ChatGPT

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

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

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

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

As a large provider of technology services operating in multiple industries, Infosys is one of the names that quickly come to mind when decision-makers consider possible providers of consultation on and practical implementation of any AI project – discrete or organisation-wide. Infosys delivers these services through its Topaz Fabric, leveraging its partnerships with specific AI technology providers. It reports that it is currently working on AI implementations with 90% of its top 200 clients and has more than 4,600 AI projects in progress. The company’s strategy for AI implementation organisation-wide looks at six areas affected and considered during projects. AI strategy and engineering focuses on designing and implementing AI strategies and architectures aligned to specific business objectives. These include the orchestration of AI agents, proprietary platforms, and third-party tools on infrastructure especially configured for AI workloads. An overarching strategy will lead to a consistent, enterprise AI-first operating model. Data for AI addresses the preparation of enterprise data, covering structured and unstructured data and processes in this area include the development of AI-ready data platforms. Infosys refers to “AI-grade” data engineering practices such as data fingerprinting and synthetic training data services. The intention is to convert siloed data assets into reliable inputs for analytics and predictive systems. Process AI concentrates on integrating AI agents into business processes, redesigning workflows if necessary so AI agents and human employees can work better together. The aim is to improve operational efficiency in general, regardless of business function. Legacy modernisation applies AI agents in the analysis and interpretation of the existing technology stack and potentially reverse-engineering legacy systems to better stage AI modernisation projects. The overall aim is to reduce technical debt and offer a greater responsiveness when AI is unleashed. Physical AI extends into products and devices in the workplace. This involves embedding AI into hardware systems such as those that collect sensor data, interpret that data, and act in the physical world. This broad definition encompasses digital twins, robotics, autonomous systems, and edge computing. In short, it’s the integration of digital intelligence and physical operations. AI trust covers governance, security, and ethics, and includes consideration of risk assessment frameworks, policy development, AI testing, and overall technology lifecycle management. Lessons for business leaders Although business leaders may be already in partnership with alternative service providers other than Infosys, the company’s strategy of demarcating the necessary action areas for AI implementations offers significant value. The six areas described provide practical reference points that can be used in any organisation to plan projects or perhaps monitor and assess ongoing implementation efforts. Among these, data preparation is central. AI systems depend on data quality and consistency, so investment in data platforms, data governance, and engineering practices that support models is central tenet on which AI initiatives are built. Embedding AI into workflows means it’s sometimes necessary to redesign the way employees work. Leaders should be aware of how AI agents and employees interact, and measure performance improvements. Changes can be made both to the technologies deployed and the working methods that have existed to date. If the latter, retraining and educating affected employees will be necessary, with accompanying costs. The issue of legacy systems requires careful attention as many organisations operate complex estates that limit the agility necessary for AI to improve operations. AI tools themselves can help to analyse existing dependencies and even plan modernisation, implemented, ideally, over several stages or in separate sprints. Physical operations intersect increasingly with digital systems. For companies with physical products, such as in manufacturing or logistics, embedding AI into devices and equipment can improve monitoring and devices’ responsiveness. This will require coordination between IT, OT, engineering, and operational teams, and line-of-business leaders should be consulted in particular. Governance should accompany any scale of AI implementation. Risk assessment, security testing, security policy formulation, and the design of AI-specific guardrails should be established early on. Regulatory scrutiny of AI is increasing, particularly in sectors handling sensitive data, and statutory penalties apply for data loss or mismanagement, regardless of its source – AI or otherwise – in the enterprise. Clear accountability structures and documentation reduce these risks to operations and reputation. Taken together, these areas indicate that AI implementation is organisational rather than purely technical. Success depends on leadership alignment, sustained investment, and realistic assessment of any capability gaps. Claims of rapid transformation should be treated cautiously, and durable results are more likely when strategy, data, process design, modernisation, operational integration, and governance are addressed in parallel. (Image source: “Infosys, Bangalore, India” by theqspeaks is licensed under CC BY-NC-SA 2.0.) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Infosys AI implementation framework offers business leaders guidance appeared first on AI News. View the full article

