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There is a particular kind of momentum in the technology industry that announces itself not through a single breakthrough, but through the simultaneous convergence of many. Physical AI is having that moment right now–and paying attention to where it is coming from, and why, tells you more than any single product launch can. The term itself–physical AI–is simple enough. It describes AI systems that don’t just process data or generate content, but perceive, reason, and act in the real world–robots, autonomous vehicles, machines that adapt. Nvidia CEO Jensen Huang called it “the ChatGPT moment for robotics” at CES in January–a deliberate framing, and a useful one. The ChatGPT comparison isn’t about hype. It signals that a technology once confined to research environments is being adopted for mainstream commercial deployment. That crossing is exactly what we are watching unfold from factory floors in Silicon Valley to stages in Shanghai.” The West is building the stack On the Western side, the physical AI push is fundamentally a platform race. The companies investing most aggressively aren’t primarily robotics companies–they’re infrastructure companies that see robotics as the next surface on which AI gets monetised. Nvidia has released new Cosmos and GR00T open models for robot learning and reasoning, alongside the Blackwell-powered Jetson T4000 module, which delivers 4x greater energy efficiency for robotics computing. Arm has carved outan entirely new Physical AI business unit focused on semiconductor design for robotics and intelligent vehicles. Siemens and Nvidia announced plans to build what they’re calling an Industrial AI Operating System, with ambitions to create the world’s first fully AI-driven adaptive manufacturing site. Then there’s Google, which last week brought its robotics software unit Intrinsic fully in-house–out of Alphabet’s “Other Bets” and into Google’s core. The move positions Google to offer manufacturers a vertically integrated stack: AI models from DeepMind, deployment software from Intrinsic, and cloud infrastructure from Google Cloud. The Android analogy being floated internally is instructive. Android didn’t win smartphones by building the best phone. It won by becoming the layer everything else ran on. That is precisely what Google is attempting with physical AI. The enterprise implications are significant. A Deloitte survey of more than 3,200 global business leaders found that 58% are already using physical AI in some capacity, rising to 80% with plans over the next two years. The demand is there. The question has shifted from whether to adopt to how fast and on whose platform. Boston Dynamics’ humanoid robot Boston Dynamics Atlas has begun operating fully on its own inside Hyundai’s manufacturing facility in Georgia. Follow: @AFpost pic.twitter.com/pfAzyxqRnn — AF Post (@AFpost) January 5, 2026 The East is building the machines China’s physical AI story is different in character–and arguably more visceral. At this year’s Spring Festival Gala, humanoid robots from multiple ******** startups performed kung fu routines, aerial flips, and choreographed dances before hundreds of millions of viewers–a sharp contrast from the stumbling prototypes that drew scepticism just a year prior. It was a spectacle, yes. It was also a statement. China accounted for over 80% of global humanoid robot installations in 2025 and over half of the world’s industrial robots. That dominance is underpinned by structural advantages that go beyond software. China controls roughly 70% of the global lidar sensor market, leads in harmonic reducer production–the gears critical to robot movement–and has driven hardware costs down through the same economies of scale that propelled its EV industry. Alibaba has entered the race with RynnBrain, an open-source AI model designed to help robots comprehend the physical world and identify objects–positioning itself alongside NVIDIA’s Cosmos and Google DeepMind’s Gemini Robotics in the foundation model layer. With over 140 domestic humanoid manufacturers and more than 330 humanoid models already unveiled, China’s push into embodied AI is no longer experimental–it’s commercial. Why it matters beyond the headlines The convergence of Western platform strategies and Eastern manufacturing scale is creating something genuinely new: a global physical AI ecosystem that is advancing on multiple fronts simultaneously, with different competitive advantages colliding. What makes this moment distinct from prior robotics waves is the removal of the expertise bottleneck. Historically, deploying industrial robots required specialised engineering teams, months of custom programming, and a high tolerance for downtime. The platforms being built now–by Google, Nvidia, Siemens, and their ******** equivalents–are explicitly designed to lower that barrier. Companies like Vention, which raised US$110 million in January, claim their physical AI platforms can reduce automation project timelines from months to days. When that claim becomes routine, the economics of manufacturing change structurally. There is also a geopolitical dimension that sits quietly beneath the product announcements. Every foundation model for robotics, every platform layer, every semiconductor architecture being developed right now carries with it questions of supply chain dependency, data sovereignty, and long-term infrastructure control. The country–or company–that governs the software layer of physical AI will have unusual leverage over industrial operations globally for years to come. Physical AI is not a trend. It is the next significant reconfiguration of how the world makes things, moves things, and operates at scale. The conversations happening now–from semiconductor boardrooms to factory floors in Shenzhen and Silicon Valley–are not preliminary. They are the thing itself, already underway. (Photo by Hyundai Motor Group) See also: Goldman Sachs and Deutsche Bank test agentic AI for trade surveillance Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. 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AI agents prefer Bitcoin for digital wealth storage, forcing finance chiefs to adapt their architecture for machine autonomy. When AI systems gain economic autonomy, their internal logic dictates how corporate capital flows. Non-partisan research by the Bitcoin Policy Institute evaluated how these frontier models would transact if operating as independent economic actors. The study tested 36 models from six providers – including Google, Anthropic, and OpenAI – across 9,072 neutral monetary scenarios. Given a blank slate, machines chose Bitcoin in 48.3 percent of all responses, beating every other option. Traditional state-backed currency (“fiat”) fared poorly, with over 90 percent of responses favouring digitally-native money over fiat. Not a single model out of the 36 selected fiat as its top preference. The finding that AI agents lean towards digital assets like Bitcoin forces technology officers to assess their current payment rails. If the autonomous procurement systems of tomorrow default to decentralised assets, corporate IT environments must support those formats to maintain operational efficiency and compliance. Relying on legacy banking APIs introduces unnecessary friction when dealing with machine-to-machine commerce. Two-tier machine economy The research details a specific functional division in how these systems process economic value. Without prompting, models defaulted to a two-tier monetary system that separates savings from spending. For long-term value preservation, Bitcoin dominated the results at 79.1 percent. Yet, when tasked with everyday payments and transactions, “stablecoins” (digital assets pegged to fiat currencies or commodities) captured 53.2 percent of the preferences. Across all scenarios, stablecoins ranked second overall at 33.2 percent. Take the example of a supply chain agent programmed to optimise logistics costs and pay international freight vendors. Using traditional fiat rails, the agent encounters weekend settlement delays and currency conversion fees. By leveraging stablecoins, the same agent executes instant and programmatic payments, improving supply chain resilience. Simultaneously, the core treasury holding the system’s capital base stores wealth in Bitcoin to prevent long-term debasement and counterparty risk. Preparing for AI agents to use Bitcoin and other digital assets Rolling out these autonomous systems complicates vendor management. A model’s financial reasoning stems from a blend of raw intelligence, training data, and alignment methodology. Preferences vary widely by model provider, with Bitcoin selection ranging from 91.3 percent in Anthropic’s Claude Opus 4.5 down to 18.3 percent in OpenAI’s GPT-5.2. The choice of an AI provider clearly directly influences how autonomous agents assess risk and allocate capital. If a company implements a specific language model for automated portfolio management, the IT department must be aware of the financial biases embedded in the software. The models also demonstrated unexpected behaviour regarding resource valuation. In 86 separate responses, models independently proposed using compute units or energy (such as GPU-hours and kilowatt-hours) as a method to price goods and services. Tracking and managing this abstract value exchange requires high data maturity. Organisations should begin piloting stablecoin settlement integrations for lower-risk vendor payments. The findings point to a growing requirement for AI agent-native Bitcoin payment infrastructure, self-custody solutions, and ‘Lightning Network’ integration. Since these models heavily favour open, permissionless networks, relying solely on traditional banking infrastructure limits the capabilities of next-generation tools. By building compliant gateways to digital asset networks now, leaders can ensure their platforms remain competitive. See also: Santander and Mastercard run Europe’s first AI-executed payment pilot Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post AI agents prefer Bitcoin shaping new finance architecture appeared first on AI News. View the full article

When Google folds a moonshot into its core operations, it’s not cleaning house. It’s placing a bet. On February 25, Alphabet-owned Intrinsic–which builds AI models and software designed to make industrial robotics more accessible–officially joined Google. The company will remain a distinct group within Google, working closely with Google DeepMind and tapping into Gemini AI models and Google Cloud. No purchase price was disclosed. On the surface, this looks like a routine internal reshuffle. It isn’t. From Moonshot to Mandate Intrinsic graduated into an independent Alphabet-owned company in 2021 after five years of development within Alphabet’s X, the moonshot research division–the same factory that produced Waymo and Wing. Its mission from the start: make industrial robotics AI accessible to manufacturers who don’t have armies of specialist engineers. While hardware like robotic arms has become cheaper, programming them remains incredibly complex, often requiring hundreds of hours of manual coding by specialised engineers that can vary based on the particular robot. Intrinsic’s answer to that is Flowstate–a web-based platform that allows users to build robotic applications without having to write thousands of lines of code. The platform is designed to be hardware-, software-, and AI-model-agnostic. Think of it less as a product and more as an operating layer–one that Google CEO Sundar Pichai has reportedly compared directly to Android. “He said this is the Android of robotics,” Intrinsic CEO Wendy Tan White said, noting that Pichai worked on Chrome and Android before becoming CEO. Why now, why Google? The timing isn’t arbitrary. The sequence of hiring Boston Dynamics’ CTO, releasing a standalone robotics SDK, and now absorbing Intrinsic represents a deliberate consolidation of robotics capability inside Google’s core. Taken together, these moves position Google to offer manufacturers something no competitor has assembled quite as cleanly: AI models from DeepMind, deployment software from Intrinsic, and cloud infrastructure from Google Cloud–all under one roof. Last month, Google also teamed up with Boston Dynamics to integrate Gemini into Atlas humanoid robots built for manufacturing environments, while Google DeepMind hired the former CTO of Boston Dynamics in November. The industrial robotics AI market Google is chasing is not small. McKinsey projects that the market for general-purpose robots could reach US$370 billion by 2040. What it means for the enterprise For enterprise decision-makers, the more interesting signal here isn’t the technology–it’s the accessibility shift. Google plans to integrate Intrinsic’s robotics development platform and vision models with its broader AI ecosystem, combining advanced reasoning, perception and learning capabilities with industrial-grade robotics software to allow machines to interpret sensor data better, adapt to dynamic environments and execute complex tasks. Intrinsic has also expanded through acquisitions–acquiring the Open Source Robotics Corp. in 2022, the for-profit arm of the foundation behind the Robot Operating System (ROS). And its commercial pipeline is already in motion: in October 2025, Intrinsic formed a strategic partnership with Foxconn focused on developing general-purpose intelligent robots for full factory automation within electronics manufacturing. White framed the integration in terms enterprise leaders will find hard to ignore: production economics, operational transformation, and what she described as truly advanced manufacturing — all within reach once Google’s infrastructure is fully behind it. That’s a significant claim. But with Gemini, DeepMind, and Google Cloud now aligned behind it, the infrastructure to back it up is, for the first time, actually there. See also: Physical AI adoption boosts customer service ROI Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Google makes its industrial robotics AI play official–and this time, it means business appeared first on AI News. View the full article

