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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

Shifting from price hikes to persuasion, Coca-Cola’s latest strategy signals how AI is moving deeper into the core of corporate marketing. Recent coverage of the company’s leadership discussions shows that Coca-Cola is entering what executives describe as a new phase focused on influence not pricing power. According to Mi-3, the company is changing its focus from “price to persuasion,” with digital platforms, AI, and in-store execution becoming increasingly important in building demand. This reflects a change in consumer brand behaviour as inflation pressures ease and companies seek new strategies to maintain revenue growth. That means expanding the role of AI in Coca-Cola’s marketing production and decision-making. The company has already experimented with generative AI in creative campaigns and continues testing how automation can help with content creation, campaign planning, and distribution. Industry analysis from The Current points out that Coca-Cola has been embedding AI into marketing workflows and scaling its use in creative production and campaign execution. These efforts include using AI tools to generate images, assist with storytelling, and adjust campaigns in channels. Testing AI in the marketing pipeline The week’s reporting suggests the company is now testing AI-driven systems that can help automate parts of the advertising process, including drafting scripts or preparing social media content. While these initiatives remain in testing not full rollout, they illustrate how large brands are moving toward more automated marketing pipelines. Instead of relying only on agencies or long creative cycles, companies are exploring ways to shorten the path from concept to campaign. During the past two years, many consumer goods have firms relied on price increases to offset rising costs. As inflation slows in several markets, analysts say that strategy has limits. Growth increasingly depends on persuading consumers to buy more often or choose higher-margin products. AI offers a way to refine that persuasion at scale, using data to shape messages, target audiences, and adjust campaigns in near real time. Coca-Cola’s approach fits a wider trend in marketing technology. Generative AI tools have quickly moved from experimental use to regular deployment in large enterprises. According to McKinsey’s 2024 global AI survey, about one-third of organisations already use generative AI in at least one business function, with marketing and sales among the most common areas of adoption. Analysts expect that share to keep rising as companies test automation in creative work and customer engagement. AI moves upstream in enterprise strategy What strikes out in Coca-Cola’s case is how the corporation frames AI not only as a cost-saving tool, but also as part of a broader operating shift. By focusing on persuasion, the company signals that AI’s value lies in shaping demand, not improving efficiency. That includes using AI to analyse consumer behaviour, tailor messaging to different markets, and support local teams with adaptable content. The strategy also reflects a growing tension in the marketing sector. Automation can speed up production and test more campaign ideas, but it also raises questions about creative quality, brand consistency, and the role of human teams. Companies experimenting with AI-generated content must still ensure that messaging aligns with their brand identity and cultural context. For global brands like Coca-Cola, that challenge becomes more complex because campaigns frequently need to work in many regions. Another factor shaping this transition is the rapid growth of digital advertising channels. As spending shifts toward social platforms, streaming services, and online retail media, the volume of content required has expanded. AI tools offer a way to produce many versions of ads, test different approaches, and adjust messaging based on performance data. This makes automation appealing not only for cost reasons, but also for speed and flexibility. Coca-Cola’s move reflects a broader pattern: AI adoption is moving upstream in business processes. Early deployments frequently centred on analytics or internal automation. Companies are now applying AI in customer-facing functions like marketing strategy, creative development, and campaign management. That change suggests that AI is becoming part of how companies compete for market share, not how they reduce expenses. The firm has not indicated that AI will replace creative teams or agencies. Instead, the current direction indicates a hybrid model in which automation handles repetitive or data-heavy tasks while human teams guide brand voice and campaign concepts. Many marketing leaders believe that this blended approach will define the next phase of AI adoption. Coca-Cola’s emphasis on persuasion over pricing may impact how other consumer brands approach growth in a post-inflation environment. If AI can assist businesses in more precisely shaping demand, it may minimise reliance on price increases or mass-market campaigns. (Photo by James Yarema) See also: PepsiCo is using AI to rethink how factories are designed and updated 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 Coca-Cola turns to AI marketing as price-led growth slows appeared first on AI News. View the full article

The adoption of AI for enterprise treasury management enables businesses to abandon manual spreadsheets for automated data pipelines. Corporate finance departments face pressure from market volatility, regulatory demands, and digital finance requirements. Ashish Kumar, head of Infosys Oracle Sales for North America, and CM Grover, CEO of IBS FinTech, recently discussed the realities of corporate treasuries. IBS FinTech has operated for 19 years and currently ranks in the top five globally according to an IDC report. Grover notes that while AI-powered automation has reached many areas of corporate life, treasury departments often still rely on manual spreadsheets. “IBS FinTech has identified the gap in the CFO’s office in corporations where they are managing their most critical information system, that is, treasury management on Excel,” Grover said. Treasury teams manage cash, liquidity, and risk. Companies face foreign currency risk through imports and exports, alongside related commodity risks. Cash surplus companies also need to invest in operations to generate returns. The key problem for many enterprises is a lack of real-time data connection. Teams often execute trades on platforms like Bloomberg, Reuters, or 360D, manually enter the data into spreadsheets, and then post accounting entries into an enterprise resource planning system. Successfully implementing AI in enterprise treasury management AI implementations in finance depend on resolving these manual bottlenecks. Enterprise leaders often view the technology as a fast solution, but the technology requires digitised and automated data as a foundation. “It is not by talking you can do AI in treasury,” Grover said. “You have to create that underlying data set that has to be digitised and automated.” Integrating treasury management systems with existing enterprise resource planning platforms allows companies to establish this data foundation. IBS FinTech built its backend on Oracle databases from its inception and now integrates with Oracle Cloud, NetSuite, and Fusion. A connected ecosystem requires the treasury management system to communicate directly with the enterprise resource planning platform, trading platforms, and banks. This integration provides executives with accurate information to manage liquidity, mitigate risk, and monitor compliance violations across the system. Grover expects global volatility to increase due to geopolitical and economic factors impacting commodities, equities, and foreign exchange. Executives must prioritise automation and real-time information systems to operate in this uncertain environment. Kumar noted that modernising treasury management with AI and connecting it to enterprise resource planning systems builds financial resilience. Enterprise leaders should audit their existing data workflows. If a finance team relies on manual entry between a trading platform and an enterprise resource planning platform, AI initiatives will fail due to poor data quality. Implementing direct integrations ensures data flows in real time without error, providing the necessary baseline for future technology deployment. 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 How AI upgrades enterprise treasury management appeared first on AI News. View the full article