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

American International Group (AIG) has reported faster than expected gains from its use of generative AI, with implications for underwriting capacity, operating cost, and portfolio integration. The company’s recent disclosures at an Investor Day merit attention from AI decision-makers as they contain assertions about measurable throughput and workflow redesign. AIG has outlined potential benefits from generative AI. Chief executive Peter Zaffino later described the company’s early projections as “aspirational,” yet in a fourth quarter earnings call, he stated that “we see the abilities are much greater.” The change in tone is indicative of positive internal results, and according to Zaffino, “We’re seeing a massive change in our ability to process a submission flow way […] without additional human capital resources. That has been the biggest surprise.” The company’s claims that generative AI has increased submission processing capacity, the economic impact is direct. AIG reports that in 2025 it “made progress embedding generative AI in our core underwriting and claims processes, and expanding it.” The company’s internal tool, AIG Assist, is implemented in most commercial lines of businesses. Lexington Insurance, AIG’s excess and surplus unit has targetted reaching 500,000 submissions by 2030. Zaffino reports that Lexington has already surpassed 370,000 submissions in 2025. AIG uses generative models to extract and summarise incoming data, and has developed an orchestration layer in the technology stack “to coordinate AI agents to drive better decision-making and reduce costs in the organisation.” Previous Investor Days, this level of orchestration was not a focus. The chief executive describes AI agents “as companions that operate with our teams” that provide real-time information, draw on historical cases, and challenge underwriting decisions. The company relies on its ability to manage incoming data “at a fraction of the time” and to orchestrate agents so they can “scale and be able to analyse that information that’s not biased in any way; that’s through the entire workflow.” AIG links orchestration to compression of what it terms a “front-to-back workflow,” a tighter integration between intake, risk assessment and claims handling. The company states that multiple agents, coordinated through a orchestration layer, streamlines repetitive and previously-lengthy processes. AIG has applied its generative AI stack in specific transactions. During the conversion of Everest’s retail commercial business, the company reports that accounts were prioritised for renewal “in a fraction of the time.” Management states that it built an ontology of Everest’s portfolio and combined it with its own, which “allowed [the company] to prioritise how the portfolios could blend together.” Ontological alignment is technically demanding and often creates underestimated costs. The launch of Lloyd’s Syndicate 2479, in partnership with Amwins and Blackstone, extended the ontological approach to a special purpose vehicle. In conjunction with Palantir, AIG used LLMs to assess whether Amwins’ programme portfolio aligned with the syndicate’s stated risk appetite. Zaffino stated that AIG has a “strong pipeline of SPV opportunities.” For AI decision-makers, the case illustrates the use that orchestration and workflow integration can provide when generative models are embedded in core processes, and the degree to which economic impact depends on measurable changes in capacity and cycle time. (Image source: “Nagasaki, AIG (Insurance company) building” by Admanchester is licensed under CC BY-NC-ND 2.0. ) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Insurance giant AIG deploys agentic AI with orchestration layer appeared first on AI News. View the full article