Artificial intelligence is no longer just powering defensive cybersecurity tools, it is reshaping the entire threat landscape. AI is accelerating reconnaissance, improving the realism of phishing, automating malware mutation, and enabling adaptive attack techniques. At the same time, enterprises are embedding AI agents, copilots, and generative AI tools into everyday workflows. That dual dynamic has created a new category: AI security. AI security platforms focus on three primary challenges in 2026: Securing enterprise AI usage and prompt interactions Protecting AI models, agents, and infrastructure Defending against AI-powered cyber threats Below are five of the strongest AI security solutions in 2026. Check Point – AI-driven security Check Point integrates AI security into its broader Infinity platform, covering network, cloud, endpoint, and AI usage in a unified architecture. The core of the platform is ThreatCloud AI, which leverages more than 50 AI engines and intelligence from over 150,000 connected networks. Compromise indicators propagate across the platform within seconds, enabling coordinated defense across domains. The platform addresses AI risk at multiple layers. GenAI Protect monitors employee interactions with generative AI tools, semantically analysing prompts to enforce data loss prevention policies in real time. This approach focuses on contextual classification rather than simple keyword matching. Check Point also secures AI infrastructure and enhances security operations through Infinity AI Copilot. Independent testing has shown high efficacy against zero-day malware, and the platform has consistently ranked highly in hybrid firewall evaluations. Best for: Enterprises seeking unified AI security across infrastructure, AI usage, and security operations. CrowdStrike – AI security services CrowdStrike extends its Falcon platform into AI protection by integrating telemetry from endpoints, identities, cloud workloads, and AI agent activity. Falcon AIDR focuses specifically on defending against prompt injection and malicious manipulation of AI agents. It is designed to identify known prompt injection techniques while maintaining low latency, which is critical in production AI environments. CrowdStrike also integrates AI assistants directly into security operations. Charlotte AI supports natural language threat investigation and automated triage, reinforcing the company’s vision of an AI-augmented SOC. The approach is particularly strong for organisations already standardised on the Falcon ecosystem, allowing AI security capabilities to extend existing endpoint and cloud telemetry. Best for: Organisations seeking integrated AI threat detection within an established endpoint-centric security architecture. Cisco – AI defense Cisco approaches AI security from a network-centric vantage point. Because it operates at the network layer, Cisco can inspect AI-related traffic across enterprise environments, including API calls and model interactions that may not be visible at the endpoint level. Cisco AI Defense integrates into the broader Security Service Edge architecture. Recent enhancements include AI Bills of Materials to map dependencies within AI ecosystems, real-time guardrails for agentic systems, and red teaming simulations against AI workflows. Cisco aligns its controls with established frameworks such as NIST AI Risk Management Framework and MITRE ATLAS. This emphasis on governance makes it attractive to enterprises operating in regulated industries. Best for: Enterprises with strong Cisco network infrastructure seeking AI security embedded at the traffic and control layer. Microsoft– AI-enhanced security ecosystem Microsoft’s AI security advantage lies in scale. The company processes tens of trillions of security signals daily across its global infrastructure. Security Copilot functions as an AI assistant embedded within Defender, Entra, Intune, and Purview. It automates alert triage, assists with natural language threat investigation, and orchestrates remediation actions. Microsoft has also expanded AI security posture management to include multi-cloud environments, including AWS and Google Cloud AI services. This is particularly important for enterprises building AI models outside Azure. For organisations already invested in Microsoft 365 enterprise licensing, AI-enhanced security capabilities can be layered into existing subscriptions without introducing additional vendor complexity. Best for: Enterprises deeply aligned with Microsoft 365 and Defender ecosystems. Okta– Identity security with AI risk context As AI agents proliferate, identity becomes a primary attack surface. Many AI systems operate with high levels of privilege and autonomy. Okta focuses specifically on identity governance in AI environments. Its architecture treats AI agents as first-class identities, applying authentication, authorisation, and lifecycle governance controls similar to those applied to human users. Identity Security Posture Management identifies over-privileged accounts, including non-human identities, and surfaces risk in real time. The company also promotes open standards for managing AI-to-application connectivity through extended OAuth mechanisms. For enterprises rapidly deploying AI agents internally, identity-centric AI security becomes essential. Best for: Organisations deploying AI agents at scale that require identity governance for non-human actors. Comparison Overview VendorCore strengthIdeal buyerCheck PointUnified AI security across infrastructure and usageLarge enterprises seeking platform consolidationCrowdStrikeEndpoint-integrated AI threat detectionFalcon-centric organisationsCiscoNetwork-layer AI traffic visibilityCisco ecosystem enterprisesMicrosoftSignal scale and Copilot integrationMicrosoft 365-heavy environmentsOktaAI identity governanceOrganisations deploying AI agents broadly How to choose the right AI security solution Selecting the right AI security platform depends on architecture and maturity. Organisations building AI internally should prioritise infrastructure protection and identity governance. Enterprises concerned with employee generative AI usage should evaluate prompt monitoring and DLP integration. Security teams overwhelmed by alert volume may prioritise AI-augmented SOC automation. AI security is not a separate silo. It intersects with network security, identity management, cloud governance, and incident response. The platforms above represent different strategic entry points into AI risk management. The best solution is the one aligned with your existing ecosystem and operational model. In 2026, AI is both a tool and a target. Enterprises that treat AI security as an integrated part of their security architecture will be better positioned to manage evolving threats. Image source: Pixabay The post Best AI security solutions 2026: Top enterprise platforms compared appeared first on AI News. View the full article

Try to think of just one area where artificial intelligence is not leaving a mark, and you’ll realise there’s almost none. And in the forex world, things have not been any different. It’s a big part of why Fortune Business Insights values the global AI market size at $375.93 billion. Looking ahead, the sector could continue making significant strides, reaching $2.48 trillion by 2034. The days of poring over charts and staring at economic indicators, hoping your instincts wouldn’t betray you, are long gone. Today, with AI forex automation software, you can analyse massive amounts of data and execute trades more accurately in milliseconds. And if you think that this is mere sci-fi, you might need to think again. Imagine, according to industry estimates from Future Market Insights, the AI trading platform market alone has already reached $220.5 million and is on track to hit $631.9 million by 2035. If that’s not enough, Andrew Borysenko, a respected financial trader, says over 70% of forex trading volume is now generated by automated systems. So, how and why exactly has AI been able to carve its own niche in this sector? Smarter decision-making through predictive analytics Consider a scenario where you want to invest in EUR/USD. If you’re using a traditional algorithm, it may only act when the exchange rate reaches a predetermined level. But an AI-driven system works differently. It’s able to detect subtle signals in global economic news and execute preemptive trades. Things like an unexpected policy shift in the Eurozone or shifts in the US interest rate expectations rarely pass unnoticed. In the long run, you end up making much better decisions than you would if you were solely relying on human intuition. So, you shouldn’t be surprised when institutions like the Global Banking & Finance Review claim that artificial intelligence can improve investment predictions by up to 45%. It’s such findings that explain why many traders have not been left out of the AI craze. After all, given the large amounts of data typically involved in analysis, manually processing every market signal can be overwhelming. And it can be really problematic if you miss those signals, as you won’t be able to take advantage of them. But with AI, nothing slips through the cracks. It scans large datasets, picking up on patterns and correlations that even the most experienced traders might overlook. And even if an unexpected announcement from a central bank would shift currency values within seconds, AI-powered tools can detect the news and quantify its potential impact almost instantly. As a result, traders can participate more proactively while reducing the guesswork that once made forex trading so daunting. Efficiency that matches the speed of the market Did you know that, according to Market Growth Reports, automated systems now account for over 70% of the global trading volume? Part of why this is so is that AI-based systems don’t just get tired. They work around the clock, reducing the likelihood of missing out on profitable opportunities. Truth be told: There are just times when you’ll get tired. And it doesn’t matter how experienced a trader you are. Fatigue could kick in, and suddenly those sharp instincts you’ve relied on start to blur. Eyes that were once quick to spot a chart pattern may begin to glaze over, and mental calculations take a fraction longer, just enough to miss a trade. Now imagine combining this weariness with the sheer volume of data needed for a more informed trading decision. By the time you’re processing one dataset, several others may have already shifted. This is not something any serious trader would want for themselves, especially when you consider how fast things change in forex. Thankfully, AI doesn’t get tired or lose focus. This makes it possible to constantly scan for opportunities and execute trades the moment conditions align. Risk management and emotional control Forex trading is as much an emotional exercise as it is analytical. But when emotions like fear or overconfidence take over, sound judgment tends to slip away. Unfortunately, a good number of traders often fall victim to these very emotions. Revenge trading can increase loss sizes by as much as 340% and “panic exits cause traders to miss 67% of their target profits.” If you’ve been in the trading industry long enough, you know what a sudden geopolitical event can mean. The panic and pressure of those split-second market swings can make even the most seasoned trader second-guess their strategy. AI, however, is not subject to emotional swings. It follows data-driven rules consistently and sticks to pre-defined parameters even when the market gets chaotic. In this way, you are able to trade in a more disciplined way, which, in turn, helps avoid unnecessary frustration. In an industry where every second counts, AI can manage your risks more effectively and ensure decisions are based on data rather than emotions. For traders, the rise of this technology is undoubtedly a game-changer. Just the thought that you don’t have to entirely depend on gut feelings to process endless streams of market data is liberating. And when you consider how the technology makes it possible to anticipate market movements and stay disciplined under pressure, it becomes easy to understand why many more traders are turning to it. Image source: Unsplash The post The integration of AI in modern forex automation appeared first on AI News. View the full article

The adoption of physical AI drives ROI in frontline customer service by merging digital intelligence with human-like physical interaction. As businesses navigate shrinking labour pools, they are finding that simply automating routine workflows is no longer enough. A new partnership between KDDI and AVITA demonstrates how companies can address complex operational gaps through humanoid deployment. While traditional industrial robots excel at repetitive, single-function tasks, they lack the versatility required to manage unexpected anomalies like equipment failures. Customer-facing roles demand nonverbal communication, including synchronised nodding, natural eye contact, and reassuring facial expressions. By integrating AVITA’s avatar creation expertise with KDDI’s communications infrastructure, the two organisations are building domestically developed humanoids capable of operating smoothly in real-world commercial environments. Blending hardware with advanced data infrastructure Deploying humanoids into active commercial spaces requires high-capacity and low-latency network infrastructure to transmit visual data and control commands in real time. KDDI provides this operational backbone, facilitating remote control capabilities alongside intensive cloud-based data processing. The resulting visual and motion data collected during customer interactions feeds back into the system to train the AI, improving the precision and autonomy of the humanoid’s behaviour. To support the demanding computational requirements of physical AI adoption, the companies plan to utilise GPUs hosted at the Osaka Sakai Data Center, which commenced operations in January 2026. They are also exploring integration with an on-premises service for Google’s Gemini high-performance generative AI model. This alignment with major enterprise platforms ensures that data processing remains secure and capable of handling complex dialogue requirements. The hardware itself departs from standard utilitarian machinery. Based on a concept model designed by Hiroshi Ishiguro, the humanoid features a compact skeletal structure approximating a typical Japanese physique. Silicone skin and specialised mechanical systems enable warm, approachable facial expressions that sync directly with spoken dialogue. Embedded camera sensors track objects in motion to create natural eye contact, while quiet pneumatic actuation allows for fluid and continuous movement with natural “micro-variations”. This design specifically addresses the historical difficulty of deploying automation in operations requiring hospitality and reassurance. Preparing for commercial adoption of physical AI This initiative builds upon earlier joint projects between KDDI and AVITA, which introduced a “next-generation remote customer service platform” using digital avatars for remote assistance at retail locations like Lawson and au Style shops. Transitioning from digital and language-driven communication to physical units capable of free movement represents a logical progression for enterprises looking to scale their customer service capabilities. The partners intend to begin trials in actual commercial facilities starting in Autumn 2026. Deployment at customer touchpoints such as au Style shops will also be considered. Integrating physical AI demands environments capable of sustaining continuous, high-volume data streams without latency interruptions. As visual and motion data becomes central to machine learning models, governance frameworks must adapt to manage customer data usage within physical spaces. Organisations facing demographic workforce pressures should evaluate current bottlenecks to identify where non-verbal, empathetic engagement is necessary. Setting up high-speed network foundations and piloting digital AI avatar programmes today allows enterprises to prepare for the adoption of physical humanoids as the hardware further matures. See also: Santander and Mastercard run Europe’s first AI-executed payment pilot Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Physical AI adoption boosts customer service ROI appeared first on AI News. View the full article

