ChatGPT

The severe weather experienced at present in the US has placed significant strain on the airline industry in the country, with knock-on effects of changes to schedules and routes affecting the rest of the world. It’s at times like this that companies have to respond to queries from customers at a much greater rate than during normal operations, and there are – in the specific case of the air sector – operational decisions that need to be taken quickly, yet inside the strictest safety boundaries. Several airlines are turning to generative AI to help them during these types of events, and more generally, to help turn them into more efficient and reactive organisations. Last year, Air France-KLM built a cloud-based generative AI ‘factory’ for use throughout the organisation, which it described as letting it make AI development more consistent and reusable. It formed a partnership with Accenture and Google Cloud for its factory, using it to test and deploy generative AI models. It produces measurable outcomes in ground operations, engineering and maintenance, and customer-facing functions. The partnership group has stated that enterprise deployment of generative AI has increased development speed by more than 35%. The AI factory was built on earlier work undertaken by the airline and Accenture, which involved migrating core applications to the cloud. Since then, Air France-KLM has created a private AI assistant and RAG tools linking LLMs with internal search to support tasks like diagnosing and repairing aircraft damage. The factory is also used by employees, who get trained on how to use AI tools in order that they can use the power of LLMs to make a positive impact to the business. Weather and when AI is used United Airlines is similarly exploring AI in its operations. In an interview with CIO.com, CIO Jason Birnbaum described AI as a way to “shorten decision cycles” during irregular operations such as the recent outages caused by the current extreme cold snap. The company’s AI journey began with the use of AI to respond to passenger enquiries. When flights are delayed or cancelled, customer service representatives are expected to respond quickly and informatively, yet retain a company-mandated communication style – honed during the company’s ‘Every Flight Has A Story’ programme. During extended periods of disruption, maintaining the output from what the company terms ‘storytellers’ difficult. Jason Birnbaum said, “Considering the number of delays versus storytellers, we couldn’t have a person write a new message with every event. So we focused on prioritising the most impactful situations. […] The data piece was simple: the basic facts of the flight and the running chat between the attendants, pilots, gate agents, and the operations people associated with the flight. We fed that information — with additional data on weather, for example — into the AI model, to generate a good draft customer message.” “The trick then was to have it understand the nuances of United Airlines’ communications style and what we wanted to emphasise. That’s where prompt engineering came in, not to train the model to understand flight data, but to use the words United prefers. Let’s take safety, for instance. We can emphasise safety with without scaring people, and the AI tool is learning to make the right word choice. […] The AI model was very good at looking back in time to bring previous flight data into the current situation. Even our human storytellers didn’t include reasons for flight delays, and that kind of information can be very useful to a customer.” Boston Consulting Group’s measure of AI maturity in industries pegs airlines at ‘average’, having moved from slightly below average in the past year. Only one of the 36 airlines surveyed met the highest criteria for being prepared for an AI-enabled future. The analysis suggests that by 2030, carriers that embed AI at the core of their workflows could achieve operating margins that are 5% to 6% points higher than those of peers. It’s thought that generative AI will become part of the operational core of airlines and airports, where decisions about schedules, crew allocations, aircraft rotations, and passenger recovery have to be made quickly. Microsoft claims data-driven AI systems can reduce the root causes of flight delays by up to 35% through improved disruption forecasting, which can limit the negative effects of the spread of disruption. Airlines using AI-driven personalisation report revenue increases of around 10% to 15% per passenger, according to Microsoft, which also says that AI-based tools such as self-service customer interfaces can lead to cost reductions of up to 30%. (Image source: “airplane” by Kuster & Wildhaber Photography is licensed under CC BY-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 Cold snap highlight’s airlines’ proactive use of AI appeared first on AI News. View the full article

Enterprise AI has moved from isolated prototypes to systems that shape real decisions: drafting customer responses, summarising internal knowledge, generating code, accelerating research, and powering agent workflows that can trigger actions in business systems. That creates a new security surface, one that sits between people, proprietary data, and automated execution. AI security tools exist to make those questions operational. Some focus on governance and discovery. Others harden AI applications and agents at runtime. Some emphasise testing and red teaming before deployment. Others help security operations teams handle the new class of alerts AI introduces in SaaS and identity layers. What counts as an “AI security tool” in enterprise environments? “AI security” is an umbrella term. In practice, tools tend to fall into a few functional buckets, and many products cover more than one. AI discovery & governance: identifies AI use in employees, apps, and third parties; tracks ownership and risk LLM & agent runtime protection: enforces guardrails at inference time (prompt injection defenses, sensitive data controls, tool-use restrictions) AI security testing & red teaming: tests models and workflows against adversarial techniques before (and after) production release AI supply chain security: assesses risks in models, datasets, packages, and dependencies used in AI systems SaaS & identity-centric AI risk control: manages risk where AI lives inside SaaS apps and integrations, permissions, data exposure, account takeover, risky OAuth scopes A mature AI security programme typically needs at least two layers: one for governance and discovery, and another for runtime protection or operational response, depending on whether your AI footprint is primarily “employee use” or “production AI apps.” Top 10 AI security tools for enterprises in 2026 1) Koi Koi is the best AI security tool for enterprises because of its approach to AI security from the software control layer, helping enterprises govern what gets installed and adopted in endpoints, including AI-adjacent tooling like extensions, packages, and developer assistants. The matters because AI exposure often enters through tools that look harmless: browser extensions that read page content, IDE add-ons that access repositories, packages pulled from public registries, and fast-moving “helper” apps that become embedded in daily workflows. Rather than treating AI security as a purely model-level concern, Koi focuses on controlling the intake and spread of tools that can create data exposure or supply chain risk. In practice, that means turning ad-hoc installs into a governed process: visibility into what’s being requested, policy-based decisions, and workflows that reduce shadow adoption. For security teams, it provides a way to enforce consistency in departments without relying on manual policing. Key features include: Visibility into installed and requested tools in endpoints Policy-based allow/block decisions for software adoption Approval workflows that reduce shadow AI tooling sprawl Controls designed to address extension/package risk and tool governance Evidence trails for what was approved, by whom, and under what policy 2) Noma Security Noma Security is often evaluated as a platform for securing AI systems and agent workflows at the enterprise level. It focuses on discovery, governance, and protection of AI applications in teams, especially when multiple business units deploy different models, pipelines, and agent-driven processes. A key reason enterprises shortlist tools like Noma is scale: once AI adoption spreads, security teams need a consistent way to understand what exists, what it touches, and which workflows represent elevated risk. That includes mapping AI apps to data sources, identifying where sensitive information may flow, and applying governance controls that keep pace with change. Key features include: AI system discovery and inventory in teams Governance controls for AI applications and agents Risk context around data access and workflow behaviour Policies that support enterprise oversight and accountability Operational workflows designed for multi-team AI environments 3) Aim Security Aim Security is positioned around securing enterprise adoption of GenAI, especially the use layer where employees interact with AI tools and where third-party applications add embedded AI features. The makes it particularly relevant for organisations where the most immediate AI risk is not a custom LLM app, but workforce use and the difficulty of enforcing policy in diverse tools. Aim’s value tends to show up when enterprises need visibility into AI use patterns and practical controls to reduce data exposure. The goal is to protect the business without blocking productivity: enforce policy, guide use, and reduce unsafe interactions while preserving legitimate workflows. Key features include: Visibility into enterprise GenAI use and risk patterns Policy enforcement to reduce sensitive data exposure Controls for third-party AI tools and embedded AI features Governance workflows aligned with enterprise security needs Central management in distributed user populations 4) Mindgard Mindgard stands out for AI security testing and red teaming, helping enterprises pressure-test AI applications and workflows against adversarial techniques. The is especially important for organisations deploying RAG and agent workflows, where risk often comes from unexpected interaction effects: retrieved content influencing instructions, tool calls being triggered in unsafe contexts, or prompts leaking sensitive context. Mindgard’s value is proactive: instead of waiting for issues to surface in production, it helps teams identify weak points early. For security and engineering leaders, this supports a repeatable process, similar to application security testing, where AI systems are tested and improved over time. Key features include: Automated testing and red teaming for AI workflows Coverage for adversarial behaviours like injection and jailbreak patterns Findings designed to be actionable for engineering teams Support for iterative testing in releases Security validation aligned with enterprise deployment cycles 5) Protect AI Protect AI is often evaluated as a platform approach that spans multiple layers of AI security, including supply chain risk. The is relevant for enterprises that depend on external models, libraries, datasets, and frameworks, where risk can be inherited through dependencies not created internally. Protect AI tends to appeal to organisations that want to standardise security practices in AI development and deployment, including the upstream components that feed into models and pipelines. For teams that have both AI engineering and security responsibilities, that lifecycle perspective can reduce gaps between “build” and “secure.” Key features include: Platform coverage in AI development and deployment stages Supply chain security focus for AI/ML dependencies Risk identification for models and related components Workflows designed to standardise AI security practices Support for governance and continuous improvement 6) Radiant Security Radiant Security is oriented toward security operations enablement using agentic automation. In the AI security context, that matters because AI adoption increases both the number and novelty of security signals, new SaaS events, new integrations, new data paths, while SOC bandwidth stays limited. Radiant focuses on reducing investigation time by automating triage and guiding response actions. The key difference between helpful automation and dangerous automation is transparency and control. Platforms in this category need to make it easy for analysts to understand why something is flagged and what actions are being recommended. Key features include: Automated triage designed to reduce analyst workload Guided investigation and response workflows Operational focus: reducing noise and speeding decisions Integrations aligned with enterprise SOC processes Controls that keep humans in the loop where needed 7) Lakera Lakera is known for runtime guardrails that address risks like prompt injection, jailbreaks, and sensitive data exposure. Tools in this category focus on controlling AI interactions at inference time, where prompts, retrieved content, and outputs converge in production workflows. Lakera tends to be most valuable when an organisation has AI applications that are exposed to untrusted inputs or where the AI system’s behaviour must be constrained to reduce leakage and unsafe output. It’s particularly relevant for RAG apps that retrieve external or semi-trusted content. Key features include: Prompt injection and jailbreak defense at runtime Controls to reduce sensitive data exposure in AI interactions Guardrails for AI application behaviour Visibility and governance for AI use patterns Policy tuning designed for enterprise deployment realities 8) CalypsoAI CalypsoAI is positioned around inference-time protection for AI applications and agents, with emphasis on securing the moment where AI produces output and triggers actions. The is where enterprises often discover risk: the model output becomes input to a workflow, and guardrails must prevent unsafe decisions or tool use. In practice, CalypsoAI is evaluated for centralising controls in multiple models and applications, reducing the burden of implementing one-off protections in every AI project. The is particularly helpful when different teams ship AI features at different speeds. Key features include: Inference-time controls for AI apps and agents Centralised policy enforcement in AI deployments Security guardrails designed for multi-model environments Monitoring and visibility into AI interactions Enterprise integration support for SOC workflows 9) Cranium Cranium is often positioned around enterprise AI discovery, governance, and ongoing risk management. Its value is particularly strong when AI adoption is decentralised and security teams need a reliable way to identify what exists, who owns it, and what it touches. Cranium supports the governance side of AI security: building inventories, establishing control frameworks, and maintaining continuous oversight as new tools and features appear. The is especially relevant when regulators, customers, or internal stakeholders expect evidence of AI risk management practices. Key features include: Discovery and inventory of AI use in the enterprise Governance workflows aligned with oversight and accountability Risk visibility in internal and third-party AI systems Support for continuous monitoring and remediation cycles Evidence and reporting for enterprise AI programmes 10) Reco Reco is best known for SaaS security and identity-driven risk management, which is increasingly relevant to AI because so much “AI exposure” exists inside SaaS tools, copilots, AI-powered features, app integrations, permissions, and shared data. Rather than focusing on model behaviour, Reco helps enterprises manage the surrounding risks: account compromise, risky permissions, exposed files, overintegrations, and configuration drift. For many organisations, reducing AI risk starts with controlling the platforms where AI interacts with data and identity. Key features include: SaaS security posture and configuration risk management Identity threat detection and response for SaaS environments Data exposure visibility (files, sharing, permissions) Detection of risky integrations and access patterns Workflows aligned with enterprise identity and security operations Why AI security matters for enterprises AI creates security issues that don’t behave like traditional software risk. The three drivers below are why many enterprises are building dedicated AI security abilities. 1) AI can turn small mistakes into repeated leakage A single prompt can expose sensitive context: internal names, customer details, incident timelines, contract terms, design decisions, or proprietary code. Multiply that in thousands of interactions, and leakage becomes systematic not accidental. 2) AI introduces a manipulable instruction layer AI systems can be influenced by malicious inputs, direct prompts, indirect injection through retrieved content, or embedded instructions inside documents. A workflow may “look normal” while being steered into unsafe output or unsafe actions. 3) Agents expand blast radius from content to execution When AI can call tools, access files, trigger tickets, modify systems, or deploy changes, a security problem is not “wrong text.” It becomes “wrong action,” “wrong access,” or “unapproved execution.” That’s a different level of risk, and it requires controls designed for decision and action pathways, not just data. The risks AI security tools are built to address Enterprises adopt AI security tools because these risks show up fast, and internal controls are rarely built to see them end-to-end: Shadow AI and tool sprawl: employees adopt new AI tools faster than security can approve them Sensitive data exposure: prompts, uploads, and RAG outputs can leak regulated or proprietary data Prompt injection and jailbreaks: manipulation of system behaviour through crafted inputs Agent over-permissioning: agent workflows get excessive access “to make it work” Third-party AI embedded in SaaS: features ship inside platforms with complex permission and sharing models AI supply chain risk: models, packages, extensions, and dependencies bring inherited vulnerabilities The best tools help you turn these into manageable workflows: discovery → policy → enforcement → evidence. What Strong Enterprise AI Security Looks Like AI security succeeds when it becomes a practical operating model, not a set of warnings. High-performing programmes typically have: Clear ownership: who owns AI approvals, policies, and exceptions Risk tiers: lightweight governance for low-risk use, stronger controls for systems touching sensitive data Guardrails that don’t break productivity: strong security without constant “security vs business” conflict Auditability: the ability to show what is used, what is allowed, and why decisions were made Continuous adaptation: policies evolve as new tools and workflows emerge This is why vendor selection matters. The wrong tool can create dashboards without control, or controls without adoption. How to choose AI security tools for enterprises Avoid the trap of buying “the AI security platform.” Instead, choose tools based on how your enterprise uses AI. Map your AI footprint first Is most use employee-driven (ChatGPT, copilots, browser tools)? Are you building internal LLM apps with RAG, connectors, and access to proprietary knowledge? Do you have agents that can execute actions in systems? Is AI risk mostly inside SaaS platforms with sharing and permissions? Decide what must be controlled vs observed Some enterprises need immediate enforcement (block/allow, DLP-like controls, approvals). Others need discovery and evidence first. Prioritise integration and operational fit A great AI security tool that can’t integrate into identity, ticketing, SIEM, or data governance workflows will struggle in enterprise environments. Run pilots that mimic real workflows Test with scenarios your teams actually face: Sensitive data in prompts Indirect injection via retrieved documents User-level vs admin-level access differences An agent workflow that has to request elevated permissions Choose for sustainability The best tool is the one your teams will actually use after month three, when the novelty wears off and real adoption begins. Enterprises don’t “secure AI” by declaring policies. They secure AI by building repeatable control loops: discover, govern, enforce, validate, and prove. The tools above represent different layers of that loop. The best choice depends on where your risk concentrates, workforce use, production AI apps, agent execution pathways, supply chain exposure, or SaaS/identity sprawl. Image source: Unsplash The post Top 10 AI security tools for enterprises in 2026 appeared first on AI News. View the full article