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

Goldman Sachs plans to deploy Anthropic’s Claude model in trade accounting and client onboarding, and, according to an article in American Banker, presents this as part of a broader push among large banks to use generative artificial intelligence to improve efficiency. The focus is on operational processes that sit in the back office and have traditionally relied on large teams performing tasks like document review, reconciliation, and compliance checks. Several banks already use generative AI in knowledge work. JPMorganChase provides employees with access to a large language model suite for information retrieval and data analysis, while the Bank of America’s Erica assistant answers internal technology and human resources queries. Citi and Goldman both use AI to help developers with coding. The article suggests a more recent development is the application of generative AI to operational tasks like trade accounting and know-your-customer (KYC). Automating the edge-cases Automatable processes in the sector are often rules-based, involving collecting data, validating it against internal and external databases, and assembling required documentation. In theory, conventional software has been used to automate such work. However, Marco Argenti, Goldman’s chief information officer, argues that if a rules-based system resolves most cases, a small percentage of transactions fall outside defined parameters that can translate into thousands of individual items at the type of scale in question. He cites the example of identity verification in KYC compliance, where minor discrepancies or documents approaching expiry can create edge cases requiring judgement. Argenti says that neural networks can address these micro-decisions as they’re capable of applying contextual reasoning where fixed rules might be missing or don’t necessarily give a clear answer. In this scenario, generative AI augments existing rules systems rather than supplanting them. Operational improvements, therefore lie in the reduction in the number of cases that require manual intervention and thus shortening time needed to resolve the exceptions. The coding experience Goldman’s prior experience with Claude models used internally for software development informed its decision to extend AI to other areas of operations. Developers use a version of Claude with Cognition’s Devin agent to aid them with programming. In this context, human developers set specifications and regulatory parameters, the agent produces code, and humans review outputs. The agent is also used to run code tests and validations. He describes this as a change to devs’ workflows, with agents operating according to defined instructions. The benefit is increased developer productivity and the faster completion of projects.s For trade accounting and client onboarding, Goldman and Anthropic AI project owners observed existing workflows with domain experts to identify work bottlenecks. The implemented agents review documents, extract entities, determine whether additional documentation is required, assess ownership structures, and can trigger further compliance checks. Tasks automated in this way tend to be document-heavy and require individual judgement. By automating extraction and preliminary assessment, the agents reduce the time analysts spend on comparison work. Indranil Bandyopadhyay, principal analyst at Forrester, says that reconciliation in trade accounting requires comparing fragmented data in internal ledgers, counterparty confirmations, and the perusal of bank statements, and that a typical workflow depends on accurate extraction and matching of figures and text to existing documents. Claude’s ability to process large context windows and follow instructions, he says, makes it suited to just such workflows. The labour involved in client onboarding, such as parsing passports and corporate registration documents, and the cross-referencing of all sources means AI’s ability to extract structured data and flag inconsistencies makes the technology a good fit, reducing overall workloads. Bandyopadhyay stresses that accounting and compliance platforms remain the canonical systems of record. Claude operates in the workflow layer, handling extraction and comparison so human analysts can handle the code’s exceptions.. In his assessment, the operational value in a regulated environments like banking lies in such a division of labour. Jonathan Pelosi, head of financial services at Anthropic says Claude is trained to surface uncertainty and to provide source attribution, creating an audit trail – reducing the effect of hallucinations. Bandyopadhyay also notes the importance of human oversight and validation, saying institutions should design systems so that errors are detected early. Goldman’s Marco Argenti rejects the view that AI systems are inherently easier to deceive than people, arguing that social engineering exploits human vulnerabilities and that AI can detect subtle anomalies at scale, and reiterates the need to combine human judgement with automated scrutiny in teams. His claim implies a increase in operational capacity without proportional increases in staff, even with the issues known to affect AI rollouts. AI in banking operations In the banking sector, generative AI is a tool that improves operational performance by accelerating document processing, reducing exception handling time, and increasing throughput in high volume workflows. But the need to retain human oversight to counteract AI’s errors means the retention of and reliance on existing systems of records remains. (Image source: “Dreams…” by noahwesley is licensed under CC BY-NC-SA 2.0) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Goldman Sachs deploys Anthropic systems with success appeared first on AI News. View the full article