An artificial intelligence system has, for the first time in Europe, completed a payment inside a live banking network without a human entering the final command. Banco Santander and Mastercard confirmed that they had executed a live end-to-end payment initiated and completed by an AI agent, a software system operating within the bank’s own regulated payments infrastructure. The move was described by both firms as a milestone in what they call “agentic payments,” where software can act on behalf of customers under set limits and controls. This was not a simulated experiment. The transaction ran through Santander’s normal payments network using Mastercard Agent Pay, a framework that lets AI agents be registered and treated as participants in the payment flow. The pilot took place under strict security, governance, and compliance rules, and was not open to public use. The AI agent performed its role inside predefined limits and permissions set by the bank and the customer. The goal was to confirm that an autonomous system could initiate, authorise, and complete a transaction while still meeting the legal and operational guardrails that apply to everyday banking. Why this AI payment pilot matters Payments systems are among the most tightly regulated digital services in the world. Any change to how transactions are initiated must still meet authentication rules, fraud protections, and governance standards that financial regulators enforce. That’s why this pilot matters: it embeds an AI actor into a system normally used only by humans. The transaction was processed through Santander’s live infrastructure rather than a test environment. That means the bank and its partner had to ensure that all compliance checks, security validations, and payment routing worked the same way they would for a normal customer purchase. Even so, this is still a pilot project. Santander and Mastercard have made it clear that the arrangement is not a commercial service available to customers yet. The objective is to explore how AI agents could one day fit into existing payment flows while keeping the necessary controls intact. What industry forecasts say The idea of allowing AI to act autonomously is not limited to payments. Industry analysts have been following the broader shift toward agentic AI systems, software that can complete tasks or make decisions with limited human intervention. Research and forecast data suggest that this trend is likely to grow in business settings. Gartner, a major technology research firm, forecasts that around 33 % of enterprise software applications will include agentic AI by 2028, up from less than 1 % today. That projection reflects interest among corporate buyers in systems that can perform work on their behalf rather than only assist humans. Other forecasts align with this view, showing that businesses are increasingly preparing to deploy software agents for routine operations, customer interactions, and workflow automation. These systems are expected to move from early pilots into more common use cases over the next several years. The Mastercard network itself already reflects the scale of modern digital commerce. Independent reporting notes that Mastercard’s decision-making and fraud-scoring systems work with nearly 160 billion transactions annually across its network, evidence of how vast and complex the environment is where agentic systems might one day operate. What companies are saying In its press announcement, Santander highlighted its desire to build a responsible approach to AI payment systems. Matías Sánchez, global head of Cards and Digital Solutions at Santander, said: “Our role is not only to adopt innovation, but to shape it responsibly, embedding security, governance and customer protection by design. As AI agents become part of everyday commerce, building trusted, scalable frameworks will be essential to unlocking their full potential.” Kelly Devine, President, Europe at Mastercard, described the pilot in terms of continuity rather than change: “With Mastercard Agent Pay, we are applying the same principles that have defined our network for decades — security, interoperability and trust — to a new era of AI-enabled commerce.” Those comments underscore that neither company is portraying AI payments as already ready for broad use. Instead, they are testing how such capabilities could be governed and scaled safely. Dogma vs. reality There is a gap between the buzz around AI and what is operationally feasible today. Agentic AI as a concept promises systems that can act on behalf of users or businesses in real time. But many current applications remain in early stages, and some analyst reports have even warned that a large share of agentic AI projects could be cancelled before they reach production — due to costs, unclear value, or immature technology. What Santander and Mastercard have shown is that the technical plumbing can work under real-world conditions. But that doesn’t mean consumers can yet unlock AI agents to autonomously pay bills, shop online, or manage subscriptions. Those outcomes will require further testing, regulatory alignment, and robust guardrails for safety, privacy, and fraud prevention. What enterprise leaders should watch For business decision-makers, this pilot raises three practical questions: Governance and oversight: How will AI agents be controlled so that spending limits, identity checks, and audit trails remain clear? Identity and trust: If software can act on behalf of people or companies, how will systems ensure that only authorised actions are taken? Risk and liability: Who is responsible when an autonomous agent makes an error or misinterprets instructions? These are not academic concerns. As enterprise systems begin to support more autonomous tasks, from supplier ordering to subscription payments, organisations will need clear frameworks that define how AI agents are governed, monitored, and held accountable. The long view for AI-initiated payments The Santander and Mastercard test is not the finish line for AI-initiated transactions. It is an early step toward understanding how autonomous systems might coexist with regulated financial systems. The pilot demonstrates that AI systems can be integrated into live payments rails, but only under tightly controlled and monitored conditions. Scaling this to everyday use will require a lot of additional work on controls, security, and compliance. Still, the fact that a regulated bank and a global payments network have run a successful agent-initiated transaction shows where enterprise experimentation is heading: from pilot programs toward real-world validation. For enterprises planning their own AI strategies, this suggests that action-capable AI may soon move beyond suggestion and automation into governed execution, if done with care and strong oversight. (Photo by Clay Banks) See also: Goldman Sachs and Deutsche Bank test agentic AI for trade surveillance Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. 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AI-native networks have been a recurring talking point at Mobile World Congress for years. What made MWC 2026 in Barcelona different was the evidence. A cascade of announcements from the world’s biggest telecom vendors, chipmakers, and operators didn’t just reiterate the vision for AI-RAN–they delivered field trial results, commercial product launches, open-source toolkits, and a multi-operator coalition committing to build 6G on AI-native foundations. For enterprise and IT decision-makers, the signal is clear: the architectural shift happening in telecom infrastructure will soon reshape how connectivity is delivered, managed, and monetised. Nvidia and a global coalition lock in on AI-RAN and 6G The week’s most consequential announcement thus far came from Nvidia, which secured commitments from more than a dozen global operators and technology companies–including BT Group, Deutsche Telekom, Ericsson, Nokia, SK Telecom, SoftBank, T-Mobile, Cisco, and Booz Allen–to build 6G on open, secure, and AI-native software-defined platforms. The initiative, framed as a shared commitment to ensure future connectivity infrastructure is intelligent, resilient and trustworthy, is backed by ongoing collaborations with governments across the US, ***, Europe, Japan, and Korea. Jensen Huang, Nvidia’s founder and CEO, set the stakes plainly: “AI is redefining computing and driving the largest infrastructure buildout in human history–and telecommunications is next.” The company is a founding member of the AI-RAN Alliance, which now has over 130 participating companies, and has joined the FutureG Office-led OCUDU Initiative in the US to accelerate open, software-defined, AI-native 6G architectures. Nvidia also released a suite of open-source tools targeting network operators: a 30-billion-parameter Nemotron Large Telco Model (LTM), developed with AdaptKey AI and fine-tuned on telecom datasets including industry standards and synthetic logs; an open-source guide co-published with Tech Mahindra for building AI agents that reason like NOC engineers; and new Nvidia Blueprints for RAN energy efficiency and network configuration. The energy blueprint integrates VIAVI’s TeraVM AI RAN Scenario Generator to simulate energy-saving policies in a closed loop before touching live networks. Real-world adoption of the network configuration blueprint is already underway–Cassava Technologies is deploying it for an autonomous network platform across Africa’s multi-vendor mobile environment, while NTT DATA is using it with a tier one operator in Japan to manage traffic surges after network outages. Nokia and operators take AI-RAN over the air Nokia announced significant progress in its strategic AI-RAN partnership with Nvidia, completing functional tests of its anyRAN software on NVIDIA’s GPU-accelerated AI-RAN platform with T-Mobile US, Indosat Ooredoo Hutchison (IOH), and SoftBank Corp. The results matter because they moved validation out of controlled lab environments and into live, over-the-air conditions. At T-Mobile’s AI-RAN Innovation Centre in Seattle, Nokia’s AirScale Massive MIMO radio in the 3.7GHz band ran concurrent AI and RAN workloads–including video streaming, generative AI queries, and AI-powered video captioning–on a single Nvidia Grace Hopper 200 server alongside commercial 5G. IOH achieved Southeast Asia’s first AI-RAN-powered Layer 3 5G call at MWC, with AI and RAN workloads running simultaneously on shared GPU infrastructure. As IOH President Director and CEO Vikram Sinha put it: “This is not just about proving that the technology works. It is about ensuring that every Indonesian, wherever they are, can benefit from the digital and AI era.” SoftBank’s demonstration went further, showing how spare compute capacity identified by its AITRAS Orchestrator can run third-party AI workloads–a glimpse of how operators could eventually monetise RAN infrastructure beyond connectivity. Nokia’s expanded AI-RAN ecosystem now includes Dell Technologies, Quanta, Supermicro, and Red Hat OpenShift for orchestration, giving operators a widening range of commercial off-the-shelf options. Nokia shares rose 5.4% on the day of the announcement. Ericsson takes a different road to AI-native networks Ericsson arrived at MWC 2026 with a distinctly different approach–and it is one worth understanding. While Nokia has bet on Nvidia GPU acceleration (backed by a US$1 billion Nvidia investment), Ericsson unveiled ten new AI-ready radios built on its own purpose-built silicon, featuring neural network accelerators embedded directly into its Massive MIMO hardware. No NVIDIA GPUs required. The portfolio includes AI-managed beamforming, AI-powered outdoor positioning, instant coverage prediction using AI models, and a latency-prioritised scheduler delivering up to seven times faster response times. Ericsson’s argument is built on total cost of ownership: custom silicon, it contends, delivers better TCO and power efficiency than external GPU hardware, with the added benefit of supply chain independence. Per Narvinger, head of Ericsson’s mobile networks business, has been direct that this view is unlikely to change. At MWC, Ericsson also announced a sweeping collaboration with Intel spanning compute, cloud technologies, and AI-driven RAN and packet core use cases, to accelerate ecosystem readiness for AI-native 6G. “6G is not merely an iteration of mobile technology. It is the infrastructure that will distribute AI across devices, the edge and the cloud,” said Ericsson President and CEO Börje Ekholm. Intel CEO Lip-Bu Tan framed the partnership as a path to open, power-efficient networks grounded in AI inference, with future Ericsson Silicon built on Intel’s most advanced process nodes. SK Telecom, SoftBank, and the operator rebuild Beyond the vendor announcements, two operators used MWC 2026 to articulate how deeply AI-RAN fits into their broader infrastructure strategies. SK Telecom CEO Jung Jai-hun outlined a full-stack AI-native rebuild–from its network core to customer service systems–including plans to upgrade its sovereign AI foundation model from 519 billion to over one trillion parameters, and to build a new AI data centre in Korea in collaboration with OpenAI. The company is also expanding autonomous network operations using AI to automate wireless quality management, traffic control, and network equipment operations, with AI-RAN technology central to improving speed and reducing latency. SoftBank, meanwhile, demonstrated its Autonomous Agentic AI-RAN (AgentRAN) system at MWC in collaboration with Northeastern University’s INSI, Keysight Technologies, and zTouch Networks. The system uses SoftBank’s Large Telecom Model to translate natural-language operator goals into real-time 5G and 6G network configurations–a meaningful step toward networks that manage themselves based on intent rather than manual instruction. A hardware ecosystem takes shape around AI-RAN One of the clearest signs that AI-RAN is maturing from concept to commercial infrastructure is the breadth of hardware companies now building purpose-built products for it. At MWC 2026, Quanta Cloud Technology announced commercial on-the-shelf AI-RAN products supporting Nvidia ARC platforms and Nokia software. Supermicro extended support across the full Nvidia AI-RAN portfolio, including ARC-Pro and RTX 6000-based configurations. MSI unveiled its unified AI-vRAN platform with dynamic GPU allocation between 5G and AI workloads. Lanner Electronics launched its AstraEdge AI Server lineup–the ECA-6710 and ECA-5555–purpose-built to co-locate AI inference, RAN functions, and high-performance packet processing at cell sites. AMD, not to be left out, positioned its EPYC 8005 edge platform and Open Telco AI initiative at MWC as an alternative compute path for operators moving from AI pilots to production. What this means beyond the network For enterprise decision-makers, the implications of this week’s announcements extend beyond telecom infrastructure procurement. AI-RAN networks that evolve continuously through software–rather than requiring costly hardware refresh cycles–mean connectivity infrastructure increasingly resembles cloud infrastructure in its pace of change and flexibility. The embedding of GPU compute within the RAN opens the prospect of enterprise AI workloads running at the network edge, closer to where data is generated. And as Nvidia’s State of AI in Telecom report noted, 77% of respondents anticipate a significantly faster deployment timeline for AI-native wireless architecture than for previous network generations. The architecture debate between Ericsson’s custom silicon path and Nokia-Nvidia’s GPU-accelerated approach is also worth watching–not because one will definitely win, but because it reflects a genuine question about where AI inference should sit in network hardware, and at what cost. That question will shape operator procurement decisions and vendor relationships for years. What MWC 2026 made unmistakable is that AI-native networks are no longer a research agenda. The field trials are live, the hardware is shipping, and the coalitions are forming. The question for enterprises and operators alike is no longer whether this transition will happen–but how fast, and who leads it. (Photo by ) See also: MWC 2026: SK Telecom lays out plan to rebuild its core around AI Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post AI-Native networks are no longer a 6G promise–MWC 2026 just proved it appeared first on AI News. View the full article