Artificial intelligence is moving closer to the point where it can act, not just advise. A new pilot by DBS Bank shows how that shift may soon affect everyday payments, as financial institutions begin testing systems that allow AI agents to complete purchases on behalf of customers. DBS is working with Visa to trial Visa Intelligent Commerce, a framework designed to support transactions initiated by AI software rather than humans. The system allows digital agents to search for products, select options, and complete purchases using payment credentials issued and controlled by the bank. According to reports from Asian Banking & Finance and Fintech Futures, the pilot has already processed real transactions, including food and beverage purchases made using DBS or POSB cards. Moving from recommendations to real transactions The trial highlights how banks are preparing for what some in the industry call “agent-driven commerce.” In this model, AI tools do more than recommend products or compare prices. They can execute the purchase itself, subject to rules set by both the customer and the issuing bank. Visa’s approach keeps the bank at the centre of the process. Payment details are tokenised, and transactions pass through issuer-controlled approval flows designed to confirm identity, intent, and spending limits. This means the bank still decides whether the agent’s action fits the user’s permissions before money moves. The structure aims to address one of the biggest concerns around autonomous AI: how to maintain control and trust when software begins making financial decisions. The DBS pilot is part of a wider effort to test where AI fits into financial infrastructure. Rather than treating AI as a customer-facing tool, banks are increasingly examining how it might change the mechanics of payments, fraud checks, and authorisation. Industry observers note that this marks a shift from AI as a productivity assistant to AI as an operational participant in transactions. Early use cases focus on routine purchases Early use cases for agent-based commerce are practical rather than futuristic. These include routine purchases such as ordering groceries, renewing subscriptions, booking travel, or restocking household items. In these cases, the agent follows instructions set in advance by the user, such as budget limits or preferred brands. DBS and Visa plan to expand the pilot into broader online shopping and travel bookings as testing continues, according to Fintech Futures. The idea of AI executing purchases raises both opportunity and risk for financial institutions. On one hand, banks that support agent-based payments could gain a stronger role in digital commerce by acting as the control layer that manages consent and security. On the other, they must handle new questions about liability, authentication, and dispute handling if an agent makes a purchase the customer later challenges. Security and governance will likely shape how fast this model spreads. Analysts often point out that customers may accept AI suggestions long before they accept AI decisions involving money. By keeping approval logic within the issuing bank’s systems, Visa’s framework attempts to reassure users that human oversight remains embedded in the process. A wider shift in how enterprises deploy AI agents The pilot also reflects a broader pattern in enterprise AI adoption. Over the past year, many companies have moved beyond testing chatbots or internal assistants and started placing AI into workflows that directly affect revenue, operations, or customer transactions. In banking, this includes fraud monitoring, credit scoring support, and automated customer service. Allowing AI to trigger payments could be the next step in that progression. For DBS, which has invested heavily in digital banking systems, the trial fits into a longer push to integrate automation into financial services. The bank has previously focused on using data analytics and AI tools to streamline operations and personalise services. The new payment pilot extends that strategy into commerce itself. Whether agent-based payments become common will depend on how comfortable customers feel delegating financial decisions to software. It will also depend on how clearly banks define the boundaries of what AI agents can and cannot do. Industry experts say adoption may begin with low-risk, repeat purchases before expanding to more complex transactions. For now, the DBS and Visa pilot offers a glimpse of how payment systems may adapt if AI agents become part of daily digital life. Instead of only helping users choose what to buy, future systems may allow trusted software to complete the purchase — with banks acting as the gatekeepers that decide when those actions are allowed. (Photo by Patrick Tomasso) 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. The post DBS pilots system that lets AI agents make payments for customers appeared first on AI News. View the full article

For leaders in the financial sector, the experimental phase of generative AI has concluded and the focus for 2026 is operational integration. While early adoption centred on content generation and efficiency in isolated workflows, the current requirement is to industrialise these capabilities. The objective is to create systems where AI agents do not merely assist human operators, but actively run processes within strict governance frameworks. This transition presents specific architectural and cultural challenges. It requires a move from disparate tools to joined-up systems that manage data signals, decision logic, and execution layers simultaneously. Financial institutions integrate agentic AI workflows The primary bottleneck in scaling AI within financial services is no longer the availability of models or creative application, it is coordination. Marketing and customer experience teams often struggle to convert decisions into action due to friction between legacy systems, compliance approvals, and data silos. Saachin Bhatt, Co-Founder and COO at Brdge, notes the distinction between current tools and future requirements: “An assistant helps you write faster. A copilot helps teams move faster. Agents run processes.” For enterprise architects, this means building what Bhatt terms a ‘Moments Engine’. This operating model functions through five distinct stages: Signals: Detecting real-time events in the customer journey. Decisions: Determining the appropriate algorithmic response. Message: Generating communication aligned with brand parameters. Routing: Automated triage to determine if human approval is required. Action and learning: Deployment and feedback loop integration. Most organisations possess components of this architecture but lack the integration to make it function as a unified system. The technical goal is to reduce the friction that slows down customer interactions. This involves creating pipelines where data flows seamlessly from signal detection to execution, minimising latency while maintaining security. Governance as infrastructure In high-stakes environments like banking and insurance, speed cannot come at the cost of control. Trust remains the primary commercial asset. Consequently, governance must be treated as a technical feature rather than a bureaucratic hurdle. The integration of AI into financial decision-making requires “guardrails” that are hard-coded into the system. This ensures that while AI agents can execute tasks autonomously, they operate within pre-defined risk parameters. Farhad Divecha, Group CEO at Accuracast, suggests that creative optimisation must become a continuous loop where data-led insights feed innovation. However, this loop requires rigorous quality assurance workflows to ensure output never compromises brand integrity. For technical teams, this implies a shift in how compliance is handled. Rather than a final check, regulatory requirements must be embedded into the prompt engineering and model fine-tuning stages. “Legitimate interest is interesting, but it’s also where a lot of companies could trip up,” observes Jonathan Bowyer, former Marketing Director at Lloyds Banking Group. He argues that regulations like Consumer Duty help by forcing an outcome-based approach. Technical leaders must work with risk teams to ensure AI-driven activity attests to brand values. This includes transparency protocols. Customers should know when they are interacting with an AI, and systems must provide a clear escalation path to human operators. Data architecture for restraint A common failure mode in personalisation engines is over-engagement. The technical capability to message a customer exists, but the logic to determine restraint is often missing. Effective personalisation relies on anticipation (i.e. knowing when to remain silent is as important as knowing when to speak.) Jonathan Bowyer points out that personalisation has moved to anticipation. “Customers now expect brands to know when not to speak to them as opposed to when to speak to them.” This requires a data architecture capable of cross-referencing customer context across multiple channels – including branches, apps, and contact centres – in real-time. If a customer is in financial distress, a marketing algorithm pushing a loan product creates a disconnect that erodes trust. The system must be capable of detecting negative signals and suppressing standard promotional workflows. “The thing that kills trust is when you go to one channel and then move to another and have to answer the same questions all over again,” says Bowyer. Solving this requires unifying data stores so that the “memory” of the institution is accessible to every agent (whether digital or human) at the point of interaction. The rise of generative search and SEO In the age of AI, the discovery layer for financial products is changing. Traditional search engine optimisation (SEO) focused on driving traffic to owned properties. The emergence of AI-generated answers means that brand visibility now occurs off-site, within the interface of an LLM or AI search tool. “Digital PR and off-site SEO is returning to focus because generative AI answers are not confined to content pulled directly from a company’s website,” notes Divecha. For CIOs and CDOs, this changes how information is structured and published. Technical SEO must evolve to ensure that the data fed into large language models is accurate and compliant. Organisations that can confidently distribute high-quality information across the wider ecosystem gain reach without sacrificing control. This area, often termed ‘Generative Engine Optimisation’ (GEO), requires a technical strategy to ensure the brand is recommended and cited correctly by third-party AI agents. Structured agility There is a misconception that agility equates to a lack of structure. In regulated industries, the opposite is true. Agile methodologies require strict frameworks to function safely. Ingrid Sierra, Brand and Marketing Director at Zego, explains: “There’s often confusion between agility and chaos. Calling something ‘agile’ doesn’t make it okay for everything to be improvised and unstructured.” For technical leadership, this means systemising predictable work to create capacity for experimentation. It involves creating safe sandboxes where teams can test new AI agents or data models without risking production stability. Agility starts with mindset, requiring staff who are willing to experiment. However, this experimentation must be deliberate. It requires collaboration between technical, marketing, and legal teams from the outset. This “compliance-by-design” approach allows for faster iteration because the parameters of safety are established before the code is written. What’s next for AI in the financial sector? Looking further ahead, the financial ecosystem will likely see direct interaction between AI agents acting on behalf of consumers and agents acting for institutions. Melanie Lazarus, Ecosystem Engagement Director at Open Banking, warns: “We are entering a world where AI agents interact with each other, and that changes the foundations of consent, authentication, and authorisation.” Tech leaders must begin architecting frameworks that protect customers in this agent-to-agent reality. This involves new protocols for identity verification and API security to ensure that an automated financial advisor acting for a client can securely interact with a bank’s infrastructure. The mandate for 2026 is to turn the potential of AI into a reliable P&L driver. This requires a focus on infrastructure over hype and leaders must prioritise: Unifying data streams: Ensure signals from all channels feed into a central decision engine to enable context-aware actions. Hard-coding governance: Embed compliance rules into the AI workflow to allow for safe automation. Agentic orchestration: Move beyond chatbots to agents that can execute end-to-end processes. Generative optimisation: Structure public data to be readable and prioritised by external AI search engines. Success will depend on how well these technical elements are integrated with human oversight. The winning organisations will be those that use AI automation to enhance, rather than replace, the judgment that is especially required in sectors like financial services. See also: Goldman Sachs deploys Anthropic systems with success 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 financial institutions are embedding AI decision-making appeared first on AI News. View the full article