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

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

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

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

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

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

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

For many enterprises, the first real test of AI is not customer-facing products or flashy automation demos. It is the quiet machinery that runs the organisation itself. Human resources, with its mix of routine workflows, compliance needs, and large volumes of structured data, is emerging as one of the earliest areas where companies are pushing AI into day-to-day operations. That shift is visible in how large employers are rethinking workforce systems. The telecommunications group e& began moving its human resources operations to what it describes as an AI-first model, covering roughly 10,000 employees across its organisation. The transition is built on Oracle Fusion Cloud Human Capital Management (HCM), running in an Oracle Cloud Infrastructure dedicated region. Details of the deployment were outlined in a recent Oracle announcement. The change is less about introducing a single AI feature and more about restructuring how HR processes are handled. Automated and AI-driven tools are expected to help HR departments with recruitment screening, interview coordination, and employee learning recommendations. The stated goal is to standardise processes across regions and provide managers with faster access to workforce data and insights. HR as an enterprise AI proving ground From an enterprise perspective, HR is a logical entry point. Many HR tasks follow repeatable patterns: candidate matching, onboarding documentation, leave management, and training assignments. These workflows produce consistent data trails, which makes them easier to model and automate than loosely defined knowledge work. Moving such functions onto AI-supported systems allows organisations to test reliability, governance, and user acceptance in a controlled environment before expanding into more sensitive areas. The infrastructure choice also indicates how enterprises are balancing innovation with compliance. Oracle claims that the system is deployed in a dedicated cloud region designed to address data sovereignty and regulatory requirements. For multinational corporations, workforce data sits at the intersection of privacy law, employment regulation, and corporate governance. Running AI tools in a controlled environment is part of how companies are trying to contain risk while experimenting with automation. Governance, compliance, and internal risk management The e& rollout reflects a broader pattern in enterprise AI adoption: internal transformation is often more achievable than external disruption. Customer-facing AI systems attract attention, but they introduce reputational and operational risk if they fail. HR platforms, by contrast, operate behind the scenes. Errors can still carry consequences, yet they are easier to monitor, audit, and correct within existing governance structures. Industry research supports the idea that internal operations are becoming a primary testing ground. Deloitte’s 2026 State of AI in the Enterprise report found that organisations are increasingly shifting AI projects from pilot stages into production environments, with productivity and workflow automation cited as early areas of return. The report is based on a survey of more than 3,000 senior leaders involved in AI initiatives, including respondents in Southeast Asia. While the study spans multiple business functions, administrative and operational processes were repeatedly identified as practical entry points for scaled deployment. Workforce systems also provide a natural setting for AI agents and assistants. HR teams handle frequent employee queries about policies, benefits, and training options. Embedding conversational tools into these workflows may reduce manual workload while giving employees faster access to information. According to Oracle’s description of the deployment, e& plans to introduce digital assistants designed to support candidate engagement and employee development tasks. Whether such tools deliver consistent value will depend on accuracy, oversight, and how well they integrate with existing HR processes. Scaling AI inside the organisation The lesson is not that HR automation is new, but that AI is changing the scope of what can be automated. Traditional HR software focused on record-keeping and workflow management. AI layers add predictive matching, pattern analysis, and decision support. That expansion raises familiar governance questions: data quality, bias, auditability, and employee trust. There is also a workforce dimension. Automating parts of HR does not eliminate the need for human oversight; it changes where effort is concentrated. HR professionals may spend less time on routine coordination and more on policy interpretation, employee engagement, and exception handling. Enterprises adopting AI-driven systems will need clear escalation paths and review processes to avoid over-reliance on automated outputs. What makes the current moment different is scale. Deployments that cover thousands of employees turn AI from an experiment into operational infrastructure. They force organisations to confront issues of reliability, training, and change management in real time. The systems must work consistently across jurisdictions, languages, and regulatory frameworks. As enterprises look for low-risk entry points into AI, workforce operations are likely to remain high on the list. They combine structured data, repeatable workflows, and measurable outcomes — conditions that suit automation while still allowing room for human judgement. The experience of early adopters will shape how quickly other internal functions, from finance to procurement, follow a similar path. (Photo by Zulfugar Karimov) See also: Barclays bets on AI to cut costs and boost returns 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, clickhere for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post How e& is using HR to bring AI into enterprise operations appeared first on AI News. View the full article