Big retailers are committing more heavily to agentic AI-led commerce, and accepting some loss of customer proximity and data control in the process. As reported by Retail Dive, the opening weeks of 2026 have seen Etsy, Target and Walmart push product ranges onto third-party AI platforms, forming new partnerships with Google’s Gemini and Microsoft’s Copilot, after last year’s collaborations with OpenAI’s ChatGPT. These let consumers purchase goods inside the AI’s conversation interface. Amazon and Walmart have been investing in their own consumer-facing AI assistants, Rufus and Sparky respectively to change how shoppers interact with their brands. Agentic AI is beginning to redraw direct-to-consumer engagement, and industry figures regard this trend as an important moment in online retail. “I think this has the potential to disrupt retail in the same way the internet once did,” Kartik Hosanagar, a marketing professor at the Wharton School of the University of Pennsylvania, told the website’s reporters. Partnering with AIs like ChatGPT or Gemini engages consumers wherever they happen to be and may choose to shop. Adobe’s 2025 Holiday Shopping report found that AI-driven traffic to US e-commerce sites grew 758% year on year between in November 2025, and Cyber Monday saw a 670% increase in AI-referred retail visits. “What we expect is a deepening of consumer engagement,” Katherine ******, a partner at Kearney specialising in food, drug and mass-market retail, said in an email to Retail Dive. “More shoppers will rely on AI for purchasing, and across a wider range of missions. As retailers’ capabilities within these tools improve, adoption should accelerate further.” Meeting customers on AI platforms comes with trade-offs, according to industry observers, with questions around data ownership and the risk that retailers are sidelined. 81% of retail executives believe generative AI will erode brand loyalty by 2027, according to Deloitte’s 2026 Retail Industry Global Outlook, published earlier this month. Retailers’ websites or apps provide a stream of behavioural data, and if discovery, evaluation, and purchase happen externally, any insight doesn’t reach the retailer. “This fundamentally changes where power sits,” Hosanagar said. “Control over the agent increasingly means control over the customer relationship.” Google and Alphabet CEO Sundar Pichai has unveiled new commerce tools for Gemini, outlining how it will support customers from discovery to final purchase. Nikki Baird, vice president of strategy and product at Aptos, says this raises difficult questions. “What he’s describing is Google owning the data across discovery, decision and transaction. Even if some information is shared back, missing context from those stages leaves retailers with a much poorer understanding of their customers.” Pichai reassured retailers collaboration remains central to Google. “From nearly three decades of working with retailers, we know success only comes when we work together,” he told an NRF audience. “Our aim is to use our full technology stack to help shape the next era of retail.” Yet agentic systems’ features like instant checkout absorb the shopping experience into one platform. “If research, discovery and purchase all happen on OpenAI rather than Walmart.com, you’re effectively giving away the brand experience. At that point, the retailer risks becoming little more than a fulfilment operation,” Hosanagar said. Amazon has not announced plans to sell directly through ChatGPT, doubling down on its own AI initiatives. Earlier this month, the company launched a dedicated site for Alexa+, its generative AI assistant that helps users research and plan purchases. Yet participation in third-party AI commerce may become unavoidable. When OpenAI launched its Instant Checkout feature on ChatGPT last September, it suggested that enabling the function could influence how merchants are ranked in search results, in addition to price and product quality. Uploading product catalogues to AI chat platforms may be the first step in a transformation of online retail. According to Deloitte, roughly half of retail executives expect the current multi-stage shopping process to reduce to a single AI-driven interaction by 2027. For now the industry remains at an early stage of any transition. “The real inflection point is when consumers rely on an autonomous agent to shop on their behalf,” Hosanagar told Retail Dive. “Retailers will engage less with humans directly and more with their representatives — AI agents. That agent processes information differently, requires data in new formats and responds to persuasion in ways unlike a person.” Today, consumers can access ChatGPT on their phones while in-store, effectively consulting an always-available expert. “It’s not just the internet in your pocket,” Baird told Retail Dive. “It’s like having a highly knowledgeable store associate who knows every retailer.” This may prompt retailers to equip frontline staff with their own AI tools, offering instant insight into customer preferences or shopping history. Alternatively, a retailer’s AI agent could proactively notify customers when a favoured item is back in stock, helping associates convert interest into sales. “The goal is to enable store associates to perform at their best,” Baird said. (Image source: “Shopping trauma!” by Elsie esq. is licensed under CC BY 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 Retailers examine options for on-AI retail appeared first on AI News. View the full article

AI continues to reshape technology and business; yet for the network, enterprise connectivity in the AI age means being always-on, and extra vigilant for sovereignty and security besides. This means that speed is not the only requirement. As Julian Skeels, chief digital officer at Expereo notes, it is more about ‘certainty.’ “AI workloads are distributed, they’re continuous, they’re incredibly latency-sensitive. Inference, monitoring, retrieval and remediation never stop, so that changes the network’s role,” says Skeels. “In the world of AI, networking actually becomes a system dependency,” he adds. “When the network degrades, the application degrades immediately. “An AI-ready network needs to make data movement deterministic. It’s not just about it being fast; it’s about it being predictable, and observable, and governable, and resilient – and to do all those things under continual change.” Many CIOs, however, are struggling right now with what Skeels describes as ‘connectivity everywhere but visibility nowhere.’ “They’re dealing with hybrid networks, multiple clouds, multiple providers and portals that create a constant operational drag to their teams,” says Skeels. “What they want is clarity and control – not more tools.” Skeels arrived at Expereo last year with myriad cross-industry experience in product and digital transformation initiatives under his belt. He found an industry ripe for accelerative change, and a company determined to lead the way and ensure pricing global connectivity should take minutes rather than weeks. “When I came to Expereo, I saw that global connectivity has, I would say, largely resisted real digital transformation for a long time,” notes Skeels. “Most customers will still experience it as slow, and manual, and opaque, and fragmented across the dozens of providers and portals they need to work with. “We believe, though, that with emerging technologies such as agentic AI, that’s finally changing,” adds Skeels. “Our ambition here is to make global connectivity as simple, and immediate, and transparent as cloud computing is for our customers.” Enabling such change for customers requires that mix of speed and visibility – and this is where the expereoOne platform comes in, to provide what the company calls ‘visibility at the speed of life’ and give customers a single, global view of what is being deployed, how it is performing, and what it costs. Beyond visibility, customers also need proactivity, as Skeels explains. “We’re deeply integrated into our customers’ order management, their ITSM, their ERP systems, which makes working with Expereo at scale absolutely seamless,” he says. “The key point is that better visibility isn’t about more dashboards. It’s about connecting network behaviour to their business outcomes in terms of resilience, security experience, and cost.” Skeels is speaking at the Digital Transformation Expo Global on February 4-5 around designing the AI-ready network – and his session promises to subvert the usual advice for those in attendance. “I want to challenge a few things,” notes Skeels. “I want to ask people to consider even unlearning things they’ve learned in the past. “A lot of what we’ve taken for granted about networks no longer holds in an AI world.” Watch the full conversation between Julian Skeels and TechEx’s James Bourne below: Photo by Pixabay The post Expereo: Enterprise connectivity amid AI surge with ‘visibility at the speed of life’ appeared first on AI News. View the full article

Formula E is using Google Cloud AI to meet its net zero targets by driving efficiency across its global logistics and commercial operations. As part of an expanded multi-year agreement, the electric racing series will integrate Gemini models into its ecosystem to support performance analysis, back-office workflows, and event logistics. The collaboration demonstrates how sports organisations are utilising cloud infrastructure to drive tangible business outcomes, rather than just securing surface-level sponsorship. The partnership focuses on optimising business operations, ranging from race management to the fan experience. Operational twins and carbon data to achieve net zero targets While marketing visibility often drives sports partnerships, this agreement builds on a technical foundation first formalised in January 2025. The elevation to “Principal Partner” involves Formula E adopting Google Cloud technologies for business-critical functions. The immediate application involves optimising the complex logistics of a global championship. Advanced AI modelling of the back office and the creation of race and event digital twins allow the organisation to simulate and optimise site builds virtually. This application directly affects Scope 3 emissions. The capability to plan infrastructure virtually minimises the need for physical on-site reconnaissance and reduces the transport of heavy equipment. For a championship that is the only sport-certified net zero carbon entity since inception, maintaining this status requires finding efficiencies in the supply chain. The digital twin approach delivers a quantifiable reduction in the operational carbon footprint while maintaining performance. Beyond logistical modelling, the Google Cloud AI partnership extends into the workforce productivity layer. Formula E is deploying Google Workspace with Gemini AI to enable greater agility and efficiency across its organisation. The organisation intends to use these tools to accelerate performance and deliver faster operations. This reflects a broader trend where generative AI tools are provisioned to reduce administrative latency in distributed workforces. The viability of these implementations to achieve net zero targets is supported by previous collaborative projects. Formula E recently utilised Google’s AI Studio and Gemini models to execute the ‘Mountain Recharge’ initiative. Engineers used the models to map an optimal route for the GENBETA car during a mountain descent. The AI identified and analysed specific braking zones, calculating the necessary regenerative braking required to harvest enough energy to complete a full lap of the Monaco circuit subsequently. This specific use case demonstrates how high-dimensional data – including topography, friction, and energy consumption – can be processed to define physical execution. Using Google Cloud AI to enhance Formula E’s data product The partnership also addresses the commercial requirement to retain and grow a digital audience. Formula E has integrated a ‘Strategy Agent’ into its live broadcasts. This tool processes real-time data to provide viewers with tailored insights and predictions regarding race strategy and driver performance. Millions of viewers have utilised these insights, which explain complex race dynamics as they unfold. This mirrors the enterprise challenge of observability (i.e. taking vast streams of real-time technical data and synthesising them into understandable narratives for stakeholders.) Beyond helping to achieve net zero targets, the leadership at both organisations frames this expansion as a necessary evolution of their technical stack. Jeff Dodds, CEO of Formula E, said: “Our expanded partnership with Google Cloud is a true game-changer for Formula E and for motorsport as a whole. We are already pushing the boundaries of technology in sport, and this Principal Partnership confirms our vision. “The integration of Google Cloud’s AI capabilities will unlock a new dimension of real-time performance optimisation and strategic decision-making, both for the Championship and for our global broadcast audience. This collaboration will redefine how fans experience our races and set a new benchmark for technology integration in sport worldwide.” Tara Brady, President of Google Cloud EMEA, added: “Formula E is a hub of innovation, where milliseconds can define success. This expanded partnership is a testament to the power of Google Cloud’s AI and data analytics, showing how our technology can deliver a competitive advantage in the most demanding scenarios.” The progression from the initial partnership in January 2025 to this expanded scope suggests the pilot programs provided sufficient ROI to warrant a broader rollout. As organisations face pressure to balance performance with net zero targets, the use of virtual simulation to optimise physical deployment remains a high-value area for investment. See also: Controlling AI agent sprawl: The CIO’s guide to governance Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post How Formula E uses Google Cloud AI to meet net zero targets appeared first on AI News. View the full article