As a large provider of technology services operating in multiple industries, Infosys is one of the names that quickly come to mind when decision-makers consider possible providers of consultation on and practical implementation of any AI project – discrete or organisation-wide. Infosys delivers these services through its Topaz Fabric, leveraging its partnerships with specific AI technology providers. It reports that it is currently working on AI implementations with 90% of its top 200 clients and has more than 4,600 AI projects in progress. The company’s strategy for AI implementation organisation-wide looks at six areas affected and considered during projects. AI strategy and engineering focuses on designing and implementing AI strategies and architectures aligned to specific business objectives. These include the orchestration of AI agents, proprietary platforms, and third-party tools on infrastructure especially configured for AI workloads. An overarching strategy will lead to a consistent, enterprise AI-first operating model. Data for AI addresses the preparation of enterprise data, covering structured and unstructured data and processes in this area include the development of AI-ready data platforms. Infosys refers to “AI-grade” data engineering practices such as data fingerprinting and synthetic training data services. The intention is to convert siloed data assets into reliable inputs for analytics and predictive systems. Process AI concentrates on integrating AI agents into business processes, redesigning workflows if necessary so AI agents and human employees can work better together. The aim is to improve operational efficiency in general, regardless of business function. Legacy modernisation applies AI agents in the analysis and interpretation of the existing technology stack and potentially reverse-engineering legacy systems to better stage AI modernisation projects. The overall aim is to reduce technical debt and offer a greater responsiveness when AI is unleashed. Physical AI extends into products and devices in the workplace. This involves embedding AI into hardware systems such as those that collect sensor data, interpret that data, and act in the physical world. This broad definition encompasses digital twins, robotics, autonomous systems, and edge computing. In short, it’s the integration of digital intelligence and physical operations. AI trust covers governance, security, and ethics, and includes consideration of risk assessment frameworks, policy development, AI testing, and overall technology lifecycle management. Lessons for business leaders Although business leaders may be already in partnership with alternative service providers other than Infosys, the company’s strategy of demarcating the necessary action areas for AI implementations offers significant value. The six areas described provide practical reference points that can be used in any organisation to plan projects or perhaps monitor and assess ongoing implementation efforts. Among these, data preparation is central. AI systems depend on data quality and consistency, so investment in data platforms, data governance, and engineering practices that support models is central tenet on which AI initiatives are built. Embedding AI into workflows means it’s sometimes necessary to redesign the way employees work. Leaders should be aware of how AI agents and employees interact, and measure performance improvements. Changes can be made both to the technologies deployed and the working methods that have existed to date. If the latter, retraining and educating affected employees will be necessary, with accompanying costs. The issue of legacy systems requires careful attention as many organisations operate complex estates that limit the agility necessary for AI to improve operations. AI tools themselves can help to analyse existing dependencies and even plan modernisation, implemented, ideally, over several stages or in separate sprints. Physical operations intersect increasingly with digital systems. For companies with physical products, such as in manufacturing or logistics, embedding AI into devices and equipment can improve monitoring and devices’ responsiveness. This will require coordination between IT, OT, engineering, and operational teams, and line-of-business leaders should be consulted in particular. Governance should accompany any scale of AI implementation. Risk assessment, security testing, security policy formulation, and the design of AI-specific guardrails should be established early on. Regulatory scrutiny of AI is increasing, particularly in sectors handling sensitive data, and statutory penalties apply for data loss or mismanagement, regardless of its source – AI or otherwise – in the enterprise. Clear accountability structures and documentation reduce these risks to operations and reputation. Taken together, these areas indicate that AI implementation is organisational rather than purely technical. Success depends on leadership alignment, sustained investment, and realistic assessment of any capability gaps. Claims of rapid transformation should be treated cautiously, and durable results are more likely when strategy, data, process design, modernisation, operational integration, and governance are addressed in parallel. (Image source: “Infosys, Bangalore, India” by theqspeaks is licensed under CC BY-NC-SA 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 Infosys AI implementation framework offers business leaders guidance appeared first on AI News. View the full article