Alibaba has entered the race to build AI that powers robots, not just chatbots. The ******** tech giant this week unveiled RynnBrain, an open-source model designed to help robots perceive their environment and execute physical tasks. The move signals China’s accelerating push into physical AI as ageing populations and labour shortages drive demand for machines that can work alongside—or replace—humans. The model positions Alibaba alongside Nvidia, Google DeepMind, and Tesla in the race to build what Nvidia CEO Jensen Huang calls “a multitrillion-dollar growth opportunity.” Unlike its competitors, however, Alibaba is pursuing an open-source strategy—making RynnBrain freely available to developers to accelerate adoption, similar to its approach with the Qwen family of language models, which rank among China’s most advanced AI systems. Video demonstrations released by Alibaba’s DAMO Academy show RynnBrain-powered robots identifying fruit and placing it in baskets—tasks that seem simple but require complex AI governing object recognition and precise movement. The technology falls under the category of vision-language-action (VLA) models, which integrate computer vision, natural language processing, and motor control to enable robots to interpret their surroundings and execute appropriate actions. Unlike traditional robots that follow preprogrammed instructions, physical AI systems like RynnBrain enable machines to learn from experience and adapt behaviour in real time. This represents a fundamental shift from automation to autonomous decision-making in physical environments—a shift with implications extending far beyond factory floors. HUGE: Alibaba just launched "RynnBrain" an open-source AI model that lets robots see, think, and act in the real world, with the aim to steal market share from Google and Nvidia. pic.twitter.com/ULe3VcFlcE — AI Flash (@aiflash_) February 10, 2026 From prototype to production The timing signals a broader inflexion point. According to Deloitte’s 2026 Tech Trends report, physical AI has begun “shifting from a research timeline to an industrial one,” with simulation platforms and synthetic data generation compressing iteration cycles before real-world deployment. The transition is being driven less by technological breakthroughs than by economic necessity. Advanced economies face a stark reality: demand for production, logistics, and maintenance continues rising while labour supply increasingly fails to keep pace. The OECD projects that working-age populations across developed nations will stagnate or decline over the coming decades as ageing accelerates. Parts of East Asia are encountering this reality earlier than other regions. Demographic ageing, declining fertility, and tightening labour markets are already influencing automation choices in logistics, manufacturing, and infrastructure—particularly in China, Japan, and South Korea. These environments aren’t exceptional; they’re simply ahead of a trajectory other advanced economies are likely to follow. When it comes to humanoid robots specifically—machines designed to walk and function like humans—China is “forging ahead of the U.S.,” with companies planning to ramp up production this year, according to Deloitte. UBS estimates there will be two million humanoids in the workplace by 2035, climbing to 300 million by 2050, representing a total addressable market between $1.4 trillion and $1.7 trillion by mid-century. The governance gap Yet as physical AI capabilities accelerate, a critical constraint is emerging—one that has nothing to do with model performance. “In physical environments, failures cannot simply be patched after the fact,” according to a World Economic Forum analysis published this week. “Once AI begins to move goods, coordinate labour or operate equipment, the binding constraint shifts from what systems can do to how responsibility, authority and intervention are governed.” Physical industries are governed by consequences, not computation. A flawed recommendation in a chatbot can be corrected in software. A robot that drops a part during handover or loses balance on a factory floor designed for humans causes operations to pause, creating cascading effects on production schedules, safety protocols, and liability chains. The WEF framework identifies three governance layers required for safe deployment: executive governance setting risk appetite and non-negotiables; system governance embedding those constraints into engineered reality through stop rules and change controls; and frontline governance giving workers clear authority to override AI decisions. “As physical AI accelerates, technical capabilities will increasingly converge, but governance will not,” the analysis warns. “Those that treat governance as an afterthought may see early gains, but will discover that scale amplifies fragility.” This creates an asymmetry in the US-China competition. China’s faster deployment cycles and willingness to pilot systems in controlled industrial environments could accelerate learning curves. However, governance frameworks that work in structured factory settings may not translate to public spaces where autonomous systems must navigate unpredictable human behaviour. Early deployment signals Current deployments remain concentrated in warehousing and logistics, where labour market pressures are most acute. Amazon recently deployed its millionth robot, part of a diverse fleet working alongside humans. Its DeepFleet AI model coordinates this massive robot army across the entire fulfilment network, which Amazon reports will improve travel efficiency by 10%. BMW is testing humanoid robots at its South Carolina factory for tasks requiring dexterity that traditional industrial robots lack: precision manipulation, complex gripping, and two-handed coordination. The automaker is also using autonomous vehicle technology to enable newly built cars to drive themselves from the assembly line through testing to the finishing area, all without human assistance. But applications are expanding beyond traditional industrial settings. In healthcare, companies are developing AI-driven robotic surgery systems and intelligent assistants for patient care. Cities like Cincinnati are deploying AI-powered drones to autonomously inspect bridge structures and road surfaces. Detroit has launched a free autonomous shuttle service for seniors and people with disabilities. The regional competitive dynamic intensified this week when South Korea announced a $692 million national initiative to produce AI semiconductors, underscoring how physical AI deployment requires not just software capabilities but domestic chip manufacturing capacity. NVIDIA has released multiple models under its “Cosmos” brand for training and running AI in robotics. Google DeepMind offers Gemini Robotics-ER 1.5. Tesla is developing its own AI to power the Optimus humanoid robot. Each company is betting that the convergence of AI capabilities with physical manipulation will unlock new categories of automation. As simulation environments improve and ecosystem-based learning shortens deployment cycles, the strategic question is shifting from “Can we adopt physical AI?” to “Can we govern it at scale?” For China, the answer may determine whether its early mover advantage in robotics deployment translates into sustained industrial leadership—or becomes a cautionary tale about scaling systems faster than the governance infrastructure required to sustain them. (Photo by Alibaba) See also: EY and NVIDIA to help companies test and deploy physical AI 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, clickhere for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Alibaba enters physical AI race with open-source robot model RynnBrain appeared first on AI News. View the full article