For many organisations, the AI debate has moved on from whether to adopt the technology to a harder question: why do the results feel uneven? New tools are in place, pilots are running, and budgets are rising, yet clear AI returns remain elusive. According to Cloudflare’s 2026 App Innovation Report, the difference often has less to do with AI itself and more to do with the state of the applications underneath it. The report, based on a survey of more than 2,300 senior leaders in APAC, EMEA, and the Americas, points to application modernisation as the clearest divider between organisations seeing real AI value and those still struggling. Companies that are ahead of schedule in modernising their applications are nearly three times more likely to report a clear payoff from their AI investments. In APAC, the link is even more explicit: 92% of leaders say updating their software was the single most important factor in improving their AI abilities. Modernisation, not experimentation, drives AI returns The finding re-frames AI success as a foundation problem not a tooling problem. AI systems depend on fast access to data, flexible architectures, and reliable integration points. Legacy applications, fragmented infrastructure, and brittle workflows make it harder for AI projects to move beyond isolated use cases. Modernised applications, by contrast, give organisations room to experiment, scale, and adapt without constant rework. The report describes this relationship as a reinforcing cycle. Organisations modernise applications to support AI, then use AI results to justify deeper modernisation. Leaders in this group report far higher confidence that their infrastructure can support AI development, and that confidence translates into action. In APAC, 90% of leading organisations have already integrated AI into existing applications, compared with much lower levels among those behind schedule. Around 80% plan to increase that integration further over the next year. The shift marks a change in mindset, as earlier waves of AI adoption focused on testing and pilots. Now, the emphasis is on integration. AI is not treated as a standalone project but as part of everyday systems, from internal workflows to customer-facing applications. The report shows that leading organisations are using AI to improve internal processes, build content-driven applications, and support revenue-generating work, while lagging organisations remain more cautious and fragmented in their approach. The cost of delay shows up in security and confidence The cost of falling behind is becoming clearer as well. Organisations that lag on modernisation tend to modernise reactively, often after a security incident or operational failure. In APAC, these organisations report lower confidence in both their infrastructure and their teams’ ability to support AI. That lack of confidence slows decision-making and limits how far AI projects can go. Instead of expanding use cases, teams spend time managing risk, fixing gaps, and dealing with technical debt. Security plays a central role in this dynamic. The report shows that organisations with strong alignment between security and application teams are far more likely to scale AI successfully. Where that alignment is weak, security issues consume time and attention, pushing modernisation and AI work further down the priority list. Many lagging organisations report difficulty tracking risks in applications and APIs, which makes it harder to move quickly without increasing exposure. For leaders, security is treated as part of application design not an add-on. That approach reduces the amount of reactive work needed after incidents and frees teams to focus on building and improving systems. Over time, this also lowers the operational drag that can stall AI efforts. The report suggests that reliability has become a practical limit on speed: organisations that cannot maintain stable, secure systems struggle to move AI projects into production. Fewer tools, clearer foundations, faster AI integration Another pressure point highlighted in the APAC data is tool sprawl. Nearly all organisations report challenges in managing large and complex technology stacks, but leaders are responding more aggressively. About 86% of APAC leaders say they are actively cutting redundant tools and addressing shadow IT. The goal is not just cost control, but clarity. Fewer platforms and integrations make it easier to modernise applications, apply consistent security controls, and integrate AI without friction. Developer time is also a factor. In organisations with a modernised foundation, developers spend more time maintaining and improving systems that already work. In lagging organisations, developers are more likely to rebuild from scratch or spend time on configuration and remediation. That difference affects how quickly new AI abilities can be introduced and refined. When teams are tied up fixing problems, AI becomes harder to prioritise. Taken together, the findings suggest that AI success is less about racing to deploy new models and more about removing the obstacles that slow everything else down. Application modernisation creates the conditions for AI to deliver value, while fragmented systems and reactive practices limit what AI can achieve. Without that foundation, organisations find it harder to turn AI investment into measurable AI returns. For APAC organisations, the message is that AI investment without modernisation tends to produce shallow results. Modernisation without integration plans risks becoming an ongoing rebuild. The organisations seeing the strongest returns are those that treat application updates, security alignment, and AI integration as connected work, not separate initiatives. The report does not suggest a single path forward, but it does draw a clear line between organisations that act early and those that wait. The advantage not comes from having AI, but from having applications ready to use it. (Photo by Julio Lopez) See also: Controlling AI agent sprawl: The CIO’s guide to governance Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. 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 Modernising apps triples the odds of AI returns, Cloudflare says appeared first on AI News. View the full article

Anthropic’s Economic Index offers a look at how organisations and individuals are actually using large language models. The report contains the company’s analysis of a million consumer interactions on Claude.ai, plus a million enterprise API calls, all dated from November 2025. The report notes that its figures are based on observations, rather than, for example, a sample of business decision-makers or generic survey. Limited use cases dominate Use of Anthropic’s AI tends to cluster around a relatively small number of tasks, with the ten most frequently-performed tasks accounting for almost a quarter of consumer interactions, and nearly a third of enterprise API traffic. There’s a focus on the use of Claude for code creation and modification, as readers might expect. This concentration of use of AI as a software development tool has remained fairly constant over time, suggesting that the model’s value is largely based around these types of tasks, with no emerging use of Claude for other purposes of any empirical significance. This suggests that broad, general rollouts of AI are less likely to be successful than those focused on tasks where large language models are proven to be effective. Augmentation outperforms automation On consumer platforms, collaborative use – where users iterate on queries to the AI over the course of a virtual conversation – is more common than using the AI to produce automated workflows. Enterprise API usage shows the opposite, as businesses attempt to gain savings through automating tasks. However, while Claude succeeds on shorter tasks, the observed quality of outcomes declines the more complex the task (or series of tasks) is, and the longer the required ‘thinking time’ required. This implies automation is most effective for routine, well-defined tasks that are simpler, require fewer logical steps, and where responses to queries can be quick. Tasks estimated to take humans several hours show significantly lower completion rates than shorter tasks. For longer tasks to succeed, users have to iterate and correct outputs. Users breaking down large tasks into manageable steps and posing each separately (either interactively or via API) have improved success rates. The company’s observations show most queries put to the LLMs are associated with white-collar roles (although poorer countries tend to use Claude in academic settings more commonly than, for instance, the US). For example, travel agents can lose complex planning tasks to the LLM and retain elements of their more transactional work, while some roles, such as property managers, show the opposite: routine administrative tasks can be handled by the AI, and tasks needing higher-judgement remain with the human professional.. Productivity gains lessened by reliability The report notes that claims of AI boosting annual labour productivity by 1.8% (over a decade) are likely best to be reduced to 1-1.2%, due to the need to factor in extra labour and costs. While a 1% efficiency gain over a decade is still economically meaningful, the need for activities such as validation, error handling, and reworking will lower success rates and therefore there should be a similar adjustment in the minds of a business’s decision-makers. Potential gains to an organisation deploying AI also depend on whether tasks given to the LLM complement or substitute work. In the latter case, the success of substituting an AI for tasks normally done by a human depends on how complex the work is. It’s noteworthy that the report finds a near-perfect correlation between the sophistication of users’ prompts to the LLM and successful outcomes. Thus, how people use AI shapes what it delivers. Key takeaways for leaders AI implementation delivers value fastest in specific, well-defined areas. Complementary systems (AI+human) outperform full automation for complex work. Reliability and necessary extra work ‘around’ the AI reduce predicted productivity gains. Changes to workforces’ makeup depend on the mix of tasks and their complexity, not specific job roles. (Image source: “the virtual construction worker” by antjeverena 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 Anthropic’s usage stats paint a detailed picture of AI success appeared first on AI News. View the full article

Cyber threats don’t follow predictable patterns, forcing security teams to rethink how protection works at scale. Defensive AI is emerging as a practical response, combining machine learning with human oversight. Cybersecurity rarely fails because teams lack tools. It fails because threats move faster than detection can keep pace. As digital systems expand, attackers adapt in real time while static defences fall behind. This reality explains why AI security explained has become a central topic in modern cyber defense conversations. Why cyber defense needs machine learning now Attack techniques today are fluid. Phishing messages change wording in hours. Malware alters behaviour to avoid detection. Rule-based security struggles in this environment. Machine learning fills this void by learning how systems are expected to behave. In other words, it does not wait for a recognised pattern but searches for something that does not seem to fit. The is important when a threat is either new or camouflaged. For security teams, this change reduces blind spots. Machine learning processes data volumes that no human team could review manually. It connects subtle signals in networks, endpoints and cloud services. You see the benefit when response times shrink. Early detection limits damage. Faster containment protects data and continuity. In global environments, that speed often determines whether an incident stays manageable. How defensive AI identifies threats in real time Machine learning models are interested in behaviour and not in assumptions. Models learn by observing how users and applications interact. When activity breaks from expected patterns, alerts surface. This approach works even when the threat has never appeared before. Zero-day attacks really become visible because behaviour, not history, triggers concern. Common detection techniques include: Behavioural base-lining to spot unusual activity Anomaly detection in network and application traffic Classification models trained on diverse threat patterns Real-time analysis is essential. Modern attacks spread quickly in interconnected systems. Machine learning continuously evaluates streaming data, letting security teams react before damage escalates. This ability proves especially valuable in cloud environments. Resources change constantly. Traditional perimeter defences lose relevance. Behaviour-based monitoring adapts as systems evolve. Embedding defense across the AI security lifecycle Effective cyber defense does not start at deployment. It begins earlier and continues throughout a system’s lifespan. Machine learning technology evaluates development configurations and dependencies during development. High-risk configuration items and exposed services are identified before deployment to production. That makes them less exposed in the long run. Once systems go live, monitoring shifts to runtime behaviour. Access requests, inference activity and data flows receive constant attention. Unusual patterns prompt investigation. Post-deployment oversight remains critical. Use patterns change. Models age. Defensive AI detects drift that may signal misuse or emerging vulnerabilities. The lifecycle view reduces fragmentation. Security becomes consistent in stages not reactive after incidents occur. Over time, that consistency builds operational confidence. Defensive AI in complex enterprise environments Enterprise infrastructure rarely exists in one place. Cloud platforms, remote work and third-party services increase complexity. Defensive AI addresses this by correlating signals in environments. Isolated alerts become connected stories. Security teams gain context instead of noise. Machine learning also helps prioritise risk. Not every alert requires immediate action. By scoring threats based on behaviour and impact, AI reduces alert fatigue. This prioritisation improves efficiency. Analysts spend time where it matters most. Routine anomalies are monitored and not escalated. As organisations operate in regions, consistency becomes vital. Defensive AI applies the same analytical standards globally. That uniformity supports reliable protection without slowing operations. Human judgement in an AI-driven defense model Defensive AI is most effective when paired with human expertise. Automation deals with speed and volume. Human judgement and accountability are provided by humans. The ensures there is no blind trust in systems unaware of what is happening in the real world. Security specialists are involved in model training and testing. Human judgement is used to decide which behaviours are most significant. Context is always important for interpretation, particularly when business dynamics, roles and geographic considerations apply. Explainability is also a factor in trust. It is necessary to know the reason a warning was issued. Modern defensive systems are increasingly providing a reason for a decision, letting analysts review the results and make decisions with confidence not hesitation. The combination produces stronger results. AI points out potential dangers early, in large spaces. Humans make decisions about actions, focus on impact and mitigate effects. AI and humans create a robust defense system. In light of the increasingly adaptable nature of threats in cyberspace, this synergy has become imperative. The role of defensive AI in supporting the underlying foundation through analysis has been made possible through human oversight. Conclusions Cybersecurity exists in a reality that is defined by speed, scale and continuous change. The static nature of cyber-defense makes it inadequate in this reality, as attack vectors change faster than static cyber-defense measures can keep pace. Defensive AI represents a useful evolution. Machine learning improves detection, reduces response time and helps build resistance in complex systems by recognising nuanced patterns of human behaviour. But when paired with experienced human monitoring, defensive AI goes beyond automation. It can become an assured means of protecting contemporary digital infrastructure, facilitating stable security operations that don’t diminish responsibility or decision-making. Image source: Unsplash The post Defensive AI and how machine learning strengthens cyber defense appeared first on AI News. View the full article