For organizations who are still wedded to the rules and structures of robotic process automation (RPA), then considering agentic AI as the next step for automation may be faintly terrifying. SS&C Blue Prism, however, is here to help, taking customers on the journey from RPA to agentic automation at a pace with which they’re comfortable. Big as it may be, this move is a necessary one. Modern workflows are at a level of complexity that outlines what traditional RPA was designed to do, according to Steven Colquitt, VP Software Engineering, SS&C Blue Prism. Unstructured data comes from various sources resembling non-deterministic real-world interactions. “Inputs can vary, outcomes can shift and decisions depend on context in real-time,” notes Colquitt. Brian Halpin, Managing Director, Automation, SS&C Blue Prism, gives the example of a credit agreement where you might need to get 30 or 40 answers from it. He uses the word “answers” deliberately as opposed to data points to account for the level of reasoning that a large language model (LLM) performs. The element of this being a journey continues to resonate, however. “We’re now saying we’re giving an AI agent the outcome that we want, but we’re not giving it the instructions on how to complete,” says Halpin. “We’re not saying, ‘follow step one, two, three, four, five.’ We’re saying, ‘I want this loan reviewed’ or ‘I want this customer onboarded.’ “Ultimately, I think that’s where the market will go,” adds Halpin. “Is it ready for that? No. Why? Because there’s trust, there’s regulations, there’s auditability […] stability, security. We know LLMs are prone to hallucinations, we know they drift, and [if] you change the underlying model, things change and responses get different. “There’s an awful lot of learning to happen before I think companies go fully autonomous and real agentic workflows [are] driven from that sort of non-deterministic perspective,” says Halpin. “But then, there will be something else, right? There will be another model. So really, it is all a journey right now.” SS&C Blue Prism has thousands of customers who have automated processes in place, from centers of excellence (CoEs) to running digital workers in their operations, who they’re hoping to upgrade into the “world of AI”, as Halpin puts it. Sometimes it’s about connecting two separate areas. “It’s been interesting,” Halpin notes. “As I talk to [our] customers, I see a common thread among companies right now where, in a lot of cases, AI has been established as a separate unit in a company. You go over to the process automation team, and they’re maybe not even allowed to use the AI. “So, it’s about, ‘How do you help them get that capability and blend it into their process efficiency and allow them to get to the next 20%, 30% of automation, in terms of the end-to-end process?’” As part of this, SS&C Blue Prism is soon to launch new technology which helps organizations build and embed AI agents within workflows, as well as assist with orchestration. Those who attended TechEx Global, on February 4-5 as part of the Intelligent Automation conference, where SS&C Blue Prism participated, got the full story, as well as understanding the company’s ongoing path. “[SS&C Technologies] are one of the biggest users of RPA in the world,” adds Halpin. “We have over three and a half thousand digital workers deployed [across the SS&C estate]. We’re saving hundreds of millions in run-rate benefit. We’ve about 35 AI agents in production attached to those digital workers doing […] complex tasks, and really, we just want to share that journey.” Watch the full interview with Brian Halpin below: The post SS&C Blue Prism: On the journey from RPA to agentic automation appeared first on AI News. View the full article

American International Group (AIG) has reported faster than expected gains from its use of generative AI, with implications for underwriting capacity, operating cost, and portfolio integration. The company’s recent disclosures at an Investor Day merit attention from AI decision-makers as they contain assertions about measurable throughput and workflow redesign. AIG has outlined potential benefits from generative AI. Chief executive Peter Zaffino later described the company’s early projections as “aspirational,” yet in a fourth quarter earnings call, he stated that “we see the abilities are much greater.” The change in tone is indicative of positive internal results, and according to Zaffino, “We’re seeing a massive change in our ability to process a submission flow way […] without additional human capital resources. That has been the biggest surprise.” The company’s claims that generative AI has increased submission processing capacity, the economic impact is direct. AIG reports that in 2025 it “made progress embedding generative AI in our core underwriting and claims processes, and expanding it.” The company’s internal tool, AIG Assist, is implemented in most commercial lines of businesses. Lexington Insurance, AIG’s excess and surplus unit has targetted reaching 500,000 submissions by 2030. Zaffino reports that Lexington has already surpassed 370,000 submissions in 2025. AIG uses generative models to extract and summarise incoming data, and has developed an orchestration layer in the technology stack “to coordinate AI agents to drive better decision-making and reduce costs in the organisation.” Previous Investor Days, this level of orchestration was not a focus. The chief executive describes AI agents “as companions that operate with our teams” that provide real-time information, draw on historical cases, and challenge underwriting decisions. The company relies on its ability to manage incoming data “at a fraction of the time” and to orchestrate agents so they can “scale and be able to analyse that information that’s not biased in any way; that’s through the entire workflow.” AIG links orchestration to compression of what it terms a “front-to-back workflow,” a tighter integration between intake, risk assessment and claims handling. The company states that multiple agents, coordinated through a orchestration layer, streamlines repetitive and previously-lengthy processes. AIG has applied its generative AI stack in specific transactions. During the conversion of Everest’s retail commercial business, the company reports that accounts were prioritised for renewal “in a fraction of the time.” Management states that it built an ontology of Everest’s portfolio and combined it with its own, which “allowed [the company] to prioritise how the portfolios could blend together.” Ontological alignment is technically demanding and often creates underestimated costs. The launch of Lloyd’s Syndicate 2479, in partnership with Amwins and Blackstone, extended the ontological approach to a special purpose vehicle. In conjunction with Palantir, AIG used LLMs to assess whether Amwins’ programme portfolio aligned with the syndicate’s stated risk appetite. Zaffino stated that AIG has a “strong pipeline of SPV opportunities.” For AI decision-makers, the case illustrates the use that orchestration and workflow integration can provide when generative models are embedded in core processes, and the degree to which economic impact depends on measurable changes in capacity and cycle time. (Image source: “Nagasaki, AIG (Insurance company) building” by Admanchester 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 Insurance giant AIG deploys agentic AI with orchestration layer appeared first on AI News. View the full article

The release of Alibaba’s latest Qwen model challenges proprietary AI model economics with comparable performance on commodity hardware. While US-based labs have historically held the performance advantage, open-source alternatives like the Qwen 3.5 series are closing the gap with frontier models. This offers enterprises a potential reduction in inference costs and increased flexibility in deployment architecture. The central narrative of the Qwen 3.5 release is this technical alignment with leading proprietary systems. Alibaba is explicitly targeting benchmarks established by high-performance US models, including GPT-5.2 and Claude 4.5. This positioning indicates an intent to compete directly on output quality rather than just price or accessibility. Technology expert Anton P. states that the model is “trading blows with Claude Opus 4.5 and GPT-5.2 across the board.” He adds that the model “beats frontier models on browsing, reasoning, instruction following.” Alibaba Qwen’s performance convergence with closed models For enterprises, this performance parity suggests that open-weight models are no longer solely for low-stakes or experimental use cases. They are becoming viable candidates for core business logic and complex reasoning tasks. The flagship Alibaba Qwen model contains 397 billion parameters but utilises a more efficient architecture with only 17 billion active parameters. This sparse activation method, often associated with Mixture-of-Experts (MoE) architectures, allows for high performance without the computational penalty of activating every parameter for every token. This architectural choice results in speed improvements. Shreyasee Majumder, a Social Media Analyst at GlobalData, highlights a “massive improvement in decoding speed, which is up to nineteen times faster than the previous flagship version.” Faster decoding ultimately translates directly to lower latency in user-facing applications and reduced compute time for batch processing. The release operates under an Apache 2.0 license. This licensing model allows enterprises to run the model on their own infrastructure, mitigating data privacy risks associated with sending sensitive information to external APIs. The hardware requirements for Qwen 3.5 are relatively accessible compared to previous generations of large models. The efficient architecture allows developers to run the model on personal hardware, such as Mac Ultras. David Hendrickson, CEO at GenerAIte Solutions, observes that the model is available on OpenRouter for “$3.6/1M tokens,” a pricing that he highlights is “a steal.” Alibaba’s Qwen 3.5 series introduces native multimodal capabilities. This allows the model to process and reason across different data types without relying on separate, bolted-on modules. Majumder points to the “ability to navigate applications autonomously through visual agentic capabilities.” Qwen 3.5 also supports a context window of one million tokens in its hosted version. Large context windows enable the processing of extensive documents, codebases, or financial records in a single prompt. If that wasn’t enough, the model also includes native support for 201 languages. This broad linguistic coverage helps multinational enterprises deploy consistent AI solutions across diverse regional markets. Considerations for implementation While the technical specifications are promising, integration requires due diligence. TP Huang notes that he has “found larger Qwen models to not be all that great” in the past, though Alibaba’s new release looks “reasonably better.” Anton P. provides a necessary caution for enterprise adopters: “Benchmarks are benchmarks. The real test is production.” Leaders must also consider the geopolitical origin of the technology. As the model comes from Alibaba, governance teams will need to assess compliance requirements regarding software supply chains. However, the open-weight nature of the release allows for code inspection and local hosting, which mitigates some data sovereignty concerns compared to closed APIs. Alibaba’s release of Qwen 3.5 forces a decision point. Anton P. asserts that open-weight models “went from ‘catching up’ to ‘leading’ faster than anyone predicted.” For the enterprise, the decision is whether to continue paying premiums for proprietary US-hosted models or to invest in the engineering resources required to leverage capable yet lower-cost open-source alternatives. 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 Alibaba Qwen is challenging proprietary AI model economics appeared first on AI News. View the full article