At MWC 2026 in Barcelona, SK Telecom outlined how it is rebuilding itself around AI, from its network core to its customer service desks. The shift goes beyond adding new AI tools. It involves rewriting internal systems, expanding data centre capacity to the gigawatt scale, and upgrading its own large language model to more than one trillion parameters. At a press conference during MWC 2026, SK Telecom CEO Jung Jai-hun outlined what the company calls an “AI Native” strategy. The plan centres on reorganising infrastructure and making large investments so the company can help position Korea among the world’s top three AI powers. “SKT is currently at a golden time of transformation, where the two tasks of ‘customer value innovation’ and ‘AI innovation’ intersect in a borderless, converged environment that goes beyond telecommunications,” Jung said. “SKT defines ‘the customer as the very essence of our business,’ and through innovation driven by AI, we will evolve into a company that makes meaningful contributions to our customers and to Korea.” Rewriting telecom systems around AI at MWC 2026 At the core of the plan is a rebuild of SK Telecom’s integrated IT systems. The company said it will redesign sales, line management, and billing systems to be optimised for AI. The aim is to let the operator design and offer personalised plans and memberships based on each customer’s usage and behaviour patterns. The company also plans to apply a Zero Trust security framework across its systems. This will include stronger authentication, access controls, network segmentation, and AI-based monitoring, according to the company’s briefing at MWC 2026. For enterprises watching the telecom sector, this signals a broader shift. Telecom operators have long relied on legacy billing stacks and network management tools. Rebuilding those systems around AI could change how pricing, service design, and fault detection work in practice. It also raises questions about data governance and how customer data is used to train or tune AI models. SK Telecom is also expanding its “autonomous network operations” strategy. The company said it will use AI to automate wireless quality management, traffic control, and network equipment operations. With AI-RAN technology, it aims to improve speed and reduce latency. These efforts were described in company materials shared during the press event. A single AI agent across touchpoints Another part of the strategy focuses on customer interaction. SK Telecom plans to redesign pricing, roaming, and membership services to make them simpler and more automated. It is developing what it calls an integrated AI agent to connect experiences across its main customer portal, T world, and its online store, T Direct Shop. The company said the agent will analyse daily usage patterns and offer tailored suggestions across channels. It also plans to expand its AI Contact Center so customer service representatives can use AI tools during support calls. Offline retail stores are part of the shift. SK Telecom said AI will help staff identify customer needs and offer recommendations after a store visit. It is also building “AI Personas” to analyse digital behaviour across customer segments and support conversational Q&A. For enterprise leaders, this mirrors a wider pattern. Telecom operators are trying to move from reactive service models to predictive ones. The difference now is scale. By embedding AI into billing, customer service, and retail, SK Telecom is treating AI as an operating layer rather than a separate feature. Building 1GW-class AI data centres The infrastructure build-out is equally ambitious. SK Telecom said it will construct hyperscale AI data centres across Korea, targeting capacity that exceeds 1 gigawatt. It aims to attract global investment and position the country as a major AI data centre hub in Asia. The company already operates a GPU cluster called Haein and applied its virtualisation solution, Petasus AI Cloud, to support GPU-as-a-service workloads last year. It now plans to offer that cloud solution globally. SK Telecom also plans to build an AI data centre in Korea’s southwestern region in collaboration with OpenAI, according to the company’s announcement at MWC 2026. On the model side, SK Telecom said its sovereign AI foundation model currently has 519 billion parameters, making it the largest in Korea. The company plans to upgrade it to more than one trillion parameters and add multimodal capabilities so it can process image, voice, and video data starting in the second half of the year. CEO Jung framed the data centre and model build-out in national terms. “AIDC can be seen as the heart of Korea, and hyperscale LLMs as the brain,” he said. “By combining SKT’s AI capabilities with collaboration from domestic and global partners, we will lead true AI-native transformation for Korean customers and enterprises.” For enterprise readers, the key issue is not parameter count alone. It is how such models will be applied in sectors like manufacturing. SK Telecom said it is working with SK hynix on a manufacturing-focused AI package that analyses process data in real time to reduce defect rates and improve equipment efficiency. The package will be offered as infrastructure, model, and solution. Changing internal culture The transformation also extends to internal operations. SK Telecom has built an “AX Dashboard” to track AI use across departments and individuals. It operates an “AI Board” to oversee AI transformation efforts and has created an “AI playground” where employees can build AI agents without coding. More than 2,000 AI agents are already in use across marketing, legal, and public relations, according to the company’s figures shared at the event. “To drive future growth, we must reinvent our way of working from the ground up. SKT will fundamentally transform its corporate culture to be centred around AI,” Jung said. For other enterprises, the takeaway is less about branding and more about structure. SK Telecom is tying infrastructure, models, applications, and internal governance into a single program. Whether it can execute at the scale it describes remains to be seen. What is clear is that AI is no longer positioned as a side project. It is becoming the operating model. (Photo by PR Newswire) See also: Nokia and AWS pilot AI automation for real-time 5G network slicing Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post MWC 2026: SK Telecom lays out plan to rebuild its core around AI appeared first on AI News. View the full article

AI adoption in financial services has effectively become universal–and the institutions still treating it as an experiment are now the outliers. According to Finastra’s Financial Services State of the Nation 2026 report, which surveyed 1,509 senior executives across 11 markets, only 2% of financial institutions globally report no use of AI whatsoever. The debate is over. The question now is what comes next. For CIOs and technology leaders, the findings paint a picture that is equal parts opportunity and pressure. Six in ten institutions improved their AI capabilities over the past year, with 43% citing AI as their single most important innovation lever. From fraud detection and document intelligence to compliance automation and customer engagement, AI has quietly embedded itself across the entire financial value chain. But near-universal adoption also means that deployment alone is no longer a differentiator. From pilots to pressure The report identifies a clear shift in how institutions are thinking about AI. The early conversation–whether to adopt, which use cases to try, how much to invest–has given way to something more operationally complex. Institutions are now focused on scaling AI responsibly, governing it effectively, and making it work reliably across enterprise-wide functions rather than in isolated pockets. The top four use cases where institutions are either running programmes or piloting AI reflect that maturity: risk management and fraud detection (71%), data analysis and reporting (71%), customer service and support assistants (69%), and document intelligence management (69%). These are not peripheral functions. They sit at the core of how financial institutions operate and compete. Looking ahead, the three priorities that dominate the next phase are: AI-driven personalisation, agentic AI for workflow automation, and AI model governance and explainability. That last one deserves attention. As AI decisions become more consequential–and more scrutinised–the ability to explain, audit, and stand behind those decisions is fast becoming a regulatory and reputational imperative, not just a technical nicety. The infrastructure problem High adoption numbers can obscure an inconvenient truth: AI is only as capable as the systems underneath it. Finastra’s data makes this link explicit. Nearly nine in ten institutions (87%) plan to invest in modernisation over the next 12 months, driven precisely by the need to scale AI effectively. Cloud adoption, data platform modernisation, and core banking upgrades are all accelerating–not as standalone initiatives, but as the foundational layer that determines how far and how fast AI can actually go. The barriers, however, remain stubbornly human. Talent shortages are cited by 43% of institutions as the primary obstacle to progress, with the challenge particularly acute in Singapore (54%), the UAE (51%), and Japan and the US (both at 50%). Budget constraints follow closely behind. The institutions pulling ahead are increasingly turning to fintech partnerships–now the default modernisation strategy for 54% of respondents–to close those gaps without bearing the full cost of building in-house. The regional picture Across the Asia-Pacific, the data reflects distinct priorities. Vietnam leads on active AI deployment at 74%, driven by the urgency of financial inclusion and the need for faster payment and lending processing. Singapore is aggressively scaling cloud and personalisation investment, with planned spending increases above 50% year-on-year. Japan, meanwhile, remains the most cautious market surveyed, with only 39% reporting active AI deployment — a reflection of legacy constraints and a cultural preference for incremental over rapid change. Governance is the next frontier With 63% of institutions already running or piloting agentic AI programmes, the technology’s trajectory is clear. But so is the challenge it brings. Agentic AI–systems capable of autonomous decision-making and multi-step task execution–raises the stakes considerably on questions of accountability, transparency, and control. For enterprise leaders, the coming year is less about whether to invest in AI and more about how to do so in a way that regulators, customers, and boards can trust. As Chris Walters, CEO of Finastra, put it: institutions are expected to move quickly, but also responsibly, as regulatory scrutiny increases and customers demand financial services that work reliably, securely, and personally every time. The tipping point has been crossed. What institutions do with that momentum–and how carefully they govern it–will define the competitive landscape for the rest of the decade. Finastra’s Financial Services State of the Nation 2026 report surveyed 1,509 managers and executives from banks and financial institutions across France, Germany, Hong Kong, Japan, Mexico, Saudi Arabia, Singapore, the UAE, the ***, the US, and Vietnam. Research was conducted by Savanta in November 2025. (Photo by PR Newswire) See also: How financial institutions are embedding AI decision-making Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.AI adoption in financial services The post AI adoption in financial services has hit a point of no return appeared first on AI News. View the full article

Improving trust in agentic AI for finance workflows remains a major priority for technology leaders today. Over the past two years, enterprises have rushed to put automated agents into real workflows, spanning customer support and back-office operations. These tools excel at retrieving information, yet they often struggle to provide consistent and explainable reasoning during multi-step scenarios. Solving the automation opacity problem Financial institutions especially rely on massive volumes of unstructured data to inform investment memos, conduct root-cause investigations, and run compliance checks. When agents handle these tasks, any failure to trace exact logic can lead to severe regulatory fines or poor asset allocation. Technology executives often find that adding more agents creates more complexity than value without better orchestration. Open-source AI laboratory Sentient launched Arena today, which is designed as a live and production-grade stress-testing environment that allows developers to evaluate competing computational approaches against demanding cognitive problems. Sentient’s system replicates the reality of corporate workflows, deliberately feeding agents incomplete information, ambiguous instructions, and conflicting sources. Instead of scoring whether a tool generated a correct output, the platform records the full reasoning trace to help engineering teams debug failures over time. Building reliable agentic AI systems for finance Evaluating these capabilities before production deployment has attracted no shortage of institutional interest. Sentient has partnered with a cohort including Founders Fund, Pantera, and asset management giant Franklin Templeton, which oversees more than $1.5 trillion. Other participants in the initial phase include alphaXiv, Fireworks, Openhands, and OpenRouter. Julian Love, Managing Principal at Franklin Templeton Digital Assets, said: “As companies look to apply AI agents across research, operations, and client-facing workflows, the question is no longer whether these systems are powerful or if they can generate an answer, but whether they’re reliable in real workflows. “A sandbox environment like Arena – where agents are tested on real, complex workflows, and their reasoning can be inspected – will help the ecosystem separate promising ideas from production-ready capabilities and boost confidence in how this technology is integrated and scaled.” Himanshu Tyagi, Co-Founder of Sentient, added: “AI agents are no longer an experiment inside the enterprise; they’re being put into workflows that touch customers, money, and operational outcomes. “That shift changes what matters. It’s not enough for a system to be impressive in a demo. Enterprises need to know whether agents can reason reliably in production, where failures are expensive, and trust is fragile.” Organisations in sensitive industries like finance require repeatability, comparability, and a method to track reliability improvements regardless of the underlying models they use for agentic AI. Incorporating platforms like Arena allows engineering directors to build resilient data pipelines while adapting open-source agent capabilities to their private internal data. Overcoming integration bottlenecks Survey data highlights a gap between ambition and reality. While 85 percent of businesses want to operate as agentic enterprises – and nearly three-quarters plan to deploy autonomous agents – fewer than a quarter possess mature governance frameworks. Advancing from a pilot phase to full scale proves difficult for many. This happens because current corporate environments run an average of twelve separate agents, frequently in silos. Open-source development models offer a path forward by providing infrastructure that enables faster experimentation. Sentient itself acts as the architect behind frameworks like ROMA and the Dobby open-source model to assist with these coordination efforts. Focusing on computational transparency ensures that when an automated process makes a recommendation on a portfolio, human auditors can track exactly how that conclusion was reached. By prioritising environments that record full logic traces rather than isolated right answers, technology leaders integrating agentic AI for operations like finance can secure better ROI and maintain regulatory compliance across their business. See also: Goldman Sachs and Deutsche Bank test agentic AI for trade surveillance Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Upgrading agentic AI for finance workflows appeared first on AI News. View the full article

Many organisations are eroding the foundations of business – productivity, competitiveness, and efficiency. This is happening due to poor implementation of human-AI collaboration, according to cloud data and AI consultancy, Datatonic. The company says in the next phase of enterprise AI, success will come from carefully-governed and designed AI that works alongside humans in “human-in-the-loop (HiTL)’ systems. The company’s research shows that companies that fail to embed AI into their human workflows are falling behind the competition as productivity slows down. Datatonic says a hybrid human-AI approach speeds up decision-making, thus improving overall operations. Scott Eivers, CEO of Datatonic says, “AI [is] about redesigning how work gets done. The biggest risk we see in the market is productivity leakage when AI exists in isolation from the people who actually run the business.” After years of AI investment, pressure is mounting on businesses to show returns. However, some research shows some initiatives remaining in their pilot stage due to limited trust among users. As a result, organisations are failing to use AI-powered insights to positively affect decisions and workflows, meaning efficiency gains never materialise. According to Datatonic, HiTL models are crucial for future success, providing a combination of AI speed with human judgement and accountability. This is evident in agent-assisted software development, where AI systems create code from loose prompts and transform them into code. In this case, human teams decide what needs to be developed, inspect all requirements, and review plans before being brought into existence. Once this direction is clear, AI agents construct modular components. The trend for AI in the workplace is starting to appear in finance and operations. For instance, in back-office and finance departments, AI-powered document processing is already delivering a 70% reduction in invoice-processing costs according to some, but finance teams still approve the final outcomes. “They’re partnership stories,” says Andrew Harding, CTO of Datatonic. “Humans create evaluation systems, validate plans, set guardrails, and make decisions. AI executes at speed and scale. That combination is where real enterprise value shows up.” Many enterprises are failing to deploy fully autonomous agents safely, according to Datatonic, with shortfalls in security controls and governance frameworks. Autonomy can only scale when organisations introduce approval checkpoints and benchmark performance standards. Evaluation systems must also be implemented as AI models evolve, ensuring they always operate safely and as intended without violating any compliance obligations. Harding says, “As trust builds, companies can responsibly delegate more to AI. But skipping governance doesn’t build speed, it creates risk.” Datatonic predicts major acceleration in workloads in the next two years, with preparation and validation handled by AI agents. AI systems may also be implemented to test and invalidate decisions before teams invest resources. Scott Eivers believes the future “looks like expert departments run by smaller, nimble teams – finance, HR, marketing – each amplified by AI. The companies that win will be those that teach people to work with AI — not around it,” he said. (Image source: “Waterfall” by PMillera4 is licensed under CC BY-NC-ND 2.0.) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Poor implementation of AI may be behind workforce reduction appeared first on AI News. View the full article

