ emotional state back to agents in real time.</p> <p>Supervisors are a second focus. AVA Supervisor Assist is described as offering automatic tagging and trackers for phrases or risk indicators, plus near‑instant summaries and transcripts so managers can intervene with full context. RingCentral claims this reduces the time required to monitor conversations and improves the speed of escalations.</p> <p>The vendor is also extending agentic ideas into workforce management. Its AI Workforce Management functionality, bolstered by RingCentral’s acquisition of CommunityWFM, is said to use anomaly detection across historical volumes to flag outliers and recommend schedule adjustments before problems materialise. The firm is promoting a more flexible, “gig‑style” scheduling model that lets agents bid for days or partial shifts, which RingCentral argues will lift efficiency and retention by matching supply to demand with finer granularity.</p> <p>RingCentral’s broader product set underpins the agentic strategy. The company’s announcement of an agentic voice AI communications suite includes AIR, an AI Receptionist claimed to capture leads and hand over contextually to humans, and ACE, an AI Conversation Expert intended to extract productivity and business insights from voice interactions. Separately, RingCentral has introduced AIR Pro, a voice‑first, omnichannel agent platform with a no‑code studio for designing and deploying AI agents, which the firm says embeds directly into its communications and contact centre stack.</p> <p>Market observers note competitors are pursuing similar integrations of CRM, telephony and AI. According to reporting on Salesforce’s Agentforce Contact Center, rival vendors are converging on unified automation that blends CRM data with voice AI to supply real‑time prompts, omni‑channel analytics and smoother AI‑to‑human transitions. That competitive backdrop underscores why vendors emphasise end‑to‑end orchestration and enterprise controls as differentiators.</p> <p>RingCentral’s materials present the agentic features as production‑ready capabilities intended for live operations. The company is promoting demonstrations at industry events, saying attendees can see the technology in action at Enterprise Connect. Industry analysts and customers will be watching whether the theoretical gains, shorter handle times, higher CSAT and improved compliance, materialise once the tools are rolled out across diverse contact‑centre environments.</p> <p>While RingCentral frames these developments as a step change in customer experience tooling, the claims rest on product demonstrations and vendor statements. Independent verification of long‑term outcomes and a clearer view of data governance, auditability and human oversight will be important as organisations weigh adoption.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69b267deddee4f0d12372078Thu, 12 Mar 2026 23:06:49 +0000http://srmtoday.makes.news/gb/en/gen-ai/2026/03/12/ford-pro-ai-transforms-fleet-management-with-data-driven-safety-and-efficiency-tools<p>Ford Motor Company has launched an AI assistant embedded in its telematics platform, promising to revolutionise commercial fleet safety and operational efficiency through enhanced data analytics and integrations, while signalling a shift towards recurring software revenue models.</p><p>INDIANAPOLIS , Ford Motor Company has introduced an artificial intelligence assistant for commercial fleets that it says will reshape how operators manage safety and efficiency, combining deeper telematics with data-driven recommendations. According to a report by Bitcoin World, the Ford Pro AI assistant was unveiled at Work Truck Week and is being offered to existing telematics subscribers within Ford Pro, the company’s commercial-vehicle division.</p> <p>The platform ingests vast quantities of connected-vehicle data and converts them into operational intelligence, with seatbelt-usage monitoring highlighted as a high-impact example for improving driver compliance and reducing risk. The Bitcoin World piece also cites Ford Pro financials, saying the division generated $66.3 billion in revenue last year, posted $6.8 billion in net income in 2025, and served more than 840,000 global subscribers, while paid software subscriptions grew roughly 30 percent.</p> <p>Ford describes the assistant as more than a chatbot. According to Ford Pro’s own documentation released in March 2025, the company has been steadily expanding telematics capabilities, adding tools such as Fleet Start Inhibit, Top Speed Limiter and Acceleration Limiter, and enhancing the Ford Pro Telematics Drive app with Google Maps and a Direction of Travel feature. Those prior additions provide the technical foundation Ford says the AI builds on, letting managers ask specific operational questions and receive tailored answers through the existing platform.