AI deployment in financial services has crossed a critical threshold, with only 2% of institutions globally reporting no AI use whatsoever—a dramatic indicator that the technology has moved decisively from boardroom discussion to operational reality. New research from Finastra surveying 1,509 senior leaders across 11 markets reveals that Singapore financial institutions are leading this transition, with nearly two-thirds already deploying AI in production environments rather than confining it to experimental pilots. The Financial Services State of the Nation 2026 report shows 73% of Singapore institutions have deployed or improved AI use cases in their payments technology over the past 12 months—nearly double the 38% global average. “Singapore institutions are showing what AI execution at scale really looks like. This is not about isolated pilots. It is about embedding AI into core operations, supported by modern infrastructure, strong data foundations, and disciplined governance,” said Chris Walters, CEO of Finastra. From experimentation to enterprise AI deployment Globally, 31% of institutions report scaled deployment across multiple functions, while 30% have achieved limited production deployment. A further 27% are piloting or testing in limited functions, with only 8% still in the exploration phase. This represents a fundamental shift in how AI deployment is approached within financial services. The technology is no longer confined to innovation labs or proof-of-concept projects but has become integral to core banking operations. In Singapore specifically, an additional 35% are piloting or researching AI applications beyond their current production deployments, indicating a robust innovation pipeline that positions the city-state as a regional AI leader. The primary objectives driving this deployment vary by market. In Singapore and the US, 43% of institutions are using AI to improve compliance and regulatory processes—reflecting the technology’s ability to navigate increasingly complex oversight requirements while maintaining operational resilience. Globally, the top AI implementation objectives are improving accuracy and reducing errors (40%), increasing employee productivity (37%), and enhancing risk management capabilities (34%). Vietnam prioritises speed, with 49% using AI to accelerate processing in payments and lending services, while Mexico emphasises customer experience and personalisation at 43%. Cloud infrastructure enables AI at scale Singapore’s AI deployment success is underpinned by advanced cloud adoption. The research shows 55% of Singapore institutions host all or most infrastructure in the cloud, with a further 30% operating hybrid environments—an 85% total that significantly exceeds many global peers. This cloud-first approach provides the scalable, resilient infrastructure required for enterprise AI deployment. Without modern data architectures and elastic compute capabilities, AI remains confined to small-scale experiments that cannot deliver enterprise-wide value. The link between modernisation and AI deployment is clear in the data. Nearly nine in ten institutions (87%) globally plan to increase modernisation investment over the next 12 months, with Singapore leading in planned spending increases above 50%. Institutions also report strong confidence in their technology foundations, with 71% of Singapore respondents rating their core infrastructure, security and reliability ahead of peers—the highest globally and well above the 72% average. Security spending surges as AI creates new threat vectors As AI deployment accelerates, so do AI-enabled security threats. The research projects a 40% average increase in security spending globally in 2026, with institutions responding to what 43% describe as constantly evolving risks. Singapore leads in deploying advanced fraud detection and transaction monitoring, with 62% having implemented or upgraded these systems in the past year. This compares to a 48% global average, underscoring the city-state’s recognition that AI-powered fraud requires AI-powered defences. Similarly, 60% of Singapore institutions have modernised their Security Information and Event Management (SIEM) and Security Orchestration, Automation and Response (SOAR) capabilities—again the highest globally—enabling real-time threat monitoring and automated response at scale. Multi-factor authentication and biometrics deployment reached 54% in Singapore, as institutions strengthen identity verification against increasingly sophisticated attack vectors that leverage generative AI and deepfake technologies. Looking ahead, API security and gateway hardening emerge as a key priority, cited by 34% globally as a focus area for the next 12 months. This reflects growing recognition that as ecosystems expand and AI systems interact across organisational boundaries, securing access points becomes paramount. Talent shortages emerge as the primary barrier Despite strong progress, barriers to AI deployment persist. Talent shortages top the list globally at 43%, but in Singapore this figure reaches 54%—the highest of any market surveyed and tied only with the UAE. This intense competition for specialised AI, cloud, and security expertise reflects the gap between institutional ambition and available human capital. Demand for professionals who can architect AI systems, ensure model governance, and integrate AI into existing workflows far outpaces supply. Budget constraints also weigh heavily, cited by 52% of Singapore institutions—again, the highest globally. Even well-funded organisations face difficult prioritisation decisions as they balance AI deployment, security investments, modernisation, and customer experience initiatives. In response, 54% of institutions globally are partnering with fintech providers as their default approach to accessing AI capabilities without bearing the full burden of talent acquisition or system development. These partnerships allow organisations to accelerate AI deployment while maintaining control over critical data and compliance requirements. The research reveals a sector that has decisively crossed the AI adoption threshold but now faces the more complex challenge of scaling responsibly. As Walters noted, success will be defined not by the breadth of AI experiments but by the ability to embed intelligence into operations while strengthening rather than compromising trust. The study surveyed managers and executives from institutions across France, Germany, Hong Kong, Japan, Mexico, Saudi Arabia, Singapore, the UAE, the ***, the US and Vietnam, representing organisations that collectively manage over $100 trillion in assets. (Photo by Peter Nguyen) See also: AI Expo 2026 Day 2: Moving experimental pilots to AI production 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, clickhere for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post AI deployment in financial services hits an inflexion point as Singapore leads the shift to production appeared first on AI News. View the full article