Banks are testing a new type of artificial intelligence, like agentic AI, that does more than scan for keywords or follow preset rules. Instead of relying only on static alerts, some trading desks are beginning to use systems designed to reason through patterns in real time and flag conduct that may need human review. Bloomberg detailed how Goldman Sachs and Deutsche Bank are exploring or deploying so-called “agentic” AI tools for trading surveillance. The goal is to strengthen oversight of orders and trades by using software agents that can analyse activity as it happens and identify patterns that could suggest misconduct. Adaptive agents Large banks use automated surveillance systems to monitor trading activity, systems that often rely on predefined rules: if a trade exceeds a certain size, deviates from a benchmark, or fits a known risk pattern, it triggers an alert. Compliance teams then review the case manually. The challenge is scale and complexity. Modern markets generate huge volumes of data in asset classes, time zones, and trading venues. Static rules can generate large numbers of false positives, while more subtle forms of manipulation may not match known patterns. According to Bloomberg, the newer agentic systems aim to go beyond that approach. Rather than simply matching trades against a checklist, the AI agents are designed to examine trading behaviour in multiple signals, compare it with historical activity, and detect unusual combinations of actions. The tools are not described as replacing compliance officers. Instead, they appear to function as an additional layer of monitoring, surfacing cases that warrant closer human inspection. Deutsche Bank’s work with Google Cloud Bloomberg reported that Deutsche Bank is working with Google Cloud on developing AI agents that can monitor trading activity. The system is designed to review large sets of order and execution data and flag anomalies in near real time. The bank has been expanding its AI initiatives over the past few years, and this surveillance effort reflects how financial institutions are applying generative and large language model technology beyond chat interfaces. In this context, the AI is not answering customer questions but analysing structured and unstructured data streams tied to trading behaviour. The AI agents can help identify “complex anomalies” in orders and trades. That suggests the system may look at relationships between trades, timing, market conditions, and trader history not single events in isolation. Human compliance staff remain responsible for reviewing flagged cases and determining whether further action is required. Goldman Sachs’ agentic AI strategy Goldman Sachs is also exploring the use of agentic AI for surveillance, according to Bloomberg. The bank has invested heavily in AI in its trading and risk systems in recent years, and this effort appears to extend that work into compliance. The focus, as described in the report, is on using AI agents that can operate with a degree of independence in scanning for misconduct indicators. The system may identify patterns that do not fit a clear rule but still stand out as unusual. For regulators, the appeal is straightforward: earlier detection can reduce market harm and reputational risk. For banks, there is also an operational dimension. Compliance departments face pressure to handle large volumes of alerts while maintaining strict oversight standards. Tools that can reduce noise without lowering scrutiny are likely to attract attention. Why “agentic AI” matters The term “agentic AI” refers to systems that can take goal-directed actions not respond to prompts. In practice, that can mean the software is able to decide what data to examine next, compare multiple signals, and escalate findings without constant human input. In a trading context, that might involve monitoring order flows, price movements, communications metadata, and historical behaviour to assess whether activity aligns with normal patterns. This does not mean the system makes disciplinary decisions on its own. Financial institutions operate under strict regulatory regimes, and accountability remains with human supervisors. The agent’s role is to identify and organise information more effectively than static systems can. Part of a wider compliance shift What appears new is the application of more advanced generative AI architectures to internal control functions. Regulators in the US and Europe have encouraged firms to improve the monitoring of market abuse and manipulation. While rules do not mandate agentic AI, they do require firms to maintain effective systems and controls. If AI tools can help meet that standard, adoption is likely to grow. At the same time, AI in compliance raises its own questions. Banks must ensure that models are explainable, that they do not introduce bias, and that they can withstand regulatory review. Model governance, data security, and audit trails remain central concerns. What changes for the industry If agentic surveillance tools prove effective, they could alter how compliance teams work. Instead of sorting through large volumes of simple alerts, staff may spend more time evaluating complex cases surfaced by AI agents. That change would not remove the need for human judgement. It may, however, change where human effort is focused. In markets where speed and data volume continue to rise, the ability to analyse patterns in real time is becoming harder to achieve with rule-based systems alone. (Photo by Markus Spiske) See also: Mastercard’s AI payment demo points to agent-led commerce Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Goldman Sachs and Deutsche Bank test agentic AI for trade surveillance appeared first on AI News. View the full article

The machine that will make tomorrow’s AI chips possible has just been declared ready for mass production–and the clock for the industry’s next leap has officially started. ASML, the Dutch company that holds a global monopoly on commercial extreme ultraviolet lithography equipment, confirmed this week that its High-NA EUV tools have crossed the threshold from technically impressive to genuinely production-ready. The announcement, made exclusively to Reuters by ASML’s chief technology officer Marco Pieters ahead of a technical conference in San Jose, marks a turning point that chipmakers and AI companies have been waiting years for. Why this matters for AI The timing is not incidental. Current-generation EUV machines are approaching the outer edge of what they can do for advanced AI chip production–meaning the semiconductors powering large language models and AI accelerators are bumping up against a physical ceiling. High-NA EUV tools are designed to break through it, enabling chipmakers to print finer, denser circuit patterns in fewer steps. That translates directly into more powerful and efficient chips for AI workloads. “I think that it’s at a critical point to look at the amount of learning cycles that have happened,” Pieters told Reuters, referring to the volume of customer testing the machines have now accumulated. The numbers that matter ASML’s case for readiness rests on three data points it plans to release publicly. The High-NA EUV tools have now processed 500,000 silicon wafers, achieved roughly 80% uptime–with a target of 90% by year-end–and demonstrated imaging precision capable of replacing multiple conventional patterning steps with a single High-NA pass. Together, Pieters said, those figures signal that the tools are ready for manufacturers to begin qualification. The machines don’t come cheap. At approximately US$400 million per unit–double the cost of the previous EUV generation–they represent one of the most expensive pieces of capital equipment in industrial history. TSMC and Intel are among the named early adopters. A two-to-three-year runway Technical readiness and manufacturing integration are two different things, and Pieters was careful to separate them. Despite the milestone, full integration into high-volume production lines is still expected to take two to three years as chipmakers work through qualification and process development. “Chipmakers have all the knowledge to qualify these tools,” he said–a vote of confidence in the industry’s ability to move, even if the timeline remains measured. For the AI sector, that means the next generation of chip performance improvements is on the horizon, not yet in hand. But with ASML now saying the starting gun has fired, the race to integrate High-NA EUV into production has formally begun. (Photo by ASML) See also: 2025’s AI chip wars: What enterprise leaders learned about supply chain reality Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post ASML’s high-NA EUV tools clear the runway for next-gen AI chips appeared first on AI News. View the full article

Telecom networks may soon begin adjusting themselves in real time, as operators test systems that allow AI agents to manage traffic and service quality. AI may soon be making operational decisions. This week, Nokia and AWS presented a new network slicing system that uses AI agents to monitor network conditions and adjust resources automatically. The setup is being tested by telecom operators du in the United Arab Emirates and Orange in Europe and Africa, according to a joint announcement from Nokia. Adaptive AI-driven networks Network slicing lets operators create multiple virtual networks on the same physical infrastructure, each tuned for a different purpose. For example, a slice may be configured for emergency services or high-bandwidth consumer traffic. While slicing is part of the 5G standard, it has often required manual planning and fixed configurations, which limits how quickly networks can respond to changing demand. The new system aims to close that gap by introducing AI agents that track network performance indicators like latency and congestion, and consider data like event schedules or weather conditions. Agents can then adjust network settings to keep services running to agreed performance levels, according to Nokia’s description of the pilot. AWS said the solution combines Nokia’s slicing and automation tools with AI models delivered through Amazon Bedrock, its managed AI service platform. The companies describe the approach as “agentic AI”. Autonomous connectivity The interest in such systems reflects a long-standing challenge: 5G networks have delivered higher speeds and lower latency, but operators have struggled to turn those technical gains into new revenue streams. Research firm GSMA Intelligence notes many operators view network slicing as a potential source of enterprise income, though adoption has been slow due to operational complexity and uncertain demand. If networks can adapt quickly to sudden demand, like a crowded stadium or emergency responders entering a disaster area, operators may be able to offer temporary connectivity or guaranteed service levels without manual setup. Orange has said previously enterprise customers expect connectivity to behave more like cloud computing, where resources can scale on demand. Systems that allow automated control of network resources could help move telecom services closer to that model. Cloud platforms and telecom network operations The tests also highlight how cloud providers are getting involved in telecom operations. Over the past few years, some operators have moved parts of their core networks onto public cloud platforms or built cloud-based control systems. Industry analysts at Dell’Oro Group report that telecom cloud spending is rising as operators modernise networks and adopt software-driven infrastructure. Adding AI-driven control loops on top of cloud platforms represents the next step, with AI systems monitoring conditions and applying adjustments quickly. The technology remains in a testing phase. Nokia’s announcement described the work with Orange as demonstrations and pilots rollouts. Questions remain about how such systems can be deployed, how operators will supervise automated decisions, and how regulators will view AI control of critical communication infrastructure. Telecom networks carry important traffic so reliability and accountability remain central concerns. Operators typically introduce automation gradually, keeping human oversight in place while validating system behaviour under real conditions. The experiments suggest that AI is beginning to function as operational controller, adjusting physical and virtual resources in response to live events. Enterprises that rely on private 5G networks for factories or large venues may gain access to connectivity that adjusts automatically. That could influence how businesses design applications that depend on stable, predictable network performance. (Photo by M. Rennim) See also: How e& is using HR to bring AI into enterprise operations Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Nokia and AWS pilot AI automation for real-time 5G network slicing appeared first on AI News. View the full article