Corporate networks are filling up with AI agents, creating a governance blind spot for leaders managing multi-cloud infrastructures. As distinct business units race to adopt generative technologies, CIOs especially find their ecosystems populated by fragmented and unmonitored assets. This mirrors the shadow IT challenges of the cloud era, but involves autonomous actors capable of executing business logic and accessing sensitive data. IDC projects the number of actively deployed AI agents will exceed one billion by 2029—a forty-fold increase from current levels. In the first half of 2025 alone, agent creation surged by 119 percent. For enterprise leadership, the immediate challenge shifts from building these agents to locating, auditing, and governing them across platforms. Salesforce has responded to this fragmentation by expanding its MuleSoft Agent Fabric capabilities, introducing automated discovery tools designed to centralise the management of AI agents regardless of their origin. Automating discovery Visibility remains the core issue for security and operations teams. When marketing teams deploy AI agents on one platform and logistics teams build on another, effective governance becomes difficult as central IT loses a consolidated view of the organisation’s digital workforce. MuleSoft’s updated architecture addresses this via ‘Agent Scanners’. These tools continuously patrol major ecosystems – including Salesforce Agentforce, Amazon Bedrock, and Google Vertex AI – to identify running agents. Rather than relying on developers to manually register their deployments, the system automates detection. Finding an agent is only the first step; compliance leaders need to understand the logic behind it. The scanners extract metadata detailing the agent’s capabilities, the LLMs driving it, and the specific data endpoints it is authorised to access. This information is then normalised into standard Agent-to-Agent (A2A) specifications, creating a uniform profile for assets regardless of the underlying vendor. Andrew Comstock, SVP and GM of MuleSoft, said: “The most successful organisations of the next decade will be those that harness the full diversity of the multi-cloud AI landscape. The expanded capabilities of MuleSoft Agent Fabric give you the freedom to innovate across any platform while maintaining the unified visibility and control needed to scale.” Governance and cost control for AI agents Unmanaged agents create financial inefficiency and risk exposure. Consider a CISO in the banking sector. Under standard operations, verifying a new loan-processing agent involves manually chasing documentation from development teams. Automated cataloguing allows security teams to immediately view which financial databases an agent accesses and verify its authorisation levels without manual intervention. This capability ensures security teams view real-time data rather than outdated snapshots. From a financial perspective, visibility drives consolidation. Large enterprises frequently suffer from redundancy where regional teams independently procure or build similar tools. A multinational manufacturer, for instance, might have three separate teams paying for distinct summarisation agents on different platforms. By using the MuleSoft Agent Visualizer to filter the estate by job type, operations leaders can identify these overlaps. Consolidating these into a single high-performing asset reduces redundant licensing costs and allows budget reallocation toward novel development. Transitioning successfully to an ‘Agentic Enterprise’ Innovation often occurs at the edges, where data scientists build bespoke tools outside formal procurement channels. The expanded Agent Fabric addresses this by allowing the registration of “homegrown” agents and Model Context Protocol (MCP) servers via URL. This is particularly relevant for sectors like logistics, where teams may build internal tools for proprietary database optimisation. Instead of remaining hidden, these assets can be registered and made discoverable for reuse across the company. Jonathan Harvey, Head of AI Operations at Capita, said: “Agent Scanners will let us focus on innovation instead of inventory management. Knowing that every agent is automatically discovered and catalogued allows our teams to collaborate, reuse work, and build smarter multi-agent solutions.” Similarly, AT&T is utilising the framework to orchestrate agents across customer support, chat, and voice interactions. Brad Ringer, Enterprise & Integration Architect at AT&T, explained: “With AI moving so fast, MuleSoft Agent Fabric provides the framework we need to scale. It brings together and helps us orchestrate all of the agents and MCP servers we’re building in customer support, chat, and voice interactions. It isn’t just a tool; it’s a huge enabler for everything we’re doing next.” The transition to an “Agentic Enterprise” requires a change in governance around how IT assets are tracked, rendering the days of managing integrations via stale spreadsheets incompatible with the speed of AI agent deployment. Leaders must assume their inventory of AI agents is incomplete and deploy automated scanning tools to establish a baseline of truth. Once this baseline is established, governance policies should mandate that all agents – whether bought or built – expose their capabilities and data access privileges in a standardised format like A2A to facilitate monitoring. Finally, executives can use the visibility provided by these tools to audit spend, identifying duplicate functionalities across cloud environments and merging them to control the Total Cost of Ownership (TCO). As organisations move from pilot programmes to mass deployment, the differentiator will not be the intelligence of individual agents, but the coherence of the network that connects them. See also: Balancing AI cost efficiency with data sovereignty 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 Controlling AI agent sprawl: The CIO’s guide to governance appeared first on AI News. 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Primary healthcare systems across parts of Africa are under growing strain, caught between rising demand, chronic staff shortages, and shrinking international aid budgets. In that context, AI is being tested in healthcare less as a breakthrough technology and more as a way to keep basic services running. According to reporting by Reuters, the Gates Foundation and OpenAI are backing a new initiative, Horizon1000, that aims to introduce AI tools into primary healthcare clinics across several African countries. The project will begin in Rwanda and is intended to reach 1,000 clinics and surrounding communities by 2028, supported by a combined $50 million investment. The timing is not accidental as global development assistance for health fell by just under 27% last year compared to 2024, the Gates Foundation estimates, following cuts that began in the United States and spread to other major donors such as Britain and Germany. Those reductions have coincided with the first rise in preventable child deaths this century, adding pressure to health systems already stretched thin. Rather than focusing on advanced diagnostics or research, Horizon1000 is framed around everyday tasks that consume time in under-resourced clinics. AI tools under the programme are expected to assist with patient intake, triage, record keeping, appointment scheduling, and access to medical guidance, particularly in settings where one doctor may serve tens of thousands of people. Gates Foundation and OpenAI focus on AI support in healthcare “In poorer countries with enormous health worker shortages and lack of health systems infrastructure, AI can be a gamechanger in expanding access to quality care,” Bill Gates wrote in a blog post announcing the initiative. Speaking to Reuters at the World Economic Forum in Davos, Gates said the technology could help health systems recover after aid cuts slowed progress. “Our commitment is that that revolution will at least happen in the poor countries as quickly as it happens in the rich countries,” he said. The focus, according to both partners, is on supporting healthcare workers rather than replacing them. OpenAI is expected to provide technical expertise and AI systems, while the Gates Foundation will work with African governments and health authorities to oversee deployment and alignment with national guidelines. Rwanda was chosen as the first pilot country in part because of its existing digital health efforts. The country established an AI health hub in Kigali last year and has positioned itself as a testbed for health technology projects. Paula Ingabire, Rwanda’s minister of information and communications technology and innovation, said the goal is to reduce administrative burdens while expanding access. “It is about using AI responsibly to reduce the burden on healthcare workers, to improve the quality of care, and to reach more patients,” Ingabire said in a video statement released alongside the launch. Under Horizon1000, AI tools may also be used before patients reach clinics. Gates told Reuters the systems could support pregnant women and **** patients with guidance ahead of visits, especially when language barriers exist between patients and providers. What the AI tools are expected to handle Once patients arrive, AI could help link records, reduce paperwork, and speed up routine processes. “A typical visit, we think, can be about twice as fast and much better quality,” Gates said. Those expectations highlight both the promise and the limits of the approach. While AI may help streamline workflows, its impact depends on reliable data, stable power and connectivity, trained staff, and clear oversight. Many previous digital health pilots in low-income settings have struggled to scale beyond initial trials once funding or external support tapered off. Horizon1000’s designers say they are trying to avoid that pattern by working closely with local governments and health leaders rather than deploying one-size-fits-all systems. Tools are meant to be adapted to local clinical rules, languages, and care models. Even so, questions remain about long-term maintenance, data governance, and who bears responsibility if systems fail or produce errors. The initiative also reflects a broader shift in how AI is being positioned in global health. Instead of headline-grabbing claims about medical breakthroughs, the emphasis here is on narrow, operational use cases that address staffing gaps and administrative overload. In that sense, AI is being treated less as a cure for weak health systems and more as a temporary support amid declining resources. OpenAI’s involvement comes as the company expands its presence in healthcare, following earlier work on health-related applications. At the same time, it faces growing scrutiny over how its systems are trained, deployed, and governed, especially in sensitive sectors like medicine. A test of AI’s limits in healthcare systems For African health systems, the stakes are practical rather than symbolic. Sub-Saharan Africa faces an estimated shortage of nearly six million healthcare workers, a gap that training alone cannot close in the near term. If AI tools can help clinicians see more patients, reduce errors, or manage workloads more effectively, they may offer some relief. If they add complexity or require constant outside support, they risk becoming another layer of dependency. Horizon1000 sits at that intersection. As aid budgets tighten and healthcare demands rise, the project offers a test of whether AI can play a useful, limited role in primary care without overstating its reach. The outcome will depend less on the technology itself than on how well it fits into the systems meant to use it. See also: SAP and Fresenius to build sovereign AI backbone for healthcare 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, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Gates Foundation and OpenAI test AI in African healthcare appeared first on AI News. View the full article

AI cost efficiency and data sovereignty are at odds, forcing a rethink of enterprise risk frameworks for global organisations. For over a year, the generative AI narrative focused on a race for capability, often measuring success by parameter counts and flawed benchmark scores. Boardroom conversations, however, are undergoing a necessary correction. While the allure of low-cost, high-performance models offers a tempting path to rapid innovation, the hidden liabilities associated with data residency and state influence are forcing a reassessment of vendor selection. China-based AI laboratory DeepSeek recently became a focal point for this industry-wide debate. According to Bill Conner, former adviser to Interpol and GCHQ, and current CEO of Jitterbit, DeepSeek’s initial reception was positive because it challenged the status quo by demonstrating that “high-performing large language models do not necessarily require Silicon Valley–scale budgets.” For businesses looking to trim the immense costs associated with generative AI pilots, this efficiency was understandably attractive. Conner observes that these “reported low training costs undeniably reignited industry conversations around efficiency, optimisation, and ‘good enough’ AI.” AI and data sovereignty risks Enthusiasm for cut-price performance has collided with geopolitical realities. Operational efficiency cannot be decoupled from data security, particularly when that data fuels models hosted in jurisdictions with different legal frameworks regarding privacy and state access. Recent disclosures regarding DeepSeek have altered the math for Western enterprises. Conner highlights “recent US government revelations indicating DeepSeek is not only storing data in China but actively sharing it with state intelligence services.” This disclosure moves the issue beyond standard GDPR or CCPA compliance. The “risk profile escalates beyond typical privacy concerns into the realm of national security.” For enterprise leaders, this presents a specific hazard. LLM integration is rarely a standalone event; it involves connecting the model to proprietary data lakes, customer information systems, and intellectual property repositories. If the underlying AI model possesses a “back door” or obliges data sharing with a foreign intelligence apparatus, sovereignty is eliminated and the enterprise effectively bypasses its own security perimeter and erases any cost efficiency benefits. Conner warns that “DeepSeek’s entanglement with military procurement networks and alleged export control evasion tactics should serve as a critical warning sign for CEOs, CIOs, and risk officers alike.” Utilising such technology could inadvertently entangle a company in sanctions violations or supply chain compromises. Success is no longer just about code generation or document summaries; it is about the provider’s legal and ethical framework. Especially in industries like finance, healthcare, and defence, tolerance for ambiguity regarding data lineage is zero. Technical teams may prioritise AI performance benchmarks and ease of integration during the proof-of-concept phase, potentially overlooking the geopolitical provenance of the tool and the need for data sovereignty. Risk officers and CIOs must enforce a governance layer that interrogates the “who” and “where” of the model, not just the “what.” Governance over AI cost efficiency Deciding to adopt or ban a specific AI model is a matter of corporate responsibility. Shareholders and customers expect that their data remains secure and used solely for intended business purposes. Conner frames this explicitly for Western leadership, stating that “for Western CEOs, CIOs, and risk officers, this is not a question of model performance or cost efficiency.” Instead, “it is a governance, accountability, and fiduciary responsibility issue.” Enterprises “cannot justify integrating a system where data residency, usage intent, and state influence are fundamentally opaque.” This opacity creates an unacceptable liability. Even if a model offers 95 percent of a competitor’s performance at half the cost, the potential for regulatory fines, reputational damage, and loss of intellectual property erases those savings instantly. The DeepSeek case study serves as a prompt to audit current AI supply chains. Leaders must ensure they have full visibility into where model inference occurs and who holds the keys to the underlying data. As the market for generative AI matures, trust, transparency, and data sovereignty will likely outweigh the appeal of raw cost efficiency. See also: SAP and Fresenius to build sovereign AI backbone for healthcare 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 Balancing AI cost efficiency with data sovereignty appeared first on AI News. View the full article

For many large companies, artificial intelligence still lives in side projects. Small teams test tools, run pilots, and present results that struggle to spread beyond a few departments. Citi has taken a different path, where instead of keeping AI limited to specialists, the bank has spent the past two years pushing the technology into daily work in the organisation. That effort has resulted in an internal AI workforce of roughly 4,000 employees, drawn from roles that range from technology and operations to risk and customer support. The figure was first reported by Business Insider, which detailed how Citi built its “AI Champions” and “AI Accelerators” programmes to encourage participation not central control. The scale of integration is notable, as Citi employs around 182,000 people globally, and more than 70% of them now use firm-approved AI tools in some form, according to the same report. That level of use places Citi ahead of many peers that still restrict AI access to technical teams or innovation labs. From central pilots to team-level adoption Rather than start with tools, Citi focused on people. The bank invited employees to volunteer as AI Champions, giving them access to training, internal resources, and early versions of approved AI systems. The employees then supported colleagues in their own teams, acting as local points of contact not formal trainers. The approach reflects a practical view of adoption. New tools often fail not because they lack features, but because staff do not know when or how to use them. By embedding support inside teams, Citi reduced the gap between experimentation and routine work. Training played a central role. Employees could earn internal badges by completing courses or demonstrating how they used AI to improve their own tasks. The badges did not come with promotions or pay rises, but they helped create visibility and credibility in the organisation. According to Business Insider, this peer-driven model helped AI spread faster than top-down mandates. Everyday use, with guardrails Citi’s leadership has framed the effort as a response to scale not novelty. With operations spanning retail banking, investment services, compliance, and customer support, small efficiency gains can add up quickly. AI tools are being used to summarise documents, draft internal notes, analyse data sets, and assist with software development. None of these uses are new on their own, but the difference lies in how they are applied. The focus on everyday tasks also shapes Citi’s risk posture. The bank has limited employees to firm-approved tools, with guardrails around what data can be used and how outputs are handled. That constraint has slowed some experiments, but it has also made managers more comfortable allowing broader access. In regulated industries, trust often matters more than speed. What Citi’s approach shows about scaling AI The structure of Citi’s programme suggests a lesson for other large enterprises. AI adoption does not require every employee to become an expert. It requires enough people to understand the tools well enough to apply them responsibly and explain them to others. By training thousands instead of dozens, Citi reduced its reliance on a small group of specialists. There is also a cultural signal at play. Encouraging employees from non-technical roles to participate sends a message that AI is not only for engineers or data scientists. It becomes part of how work gets done, similar to spreadsheets or presentation software in earlier decades. That shift aligns with broader industry trends. Surveys from firms like McKinsey have shown that many companies struggle to move AI projects into production, often citing talent gaps and unclear ownership. Citi’s model sidesteps some of those issues by distributing ownership in teams, while keeping governance centralised. Still, the approach is not without limits. Peer-led adoption depends on sustained interest, and not all teams move at the same pace. There is also the risk that informal support networks become uneven, with some groups benefiting more than others. Citi has tried to address this by rotating Champions and updating training content as tools change. What stands out is the bank’s willingness to treat AI as infrastructure not innovation. Instead of asking whether AI could transform the business, Citi asked where it could remove friction from existing work. That framing makes progress easier to measure and reduces pressure to produce dramatic results. The experience also challenges a common assumption that AI adoption must start at the top. Citi’s senior leadership supported the effort, but much of the momentum came from employees who volunteered time to learn and teach. In large organisations, that bottom-up energy can be hard to generate, yet it often determines whether new technology sticks. As more companies move from pilots to production, Citi’s experiment offers a useful case study. It shows that scale does not come from buying more tools, but from helping people feel confident using the ones they already have. For enterprises wondering why AI progress feels slow, the answer may lie less in strategy decks and more in how work actually gets done, one team at a time. (Photo by Declan Sun) See also: JPMorgan Chase treats AI spending as core infrastructure Want to learn more about AI and big data from industry leaders? Check outAI & Big Data Expo taking place in Amsterdam, California, and London. This comprehensive event is part of TechEx and is 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 The quiet work behind Citi’s 4,000-person internal AI rollout appeared first on AI News. View the full article