</p> <p>Independent trade reporting places the public debut of Ford Pro AI at NTEA Work Truck Week in March 2026. WorkTruckOnline and Automotive Fleet both reported the assistant’s arrival at that event, describing it as an embedded feature in the Ford Pro Telematics suite that transforms millions of data points into actionable insight for fleet decision-makers. These accounts differ from the earlier June 2025 timeline in Bitcoin World’s coverage; the discrepancy underscores that multiple roll-out milestones and announcements have occurred as Ford staged the product’s introduction across channels and events.</p> <p>A central technical point Ford emphasises is reliance on customer-specific operational data to train the models. According to the Bitcoin World article, that approach reduces erroneous outputs commonly labelled “AI hallucinations,” because the assistant learns from patterns within each fleet rather than from generic datasets. Ford Pro’s public materials similarly frame the AI as a tool to surface vehicle-health issues, driving behaviour trends and utilisation metrics so fleet managers can prioritise maintenance, coaching and route decisions.</p> <p>The company has also been forging ecosystem connections to extend the platform’s practical value. A March 2025 partnership with Work Truck Solutions created an online vehicle-locator service to help businesses find work-ready Ford trucks, according to a GlobeNewswire release. Later in 2025 Ford Pro announced a multi-year integration with ServiceTitan to feed real-time Ford vehicle data into ServiceTitan’s Fleet Pro software, a move described on Ford’s From the Road site as intended to reduce downtime and simplify fleet management for trade-service businesses. Those collaborations aim to embed Ford Pro data into the broader software tools fleets already use.</p> <p>Ford’s executives have signalled the commercial strategy behind these moves: software and subscriptions offer recurring, higher-margin revenue than one-off vehicle sales. Industry observers note the same trend across automakers as manufacturers increasingly treat vehicles as platforms for post-sale services. The Bitcoin World coverage frames Ford’s AI push as both a safety initiative and a revenue play within that wider shift.</p> <p>The rollout also raises questions about workforce effects. Bitcoin World quotes CEO Jim Farley warning that AI could shrink some white-collar roles in the United States by roughly half, while also creating demand for workers to build, secure and maintain the systems that support such technology. That tension reflects a broader challenge for the sector: realising operational gains without neglecting reskilling and transition support for affected employees.</p> <p>For commercial customers, the immediate business case is straightforward. Ford Pro AI’s seatbelt monitoring, speeding and harsh-driving alerts, idle-time analysis and fuel-usage reporting are presented as levers to cut claims, lower insurance costs, target driver training and optimise maintenance scheduling. Ford’s March 2025 product notes and subsequent trade reporting say the assistant is accessible through existing telematics subscriptions, positioning it as an incremental service rather than a separate hardware sale.</p> <p>Ford has also signalled plans to take lessons from the commercial product into consumer services. Bitcoin World reports that an occupant-facing AI assistant for passenger-vehicle owners was announced at CES 2026, with an initial rollout inside Ford’s smartphone app and vehicle integration targeted for 2027. According to Ford Pro materials, the commercial platform’s data-centric model will likely inform the consumer experience, though company statements emphasise the need to adapt privacy, safety and user-experience design for retail drivers.</p> <p>Taken together, Ford’s announcements and the surrounding industry reporting depict a deliberate pivot: telematics and AI-driven services are being deployed to improve fleet safety and extract new recurring revenues, while integrations with partners aim to make that data more actionable within operators’ existing workflows. Conflicting timelines in early coverage indicate the launch was staged across different forums, but both Ford’s product notes and trade outlets agree the company has moved from trial features toward a market-ready assistant embedded in the Ford Pro ecosystem.</p> <p>As fleets begin to adopt these tools, their effectiveness will depend on accurate in-vehicle sensing, clear reporting to managers, and sustained investment in training and integrations. Ford’s combination of enhanced telematics, partner connections and a subscription delivery model positions the company to capitalise on the trend toward software-defined fleet operations while raising familiar questions about job displacement, data governance and the real-world performance of enterprise AI.</p> <p>Source: <a href="https://www.noahwire.com" rel="nofollow" target="_blank">Noah Wire Services</a></p>69b267deddee4f0d12372080Thu, 12 Mar 2026 23:06:49 +0000