Goldman Sachs plans to deploy Anthropic’s Claude model in trade accounting and client onboarding, and, according to an article in American Banker, presents this as part of a broader push among large banks to use generative artificial intelligence to improve efficiency. The focus is on operational processes that sit in the back office and have traditionally relied on large teams performing tasks like document review, reconciliation, and compliance checks. Several banks already use generative AI in knowledge work. JPMorganChase provides employees with access to a large language model suite for information retrieval and data analysis, while the Bank of America’s Erica assistant answers internal technology and human resources queries. Citi and Goldman both use AI to help developers with coding. The article suggests a more recent development is the application of generative AI to operational tasks like trade accounting and know-your-customer (KYC). Automating the edge-cases Automatable processes in the sector are often rules-based, involving collecting data, validating it against internal and external databases, and assembling required documentation. In theory, conventional software has been used to automate such work. However, Marco Argenti, Goldman’s chief information officer, argues that if a rules-based system resolves most cases, a small percentage of transactions fall outside defined parameters that can translate into thousands of individual items at the type of scale in question. He cites the example of identity verification in KYC compliance, where minor discrepancies or documents approaching expiry can create edge cases requiring judgement. Argenti says that neural networks can address these micro-decisions as they’re capable of applying contextual reasoning where fixed rules might be missing or don’t necessarily give a clear answer. In this scenario, generative AI augments existing rules systems rather than supplanting them. Operational improvements, therefore lie in the reduction in the number of cases that require manual intervention and thus shortening time needed to resolve the exceptions. The coding experience Goldman’s prior experience with Claude models used internally for software development informed its decision to extend AI to other areas of operations. Developers use a version of Claude with Cognition’s Devin agent to aid them with programming. In this context, human developers set specifications and regulatory parameters, the agent produces code, and humans review outputs. The agent is also used to run code tests and validations. He describes this as a change to devs’ workflows, with agents operating according to defined instructions. The benefit is increased developer productivity and the faster completion of projects.s For trade accounting and client onboarding, Goldman and Anthropic AI project owners observed existing workflows with domain experts to identify work bottlenecks. The implemented agents review documents, extract entities, determine whether additional documentation is required, assess ownership structures, and can trigger further compliance checks. Tasks automated in this way tend to be document-heavy and require individual judgement. By automating extraction and preliminary assessment, the agents reduce the time analysts spend on comparison work. Indranil Bandyopadhyay, principal analyst at Forrester, says that reconciliation in trade accounting requires comparing fragmented data in internal ledgers, counterparty confirmations, and the perusal of bank statements, and that a typical workflow depends on accurate extraction and matching of figures and text to existing documents. Claude’s ability to process large context windows and follow instructions, he says, makes it suited to just such workflows. The labour involved in client onboarding, such as parsing passports and corporate registration documents, and the cross-referencing of all sources means AI’s ability to extract structured data and flag inconsistencies makes the technology a good fit, reducing overall workloads. Bandyopadhyay stresses that accounting and compliance platforms remain the canonical systems of record. Claude operates in the workflow layer, handling extraction and comparison so human analysts can handle the code’s exceptions.. In his assessment, the operational value in a regulated environments like banking lies in such a division of labour. Jonathan Pelosi, head of financial services at Anthropic says Claude is trained to surface uncertainty and to provide source attribution, creating an audit trail – reducing the effect of hallucinations. Bandyopadhyay also notes the importance of human oversight and validation, saying institutions should design systems so that errors are detected early. Goldman’s Marco Argenti rejects the view that AI systems are inherently easier to deceive than people, arguing that social engineering exploits human vulnerabilities and that AI can detect subtle anomalies at scale, and reiterates the need to combine human judgement with automated scrutiny in teams. His claim implies a increase in operational capacity without proportional increases in staff, even with the issues known to affect AI rollouts. AI in banking operations In the banking sector, generative AI is a tool that improves operational performance by accelerating document processing, reducing exception handling time, and increasing throughput in high volume workflows. But the need to retain human oversight to counteract AI’s errors means the retention of and reliance on existing systems of records remains. (Image source: “Dreams…” by noahwesley is licensed under CC BY-NC-SA 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 Goldman Sachs deploys Anthropic systems with success appeared first on AI News. View the full article

NatWest Group has expanded the use of artificial intelligence in several areas of its operations, citing customer service, document management in its wealth management division, and software development. According to a blog post by its chief information officer, Scott Marcar, 2025 was the first year in which these systems were deployed at scale. The aim is to improve productivity and customer engagement. Generative AI in customer service In customer service, generative AI has been added to Cora, the bank’s digital assistant, and the number of possible customer journeys that can be supported by generative AI increased from four to 21. The bank reports this has let led to quicker resolution times and a reduced need for human intervention. Early this year, 25,000 customers will get access to a new agentic financial assistant in Cora, which is built on OpenAI models. Cora will let customers ask questions in natural language about recent transactions and their spending patterns from the bank’s app. The next phase involves adding voice-to-voice abilities that incorporate tone and conversational nuance. Customers will be able to report suspected fraud and manage related cases through the interface. The impact of AI on internal customer service operations has been largely in the creation time savings. In the bank’s retail division, for example, automated call summaries and complaint drafting tools have saved more than 70,000 hours of staff time. These generated summaries of customer calls help with written responses to complaints. Staff access to Copilot Marcar says all of its c. 60,000 employees have access to AI tools that include Microsoft Copilot Chat and the bank’s own LLM. More than half of staff have taken extra training beyond the basic training offered. Summarising wealth In the NatWest’s private banking and wealth management operations, AI is used to improve document management and client records. Relationship managers use notes, meeting summaries, and correspondence to understand clients’ circumstances. The systems generate summaries of meetings and documents, reducing the time required to review and record information, releasing 30% more time for direct client face time: Advisers allocate more hours to the giving of advice rather than administration. AWS Cloud The above changes depends alterations NatWest has made to its data infrastructure. It’s restructured its data estate to create unified customer views, and moved workloads to Amazon Web Services while simplifying some legacy systems. Access to data and scalable computing capacity supports the summarisation tools and the conversational systems used in customer service. Software development Software development is the third area in which AI is deployed. The bank’s 12,000 engineers use AI coding tools, and Marcar says AI now produces over a third of the company’s code, drafting, reviewing and testing software. In 2025, NatWest hired nearly 1,000 graduate software engineers in India and the ***. Trials of agentic engineering in its financial crime units led to a tenfold increase in productivity, and NatWest plans to extend agentic engineering practices more widely. Its stated objective is to build and iterate systems more quickly. Fraud prevention The bank has also invested in AI-powered analytics fraud detection and risk monitoring, designed to identify unusual activity and advise customers when risk is detected. Alongside operational deployment, NatWest has established an AI research office that focuses on technologies like audiovisual conversational systems and proprietary small language models. It’s also formalised governance structures through an AI and Data Ethics Code of Conduct and the organisation is part of the Financial Conduct Authority’s Live AI Testing programme. Conclusions Across customer service, wealth management document processing, and software development, AI is embedded in workflows at NatWest, producing time savings and productivity increases. The scale of deployment, covering tens of thousands of employees and a growing proportion of customer interactions, indicates that AI now forms part of NatWest’s operating model not an experimental adjunct. (Image source: Pixabay) 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 Banking AI in multiple business functions at NatWest appeared first on AI News. View the full article