State-sponsored hackers are exploiting AI to accelerate cyberattacks, with threat actors from Iran, North Korea, China, and Russia weaponising models like Google’s Gemini to craft sophisticated phishing campaigns and develop malware, according to a new report from Google’s Threat Intelligence Group (GTIG). The quarterly AI Threat Tracker report, released today, reveals how government-backed attackers have integrated artificial intelligence throughout the attack lifecycle – achieving productivity gains in reconnaissance, social engineering, and malware development during the final quarter of 2025. “For government-backed threat actors, large language models have become essential tools for technical research, targeting, and the rapid generation of nuanced phishing lures,” GTIG researchers stated in the report. AI-powered reconnaissance by state-sponsored hackers targets the defence sector Iranian threat actor APT42 used Gemini to augment reconnaissance and targeted social engineering operations. The group misused the AI model to enumerate official email addresses for specific entities and conduct research to establish credible pretexts for approaching targets. By feeding Gemini a target’s biography, APT42 crafted personas and scenarios designed to elicit engagement. The group also used the AI to translate between languages and better understand non-native phrases – abilities that help state-sponsored hackers bypass traditional phishing red flags like poor grammar or awkward syntax. North Korean government-backed actor UNC2970, which focuses on defence targeting and impersonating corporate recruiters, used Gemini to synthesise open-source intelligence and profile high-value targets. The group’s reconnaissance included searching for information on major cybersecurity and defence companies, mapping specific technical job roles, and gathering salary information. “This activity blurs the distinction between routine professional research and malicious reconnaissance, as the actor gathers the necessary components to create tailored, high-fidelity phishing personas,” GTIG noted. Model extraction attacks surge Beyond operational misuse, Google DeepMind and GTIG identified a increase in model extraction attempts – also known as “distillation attacks” – aimed at stealing intellectual property from AI models. One campaign targeting Gemini’s reasoning abilities involved over 100,000 prompts designed to coerce the model into outputting full reasoning processes. The breadth of questions suggested an attempt to replicate Gemini’s reasoning ability in non-English target languages in various tasks. How model extraction attacks work to steal AI intellectual property. (Image: Google GTIG) While GTIG observed no direct attacks on frontier models from advanced persistent threat actors, the team identified and disrupted frequent model extraction attacks from private sector entities globally and researchers seeking to clone proprietary logic. Google’s systems recognised these attacks in real-time and deployed defences to protect internal reasoning traces. AI-integrated malware emerges GTIG observed malware samples, tracked as HONESTCUE, that use Gemini’s API to outsource functionality generation. The malware is designed to undermine traditional network-based detection and static analysis through a multi-layered obfuscation approach. HONESTCUE functions as a downloader and launcher framework that sends prompts via Gemini’s API and receives C# source code as responses. The fileless secondary stage compiles and executes payloads directly in memory, leaving no artefacts on disk. HONESTCUE malware’s two-stage attack process using Gemini’s API. (Image: Google GTIG) Separately, GTIG identified COINBAIT, a phishing kit whose construction was likely accelerated by AI code generation tools. The kit, which masquerades as a major cryptocurrency exchange for credential harvesting, was built using the AI-powered platform Lovable AI. ClickFix campaigns abuse AI chat platforms In a novel social engineering campaign first observed in December 2025, Google saw threat actors abuse the public sharing features of generative AI services – including Gemini, ChatGPT, Copilot, DeepSeek, and Grok – to host deceptive content distributing ATOMIC malware targeting macOS systems. Attackers manipulated AI models to create realistic-looking instructions for common computer tasks, embedding malicious command-line scripts as the “solution.” By creating shareable links to these AI chat transcripts, threat actors used trusted domains to host their initial attack stage. The three-stage ClickFix attack chain exploiting AI chat platforms. (Image: Google GTIG) Underground marketplace thrives on stolen API keys GTIG’s observations of English and Russian-language underground forums indicate a persistent demand for AI-enabled tools and services. However, state-sponsored hackers and cybercriminals struggle to develop custom AI models, instead relying on mature commercial products accessed through stolen credentials. One toolkit, “Xanthorox,” advertised itself as a custom AI for autonomous malware generation and phishing campaign development. GTIG’s investigation revealed Xanthorox was not a bespoke model but actually powered by several commercial AI products, including Gemini, accessed through stolen API keys. Google’s response and mitigations Google has taken action against identified threat actors by disabling accounts and assets associated with malicious activity. The company has also applied intelligence to strengthen both classifiers and models, letting them refuse assistance with similar attacks moving forward.\ “We are committed to developing AI boldly and responsibly, which means taking proactive steps to disrupt malicious activity by disabling the projects and accounts associated with bad actors, while continuously improving our models to make them less susceptible to misuse,” the report stated. GTIG emphasised that despite these developments, no APT or information operations actors have achieved breakthrough abilities that fundamentally alter the threat landscape. The findings underscore the evolving role of AI in cybersecurity, as both defenders and attackers race to use the technology’s abilities. For enterprise security teams, particularly in the Asia-Pacific region where ******** and North Korean state-sponsored hackers remain active, the report serves as an important reminder to enhance defences against AI-augmented social engineering and reconnaissance operations. (Photo by SCARECROW artworks) See also: Anthropic just revealed how AI-orchestrated cyberattacks actually work – Here’s what enterprises need to know 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 State-sponsored hackers exploit AI for advanced cyberattacks appeared first on AI News. View the full article