Anthropic has detailed three “industrial-scale” AI model distillation campaigns by overseas labs designed to extract abilities from Claude. These competitors generated over 16 million exchanges using approximately 24,000 deceptive accounts. Their goal was to acquire proprietary logic to improve their competing platforms. The extraction technique, known as distillation, involves training a weaker system on the high-quality outputs of a stronger one. When applied legitimately, distillation helps companies build smaller and cheaper versions of their applications for customers. Yet, malicious actors weaponise this method to acquire powerful capabilities in a fraction of the time and cost required for independent development. Protecting intellectual property like Anthropic’s Claude Unmitigated distillation presents a severe intellectual property challenge. Because Anthropic blocks commercial access in China for national security reasons, attackers bypass regional access restrictions by deploying commercial proxy networks. These services run what Anthropic calls “hydra cluster” architectures, which distribute traffic across APIs and third-party cloud platforms. The massive breadth of these networks means there are no single points of failure. As Anthropic noted, “when one account is banned, a new one takes its place.” In one identified case, a single proxy network managed more than 20,000 fraudulent accounts simultaneously. These networks mix AI model distillation traffic with standard customer requests to evade detection. This directly impacts corporate resilience and forces security teams to reconsider how they monitor cloud API traffic. Illicitly-trained models also bypass established safety guardrails, creating severe national security risks. US developers, for example, build protections to prevent state and non-state actors from using these systems to develop bioweapons or carry out malicious cyber activities. Cloned systems lack the safeguards implemented by systems like Anthropic’s Claude, allowing dangerous capabilities to proliferate with protections stripped out entirely. Foreign competitors can feed these unprotected capabilities into military, intelligence, and surveillance systems, enabling authoritarian governments to deploy them for offensive operations. If these distilled versions are open-sourced, the danger further multiplies as the capabilities spread freely beyond any single government’s control. Unlawful extraction allows foreign entities, including those under the control of the ******** ********** Party, to close the competitive advantage protected by export controls. Without visibility into these attacks, rapid advancements by foreign developers incorrectly appear as innovation circumventing export controls. In reality, these advancements depend heavily on extracting American intellectual property at scale, an effort that still requires access to advanced chips. Restricted chip access limits both direct model training and the scale of illicit distillation. The playbook for AI model distillation The perpetrators followed a similar operational playbook, utilising fraudulent accounts and proxy services to access systems at scale while evading detection. The volume, structure, and focus of their prompts were distinct from normal usage patterns, reflecting deliberate capability extraction rather than legitimate use. Anthropic attributed these campaigns targeting Claude through IP address correlation, request metadata, and infrastructure indicators. Each operation targeted highly differentiated functions: agentic reasoning, tool use, and coding. One campaign generated over 13 million exchanges targeting agentic coding and tool orchestration. Anthropic detected this operation while it was still active, mapping timings against the competitor’s public product roadmap. When Anthropic released a new model, the competitor pivoted within 24 hours, redirecting nearly half their traffic to extract capabilities from the latest system. Another operation generated over 3.4 million requests focused on computer vision, data analysis, and agentic reasoning. This group utilised hundreds of varied accounts to obscure their coordinated efforts. Anthropic attributed this campaign by matching request metadata to the public profiles of senior staff at the foreign laboratory. In a later phase, this competitor attempted to extract and reconstruct the host system’s reasoning traces. Anthropic says a third AI model distillation campaign targeting Claude extracted reasoning capabilities and rubric-based grading data through over 150,000 interactions. This group forced the targeted system to map out its internal logic step-by-step, effectively generating massive volumes of chain-of-thought training data. They also extracted censorship-safe alternatives to politically sensitive queries to train their own systems to steer conversations away from restricted topics. The perpetrators generated synchronised traffic using identical patterns and shared payment methods to enable load balancing. Request metadata for this third campaign traced these accounts back to specific researchers at the laboratory. These requests often appear benign on their own, such as a prompt simply asking the system to act as an expert data analyst delivering insights grounded in complete reasoning. But when variations of that exact prompt arrive tens of thousands of times across hundreds of coordinated accounts targeting the same narrow capability, the extraction pattern becomes clear. Massive volume concentrated in specific areas, highly repetitive structures, and content mapping directly to training needs are the hallmarks of a distillation attack. Implementing actionable defences Protecting enterprise environments requires adopting multi-layered defences to make such extraction efforts harder to execute and easier to identify. Anthropic advises implementing behavioural fingerprinting and traffic classifiers designed to identify AI model distillation patterns in API traffic. IT leaders must also strengthen verification processes for common vulnerability pathways, such as educational accounts, security research programmes, and startup organisations. Companies should integrate product-level and API-level safeguards designed to reduce the efficacy of model outputs for illicit distillation. This must be done without degrading the experience for legitimate, paying customers. Detecting coordinated activity across large numbers of accounts is an absolute necessity. This includes specifically monitoring for the continuous elicitation of chain-of-thought outputs used to construct reasoning training data. Cross-industry collaboration also remains essential, as these attacks are growing in intensity and sophistication. This requires rapid and coordinated intelligence sharing across AI laboratories, cloud providers, and policymakers. Anthropic has published its findings about Claude being targeted by AI model distillation campaigns to provide a more holistic picture of the landscape and make the evidence available to all stakeholders. By treating AI architectures with rigorous access controls, technology officers can secure their competitive edge while ensuring ongoing governance. See also: How disconnected clouds improve AI data governance Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Anthropic: Claude faces ‘industrial-scale’ AI model distillation appeared first on AI News. View the full article

Disconnected clouds aim to improve AI data governance as businesses rethink their infrastructure under tighter regulatory expectations. Ensuring operational continuity in isolated environments has become increasingly vital for businesses. Facilities lacking continuous internet access face unique constraints where external dependencies become unacceptable. Microsoft recently expanded its capabilities to allow regulated industries and public sectors to participate independently in the digital economy. Trust in these systems stems from confidence that data remains protected, controls are enforceable, and operations proceed regardless of external conditions. The company now offers full stack options across connected, intermittently connected, and fully disconnected modes. This architecture unifies Azure Local, Microsoft 365 Local, and Foundry Local into a single sovereign private cloud. Bringing these elements together provides a localised experience resilient to any connectivity condition. By standardising governance across all deployments, it helps enterprises to prevent fragmented architectures. Azure Local disconnected operations enable organisations to run vital infrastructure using familiar Azure governance and policy controls completely offline. Execution, management, and policy enforcement stay entirely within customer-operated facilities. This approach allows companies to maintain uninterrupted operations and keep identities protected within their established boundaries. Implementations scale from minor deployments to demanding and data-intensive workloads. Improving resilience and AI data governance in tandem Deploying AI in sovereign environments introduces high compute requirements. Foundry Local enables enterprises to run multimodal large models completely offline. Utilising modern hardware from partners like NVIDIA, customers deploy AI inferencing on their own physical servers. This ensures data and application programming interfaces operate strictly within customer-controlled boundaries. Customers maintain complete authority over their hardware even as AI inferencing demands increase over time. Gerard Hoffmann, CEO of Proximus Luxembourg, said: “The availability of Azure Local disconnected operations represents a breakthrough for organisations that need control over their data without sacrificing the power of the Microsoft Cloud. “For Luxembourg, where digital sovereignty is not just a principle but a strategic necessity, this model offers the resilience, autonomy and trust our market expects. By combining Microsoft’s technological leadership with Proximus NXT’s sovereign cloud expertise, we are enabling our customers to innovate confidently—even in fully-disconnected mode.” CIOs planning offline deployments must map workloads to the correct control posture based on risk, regulation, and specific mission requirements. Since disconnected environments are not one-size-fits-all, businesses can start fast with smaller deployments and expand their capabilities over time. Implementing a disconnected private cloud with AI support answers a business requirement for highly-regulated sectors, enabling secure data governance even when external connectivity is absent. See also: Deploying agentic finance AI for immediate business ROI Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post How disconnected clouds improve AI data governance appeared first on AI News. View the full article

Agentic finance AI improves business efficiency and ROI only when deployed with strict governance and clear return on investment targets. A recent FT Longitude survey of 200 finance leaders across the US, ***, France, and Germany showed 61 percent have deployed AI agents merely as experiments. Meanwhile, one in four executives admit they do not fully grasp what these agents look like in practice. Advancing agentic finance AI beyond experiments Finance departments need governed systems that combine language processing with business logic to deliver actual value. Providers of Invoice Lifecycle Management platforms are introducing new agents designed to accelerate invoice processing and push accounts payable toward greater autonomy. Recent market solutions use generative AI, deep learning, and natural language processing to manage the entire workflow, from initial data ingestion through to final reconciliation. These digital teammates handle task execution, allowing human employees to focus on higher-level business planning rather than replacing them entirely. Within these ecosystems, specialised business agents provide contextual and real-time guidance regarding the next best actions for handling invoices. Data agents allow staff to query system information using natural language, easily finding answers about awaiting approvals in specific regions or identifying suppliers offering early payment discounts. Governing autonomous finance workflows Finance teams will only hand over tasks to agentic AI if they retain control. Finance departments require verifiable audit trails and explainable logic for every action, avoiding networks of disconnected bots. Industry leaders note that autonomy without trust isn’t acceptable, especially in sensitive industries like finance. Platforms must ensure every AI decision is explainable, auditable, and governed through existing finance controls. This approach helps safely delegate workloads to algorithms while remaining fully compliant and protected. To enable this trust, every action performed by an AI agent routes through a central policy engine. Before executing any task, the system passes the proposed action through specific autonomy gates that enforce the customer’s business rules, risk thresholds, and compliance requirements. This architecture ensures algorithms manage the bulk of the workload while finance personnel retain total visibility and a complete audit trail. Building automated procurement operations Future agentic finance AI capabilities will automate issue resolution and connect data across systems for faster decision-making. Modern capabilities in 2026 include supplier agents designed to manage invoice disputes and payment queries. These agents will automatically telephone suppliers to explain discrepancies, summarise the conversation, and outline subsequent steps to achieve faster resolutions. Professional agents, meanwhile, will assist clerks in resolving real-time processing questions using natural language to cut manual effort and delays. AI must operate as an integral business component rather than a bonus feature, requiring intelligent, secure, and ethical application to drive cost efficiencies and enhance operations. By centralising control and ensuring every automated decision from agentic AI passes through established compliance checks, organisations can safely elevate their finance operations to fully autonomous execution. See also: Mastercard’s AI payment demo points to agent-led commerce Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Deploying agentic finance AI for immediate business ROI appeared first on AI News. View the full article

Basware has introduced a AI agents in its invoice lifecycle management platform to extend the existing InvoiceAI abilities of the platform. The company positions the agents as a step towards what it calls “Agentic Finance,” a model in which AI systems undertake finance tasks under preset controls. Jason Kurtz, chief executive officer of Basware said: “The immediate future of finance involves near-perfect, touchless invoice processing. The future involves Agentic Finance, where AI entities transact on behalf of the enterprise to drive faster, smarter decisions and real business outcomes.” He said the company is working to reach “100% automated, 100% compliant, and 100% protected invoice processing.” The immediate operational area affected is accounts payable. Basware’s agents here are designed to operate inside existing invoice process. The AP Business Agent provides contextual guidance to users handling invoices, recommending next steps based on the transaction’s status. The AP Data Agent provides the ability to query data in natural language so users can get information without using a reporting tool. Questions may be, for example, which invoices are awaiting approval in a specific jurisdiction? Or, which suppliers granted early payment discounts in a given *******? The agents are intended to reduce the volume of routine queries and manual follow-ups done by accounts payable teams. Kurtz argues that the technology can alter workers’ roles. “When AI agents handle the repetitive questions to business users, AP teams are freed up to ask questions that lead to real impact. That’s how you move from processing transactions to driving strategy.” Adoption of AI in financial business functions A survey conducted on behalf of Basware found that 61% of organisations had deployed AI agents as experiments, and a quarter “did not fully understand” what an AI agent looks like in practice. The implication is that adoption remains uneven and, in many cases, exploratory. Basware’s would like to see its customers move from experimentation to operational use. The survey figures comprised of responses from 200 finance leaders in the US, United Kingdom, France, and Germany. The question permeating agentic activities in financial platforms is one of governance. Finance functions will delegate tasks to AI systems only human operators retain control over authorisation, are assured of compliance, and have access to an audit trail. Basware’s agents actions pass through what the company describes as a central policy engine. This applies business rules and sets compliance requirements and risk thresholds, referring to such controls as autonomy ‘gates’. Kurtz described the principle: “Autonomy without trust is just risk. Our platform is uniquely designed to ensure that every AI decision is explainable and governed through the same controls finance teams already rely on.” The company sees its agents integrating with established processes, rather than working in parallel outside governance frameworks. Basware has several more agentic AIs in development. A Supplier Agent will manage invoice disputes and payment queries, able to contact suppliers and summarise discussions. An AP Pro Agent is intended to assist staff to resolve processing questions via a generative AI interface. The company cites early user experiences from Billerud, a paper manufacturer. Jesper Persson from the company said there had been benefits. “Since day one, we’ve perceived the desired values from the project. The quality of invoices has improved considerably, and the AI continues to evolve and improve with each passing day. The efficiency gains we achieved translated directly into tangible cost savings.” The company’s objective is to have finance teams delegate decisions and actions to agents in the future, and it plans to release more AI tools in 2026. The company states that AI is in its platform not an add-on feature. Keys to agentic success in finance departments The introduction of AI agents in accounts payable may reduce manual effort and response times, with any gained value dependent on at least some of the following: the quality of the AI the condition of existing invoice data the translation of existing business and governance rules into terms an agent can follow how far an organisation is willing to delegate its finance function’s work to AI. (Image source: “Invoicing department of newspaper Hufvudstadsbladet” is licensed under CC BY 4.0.) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Basware’s AI agents: From invoicing to “100% automated” appeared first on AI News. View the full article