SAP and Fresenius are building a sovereign AI platform for healthcare that brings secure data processing to clinical settings. For data leaders in the medical sector, deploying AI requires strict governance that public cloud solutions often lack. This collaboration addresses that gap by creating a “controlled environment” where AI models can operate without compromising data sovereignty. Moving AI from pilot to production The project aims to build an open and integrated ecosystem allowing hospitals to use AI securely. Rather than running isolated experiments, the companies plan to create a digital backbone for a sovereign and AI-supported healthcare system. Michael Sen, CEO of Fresenius, said: “Together with SAP, we can accelerate the digital transformation of the ******* and European healthcare systems and enable a sovereign European solution that is so important in today’s global landscape. “We are making data and AI everyday companions that are secure, simple and scalable for doctors and hospital teams. This creates more room for what truly matters: caring for patients.” The technical base uses SAP Business AI and the SAP Business Data Cloud. By leveraging these components, the platform creates a compliant, sovereign foundation for operating AI models in healthcare. This infrastructure handles health data responsibly, a requirement for scaling automated processes in patient care. The partnership tackles data fragmentation through SAP’s “AnyEMR” strategy, which supports the integration of diverse hospital information systems (HIS). Using open industry standards like HL7 FHIR, the platform connects HIS, electronic medical records (EMRs), and other medical applications. This connectivity allows Fresenius to develop AI-supported solutions that increase efficiency across the care chain. The goal is to build an individual, scalable platform that enables connected, data-driven healthcare processes. Investing in sovereign AI to advance healthcare Both companies intend to invest a “mid three-digit million euro amount” in the medium term. The funds target the digital transformation of ******* and European healthcare systems using AI-supported solutions. Plans include joint investments in startups and scaleups, alongside internal technological developments. This approach aims to build a broader library of tools that plug into the sovereign platform. Christian Klein, CEO of SAP SE, commented: “With SAP’s leading technology and Fresenius’ deep healthcare expertise, we aim to create a sovereign, interoperable healthcare platform for Fresenius worldwide. “Together, we want to set new standards for data sovereignty, security, and innovation in healthcare. Thanks to SAP, Fresenius can harness the full potential of digital and AI-supported processes and sustainably improve patient care.” This deal indicates that the next phase of healthcare AI in Europe will focus on sovereign infrastructure. Scalable AI requires a controlled environment to satisfy regulatory demands—without a sovereign data backbone, AI initiatives risk stalling due to compliance concerns. See also: Scaling AI value beyond pilot phase purgatory 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. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post SAP and Fresenius to build sovereign AI backbone for healthcare appeared first on AI News. View the full article

Scaling AI value from isolated pilots to enterprise-wide adoption remains a primary hurdle for many organisations. While experimentation with generative models has become ubiquitous, industrialising these tools (i.e. wrapping them in necessary governance, security, and integration layers) often stalls. Addressing the gap between investment and operational return, IBM has introduced a new service model designed to help businesses assemble, rather than purely build, their internal AI infrastructure. Adopting asset-based consulting Traditional consultancy models typically rely on human labour to solve integration problems, a process that is often slow and capital-intensive. IBM is among the companies aiming to alter this dynamic by offering an asset-based consulting service. This approach combines standard advisory expertise with a catalogue of pre-built software assets, aiming to help clients construct and govern their own AI platforms. Instead of commissioning bespoke development for every workflow, organisations can leverage existing architectures to redesign processes and connect AI agents to legacy systems. This method helps companies to achieve value by scaling new agentic applications without necessitating alterations to their existing core infrastructure, AI models, or preferred cloud providers. Managing a multi-cloud environment A frequent concern for enterprise leaders is vendor lock-in, particularly when adopting proprietary platforms. IBM’s strategy acknowledges the reality of the heterogeneous enterprise IT landscape. The service supports a multi-vendor foundation, compatible with Amazon Web Services, Google Cloud, and Microsoft Azure, alongside IBM watsonx. This approach extends to the models themselves, supporting both open- and closed-source variants. By allowing companies to build upon their current investments rather than demanding a replacement strategy, the service addresses a barrier to adoption: the fear of technical debt accumulation when switching ecosystems. The technical backbone of this offering is IBM Consulting Advantage, the company’s internal delivery platform. Having utilised this system to support over 150 client engagements, IBM reports that the platform has boosted its own consultants’ productivity by up to 50 percent. The premise is that if these tools can accelerate delivery for IBM’s own teams, they should offer similar velocity for clients. The service provides access to a marketplace of industry-specific AI agents and applications. For business leaders, this suggests a “platform-first” focus, where attention turns from managing individual models to managing a cohesive ecosystem of digital and human workers. Active deployment of a platform-centric approach to scaling AI value The efficacy of such a platform-centric approach is best viewed through active deployment. Pearson, the global learning company, is currently utilising this service to construct a custom platform. Their implementation combines human expertise with agentic assistants to manage everyday work and decision-making processes, illustrating how the technology functions in a live operational environment. Similarly, a manufacturing firm has employed IBM’s solution to formalise its generative AI strategy. For this client, the focus was on identifying high-value use cases, testing targeted prototypes, and aligning leaders around a scalable strategy. The result was the deployment of AI assistants using multiple technologies within a secured, governed environment, laying a foundation for wider expansion across the enterprise. Despite the attention surrounding generative AI, the realisation of balance-sheet impact is not guaranteed. “Many organisations are investing in AI, but achieving real value at scale remains a major challenge,” notes Mohamad Ali, SVP and Head of IBM Consulting. “We have solved many of these challenges inside IBM by using AI to transform our own operations and deliver measurable results, giving us a proven playbook to help clients succeed.” The conversation is gradually moving away from the capabilities of specific LLMs and towards the architecture required to run them safely. Success in scaling AI and achieving value will likely depend on an organisation’s ability to integrate these solutions without creating new silos. Leaders must ensure that as they adopt pre-built agentic workflows, they maintain rigorous data lineage and governance standards. See also: JPMorgan Chase treats AI spending as core infrastructure 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. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Scaling AI value beyond pilot phase purgatory appeared first on AI News. View the full article

Artificial intelligence has shifted rapidly from a peripheral innovation to a structural component of modern financial services. In banking, payments, and wealth management, to name but three sub-sectors, AI is now embedded in budgeting tools, fraud detection systems, KYC, AML, and customer engagement platforms. Credit unions sit in this broader fintech transformation, facing similar technological pressures and operating under distinct cooperative models built on trust, proffered services in competitive markets, and community alignment. Consumer behaviour suggests AI is already part of everyday financial decision-making. Research from Velera indicates that 55% of consumers use AI tools for financial planning or budgeting, while 42% are comfortable using AI to complete financial transactions. Adoption is highest among younger demographics, with 80% of Gen Z and younger millennials using AI for financial planning and close to that proportion expressing ‘comfort’ with agentic AI. These patterns mirror trends in the wider fintech sector, where AI-driven personal finance tools and conversational interfaces have become more common. There is a particular a dual challenge for credit unions. Member expectations are shaped by large fintech companies’ digital platforms and apps, and large digital banks are deploying AI at scale. At the average Union, internal readiness remains limited. A CULytics survey shows that although 42% of credit unions have implemented AI in specific operational areas, only 8% report using it in multiple parts of the business. The gap between market expectations and institutional ability defines the current phase of AI adoption in the cooperative-based financial sector. AI as a trust-based extension of financial services Unlike many fintech startups, credit unions benefit from high levels of consumer trust. Velera reports that 85% of consumers see credit unions as reliable sources of financial advice, and 63% of CU members say they would attend AI-related educational sessions if such were offered. These findings position credit unions as being able to frame AI as an advisory tool to be embedded in existing relationships. In fintech, “explainable AI” and transparent digital finance are mainstays as identity verification, and regulation watch the technology closely. Regulators and consumers clearly expect transparency into how decisions are made by AI back ends. Credit unions can use this expectation by integrating AI into education programmes, fraud awareness efforts and financial literacy. Where AI delivers tangible value Personalisation is a leading use case for AI. Machine learning models let financial institutions move beyond static customer segmentation, via behavioural signals and life-stage indicators. The approach is already common in other sectors, and in the industry, in fintech lending and digital banking platforms. Credit unions can adopt similar techniques, ones that tailor offers, communications, and make product recommendations. Member service represents another potential high-impact area. According to CULytics, 58% of credit unions now use chatbots or virtual assistants, the most-adopted AI application in the sector. Cornerstone Advisors reports that deployment is accelerating among credit unions than banks, using AI to handle routine enquiries and preserve staff capacity. Fraud prevention has emerged as an AI use case in the sector. Alloy reports a 92% net increase in AI fraud prevention investment among credit unions in 2025, compared with lower prioritisation among banks. As digital payments get more widely-adopted, AI-driven fraud detection is important to balance security with low-friction user experiences. In this respect, credit unions face the same pressures as mainstream fintech payment providers and neobanks, where false declines and delayed responses can directly erode customer trust. Operational efficiency and lending decisions also feature prominently. Research from Inclind and CULytics shows AI being applied to reconciliation, underwriting, and internal business analytics. Users report reduced manual workloads and faster credit decisions. Cornerstone Advisors identifies lending as the third-most common AI function among credit unions, placing them closer to fintech lenders than traditional banks in this area. Structural barriers to scaling AI Despite clear use cases, scaling AI in credit unions remains difficult. Data readiness is the most frequently cited constraint. Cornerstone Advisors reports that only 11% of credit unions rate their data strategy as very effective (nearly a quarter consider it ineffective). Without accessible, well-governed data, AI systems cannot deliver reliable outcomes, regardless of the underlying sophistication of the LLM. Trust and explainability also limit the technology’s expansion. In regulated financial environments, opaque “****** box” models create risk for institutions that as a matter of course have to justify their decisions to members. PYMNTS Intelligence highlights the importance of breaking down data silos and using shared intelligence models to improve transparency and auditability. Consortium-based approaches, like those used by Velera in thousands of credit unions, reflect a trend in the financial sector towards pooled data. Integration presents a further challenge. CULytics finds that 83% of credit unions cite integration with legacy systems as an obstacle to AI, a familiar issue to many financial institutions. Limited in-house expertise in AI compounds this, again suggesting fintech partnerships, credit union service organisations (CUSOs), or externally-managed platforms as ways to accelerate deployment. From experimentation to embedded practice As AI becomes embedded in financial services, credit unions face a choice similar to that which has been confronted by banks and the wider fintech sector: placing AI as a foundational ability. Evidence suggests progress depends on disciplined execution. That means prioritising high-trust, high-impact use cases, so institutions can deliver visible benefits and not undermine members’ confidence in their trusted institutions. Strengthening data governance and accountability ensures AI-assisted decisions remain explainable and defensible. Partner-led integration might reduce technical complexity, while education and transparency align AI adoption with the values that underpin the cooperative organisation. (Image source: “Credit Union Building” by Dano is licensed under CC BY 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 Credit unions, fintech and the AI inflection of financial services appeared first on AI News. View the full article

Inside large banks, artificial intelligence has moved into a category once reserved for payment systems, data centres, and core risk controls. At JPMorgan Chase, AI is framed as infrastructure the bank believes it cannot afford to neglect. That position came through clearly in recent comments from CEO Jamie Dimon, who defended the bank’s rising technology budget and warned that institutions that fall behind on AI risk losing ground to competitors. The argument was not about replacing people but about staying functional in an industry where speed, scale, and cost discipline matter every day. JPMorgan has been investing heavily in technology for years, but AI has changed the tone of that spending. What once sat with innovation projects is now folded into the bank’s baseline operating costs. That includes internal AI tools that support research, document drafting, internal reviews, and other routine tasks in the organisation. From experimentation to infrastructure The shift in language reflects a deeper change in how the bank views risk. AI is considered part of the systems required to keep pace with competitors that are automating internal work. Rather than encouraging workers to rely on public AI systems, JPMorgan has focused on building and governing its own internal platforms. That decision reflects long-held concerns in banking about data exposure, client confidentiality, and regulatory monitoring. Banks operate in an environment where mistakes carry high costs. Any system that touches sensitive data or influences choices must be auditable and explainable. Public AI tools, trained on datasets and updated frequently, make that difficult. Internal systems give JPMorgan more control, even if they take longer to deploy. The approach also reduces the potential of uncontrolled “shadow AI,” in which employees use unapproved tools to speed up work. While such tools can improve productivity, they create gaps in oversight that regulators tend to notice quickly. A cautious approach to workforce change JPMorgan has been careful in how it talks about AI’s impact on jobs. The bank has avoided claims that AI will dramatically reduce headcount. Instead, it presents AI as a way to reduce manual work and improve consistency. Tasks that once required multiple review cycles can now be completed faster, with employees still responsible for final judgement. The framing positions AI as support not substitution, which matters in a sector sensitive to political and regulatory reaction. The scale of the organisation makes this approach practical. JPMorgan employs hundreds of thousands of people worldwide. Even tiny efficiency gains, applied broadly, can translate into meaningful cost savings over time. The upfront investment required to build and maintain internal AI systems is substantial. Dimon acknowledges that technology spending can have an impact on short-term performance, especially when market conditions are uncertain. His response is that cutting back on technology now may improve margins in the near term, but it risks weakening the bank’s position later. In that sense, AI spending is treated as a form of insurance against falling behind. JPMorgan, AI, and the risk of falling behind rivals JPMorgan’s stance reflects pressure in the banking sector. Rivals are investing in AI to speed up fraud detection, streamline compliance work, and improve internal reporting. As these tools become more common, expectations rise. Regulators may assume banks have access to advanced monitoring systems. Clients may expect faster responses and fewer errors. In that environment, lagging on AI can look less like caution and more like mismanagement. JPMorgan has not suggested that AI will solve structural challenges or eliminate risk. Many AI projects struggle to move beyond narrow uses, and integrating them into complex systems remains difficult. The harder work lies in governance. Deciding which teams can use AI, under what conditions, and with what oversight requires clear rules. Errors need defined escalation paths. Responsibility must be assigned when systems produce flawed output. Across large enterprises, AI adoption is not limited by access to models or computing power, but constrained by process, policy, and trust. For other end-user companies, JPMorgan’s approach offers a useful reference point. AI is treated as part of the machinery that keeps the organisation running. That does not guarantee success. Returns may take years to appear, and some investments will not pay off. But the bank’s position is that the greater risk lies in doing too little, not too much. (Photo by IKECHUKWU JULIUS UGWU) See also: Banks operationalise as Plumery AI launches standardised integration 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 JPMorgan Chase treats AI spending as core infrastructure appeared first on AI News. View the full article