Debenhams is piloting agentic AI commerce via PayPal integration to reduce mobile friction and help solve a familiar problem for retailers. Mobile checkout abandonment remains a persistent revenue leak for digital retailers. Debenhams Group is attempting to close this gap by deploying an agentic AI interface within the PayPal app. The pilot makes Debenhams the first *** retailer to test an automated checkout flow that keeps the user entirely inside a payment provider’s ecosystem. Shoppers using PayPal can now issue natural language prompts to find items from Debenhams Group’s brands, including boohoo, boohooMAN, Karen Millen, and PrettyLittleThing. The system bypasses standard keyword search. Instead, an agentic assistant scans the shopper’s profile to align recommendations with their budget and preferences. The agentic assistant will ask follow-up questions to narrow down options and locate relevant stock. Once a user selects a product, the transaction occurs within the chat window. The backend automatically applies saved account credentials for delivery and payment, which removes the need to redirect customers to a separate mobile site or app. Business drivers for agentic AI in commerce The rationale follows transaction volume. Debenhams Group processes 16 percent of its sales through PayPal. Placing inventory discovery in a channel where a large segment of the customer base already operates allows the retailer to compress the sales funnel. Debenhams and PayPal co-developed the agentic AI project. While current testing focuses on select US customers, a wider release in both the US and *** is planned for later this year. In the US, the system also integrates with external tools such as Perplexity and Microsoft Copilot. Dan Finley, CEO of Debenhams Group, said: “At Debenhams Group, our goal is to help customers discover and be inspired by new products and brands, while making shopping as easy and enjoyable as possible. This kind of innovation has the potential to fundamentally transform online retail; in a way we haven’t seen since the shift to mobile shopping.” Finley added that the group is “proud to be the first *** retailer to partner with PayPal on this experience, bringing a faster, more intuitive way to shop to customers across our brands.” How Debenhams is integrating wider AI infrastructure The group recently partnered with Peak AI to improve forecasting across stock, sales, and pricing. An effective agentic AI deployment in commerce requires real-time inventory and pricing visibility to function without error. The Peak AI partnership indicates the group is establishing the data lineage needed to support automated interactions. Simultaneously, the company launched the Debenhams Group AI Skills Academy to train employees in applied AI, ensuring internal teams can manage these workflows. Mike Edmonds, VP of Agentic Commerce at PayPal, commented: “With agentic commerce, shopping becomes a conversation, not a search. By embedding AI-powered discovery and checkout directly into the PayPal app, we’re helping customers move seamlessly from inspiration to purchase, while giving retailers like Debenhams Group a powerful new way to engage shoppers at scale.” This agentic AI commerce deployment tests whether third-party platforms can capture high-intent traffic better than proprietary apps. Debenhams is positioning inventory where liquidity exists rather than forcing traffic to its own storefronts. Integrating discovery and payment into a single workflow reduces the steps between marketing and settlement. Success will depend on data accuracy and the ability of the agent to interpret queries without hallucination. See also: URBN tests agentic AI to automate retail reporting 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 Debenhams pilots agentic AI commerce via PayPal integration appeared first on AI News. View the full article

Retail decisions often depend on weekly performance reports, but compiling those reports can take hours of manual work. Urban Outfitters Inc. (URBN) is testing a new approach by using agentic AI systems to generate those reports automatically, changing routine analysis from staff to software. The retailer runs brands like Urban Outfitters, Anthropologie, and Free People, and has deployed AI systems that analyse store-level data and produce weekly summaries for merchandising teams. Instead of reviewing multiple spreadsheets or dashboards, staff receive a report that highlights patterns and areas that need attention. Industry coverage indicates the automation saves merchants from reviewing more than 20 separate reports each Sunday by synthesising the information into one overview. The goal is to reduce the time spent collecting and organising data before decisions are made. The rollout offers a practical example of how “agentic AI” is beginning to enter everyday enterprise operations. How agentic AI is taking over routine retail reporting Weekly reporting sits close to the core of retail management. Merchandising teams use these updates to monitor sales trends, check inventory movement, and decide where to adjust pricing, stock levels, or promotions. Because the process repeats in many stores and regions, it can consume a large share of operational time. URBN’s AI agents take over the structured parts of that workflow. The systems gather store data, organise results, and present a digestible summary for teams to review. Employees remain responsible for interpreting the findings and taking action, but the groundwork is handled automatically. This mirrors a change in enterprise AI adoption. Early deployments frequently aimed at helping individuals complete tasks faster, like drafting text or searching internal information. Instead, agentic systems run processes in the background and present completed outputs, allowing staff to focus on judgement not preparation. Retail analysts have pointed to growing interest in this model in the sector. Discussions at recent National Retail Federation events have highlighted how retailers are exploring autonomous AI workflows to support merchandising and operational monitoring at scale. URBN’s reporting automation shows how those ideas are moving into production environments not staying in pilot stages. Why reporting is an early target for automation Reporting is one of the first operational areas that many companies try to automate because it is based on organised data and predictable formats. Weekly summaries follow a repeatable pattern, making them easier to test using automation while keeping oversight in place. Starting with reporting allows URBN to evaluate how reliable the AI outputs are and how well teams adapt to receiving automated insights. If the system consistently produces accurate summaries, it can reduce delays between identifying trends and responding to them. The approach also highlights that automation does not remove accountability. Staff still review the reports and make final decisions, but they spend less time assembling information manually. A signal of changing enterprise priorities URBN’s rollout suggests that the next phase of enterprise AI adoption may be embedding automation into everyday workflows. Companies are asking increasingly whether AI can handle recurring operational tasks reliably enough to become part of normal business processes. When those tasks are automated successfully, the benefits extend beyond time savings. Consistent reporting can help ensure that teams in regions work from the same information, which may improve coordination and speed up responses to emerging issues. In large retail networks, even small improvements in how quickly insights reach decision-makers can influence stock management and sales performance. If reporting automation proves dependable, similar systems could expand into adjacent areas like demand forecasting, promotion analysis, or supply monitoring. Each step would follow the same pattern: automate the repeatable groundwork, keep people responsible for oversight and decisions. From AI assistance to agentic AI execution URBN’s use of agentic AI illustrates a gradual change in how enterprises are integrating artificial intelligence. AI is starting to run defined operational processes automatically while humans supervise results. The change moves AI from supporting individual productivity to shaping how work is organised. By starting with a recurring task like weekly reporting and keeping review firmly in human hands, URBN is testing how far automation can be trusted in real retail operations. For other enterprises watching the evolution of agentic systems, the lesson is practical, namely about deciding which everyday processes can be handed to software – and how to manage that transition. (Photo by Clark Street Mercantile) See also: Agentic AI drives finance ROI in accounts payable automation 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 URBN tests agentic AI to automate retail reporting appeared first on AI News. View the full article