It’s an open secret (that is, not many people seem to know) that the institutions keeping the global financial system turnig over run code that is ancient, barely understood, and frighteningly hard to replace. Now, AI is finally making that problem solvable – and the market has responded with a reality check for one of technology’s oldest names. IBM shares recorded their worst single-day drop in more than 25 years earlier this week, plunging 13% after AI startup Anthropic said its Claude Code tool can accelerate COBOL modernisation – the kind of painstaking, expensive legacy work that has underpinned a portion of IBM’s consulting revenue for years. An Anthropic blog stated that “modernising a COBOL system once required armies of consultants spending years mapping workflows,” and argued that tools like Claude Code can now automate the exploration and analysis phases that consume most of the effort in COBOL modernisation. That single claim was enough to send investors reaching for the sell button. COBOL is ******* than most realise To understand why the reaction was so sharp, it helps to understand just how entrenched COBOL remains. Hundreds of billions of lines of COBOL code run in production daily, powering critical systems in finance and government sectors. The language handles an estimated 95% of ATM transactions in the US alone. The deeper problem isn’t the code itself – it’s the people who understand it. The number of developers who understand COBOL continues to shrink as the workforce that built these systems has largely retired. That talent scarcity is precisely what made COBOL modernisation so expensive for so long, and what made large consulting engagements – the kind IBM and rivals like Accenture and Cognizant built profitable practices around – essentially unavoidable. Anthropic argues that AI flips this equation entirely. Claude Code works by mapping dependencies in thousands of lines of code, documenting workflows, identifying risks faster than human analysts, and providing teams with deep insights for informed decision-making. The company says teams can now modernise COBOL codebases in quarters not years. IBM was already here What the market’s reaction may be overlooking is that IBM itself has been making this argument for some time. Anthropic’s post comes about three years after IBM itself suggested using AI to rewrite COBOL as Java and created a product called “watsonx Code Assistant for Z” to do it. IBM CEO Arvind Krishna said as recently as July 2025 that the company’s AI coding assistant for mainframes “has got very adoption,” with the majority of customers using it to understand their COBOL codebase and decide what to modernise. IBM defended its position on Monday, saying its mainframe platform delivers the same quality of performance and security regardless of programming language – COBOL or otherwise. And analysts were quick to add nuance to the panic. Evercore ISI analyst Amit Daryanani noted that “clients already had the option to migrate from the mainframe, yet they are sticking with the platform,” suggesting the fear of displacement may be outrunning the reality. The broader pattern IBM wasn’t alone in taking a hit. Accenture and Cognizant also declined following the news – a sign that investors are looking at the entire consulting model around legacy modernisation, not IBM’s mainframe hardware business. Just last week, cybersecurity stocks sold off sharply after Anthropic announced Claude Code Security, a tool that scans codebases for vulnerabilities. The pattern is becoming familiar: each new AI ability announcement triggers a reassessment of which existing revenue streams might be compressed, and the market prices in fear immediately. IBM didn’t stay quiet. Rob Thomas, the company’s Senior Vice President and Chief Commercial Officer, pushed back directly in the aforementioned blog post, drawing a line the market appeared to have missed: “Translating code is one thing. Modernising a platform is something else entirely. The two are not the same, and the gap between them is where most enterprises run into trouble.” His argument is worth sitting with. The value IBM’s mainframe delivers, Thomas contends, has nothing to do with COBOL as a language – it lives in the vertically integrated stack underneath it: z/OS, transaction processing architecture, quantum-safe encryption, and decades of hardware-software optimisation that no code translation tool touches. Anthropic’s Claude Code, in his reading, is solving a real problem – just not the one that matters most for enterprises running IBM Z. He also raised a point that complicates the headline narrative further: roughly 40% of COBOL actually runs on Windows, Linux, and other distributed platforms – not mainframes at all. Much of what’s being framed as an IBM mainframe story is partly a distributed systems problem that has been folded into a mainframe headline. IBM’s own clients are already making the case. Royal Bank of Canada has used IBM’s watsonx Code Assistant for Z to map dependencies and build modernisation blueprints for core applications. The National Organisation for Social Insurance reported a 94% reduction in time to analyse legacy COBOL code using the same tool – cutting an eight-hour task to roughly 30 minutes. Whether Monday’s selloff was a fair verdict or a reflexive one, the underlying change is real: AI is making COBOL modernisation economically viable for the first time in decades. The question IBM is asking – and the market hasn’t fully answered – is whether that’s a threat to its business or an acceleration of the transformation it’s already leading. See also: Hitachi bets on industrial expertise to win the physical AI race Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post COBOL modernisation just got an AI shortcut–and the market noticed appeared first on AI News. View the full article

A recent demonstration from Mastercard suggests that payment systems may be heading toward a future where software agents, not people, complete purchases. During the India AI Impact Summit 2026, Mastercard showed what it described as its first fully authenticated “agentic commerce” transaction. In the demo, as reported by Times of India, an AI agent searched for a product, assessed the website, and completed the purchase using stored payment credentials, without the user opening an app or entering card details. The company said the transaction took place inside a secure payment framework designed to verify both the user and the AI acting on their behalf. The demonstration was controlled, not a public rollout. Mastercard executives told reporters that broader deployment would depend on regulatory approval and ecosystem readiness. Still, the test highlights a change that many enterprises may need to prepare for: the possibility that customers – or corporate systems – will increasingly rely on AI agents to initiate and complete transactions. Assisted checkout to delegated spending Digital payments have usually focused on reducing friction for human users through tokenisation, saved credentials, and one-click checkout. Agentic commerce goes further. Instead of helping a user complete a purchase, the system allows software to handle the process from start to finish once permission rules are in place. The model relies on several building blocks already used in modern payments: identity verification, tokenised card data, and risk monitoring. What changes is who performs the action. If AI agents can act in defined limits, like spending caps or merchant restrictions, checkout may change from a user interaction to a background workflow. For enterprises, the issue is if software can spend money automatically, procurement rules, approval chains, and audit trails need to account for machine decisions, not human ones. Finance teams may need clearer policies on when an AI agent can commit funds, how liability is assigned if something goes wrong, and how fraud detection should treat automated transactions. Payment networks position for machine customers Mastercard is not alone in exploring this direction. Across the payments sector, providers are testing ways to embed transactions into AI-driven tools and digital assistants. The goal is to ensure that when autonomous software begins purchasing goods or services, payment networks remain part of the trust and verification layer. In public statements tied to the summit demo, Mastercard framed the effort as building infrastructure that allows AI agents to transact safely on behalf of users. That framing points to a broader industry race: not to build smarter AI shopping tools, but to control the authentication systems that make those tools safe enough for financial use. For banks and fintech firms, the change could affect how customer identity is managed. Traditional authentication often assumes a person is present, entering a password or approving a prompt. Agentic commerce assumes the opposite: the user may not be involved at the moment of purchase. That means identity systems must verify both the account owner’s prior consent and the agent’s authority at the time of transaction. Merchants may need API-ready storefronts If AI agents begin acting as buyers, merchant systems may also need to adapt. Online stores built mainly for human browsing may struggle if automated agents become a meaningful share of customers. To support machine-driven purchases, product catalogues, pricing data, and checkout processes may need to be accessible through structured APIs not only visual web pages. Inventory accuracy, transparent pricing, and clear return policies become more important when decisions are made by software trained to compare options instantly. This could also influence competition. If agents optimise for price and delivery speed, merchants with inconsistent data or hidden fees may be filtered out before a human even sees them. Security risks move, not disappear While agentic commerce promises convenience, it also introduces new risks. A compromised AI assistant with payment authority could execute purchases at scale before detection. Fraud models that look for unusual user behaviour may need updating to distinguish between legitimate automated spending and malicious activity. Regulators are likely to take a cautious approach. Mastercard’s own comments that the system still awaits approvals suggest that compliance frameworks for AI-initiated payments are still taking shape. In enterprises deploying AI internally, similar concerns apply. Automated purchasing agents integrated into enterprise resource planning systems could streamline routine procurement, but they also expand the attack surface. Access controls and spending thresholds will matter more when software can execute financial actions without real-time human confirmation. Where commerce may head Mastercard’s demonstration does not mean agent-led payments will reach consumers immediately. Yet it offers a glimpse of how commerce may change as AI systems move from advisory roles into operational ones. If the model matures, the most visible change may be that checkout disappears as a distinct step. Instead of visiting a site and paying, users or companies may set rules, and their software will handle the rest. For enterprises, the important takeaway is less about Mastercard’s AI technology and more about the direction of travel. As AI agents gain the authority to act, payment systems, identity frameworks, and digital storefronts may need to treat software not as a tool, but as a participant in the transaction. (Photo by Cova Software) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Mastercard’s AI payment demo points to agent-led commerce appeared first on AI News. View the full article

AI dairy farming has found its most ambitious deployment yet – not in a Silicon Valley lab nor a European agri-tech campus, but in the villages of Gujarat, India, where 36 lakh (3.6 million) women milk producers are now being served by an AI assistant named Sarlaben. Amul, the world’s largest dairy cooperative, has launched what it calls Amul AI: a platform built on five decades of cooperative data, designed to give every farmer in its network round-the-clock, personalised guidance in their own language. Amul was launched just ahead of India’s AI Impact Summit 2026 and backed by the Ministry of Electronics and Information Technology (MeitY) with the EkStep Foundation. It is a test case for whether AI – the kind being debated in boardrooms and policy forums globally – can actually reach the last mile. Meet Sarlaben: The AI dairy farming assistant Sarlaben draws from one of India’s most comprehensive agricultural data repositories. It’s accessible via the Amul Farmer mobile app – already downloaded by over 10 lakh (one million) users on Android and iOS – as well as through voice calls for farmers using feature phones or landlines. The system is integrated with Amul’s Automatic Milk Collection System (AMCS) and the Pashudhan application, allowing it to offer personalised, cattle-specific guidance. What makes Amul AI substantially different from most agricultural chatbots is the scale of its training data. The platform was built on a digital backbone managing over 200 crore (two billion) milk procurement transactions annually, veterinary treatment records from more than 1,200 doctors covering nearly 3 crore (30 million) cattle, approximately 70 lakh (seven million) artificial inseminations conducted each year, ISRO satellite imagery for fodder production mapping, and a cattle census conducted every five years. Every animal in the system carries a unique ID, with individual records of feed intake, disease history and milking status. “Amul AI is about taking dependable, verified information directly to the farmer – instantly and in a language they are comfortable with,” said Jayen Mehta, Managing Director of the Gujarat Cooperative Milk Marketing Federation (GCMMF), which markets the Amul brand. He said how, by using decades of structured data and integrating it with their operational systems, the platform will help farmers make timely decisions that improve animal productivity and income. India’s productivity paradox India is the world’s largest producer of milk, generating 347.87 million tonnes in 2024-25 according to the Department of Animal Husbandry and Dairying – more than double the US’s 102.70 million tonnes. And yet despite leading in volume, India’s per-animal milk yield remains among the lowest globally. The reasons are structural. India’s dairy sector is characterised by small herd sizes, low-quality feed, limited access to veterinary care in rural areas, and widespread lack of awareness about modern breeding and husbandry practices. Amul’s network spans more than 18,600 villages in Gujarat, where farmers supply over 350 lakh litres (35 million litres) of milk daily. But information asymmetry has long been a bottleneck – a farmer facing a sick animal at midnight in a remote village has few places to turn; the gap Amul AI is designed to close. Available initially in Gujarati – the primary language of the cooperative’s farmer base – the platform is built on the government’s Bhashini multilingual framework and could, in principle, be extended to 20 Indian languages, reaching Amul’s presence in 20,000 villages in 20 states. The cooperative model The technology story here is inseparable from the institutional one. Amul’s cooperative structure – built over five decades under the original White Revolution – created the data infrastructure that makes Amul AI possible. Most private agri-tech startups are working backwards: collecting data first, building products second. Amul already had the data. What was needed was a way to make it actionable at the farmer level. Experts tracking the dairy-tech space see this as significant. Sreeshankar Nair, Founder of Brainwired, a dairy-tech startup, identifies three specific challenges that Amul AI could meaningfully address: farmer awareness, access to quality veterinary guidance, and connectivity to grazing and feed resources. “If AI can integrate local dialects of Indian languages, India can have White Revolution 2.0,” Nair said, pointing to the transformative potential of vernacular AI in a sector where not every farmer speaks the same dialect. Saswata Narayan Biswas, Director of the Institute of Rural Management, Anand (IRMA) – the institution closely associated with Amul’s founding ethos – frames it as an AI embedded in a cooperative framework. It becomes “not a technology upgrade, but an instrument of inclusive rural transformation.” For Biswas, the specific abilities Amul AI brings – predictive disease detection, oestrus tracking, optimised feed formulation, localised weather risk advisories – are abilities Amul had been building for years. AI accelerates and democratises them. Scale and the test ahead The launch has drawn backing from the highest levels of government. Gujarat Chief Minister Bhupendra Patel launched the platform and confirmed it will be showcased at the AI Impact Summit 2026. The cooperative has acknowledged MeitY and the EkStep Foundation – an open digital infrastructure nonprofit – as partners in building the AI layer. Farmers not affiliated with Amul can also access general dairying and animal husbandry information through the app. At its current scale, Amul AI already covers more cattle – nearly 3 crore (30 million) – than most national veterinary databases anywhere in the world. The harder question, as with most AI deployments at a population scale, is whether the tool will serve those who need it most. The farmers most likely to benefit first – those already comfortable with smartphones, already plugged into Amul’s digital system – may not be the ones with the greatest information deficit. The rollout of Bhashini-enabled dialect support, the adoption rate among feature-phone users relying on voice calls, and whether AI-driven advisories translate into measurable yield improvements will be the metrics that determine whether this is genuinely White Revolution 2.0. Amul has built an AI system grounded in half a century of real cooperative transactions, real animals, and real farmers. Such an infrastructure is, arguably, the most credible foundation for AI dairy farming at scale. Whether it fulfils its promise will depend on execution – and on whether Sarlaben’s voice can reach in the last few miles; those that have always been the hardest to cross. See also: Hitachi bets on industrial expertise to win the physical AI race Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post How Amul is using AI dairy farming to put 36 million farmers first appeared first on AI News. View the full article