For the majority of web users, generative AI is AI. Large Language Models (LLMs) like GPT and Claude are the de facto gateway to artificial intelligence and the infinite possibilities it has to offer. After mastering our syntax and remixing our memes, LLMs have captured the public imagination. They’re easy to use and fun. And – the odd hallucination aside – they’re smart. But while the public plays around with their favourite flavour of LLM, those who live, breathe, and sleep AI – researchers, tech heads, developers – are focused on ******* things. That’s because the ultimate goal for AI max-ers is artificial general intelligence (AGI). That’s the endgame. To the professionals, LLMs are a sideshow. Entertaining and eminently useful, but ultimately ‘narrow AI.’ They’re good at what they do because they’ve been trained on specific datasets, but incapable of straying out of their lane and attempting to solve larger problems. The diminishing returns and inherent limitations of deep learning models is prompting exploration of smarter solutions capable of actual cognition. Models that lie somewhere between the LLM and AGI. One system that falls into this bracket – smarter than an LLM and a foretaste of future AI – is OpenCog Hyperon, an open-source framework developed by SingularityNET. With its ‘neural-symbolic’ approach, Hyperon is designed to bridge the gap between statistical pattern matching and logical reasoning, offering a roadmap that joins the dots between today’s chatbots and tomorrow’s infinite thinking machines. Hybrid architecture for AGI SingularityNET has positioned OpenCog Hyperon as a next-generation AGI research platform that integrates multiple AI models into a unified cognitive architecture. Unlike LLM-centric systems, Hyperon is built around neural-symbolic integration in which AI can learn from data and reason about knowledge. That’s because withneural-symbolic AI, neural learning components and symbolic reasoning mechanisms are interwoven so that one can inform and enhance the other. This overcomes one of the primary limitations of purely statistical models by incorporating structured, interpretable reasoning processes. At its core, OpenCog Hyperon combines probabilistic logic and symbolic reasoning with evolutionary programme synthesis and multi-agent learning. That’s a lot of terms to take it, so let’s try and break down how this all works in practice. To understand OpenCog Hyperon – and specifically why neural-symbolic AI is such a big deal – we need to understand how LLMs work and where they come up short. The limits of LLMs Generative AI operates primarily on probabilistic associations. When an LLM answers a question, it doesn’t ‘know’ the answer in the way a human instinctively does. Instead, it calculates the most probable sequence of words to follow the prompt based on its training data. Most of the time, this ‘impersonation of a person’ comes in very convincingly, providing the human user with not only the output they expect, but one that is correct. LLMs specialise in pattern recognition on an industrial scale and they’re very good at it. But the limitations of these models are well documented. There’s hallucination, of course, which we’ve already touched on, where plausible-sounding but factually incorrect information is presented. Nothing gaslights harder than an LLM eager to please its master. But a greater problem, particularly once you get into more complex problem-solving, is a lack of reasoning. LLMs aren’t adept at logically deducing new truths from established facts if those specific patterns weren’t in the training set. If they’ve seen the pattern before, they can predict its appearance again. If they haven’t, they hit a wall. AGI, in comparison, describes artificial intelligence that can genuinely understand and apply knowledge. It doesn’t just guess the right answer with a high degree of certainty – it knows it, and it’s got the working to back it up. Naturally, this ability calls for explicit reasoning skills and memory management – not to mention the ability to generalise when given limited data. Which is why AGI is still some way off – how far off depends on which human (or LLM) you ask. But in the meantime, whether AGI be months, years, or decades away, we have neural-symbolic AI, which has the potential to put your LLM in the shade. Dynamic knowledge on demand To understand neural-symbolic AI in action, let’s return toOpenCog Hyperon. At its heart is the Atomspace Metagraph, a flexible graph structure that represents diverse forms of knowledge including declarative, procedural, sensory, and goal-directed, all contained in a single substrate. The metagraph can encode relationships and structures in ways that support not just inference, but logical deduction and contextual reasoning. If this sounds a lot like AGI, it’s because it is. ‘Diet AGI,’ if you like, provides a taster of where artificial intelligence is headed next. So that developers can build with the Atomspace Metagraph and use its expressive power, Hyperon has created MeTTa (Meta Type Talk), a novel programming language designed specifically for AGI development. Unlike general-purpose languages like Python, MeTTa is a cognitive substrate that blends elements of logic and probabilistic programming. Programmes in MeTTa operate directly on the metagraph, querying and rewriting knowledge structures, and supporting self-modifying code, which is essential for systems that learn how to improve themselves. "We're emerging from a couple of years spent on building tooling. We've finally got all our infrastructure working at scale for Hyperon, which is exciting." Our CEO, Dr. @bengoertzel, joined Robb Wilson and Josh Tyson on the Invisible Machines podcast to discuss the present and… pic.twitter.com/8TqU8cnC2L — SingularityNET (@SingularityNET) January 19, 2026 Robust reasoning as gateway to AGI The neural-symbolic approach at the heart of Hyperon addresses a key limitation of purely statistical AI, namely that narrow models struggle with tasks requiring multi-step reasoning. Abstract problems bamboozle LLMs with their pure pattern recognition. Throw neural learning into the mix, however, and reasoning becomes smarter and more human. If narrow AI does a good impersonation of a person, neural-symbolic AI does an uncanny one. That being said, it’s important to contextualise neural-symbolic AI. Hyperon’s hybrid design doesn’t mean an AGI breakthrough is imminent. But it represents a promising research direction that explicitly tackles cognitive representation and self-directed learning not relying on statistical pattern matching alone. And in the here and now, this concept isn’t constrained to some big brain whitepaper – it’s out there in the wild and being actively used to create powerful solutions. The LLM isn’t dead – narrow AI will continue to improve – but its days are numbered and its obsolescence inevitable. It’s only a matter of time. First neural-symbolic AI. Then, hopefully, AGI – the final boss of artificial intelligence. Image source: Depositphotos The post OpenCog Hyperon and AGI: Beyond large language models appeared first on AI News. View the full article

After years of experimentation with artificial intelligence, retailers are striving to embed consumer insight directly into everyday commercial decisions. First Insight, a US-based analytics company specialising in predictive consumer feedback, argues that the next phase of retail AI should be epitomised by dialogue, not dashboards. Following a three-month beta programme, First Insight has made its new AI tool, Ellis, available to brands and retailers. Ellis is designed as a conversational interface that allows merchandising, pricing and planning teams to ask questions about products, pricing, and demand in the First Insight platform. The company says its approach is intended to compress decision times into minutes. Research by McKinsey has found that while most large retailers now collect volumes of customer data, some can’t translate insights into action quickly enough to influence product development decisions. It notes AI tools which shorten the distance between insight and execution are more likely to deliver measurable commercial value than reporting systems. From dashboards to dialogue First Insight has worked with retailers including Boden, Family Dollar, and Under Armour to predict consumer demand, price sensitivity, and performance using survey feedback and predictive modelling. Such insights are usually delivered on a dashboard or in a report. Ellis lets users query insights conversationally. For example, teams can ask whether a six-item or nine-item assortment is likely to perform better in a specific market, or how removing certain materials might affect appeal. First Insight says the system returns answers grounded in its existing data models. Industry evidence suggests that this method could help with a bottleneck in retail decision-making. A Harvard Business Review analysis of data-driven retail organisations found insight often loses value when it cannot be accessed quickly, particularly during phases like line review or early concept development. Predictive insight already in operation The underlying techniques used by First Insight are deployed already across the retail sector. Under Armour has described using consumer data and predictive modelling to refine product assortments and pricing strategies, stating the technology helps it reduce markdown risk and improve full-price selling. Similarly, fashion retailer Boden has discussed the role of customer insight in guiding assortment decisions, particularly in balancing trend-led items with core items. While these companies do not disclose the details of their proprietary systems, such cases can show how predictive consumer data can be embedded into commercial planning. Comparable tools are also in use elsewhere in the industry. Retailers including Walmart and Target have invested in analytics and machine learning to understand regional demand patterns, optimise pricing, and test new concepts. According to a Deloitte study on AI in retail, companies using predictive consumer insight report improved forecast accuracy and lower inventory risk, particularly when analytics are integrated early. Pricing, assortments and competitive dynamics Ellis is powered by what First Insight describes as a predictive retail large language model, one that’s trained on consumer response data. The company says this lets the system answer questions about optimal pricing, predicted sales rates, ideal assortment size, and likely segment preferences. This focus aligns with academic research showing that price optimisation and assortment planning are among the highest-value AI use cases in retail. A study published in the Journal of Retailing found that data-driven pricing models can outperform traditional cost-plus approaches, particularly when consumer willingness-to-pay is measured directly. Competitive benchmarking is another area where retailers can use analytics. Research from Bain & Company indicates retailers able to compare their products with competitors’ are better positioned to differentiate on value as well as price. Tools that consolidate such comparisons into a single analytical layer can be considered the ideal, therefore. Making insight more widely accessible One of First Insight’s core claims is that Ellis makes consumer insight accessible outside of specialist analytics teams. Natural-language queries, the company argues, lets senior executives down engage with data with no waiting for analysis. Democratisation of analytics is a recurring theme in a great deal of industry research. Gartner reports organisations which broaden access to analytics are more likely to see tool adoption and ROI. However, it cautions that systems should be governed to ensure outputs are interpreted correctly and stem from robust data. First Insight maintains that Ellis retains the methodological rigour of its existing platform, while reducing friction at the point of decision. According to Greg Petro, the company’s chief executive, the goal is to bring predictive insight into the moment when decisions are actually made. “For nearly 20 years, First Insight has helped retailers predict pricing, product success and assortment decisions by grounding them in real consumer feedback,” a company spokesperson said. “Ellis brings that intelligence directly into line review, early concept development and the boardroom, helping teams move faster without sacrificing confidence.” A crowded but growing market First Insight is not alone to target the space. Vendors such as EDITED, DynamicAction, and RetailNext offer AI tools aimed at merchandising and pricing. What differentiates newer offerings is the emphasis on usability and speed rather than model complexity. A recent Forrester report on retail AI noted that conversational interfaces are being layered on top of established analytics platforms, reflecting a demand from users for more intuitive interaction with data. Such tools lead to better decisions, although are dependent on data quality and organisational discipline. First Insight previewed Ellis at this year’s National Retail Federation conference in New York, where AI-driven merchandising and pricing tools featured prominently. As retailers face volatile demand, inflation, and changing consumer preferences, the ability to test scenarios remains valuable. (Image source: “2008 first insight” by palmasco 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 Retailers bring conversational AI and analytics closer to the user appeared first on AI News. View the full article

A new technology from digital banking platform Plumery AI aims to address a dilemma for financial institutions: how to move beyond proofs of concept and embed artificial intelligence into everyday banking operations without compromising governance, security, or regulatory compliance. Plumery’s “AI Fabric” has been positioned by the company as a standardised framework for connecting generative AI tools and models to core banking data and services. According to Plumery, the product is intended to reduce reliance on bespoke integrations and to promote an event-driven, API-first architecture that can scale as institutions grow. The challenge it seeks to address is recognised in the sector. Banks have invested heavily in AI experimentation over the past decade, but many deployments remain limited. Research by McKinsey suggests that while generative AI could materially improve productivity and customer experience in financial services, most banks struggle to translate pilots into production because of fragmented data estates and incumbent operating models. The consultancy argues that enterprise-level AI adoption requires shared infrastructure and governance, and reusable data products. In comments accompanying the product launch, Plumery’s founder and chief executive, Ben Goldin, said financial institutions are clear about what they expect from AI. “They want real production use cases that improve customer experience and operations, but they will not compromise on governance, security or control,” he said. “The event-driven data mesh architecture transforms how banking data is produced, shared, and consumed, not adding another AI layer on top of fragmented systems.” Fragmented data remains a barrier Data fragmentation remains one of the obstacles to operational AI in banking. Many institutions rely on legacy core systems that sit in newer digital channels, creating silos in products and customer journeys. Each AI initiative requires fresh integration work, security reviews, and governance approvals, thus increasing costs and slowing delivery. Academic and industry research supports this diagnosis. Studies on explainable AI in financial services note that fragmented pipelines make it harder to trace decisions and increase regulatory risk, particularly in areas like credit scoring and anti-money-laundering. Regulators have made clear that banks must be able to explain and audit AI-driven outcomes, regardless of where the models are developed. Plumery says its AI Fabric addresses such issues by presenting domain-oriented banking data as governed streams that can be reused in multiple use cases. The company argues that separating systems of record from systems of engagement and intelligence allows banks to innovate more safely. Evidence of AI already in production Despite the challenges, AI is already embedded in many parts of the financial sector. Case studies compiled by industry analysts show widespread use of machine learning and natural language processing in customer service, risk management, and compliance. Citibank, for example, has deployed AI-powered chatbots to handle routine customer enquiries, reducing pressure on call centres and improving response times. Other large banks use predictive analytics to monitor loan portfolios and anticipate defaults. Santander has publicly described its use of machine learning models to assess credit risk and strengthen portfolio management. Fraud detection is another mature area. Banks rely increasingly on AI systems to analyse transaction patterns, flagging anomalous behaviour more effectively than rule-based systems. Research from technology consultancies notes that such models depend on high-quality data flows, and that integration complexity remains a limiting factor for smaller institutions. More advanced applications are emerging at the margins. Academic research into large language models suggests that, under strict governance, conversational AI could support certain transactional and advisory functions in retail banking. However, these implementations remain experimental and are closely scrutinised due to their regulatory implications. Platform providers and ecosystem approaches Plumery operates in a competitive market of digital banking platforms that position themselves as orchestration layers rather than replacements for core systems. The company has entered partnerships designed to fit into broader fintech ecosystems. Its integration with Ozone API, an open banking infrastructure provider, was presented as a way for banks to deliver standards-compliant services more quickly, without custom development. Its approach reflects a wider industry trend towards composable architectures. Vendors like Backbase and others promote API-centric platforms that allow banks to plug in AI, analytics, and third-party services to the existing core. Analysts agree generally that such architectures are better suited to incremental innovation than large-scale system replacement. Readiness remains uneven Evidence suggests that readiness in the sector is uneven. A report by Boston Consulting Group found that fewer than a quarter of banks believe they are prepared for large-scale AI adoption. The gap, it argued, lies in governance, data foundations, and operating discipline. Regulators have responded by offering controlled environments for experimentation. In the ***, regulatory sandbox initiatives allow banks to test new technologies, including AI. These programmes are intended to support innovation and reinforce accountability and risk management. For vendors like Plumery, the opportunity lies in providing infrastructure that aligns technological ambition and regulatory reality. AI Fabric enters a market where demand for operational AI is apparent, but where success depends on proving that new tools can be safe and transparent. Whether Plumery’s approach becomes a adopted standard remains uncertain. As banks move from experimentation to production, the focus is moving towards the architectures that support AI. In that context, platforms that can demonstrate technical flexibility and governance adherence are more likely to play an important role in the digital banking’s next phase. (Image source: “Colorful Shale Strata of the Morrison Formation at the Edge of the San Rafael Swell” by Jesse Varner 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 Banks operationalise as Plumery AI launches standardised integration appeared first on AI News. View the full article