An operational AI forecasting model developed by Hertfordshire University researchers aims to improve resource efficiency within healthcare. Public sector organisations often hold large archives of historical data that do not inform forward-looking decisions. A partnership between the University of Hertfordshire and regional NHS health bodies addresses this issue by applying machine learning to operational planning. The project analyses healthcare demand to assist managers with decisions regarding staffing, patient care, and resources. Most AI initiatives in healthcare focus on individual diagnostics or patient-level interventions. The project team notes that this tool targets system-wide operational management instead. This distinction matters for leaders evaluating where to deploy automated analysis within their own infrastructure. The model uses five years of historical data to build its projections. It integrates metrics such as admissions, treatments, re-admissions, bed capacity, and infrastructure pressures. The system also accounts for workforce availability and local demographic factors including age, gender, ethnicity, and deprivation. Iosif Mporas, Professor of Signal Processing and Machine Learning at the University of Hertfordshire, leads the project. The team includes two full-time postdoctoral researchers and will continue development through 2026. “By working together with the NHS, we are creating tools that can forecast what will happen if no action is taken and quantify the impact of a changing regional demographic on NHS resources,” said Professor Mporas. Using AI for forecasting in healthcare operations The model produces forecasts showing how healthcare demand is likely to change. It models the impact of these changes in the short-, medium-, and long-term. This capability allows leadership to move beyond reactive management. Charlotte Mullins, Strategic Programme Manager for NHS Herts and West Essex, commented: “The strategic modelling of demand can affect everything from patient outcomes including the increased number of patients living with chronic conditions. “Used properly, this tool could enable NHS leaders to take more proactive decisions and enable delivery of the 10-year plan articulated within the Central East Integrated Care Board as our strategy document.” The University of Hertfordshire Integrated Care System partnership funds the work, which began last year. Testing of the AI model tailored for healthcare operations is currently underway in hospital settings. The project roadmap includes extending the model to community services and care homes. This expansion aligns with structural changes in the region. The Hertfordshire and West Essex Integrated Care Board serves 1.6 million residents and is preparing to merge with two neighbouring boards. This merger will create the Central East Integrated Care Board. The next phase of development will incorporate data from this wider population to improve the predictive accuracy of the model. The initiative demonstrates how legacy data can drive cost efficiencies and shows that predictive models can inform “do nothing” assessments and resource allocation in complex service environments like the NHS. The project highlights the necessity of integrating varied data sources – from workforce numbers to population health trends – to create a unified view for decision-making. See also: Agentic AI in healthcare: How Life Sciences marketing could achieve $450B in value by 2028 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 forecasting model targets healthcare resource efficiency appeared first on AI News. View the full article

******* Mystery 2, commonly known as MM2, is often categorised as a simple social deduction game in the Roblox ecosystem. At first glance, its structure appears straightforward. One player becomes the *********, another the sheriff, and the remaining participants attempt to survive. However, beneath the surface lies a dynamic behavioural laboratory that offers valuable insight into how artificial intelligence research approaches emergent decision-making and adaptive systems. MM2 functions as a microcosm of distributed human behaviour in a controlled digital environment. Each round resets roles and variables, creating fresh conditions for adaptation. Players must interpret incomplete information, predict opponents’ intentions and react in real time. The characteristics closely resemble the types of uncertainty modelling that AI systems attempt to replicate. Role randomisation and behavioural prediction One of the most compelling design elements in MM2 is randomised role assignment. Because no player knows the ********* at the start of a round, behaviour becomes the primary signal for inference. Sudden movement changes, unusual positioning or hesitations can trigger suspicion. From an AI research perspective, this environment mirrors anomaly detection challenges. Systems trained to identify irregular patterns must distinguish between natural variance and malicious intent. In MM2, human players perform a similar function instinctively. The sheriff’s decision making reflects predictive modelling. Acting too early risks eliminating an innocent player. Waiting too long increases vulnerability. The balance between premature action and delayed response parallels risk optimisation algorithms. Social signalling and pattern recognition MM2 also demonstrates how signalling influences collective decision making. Players often attempt to appear non-threatening or cooperative. The social cues affect survival probabilities. In AI research, multi agent systems rely on signalling mechanisms to coordinate or compete. MM2 offers a simplified but compelling demonstration of how deception and information asymmetry influence outcomes. Repeated exposure allows players to refine their pattern recognition abilities. They learn to identify behavioural markers associated with certain roles. The iterative learning process resembles reinforcement learning cycles in artificial intelligence. Digital asset layers and player motivation Beyond core gameplay, MM2 includes collectable weapons and cosmetic items that influence player engagement. The items do not change fundamental mechanics but alter perceived status in the community. Digital marketplaces have formed around this ecosystem. Some players explore external environments when evaluating cosmetic inventories or specific rare items through services connected to an MM2 shop. Platforms like Eldorado exist in this broader virtual asset landscape. As with any digital transaction environment, adherence to platform rules and account security awareness remains essential. From a systems design standpoint, the presence of collectable layers introduces extrinsic motivation without disrupting the underlying deduction mechanics. Emergent complexity from simple rules The most insight MM2 provides is how simple rule sets generate complex interaction patterns. There are no elaborate skill trees or expansive maps. Yet each round unfolds differently due to human unpredictability. AI research increasingly examines how minimal constraints can produce adaptive outcomes. MM2 demonstrates that complexity does not require excessive features. It requires variable agents interacting under structured uncertainty. The environment becomes a testing ground for studying cooperation, suspicion, deception and reaction speed in a repeatable digital framework. Lessons for artificial intelligence modelling Games like MM2 illustrate how controlled digital spaces can simulate aspects of real world unpredictability. Behavioural variability, limited information and rapid adaptation form the backbone of many AI training challenges. By observing how players react to ambiguous conditions, researchers can better understand decision latency, risk tolerance and probabilistic reasoning. While MM2 was designed for entertainment, its structure aligns with important questions in artificial intelligence research. Conclusion ******* Mystery 2 highlights how lightweight multiplayer games can reveal deeper insights into behavioural modelling and emergent complexity. Through role randomisation, social signalling and adaptive play, it offers a compact yet powerful example of distributed decision making in action. As AI systems continue to evolve, environments like MM2 demonstrate the value of studying human interaction in structured uncertainty. Even the simplest digital games can illuminate the mechanics of intelligence itself. Image source: Unsplash The post What ******* Mystery 2 reveals about emergent behaviour in online games appeared first on AI News. View the full article