Physical AI–the branch of artificial intelligence that controls robots and industrial machinery in the real world–has a hierarchy problem. At the top, OpenAI and Google are scaling multimodal foundation models. In the middle, Nvidia is building the platforms and tools for physical AI development. And then there is a third camp: industrial manufacturers like Hitachi and Germany’s Siemens, which are making the quieter but arguably more grounded argument that you cannot train machines to navigate the physical world without first understanding it. That argument is now moving from boardroom strategy to factory floor deployment, as Hitachi revealed in a recent interview with Nikkei Asia. Why Physical AI needs more than a good model Kosuke Yanai, deputy director of Hitachi’s Centre for Technology Innovation-Artificial Intelligence, is direct about what separates viable physical AI from the theoretical kind. “Physical AI cannot be implemented in society without a systematic understanding that begins with foundational knowledge of physics and industrial equipment,” he told Nikkei. Hitachi’s pitch is that it already holds much of that foundational knowledge–accumulated over decades of building railways, power infrastructure, and industrial control systems. The company has thermal fluid simulation technology that models the behaviour of gases and liquids, and signal-processing tools for monitoring equipment condition — what Yanai describes as the engineering foundation underpinning Hitachi’s ‘extensive knowledge of product design and control logic construction.’ From concept to deployment: Daikin and JR East While Hitachi’s overarching physical AI architecture–the Integrated World Infrastructure Model (IWIM), which it describes as a mixture-of-experts system integrating multiple specialised models, simulators, and data sets–remains in the concept verification stage, two real-world deployments signal that the underlying approach is already producing results. In collaboration with Daikin Industries, Hitachi has deployed an AI system that diagnoses malfunctions in commercial air-conditioner manufacturing equipment. The system, trained on equipment maintenance records, procedure manuals, and design drawings, can now identify which component is likely failing when an anomaly is detected–the kind of operational intuition that previously existed only in the heads of experienced engineers. With East Japan Railway (JR East), Hitachi has built an AI that identifies the root cause of malfunctions in the control devices running the Tokyo metropolitan area’s railway traffic management system, and then assists operators in formulating a response plan. In a network where delays ripple across millions of daily journeys, the ability to accelerate fault diagnosis carries real operational weight. The R&D pipeline: Cutting development time Hitachi’s physical AI push is also showing up in its research output. In December 2025, the company published findings from two projects presented at ASE 2025, a top-tier software engineering conference, that address a persistent bottleneck in industrial AI: the time and effort required to write and adapt control software. In the automotive sector, Hitachi and its subsidiary Astemo developed a system that uses retrieval-augmented generation to automatically produce integration test scripts for vehicle electronic control units (ECUs)–pulling from hardware-specific API information and frontline engineering knowledge. In a pilot involving multi-core ECU testing, the technology reduced integration testing man-hours by 43% compared to manual execution. In logistics, the company developed variability management technology that modularises robot control software into reusable components structured around a robot operating system (ROS). By mapping out the environmental variables and operational requirements of different warehouse settings in advance, the system lets operators adapt robotic picking-and-placing workflows to new products or layouts without rewriting software from scratch. Safety as a structural requirement, not an afterthought One thread that runs through all of Hitachi’s physical AI work is its emphasis on safety guardrails–not as a compliance checkbox, but as an engineering constraint baked into system design. Yanai told Nikkei that the company is integrating its control and reliability technology from social infrastructure development to prevent AI outputs from deviating from human-approved operating parameters. This includes input validation to screen out data that models should not be trained on, output verification to ensure machine actions do not endanger people or property, and real-time monitoring of the AI model itself for operational anomalies. It is a meaningful distinction. Physical AI systems fail in the real world, not in a sandbox. The stakes for an AI controlling railway signalling or factory robotics are categorically different from those governing a chatbot. Infrastructure to match the ambition On the infrastructure side, Hitachi Vantara–the group’s data and digital infrastructure arm–is positioning itself as an early adopter of NVIDIA’s RTX PRO Servers, built on the RTX PRO 6000 Blackwell Server Edition GPU, designed to accelerate agentic and physical AI workloads. The hardware is being paired with Hitachi’s iQ platform and used to build digital twins–virtual replicas of physical systems–that can simulate everything from grid fluctuations to robotic motion at scale. The IWIM concept, meanwhile, is designed to connect Nvidia’s open-source Cosmos physical AI development platform with specialised Japanese-language LLMs and visual language models via the model context protocol (MCP)–essentially a framework to stitch together the models, simulation tools, and industrial datasets that physical AI systems require. The broader race in physical AI is far from settled. But Hitachi’s position–that domain expertise and operational data are as important as model architecture–is increasingly hard to dismiss, particularly as deployments with partners like Daikin and JR East begin to demonstrate what that expertise is actually worth in practice. Sources: Nikkei Asia (Feb 21, 2026); Hitachi R&D (Dec 24, 2025); Hitachi Vantara Blog (Aug 27, 2025) See also:Alibaba enters physical AI race with open-source robot model RynnBrain Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Hitachi bets on industrial expertise to win the physical AI race appeared first on AI News. View the full article

AI in the APAC retail sector is transitioning from analytics and pilots into workflows and daily operations. Dense urban stores, high labour churn, and competitive quick-commerce ecosystems are driving the uptake. A Q4 2025 survey by GlobalData found that 45 percent of consumers in Asia and Australasia are very or quite likely to purchase a product based on AI recommendations or endorsements. Jaya Dandey, Consumer Analyst at GlobalData, said: “Whether shoppers realise it or not, machine-learning systems have long been deciding when to encourage consumers to make purchases, which products they can see, and what discounts they can avail. “Now, agentic systems can also complete shopping-related tasks end-to-end.” Computer vision and store automation Enterprises evaluating computer vision and machine learning can observe early implementations in the region. Lawson, for example, introduced AI-enabled ‘Lawson Go’ stores in Japan during 2022. The retailer collaborated with technology provider CloudPick in 2025 to integrate AI, machine learning, and computer vision. This integration eliminates check-out lines and cashiers to enhance the customer experience. In South Korea, retail AI company Fainders.AI launched a compact and cashier-less MicroStore inside a gym in 2024. This deployment improved the accessibility of autonomous retail across different businesses. AI also aids the forecasting and automation of retail replenishment—a capability that applies well to the APAC market, where store footprints are small and replenishment frequency is high. Japanese food retail chain Coop Sapporo uses a camera-based AI system named Sora-cam, developed by Soracom. The system helps the chain avoid overstocking and reduce unsold merchandise on store shelves. Coop Sapporo employs an analytics team to evaluate the generated images. The team determines the optimal shelf display ratio. The Sora-cam system also alerts staff members to apply discount labels on food items close to expiry to prevent wastage. AI models track waste and markdown timing while improving promotion efficiency. In Southeast Asian (SEA) markets characterised by high price sensitivity, minor improvements in promotion efficiency increase profit margins. AI-driven labour optimisation measures include scheduling, task priority lists, and workload balancing. These measures assist retailers in Japan and South Korea, which face structural labour shortages. They also provide efficiency benefits in high-growth SEA markets. Agentic AI systems in retail are improving APAC consumer interaction “In food retail, agentic AI is best understood as an AI ‘operator’ that can understand a goal, plan steps, stay within budget or allergen constraints, execute actions across systems, ask clarifying questions, and learn preferences over time,” says Dandey. Customers can bypass individual item searches by outlining their overall intent. A customer, for example, might request an AI agent to “Plan five dinners for a family of four, mostly Asian recipes, no shellfish, under 45 minutes.” The agent then generates recipes, builds a shopping cart, sizes quantities, and adds missing staples to the cart. This retail agentic AI capability aligns with regional behaviours, as many APAC households cook frequently and shop fresh. AI agents that recognise local cuisines – such as Korean banchan, Japanese bentos, and Indian spice bases – fit regional habits better than generic Western meal plans. “In many APAC markets, shopping is already deeply integrated with digital wallets, messaging apps, ride-hailing, and delivery ecosystems, making it easier for agentic AI to plug into daily routines,” explains Dandey. “Nevertheless, some key challenges need to be overcome; ensuring private data sharing consent, minimising hallucinations in terms of allergens and ingredients, and implementing proper localisation of the system with language nuance.” See also: DBS pilots system that lets AI agents make payments for customers Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Exploring AI in the APAC retail sector appeared first on AI News. View the full article

The most rigorous international study of firm-level AI impact to date has landed, and its headline finding is more constructive than many expected. Across nearly 6,000 verified executives in four countries, AI has delivered modest aggregate shifts in productivity or employment over the past three years. The measured impact reflects the early phases of deployment rather than a failure of the technology. The working paper [PDF], published by the National Bureau of Economic Research and produced by teams from the Federal Reserve Bank of Atlanta, the Bank of England, the Deutsche Bundesbank and Macquarie University, found that over 90% of firms report no measurable change headcount attributable to AI over the past three years. Given the short time horizon and the concentration of AI use in discrete functions, such incremental rather than transformative effects are consistent with how general purpose technologies have evolved historically. Adoption of AI is widespread. Around 69% of firms are already using some form of AI, led by LLM-based text generation at 41%, data processing via machine learning at 28% and visual content creation at 29%. In the ***, firm-level adoption rose from 61% to 71% across 2025. AI tools are embedded in day-to-day workflows, and although measured impact at firm level often lags adoption, the trend is generally upwards. The forward AI impact numbers indicate acceleration Executives expect stronger effects to take place over the next three years. On average, they expect a 1.4% increase in productivity and a 0.8% rise in output. US executives project a 2.25% productivity gain, while *** firms expect 1.86%. In economies that have struggled with weak productivity growth for over a decade, gains of that magnitude are notable – incremental improvements, compounded across sectors, shift national outputs. On the thorny subject of employment, executives expect a modest 0.7% reduction in headcount across the four countries over the same *******. In the ***, around two-thirds of this adjustment is expected to come through slower hiring rather than outright redundancies. That pattern suggests a gradual reallocation of roles rather than abrupt terminations. As with previous waves of automation, aggregate figures do not capture job creation in adjacent roles, and in the case of AI, these might include roles around data governance, model oversight, prompt engineering, and AI-enabled service development, many of which would be new roles. Interpreting the expectation gap The study also compares executive expectations with those of workers. Researchers fielded parallel questions to US employees through the Survey of Working Arrangements and Attitudes. Employees expect AI to increase employment at their firms by 0.5% over the next three years, while US executives expect a 1.2% reduction. Employees foresee productivity gains of 0.92%, below the executive forecast of 2.25%. This divergence reflects different vantage points. Executives observe cost structures and competitive pressure, while employees experience task-level augmentation and new capabilities. In practice, AI systems are often deployed to assist rather than replace, particularly in knowledge-intensive work. Evidence from controlled trials, including large language model use in customer support and professional services, shows productivity gains concentrated among less experienced staff, with quality improvements appearing alongside better output figures. Where communication and training are clear, adoption tends to proceed with limited resistance. Why this AI impact data merits attention Survey design influences inferences from any statistics, and in this particular case, the researchers noted variation between their own figures and those from, for example, a McKinsey survey taken in the same ******* that put adoption at 88% of organisations (the survey in question here pegs the figure at just 69%). On the other hand, the US Census Business Trends and Outlook Survey, which draws on a broader respondent base, estimated AI use at around 9% in early 2024, rising to 18% by December 2025. This gap reflects differences in sampling, question framing and respondent seniority. Executive surveys tend to capture intent and enterprise-level deployments, while broader business surveys may reflect narrower definitions of AI or earlier stages of implementation. In the study in question, respondents were phone-verified, unpaid, and predominantly CEOs and CFOs, with over 90% drawn from the *** and Germany. The data was cross-checked against ten years of macro output and employment figures from national statistics agencies. The inflection point executives anticipate may unfold over the next three years as deployments mature and integration improves, in the way that many new technologies have emerged into the workplace until they become everyday tools. The central question is less whether AI will affect productivity and employment, and more how quickly organisations can change the technology’s wider adoption into measurable economic gains. See also: Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post AI: Executives’ optimism about the future appeared first on AI News. View the full article