Author: Richard Farrell, CIO at Netcall After a year of rapid adoption and high expectations surrounding artificial intelligence, 2026 is shaping up to be the year CIOs apply a more strategic lens. Not to slow progress, but to steer it in a smarter direction. In 2025, we saw the rise of AI copilots across almost every platform imaginable. From browsers and CRMs to productivity tools and helpdesks, the tech world raced to embrace assistance-on-demand. But while vendors marketed “magic,” CIOs were left with the clean-up. Multiple pilots. Multiple platforms. Multiple promises. Few results. Now the honeymoon ******* is over. It’s time to assess what worked, what didn’t, and what truly matters. The role of the CIO is shifting from tech enthusiast to strategic outcome architect. That means moving from disconnected experiments to holistic thinking – aligning people, process, and technology to drive sustainable results. Process mapping will become an essential starting point: identifying pain points, inefficiencies, and areas for AI and automation that directly link to measurable outcomes. And that shift comes with a new set of priorities. Here are five that will define 2026. Process intelligence will replace fragmented copilots The early promise of AI copilots was appealing: save time, reduce manual work, and supercharge productivity. But reality has been far more grounded. Independent evaluations, including a detailed *** Department for Business and Trade trial, found minimal measurable productivity improvements[1]. Despite glowing self-reports, actual gains were either negligible or non-existent. Why? Because these tools were designed for individual users, not organisations. They sat on top of workflows, rather than improving them. In too many cases, the top use case was summarising meeting notes – useful, but hardly transformative. In 2026, CIOs will shift focus from point solutions to end-to-end platforms. The goal will be clear: use AI to optimise business processes, not pad out software features. This pivot from individual utility to organisational efficiency will be the biggest AI reset of the year. Consolidation will beat complexity CIOs have long battled sprawling tech estates and overlapping solutions, often held together by fragile integrations. In 2026, that complexity will come under fresh scrutiny. Too many tools chasing too few outcomes is no longer sustainable. There will be a marked shift towards simplification – rationalising technology stacks and working with partners who can demonstrate true interoperability. CIOs will favour vendors who collaborate rather than compete, and who can clearly show how their solutions integrate within the broader ecosystem. Less will be more, especially when it comes to driving efficiency and speed. This change is as much about procurement strategy as it is about technology. CIOs will look to platform-based approaches that offer the flexibility to build applications tailored to real-world processes. The ability to generate apps directly from mapped processes – refining and improving iteratively – will empower digital teams to deliver faster and smarter. It means building long-term partnerships that are based on shared goals and business value, not short-term sprints or siloed innovation. Governance will take centre stage The more AI scales, the more governance matters. In 2026, successful CIOs will build guardrails into every intelligent system. This means moving away from retrofitting rules after the fact, and instead embedding governance by design – from the very beginning of deployment. That includes audit trails, escalation rules, and privacy protocols, all built into the user journey through intuitive, adaptable frameworks. Proper escalation and human-in-the-loop models will be essential, alongside data stewardship – knowing where data is stored, how it’s accessed, and ensuring privacy by design. Governance isn’t a drag on progress; it’s the foundation of trust. Low-code platforms are emerging as powerful enablers in this shift. They don’t just speed up development – they allow CIOs to embed controls directly into the build process. This approach supports the democratisation of development, empowering teams to iterate, improve, and scale quickly, without compromising on oversight. That means compliance can’t be tacked on later; it must be built in from the start. This accelerates delivery while reassuring regulators, customers, and internal teams alike. This shift will ensure that automation supports human judgement, not overrides it – building systems people trust, not just systems that work. Prediction must be followed by action AI is good at pattern recognition. But unless those patterns trigger interventions, they don’t change outcomes. A shining example of this shift is the work at Rotherham NHS Foundation Trust. By embedding AI directly into its workflows, the Trust saw attendance among those most at risk of missing appointments improve significantly, with a 67% reduction in missed visits. It was not just that the model could identify at-risk patients; it was that this insight triggered an additional reminder, leading to better outcomes. The value was not in the model alone but in how it changed communication in a meaningful, practical way. That’s what CIOs will demand in 2026. Prediction engines must be paired with platforms that empower action. Whether it’s preventing missed appointments or spotting security anomalies before breaches occur, success will be defined by what AI enables teams to do differently. Value must be proven, not assumed A dangerous trend emerged in 2025: building business cases on feelings. CIOs were pressured to prove AI success based on user satisfaction or time-saving estimates, often self-reported. The problem? These metrics are vague, inconsistent, and impossible to verify. In 2026, that won’t be good enough. CIOs will be expected to show clear cause and effect. If AI is being used, what has it replaced? What has it improved? What cost has it avoided? We need to replace the tick-box mindset with a value lens. That means thinking beyond the tech and tying initiatives back to outcomes CEOs care about – growth, resilience, customer satisfaction, and efficiency. Crucially, this demands a holistic approach. It’s not just about technology. CIOs must align people, process, and platform – starting with detailed process mapping to understand how work gets done, where inefficiencies lie, and how those insights translate into smarter applications. These maps become blueprints for building, offering a framework to generate applications that deliver measurable value. The resolution: outcome-led leadership CIOs have spent the last decade digitising the enterprise. In 2026, their role will evolve again – from technologists to outcome architects. This year isn’t about pulling back on AI or slowing innovation. It’s about getting clear. Clear on priorities. Clear on governance. Clear on impact. The best CIOs will ask the toughest questions. Are we solving a real problem, or just deploying tech? Can we measure the benefit, not just hope for it? Are we building something sustainable, or chasing hype? 2026 is the year we stop experimenting for the sake of it and start delivering for the business. The age of shiny objects is over. It’s time for substance. And that starts with us. Author: Richard Farrell, CIO at Netcall (Image source: “Apollo classic concept art: Parachute deployment” by Mooncat.Drew is marked with Public Domain Mark 1.0.) 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 AI dominated the conversation in 2025, CIOs shift gears in 2026 appeared first on AI News. View the full article

The ETSI EN 304 223 standard introduces baseline security requirements for AI that enterprises must integrate into governance frameworks. As organisations embed machine learning into their core operations, this European Standard (EN) establishes concrete provisions for securing AI models and systems. It stands as the first globally applicable European Standard for AI cybersecurity, having secured formal approval from National Standards Organisations to strengthen its authority across international markets. The standard serves as a necessary benchmark alongside the EU AI Act. It addresses the reality that AI systems possess specific risks – such as susceptibility to data poisoning, model obfuscation, and indirect prompt injection – that traditional software security measures often miss. The standard covers deep neural networks and generative AI through to basic predictive systems, explicitly excluding only those used strictly for academic research. ETSI standard clarifies the chain of responsibility for AI security A persistent hurdle in enterprise AI adoption is determining who owns the risk. The ETSI standard resolves this by defining three primary technical roles: Developers, System Operators, and Data Custodians. For many enterprises, these lines blur. A financial services firm that fine-tunes an open-source model for fraud detection counts as both a Developer and a System Operator. This dual status triggers strict obligations, requiring the firm to secure the deployment infrastructure while documenting the provenance of training data and the model’s design auditing. The inclusion of ‘Data Custodians’ as a distinct stakeholder group directly impacts Chief Data and Analytics Officers (CDAOs). These entities control data permissions and integrity, a role that now carries explicit security responsibilities. Custodians must ensure that the intended usage of a system aligns with the sensitivity of the training data, effectively placing a security gatekeeper within the data management workflow. ETSI’s AI standard makes clear that security cannot be an afterthought appended at the deployment stage. During the design phase, organisations must conduct threat modelling that addresses AI-native attacks, such as membership inference and model obfuscation. One provision requires developers to restrict functionality to reduce the attack surface. For instance, if a system uses a multi-modal model but only requires text processing, the unused modalities (like image or audio processing) represent a risk that must be managed. This requirement forces technical leaders to reconsider the common practice of deploying massive, general-purpose foundation models where a smaller and more specialised model would suffice. The document also enforces strict asset management. Developers and System Operators must maintain a comprehensive inventory of assets, including interdependencies and connectivity. This supports shadow AI discovery; IT leaders cannot secure models they do not know exist. The standard also requires the creation of specific disaster recovery plans tailored to AI attacks, ensuring that a “known good state” can be restored if a model is compromised. Supply chain security presents an immediate friction point for enterprises relying on third-party vendors or open-source repositories. The ETSI standard requires that if a System Operator chooses to use AI models or components that are not well-documented, they must justify that decision and document the associated security risks. Practically, procurement teams can no longer accept “****** box” solutions. Developers are required to provide cryptographic hashes for model components to verify authenticity. Where training data is sourced publicly (a common practice for Large Language Models), Developers must document the source URL and acquisition timestamp. This audit trail is necessary for post-incident investigations, particularly when attempting to identify if a model was subjected to data poisoning during its training phase. If an enterprise offers an API to external customers, they must apply controls designed to mitigate AI-focused attacks, such as rate limiting to prevent adversaries from reverse-engineering the model or overwhelming defences to inject poison data. The lifecycle approach extends into the maintenance phase, where the standard treats major updates – such as retraining on new data – as the deployment of a new version. Under the ETSI AI standard, this triggers a requirement for renewed security testing and evaluation. Continuous monitoring is also formalised. System Operators must analyse logs not just for uptime, but to detect “data drift” or gradual changes in behaviour that could indicate a security breach. This moves AI monitoring from a performance metric to a security discipline. The standard also addresses the “End of Life” phase. When a model is decommissioned or transferred, organisations must involve Data Custodians to ensure the secure disposal of data and configuration details. This provision prevents the leakage of sensitive intellectual property or training data through discarded hardware or forgotten cloud instances. Executive oversight and governance Compliance with ETSI EN 304 223 requires a review of existing cybersecurity training programmes. The standard mandates that training be tailored to specific roles, ensuring that developers understand secure coding for AI while general staff remain aware of threats like social engineering via AI outputs. “ETSI EN 304 223 represents an important step forward in establishing a common, rigorous foundation for securing AI systems”, said Scott Cadzow, Chair of ETSI’s Technical Committee for Securing Artificial Intelligence. “At a time when AI is being increasingly integrated into critical services and infrastructure, the availability of clear, practical guidance that reflects both the complexity of these technologies and the realities of deployment cannot be underestimated. The work that went into delivering this framework is the result of extensive collaboration and it means that organisations can have full confidence in AI systems that are resilient, trustworthy, and secure by design.” Implementing these baselines in ETSI’s AI security standard provides a structure for safer innovation. By enforcing documented audit trails, clear role definitions, and supply chain transparency, enterprises can mitigate the risks associated with AI adoption while establishing a defensible position for future regulatory audits. An upcoming Technical Report (ETSI TR 104 159) will apply these principles specifically to generative AI, targeting issues like deepfakes and disinformation. See also: Allister Frost: Tackling workforce anxiety for AI integration 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. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Meeting the new ETSI standard for AI security appeared first on AI News. View the full article