Finance leaders are driving ROI using agentic AI for accounts payable automation, turning manual tasks into autonomous workflows. While general AI projects saw return on investment rise to 67 percent last year, autonomous agents delivered an average ROI of 80 percent by handling complex processes without human intervention. This performance gap demands a change in how CIOs allocate automation budgets. Agentic AI systems are now advancing the enterprise from theoretical value to hard returns. Unlike generative tools that summarise data or draft text, these agents execute workflows within strict rules and approval thresholds. Boardroom pressure drives this pivot. A report by Basware and FT Longitude finds nearly half of CFOs face demands from leadership to implement AI across their operations. Yet 61 percent of finance leaders admit their organisations rolled out custom-developed AI agents largely as experiments to test capabilities rather than to solve business problems. These experiments often fail to pay off. Traditional AI models generate insights or predictions that require human interpretation. Agentic systems close the gap between insight and action by embedding decisions directly into the workflow. Jason Kurtz, CEO of Basware, explains that patience for unstructured experimentation is running low. “We’ve reached a tipping point where boards and CEOs are done with AI experiments and expecting real results,” he says. “AI for AI’s sake is a waste.” Accounts payable as the proving ground for agentic AI in finance Finance departments now direct these agents toward high-volume, rules-based environments. Accounts payable (AP) is the primary use case, with 72 percent of finance leaders viewing it as the obvious starting point. The process fits agentic deployment because it involves structured data: invoices enter, require cleaning and compliance checks, and result in a payment booking. Teams use agents to automate invoice capture and data entry, a daily task for 20 percent of leaders. Other live deployments include detecting duplicate invoices, identifying fraud, and reducing overpayments. These are not hypothetical applications; they represent tasks where an algorithm functions with high autonomy when parameters are correct. Success in this sector relies on data quality. Basware trains its systems on a dataset of more than two billion processed invoices to deliver context-aware predictions. This structured data allows the system to differentiate between legitimate anomalies and errors without human oversight. Kevin Kamau, Director of Product Management for Data and AI at Basware, describes AP as a “proving ground” because it combines scale, control, and accountability in a way few other finance processes can. The build versus buy decision matrix Technology leaders must next decide how to procure these capabilities. The term “agent” currently covers everything from simple workflow scripts to complex autonomous systems, which complicates procurement. Approaches split by function. In accounts payable, 32 percent of finance leaders prefer agentic AI embedded in existing software, compared to 20 percent who build them in-house. For financial planning and analysis (FP&A), 35 percent opt for self-built solutions versus 29 percent for embedded ones. This divergence suggests a pragmatic rule for the C-suite. If the AI improves a process shared across many organisations, such as AP, embedding it via a vendor solution makes sense. If the AI creates a competitive advantage unique to the business, building in-house is the better path. Leaders should buy to accelerate standard processes and build to differentiate. Governance as an enabler of speed Fear of autonomous error slows adoption. Almost half of finance leaders (46%) will not consider deploying an agent without clear governance. This caution is rational; autonomous systems require strict guardrails to operate safely in regulated environments. Yet the most successful organisations do not let governance stop deployment. Instead, they use it to scale. These leaders are significantly more likely to use agents for complex tasks like compliance checks (50%) compared to their less confident peers (6%). Anssi Ruokonen, Head of Data and AI at Basware, advises treating AI agents like junior colleagues. The system requires trust but should not make large decisions immediately. He suggests testing thoroughly and introducing autonomy slowly, ensuring a human remains in the loop to maintain responsibility. Digital workers raise concerns regarding displacement. A third of finance leaders believe job displacement is already happening. Proponents argue agents shift the nature of work rather than eliminating it. Automating manual tasks such as information extraction from PDFs frees staff to focus on higher-value activities. The goal is to move from task efficiency to operating leverage, allowing finance teams to manage faster closes and make better liquidity decisions without increasing headcount. Organisations that use agentic AI extensively report higher returns. Leaders who deploy agentic AI tools daily for tasks like accounts payable achieve better outcomes than those who limit usage to experimentation. Confidence grows through controlled exposure; successful small-scale deployments lead to broader operational trust and increased ROI. Executives must move beyond unguided experimentation to replicate the success of early adopters. Data shows that 71 percent of finance teams with weak returns acted under pressure without clear direction, compared to only 13 percent of teams achieving strong ROI. Success requires embedding AI directly into workflows and governing agents with the discipline applied to human employees. “Agentic AI can deliver transformational results, but only when it is deployed with purpose and discipline,” concludes Kurtz. See also: AI deployment in financial services hits an inflection point as Singapore leads the shift to production 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 Agentic AI drives finance ROI in accounts payable automation appeared first on AI News. View the full article

Author: Dev Pragad, CEO, Newsweek As artificial intelligence platforms increasingly mediate how people encounter news, media leaders are confronting an important change in the relationship between journalism and the public. AI-driven search and conversational interfaces now influence how audiences discover and trust information, often before visiting a publisher’s website. According to Dev Pragad, the implications for journalism extend beyond traffic metrics or platform optimisation. “AI has effectively become a front door to information, That changes how journalism is surfaced, how it is understood, and how publishers must think about sustainability.” AI is redefining news distribution For a long time, digital journalism relied on predictable referral patterns driven by search engines and social platforms. That model is now under strain as AI systems summarise reporting directly in their interfaces, reducing the visibility of original sources. While AI tools can efficiently aggregate information, Pragad argues they cannot replace the editorial judgement and accountability that define credible journalism. “AI can synthesise what exists,” he said. “Journalism exists to establish what is true.” This has prompted publishers to rethink distribution and the formats and institutional signals that distinguish professional reporting from automated outputs. Why publishers cannot rely on traffic alone One of the main challenges facing news organisations is the decoupling of audience understanding from direct website visits. Readers may consume accurate summaries of events without ever engaging with the reporting institution behind them. “That reality requires honesty from publishers. Traffic alone is not a stable foundation for sustaining journalism”, Pragad said. At Newsweek, this has led to an emphasis on revenue diversification, brand authority, and content formats that retain value even when summarised. Content AI cannot commoditise Pragad points to several forms of journalism that remain resistant to AI commoditisation: In-depth investigations Expert-led interviews and analysis Proprietary rankings and research Editorially-contextualised video journalism “These formats anchor reporting to accountable institutions,” he said. “They carry identity and credibility in ways that cannot be flattened into anonymous data.” Trust as editorial infrastructure As AI-generated content becomes more prevalent, trust has emerged as a defining competitive advantage for journalism. “When misinformation spreads easily and AI text becomes harder to distinguish from verified reporting, trust becomes infrastructure,” Pragad said. “It determines whether audiences believe what they read.” Editorial credibility is cumulative and fragile, he said. Once lost, it cannot be quickly rebuilt. The case for publisher-AI collaboration Rather than resisting AI outright, Pragad advocates for structured collaboration between publishers and technology platforms. That includes clearer attribution standards and fair compensation models when journalistic work is used to train or inform AI systems. “Journalism underpins the quality of AI outputs. If reporting weakens, AI degrades with it.” Leading Newsweek through industry transition Since taking leadership in 2018, Pragad has overseen Newsweek’s expansion in digital formats, global platforms, and diversified revenue streams. That evolution required acknowledging that legacy distribution models would not survive intact. “The goal isn’t to preserve old systems, it’s to preserve journalism’s role in society.” Redesigning, not resisting, the future of media Pragad believes the publishers best positioned for the AI era will be those that emphasise editorial identity and adaptability over scale alone. “This is not a moment for nostalgia, it’s a moment for redesign.” As AI continues to reshape how information is accessed, Pragad argues that the enduring value of journalism lies in its ability to explain and hold power accountable, regardless of the interface delivering the news. Author: Dev Pragad, CEO, Newsweek The post Newsweek CEO Dev Pragad warns publishers: adapt as AI becomes news gateway appeared first on AI News. View the full article