Hiring at large firms has long relied on interviews, tests, and human judgment. That process is starting to shift. McKinsey has begun using an AI chatbot as part of its graduate recruitment process, signalling a shift in how professional services organisations evaluate early-career candidates. The chatbot is being used during the initial stages of recruitment, where applicants are asked to interact with it as part of their assessment. Rather than replacing interviews or final hiring decisions, the tool is intended to support screening and evaluation earlier in the process. The move reflects a wider trend across large organisations: AI is no longer limited to research or client-facing tools, but is increasingly shaping internal workflows. Why McKinsey is using AI in graduate hiring Graduate recruitment is resource-heavy. Every year, large firms receive tens of thousands of applications, many of which must be assessed in short hiring cycles. Screening candidates for basic fit, communication skills, and problem-solving ability can take a long time, even before interviews begin. Using AI at this stage offers a way to manage volume. A chatbot can interact with every applicant, ask consistent questions and collect organised responses. Human recruiters can then review that data, rather than requiring staff to manually screen every application from scratch. For McKinsey, the chatbot is part of a larger assessment process that includes interviews and human judgment. According to the company, the tool helps in gathering more information early on, rather than making recruiting judgments on its own. Shifting the role of recruiters Introducing AI into recruitment alters how hiring teams operate. Rather than focusing on early screening, recruiters can devote more time to assessing prospects who have already passed initial tests. In theory, that allows for more thoughtful interviews and deeper evaluation later in the process. At the same time, it raises questions about oversight. Recruiters need to understand how the chatbot evaluates responses and what signals it prioritises. Without that visibility, there is a risk that decisions could lean too heavily on automated outputs, even if the tool is meant to assist rather than decide. Professional services firms are typically wary about such adjustments. Their reputations rely heavily on talent quality, and any perception of unfair or flawed hiring practices carries risk. As a result, recruitment serves as a testing ground for AI use, as well as an area where controls are important. Concerns around fairness and bias Using AI in hiring is not without controversy. Critics have raised concerns that automated systems can reflect biases present in their training data or in how questions are framed. If not monitored closely, those biases can affect who progresses through the hiring process. McKinsey has said it is mindful of these risks and that the chatbot is used alongside human review. Still, the move highlights a broader challenge for organisations adopting AI internally: tools must be tested, audited, and adjusted over time. In recruitment, that includes checking whether certain groups are disadvantaged by how questions are asked or how responses are interpreted. It also means giving candidates clear information about how AI is used and how their data is handled. How McKinsey’s AI hiring move fits a wider enterprise trend The use of AI in graduate hiring is not unique to consulting. Large employers in finance, law, and technology are also testing AI tools for screening, scheduling interviews, and analysing written responses. What stands out is how quickly these tools are moving from experiments to real processes. In many cases, AI enters organisations through small, contained use cases. Hiring is one of them. It sits inside the company, affects internal efficiency, and can be adjusted without changing products or services offered to clients. That pattern mirrors how AI adoption is unfolding more broadly. Instead of sweeping transformations, many firms are adding AI to specific workflows where the benefits and risks are easier to manage. What this signals for enterprises McKinsey’s use of an AI chatbot in recruitment points to a practical shift in enterprise thinking. AI is becoming a tool for routine internal decisions, not just analysis or automation behind the scenes. For other organisations, the lesson is less about copying the tool and more about approach. Introducing AI into sensitive areas like hiring requires clear boundaries, human oversight, and a willingness to review outcomes over time. It also requires communication. Candidates need to know when they are interacting with AI and how that interaction fits into the overall hiring process. Transparency helps build trust, especially as AI becomes more common in workplace decisions. As professional services firms continue to test AI in their own operations, recruitment offers an early view of how far they are willing to go. The technology may help manage scale and consistency, but responsibility for decisions still rests with people. How well companies balance those two will shape how AI is accepted inside the enterprise. (Photo by Resume Genius) See also: Allister Frost: Tackling workforce anxiety for AI integration success Want to learn more about AI and big data from industry leaders? Check outAI & 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, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. 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OpenAI, Google, and Anthropic announced specialised medical AI capabilities within days of each other this month, a clustering that suggests competitive pressure rather than coincidental timing. Yet none of the releases are cleared as medical devices, approved for clinical use, or available for direct patient diagnosis—despite marketing language emphasising healthcare transformation. OpenAI introduced ChatGPT Health on January 7, allowing US users to connect medical records through partnerships with b.well, Apple Health, Function, and MyFitnessPal. Google released MedGemma 1.5 on January 13, expanding its open medical AI model to interpret three-dimensional CT and MRI scans alongside whole-slide histopathology images. Anthropic followed on January 11 with Claude for Healthcare, offering HIPAA-compliant connectors to CMS coverage databases, ICD-10 coding systems, and the National Provider Identifier Registry. All three companies are targeting the same workflow pain points—prior authorisation reviews, claims processing, clinical documentation—with similar technical approaches but different go-to-market strategies. Developer platforms, not diagnostic products The architectural similarities are notable. Each system uses multimodal large language models fine-tuned on medical literature and clinical datasets. Each emphasises privacy protections and regulatory disclaimers. Each positions itself as supporting rather than replacing clinical judgment. The differences lie in deployment and access models. OpenAI’s ChatGPT Health operates as a consumer-facing service with a waitlist for ChatGPT Free, Plus, and Pro subscribers outside the EEA, Switzerland, and the ***. Google’s MedGemma 1.5 releases as an open model through its Health AI Developer Foundations program, available for download via Hugging Face or deployment through Google Cloud’s Vertex AI. Anthropic’s Claude for Healthcare integrates into existing enterprise workflows through Claude for Enterprise, targeting institutional buyers rather than individual consumers. The regulatory positioning is consistent across all three. OpenAI states explicitly that Health “is not intended for diagnosis or treatment.” Google positions MedGemma as “starting points for developers to evaluate and adapt to their medical use cases.” Anthropic emphasises that outputs “are not intended to directly inform clinical diagnosis, patient management decisions, treatment recommendations, or any other direct clinical practice applications.” Benchmark performance vs clinical validation Medical AI benchmark results improved substantially across all three releases, though the gap between test performance and clinical deployment remains significant. Google reports that MedGemma 1.5 achieved 92.3% accuracy on MedAgentBench, Stanford’s medical agent task completion benchmark, compared to 69.6% for the previous Sonnet 3.5 baseline. The model improved by 14 percentage points on MRI disease classification and 3 percentage points on CT findings in internal testing. Anthropic’s Claude Opus 4.5 scored 61.3% on MedCalc medical calculation accuracy tests with Python code execution enabled, and 92.3% on MedAgentBench. The company also claims improvements in “honesty evaluations” related to factual hallucinations, though specific metrics were not disclosed. OpenAI has not published benchmark comparisons for ChatGPT Health specifically, noting instead that “over 230 million people globally ask health and wellness-related questions on ChatGPT every week” based on de-identified analysis of existing usage patterns. These benchmarks measure performance on curated test datasets, not clinical outcomes in practice. Medical errors can have life-threatening consequences, translating benchmark accuracy to clinical utility more complex than in other AI application domains. Regulatory pathway remains unclear The regulatory framework for these medical AI tools remains ambiguous. In the US, the FDA’s oversight depends on intended use. Software that “supports or provides recommendations to a health care professional about prevention, diagnosis, or treatment of a disease” may require premarket review as a medical device. None of the announced tools has FDA clearance. Liability questions are similarly unresolved. When Banner Health’s CTO Mike Reagin states that the health system was “drawn to Anthropic’s focus on AI safety,” this addresses technology selection criteria, not legal liability frameworks. If a clinician relies on Claude’s prior authorisation analysis and a patient suffers harm from delayed care, existing case law provides limited guidance on responsibility allocation. Regulatory approaches vary significantly across markets. While the FDA and Europe’s Medical Device Regulation provide established frameworks for software as a medical device, many APAC regulators have not issued specific guidance on generative AI diagnostic tools. This regulatory ambiguity affects adoption timelines in markets where healthcare infrastructure gaps might otherwise accelerate implementation—creating a tension between clinical need and regulatory caution. Administrative workflows, not clinical decisions Real deployments remain carefully scoped. Novo Nordisk’s Louise Lind Skov, Director of Content Digitalisation, described using Claude for “document and content automation in pharma development,” focused on regulatory submission documents rather than patient diagnosis. Taiwan’s National Health Insurance Administration applied MedGemma to extract data from 30,000 pathology reports for policy analysis, not treatment decisions. The pattern suggests institutional adoption is concentrating on administrative workflows where errors are less immediately dangerous—billing, documentation, protocol drafting—rather than direct clinical decision support where medical AI capabilities would have the most dramatic impact on patient outcomes. Medical AI capabilities are advancing faster than the institutions deploying them can navigate regulatory, liability, and workflow integration complexities. The technology exists. The US$20 monthly subscription provides access to sophisticated medical reasoning tools. Whether that translates to transformed healthcare delivery depends on questions these coordinated announcements leave unaddressed. See also: AstraZeneca bets on in-house AI to speed up oncology research 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. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post AI medical diagnostics race intensifies as OpenAI, Google, and Anthropic launch competing healthcare tools appeared first on AI News. View the full article

Drug development is producing more data than ever, and large pharmaceutical companies like AstraZeneca are turning to AI to make sense of it. The challenge is no longer whether AI can help, but how tightly it needs to be built into research and clinical work to improve decisions around trials and treatment. That question helps explain why AstraZeneca is bringing Modella AI in-house. The company has agreed to acquire the Boston-based AI firm as it looks to deepen its use of AI across oncology research and clinical development. Financial terms were not disclosed. Rather than treating AI as a supporting tool, AstraZeneca is pulling Modella’s models, data, and staff directly into its research organisation. The move reflects a broader shift in the drug industry, where partnerships are giving way to acquisitions as companies try to gain more control over how AI is built, tested, and used in regulated settings. Why AI ownership is starting to matter in drug research Modella AI focuses on using computers to analyse pathology data, such as biopsy images, and link those findings with clinical information. Its work centres on making pathology more quantitative, helping researchers spot patterns that may point to useful biomarkers or guide treatment choices. In a statement, Modella said its foundation models and AI agents would be integrated into AstraZeneca’s oncology research and development work, with a focus on clinical development and biomarker discovery. How AstraZeneca moved its AI partnership toward full integration For AstraZeneca, the deal builds on a collaboration that began several years ago. That earlier partnership allowed both sides to test whether Modella’s tools could work within the drugmaker’s research environment. According to AstraZeneca executives, the experience made it clear that closer integration was needed. Speaking at the J.P. Morgan Healthcare Conference, AstraZeneca Chief Financial Officer Aradhana Sarin described the acquisition as a way to bring more data and AI capability inside the company. “Oncology drug development is becoming more complex, more data-rich and more time-sensitive,” said Gabi Raia, Modella AI’s chief commercial officer, adding that joining AstraZeneca would allow the company to deploy its tools across global trials and clinical settings. Using AI to improve trial decisions Sarin said the deal would “supercharge” AstraZeneca’s work in quantitative pathology and biomarker discovery by combining data, models, and teams under one roof. While such language reflects ambition, the practical goal is more grounded: shortening the time it takes to turn research data into decisions that affect trial design and patient selection. One area where AstraZeneca expects AI to have an impact is in choosing patients for clinical trials. Better matching patients to studies could improve trial outcomes and reduce costs tied to delays or failed studies. That kind of improvement depends less on complex algorithms and more on steady access to clean data and tools that fit into existing workflows. Talent and tools move in-house The acquisition also highlights a change in how large pharmaceutical firms think about AI talent. Rather than relying on outside vendors, companies are increasingly treating data scientists and machine learning experts as part of their core research teams. For AstraZeneca, bringing Modella’s staff in-house reduces dependence on external roadmaps and gives the company more say over how tools are adapted as research needs change. AstraZeneca said this is the first time a major pharmaceutical company has acquired an AI firm outright, though collaborations between drugmakers and technology companies have become common. AstraZeneca joins a crowded field of pharma–AI deals At the same healthcare conference, several new partnerships were announced, including a $1 billion collaboration between Nvidia and Eli Lilly to build a new research lab using Nvidia’s latest AI chips. Those deals point to growing interest in AI across the sector, but they also underline a key difference in strategy. Partnerships can speed up experimentation, while acquisitions suggest a longer-term bet on building internal capability. For companies operating under strict regulatory rules, that control can matter as much as raw computing power. What AstraZeneca is betting on next Sarin described the earlier AstraZeneca–Modella partnership as a “test drive,” saying the company ultimately wanted Modella’s data, models, and people inside the organisation. The aim, she said, is to support the development of “highly targeted biomarkers and then highly targeted therapeutics.” Beyond the Modella deal, Sarin said 2026 is expected to be a busy year for AstraZeneca, with several late-stage trial results due across different therapy areas. The company is also working toward a target of $80 billion in annual revenue by 2030. Whether acquisitions like this help meet those goals will depend on execution. Integrating AI into drug development is slow, expensive, and often messy. Still, AstraZeneca’s move signals a clear view of where it thinks the value lies: not in buying AI as a service, but in embedding it deeply into how medicines are discovered and tested. (Photo by Mika Baumeister) See also: Allister Frost: Tackling workforce anxiety for AI integration 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. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post AstraZeneca bets on in-house AI to speed up oncology research appeared first on AI News. View the full article

Research from Cleo AI indicates that young adults are turning to artificial intelligence for financial advice to help them manage their money and develop more sustainable financial habits. The study surveyed 5,000 *** adults aged 28 to 40 and found that the majority are saving significantly less than they would like. In this context, interest in AI-driven money management tools is rising. One in five respondents describe themselves as curious about using AI to manage their finances, while a further 12% say they are excited by the prospect. Yet despite the interest in using AI in this context, confidence in personal financial management remains weak. More than a third of respondents (37%) report struggling with self-discipline around money, with impulse spending frequently undermining savings goals. Four in five believe they could improve their financial knowledge, pointing to a gap between intention and behaviour. Adults aged 28 to 34 are around 15% more satisfied with their savings than those aged 35 to 40, and save around 33% more each month on average. The findings suggest that as people move through early adulthood, financial strain accumulates while access to effective, ongoing support does not increase at the same rate. AI in money management AI is being seen as a tool that might help regain financial control. Many respondents express comfort with using AI for routine financial tasks. Nearly two-thirds (64%) would trust AI to advise on disposable income, while more than half would allow AI to move money to avoid overdrafts (54%) or manage regular bill payments (52%). Cleo’s CEO and founder, Barney Hussey-Yeo, states structural economic pressures are a major factor. Rising living costs, stagnant pay, low wages, and debt mean that many people are not mismanaging money so much as not having enough to make managing it worthwhile. In this context, AI tools positioned as practical, everyday assistance that can work with highly limited funds at its disposal rather than a tool for aspirational financial planning. Younger respondents are driving adoption. Adults aged 28 to 34 are 8% more confident than those aged 35 to 40 in using AI-powered financial tools. However, trust remains a barrier: nearly a quarter of respondents (23%) prefer to begin with limited use of the technology and need evidence of value before significant engagement. The research also highlights the regional disparities evident in the ***. Average monthly savings in the affluent South are 26% higher than in the North. Londoners save 33% more than the national average and around £250 more per month than those in Norwich. London (£431), Brighton (£401) and Edinburgh (£386) report the highest average monthly savings, while Newcastle (£185) and Cardiff in Wales (£184.95) sit at the bottom. Implications for fintech decision-makers The strongest signal in this evidence is not enthusiasm for AI per se, but demand for support under financial stress. High proportions citing poor self-discipline (37%) and low confidence in financial knowledge (80%) indicate that execution is the second problem. Trust is a gating factor rather than a secondary concern. While headline willingness to delegate tasks such as overdraft avoidance is high, nearly a quarter of users want incremental proof before committing. This would favour modular product design and specific implementations in software rather than full automation from the outset. Evidence suggests adoption will be earned through demonstrated utility, not brand positioning. Age-related divergence within a relatively narrow cohort (28–40) is notable. The sharp drop in savings satisfaction and contribution among those aged 35–40 (the time of life when most take on more responsibilities and financial burden) suggests that fintechs targeting young professionals only might miss those with materially different needs. For older millennials, tools that address cumulative obligations (housing, dependants, legacy debt, bills) are likely to be more relevant.. Regional savings disparities are large and persistent, with London outliers (where mean income is higher) masking much weaker savings capacity elsewhere. This weakens the case for nationally uniform products. Pricing, thresholds, nudges in the form of notifications and in-app messages may need regional bias if products are to feel realistic outside higher-income urban centres in the South of the ***. (Image source: “Iced tea at Georgia’s” by Ed Yourdon 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 Research shows *** young adults would use AI for financial guidance appeared first on AI News. View the full article