Barclays recorded a 12 % jump in annual profit for 2025, reporting £9.1 billion in earnings before tax, up from £8.1 billion a year earlier. The bank also raised its performance targets out through 2028, aiming for a return on tangible equity (RoTE) of more than 14 %, up from a previous goal of above 12 % by 2026. A growing US business and cost reductions underpinned this outcome, with Barclays citing AI as a key driver of those efficiency gains. At a time when many large companies are still experimenting with AI pilots, Barclays is tying the technology directly to its cost structure and profit outlook. In public statements and investor filings, leadership positions AI as one of the levers that can help the bank sustain lower costs and improved returns, especially as macroeconomic conditions shift. Barclays’ 12 % profit rise this week matters, not just for its shareholders, but because it reflects a trend that traditional, highly regulated firms are now positioning AI as a core part of running the business, not something kept in separate innovation labs. For companies outside tech, linking AI to measurable results such as profit and efficiency marks a shift toward operational use over hype. Why AI matters for cost discipline Barclays has said that technology such as AI is part of its plan to cut costs and make its operations more efficient. That includes trimming parts of the legacy technology stack and rethinking where and how work happens. Investment in AI tools complements broader cost savings goals that stretch back multiple years. For many large companies, labour and legacy systems still make up a large chunk of operating expenses. Using AI to automate repetitive tasks or streamline data processing can reduce that burden. In Barclays’ case, these efficiencies are part of the bank’s rationale for setting higher performance targets, even though margins remain under pressure in parts of its business. It’s important to be specific about what these efficiencies mean in practice. AI technologies, for example, models that assist with risk analysis, customer service workflows, and internal reporting, can reduce the hours staff spend on manual work. That doesn’t always mean cutting jobs outright, but it can lower the overall cost base, especially in functions that are routine or transaction-driven. From investment to impact Investments in AI don’t translate to results overnight. Barclays’ approach combines these tools with structural cost reduction programs, helping the bank manage expenses at a time when revenue growth alone isn’t enough to lift returns to desired levels. Barclays’ performance targets for 2028 reflect this dual focus. The bank’s leadership has said that its plans include returning more than £15 billion to shareholders between 2026 and 2028, supported by improved efficiency and profit strength. Often, companies talk about technology investment in vague terms. Barclays’ latest figures make the link between tech and profit more concrete: the 12 % profit rise was reported in the same breath as the role of technology in trimming costs. It’s not the only factor; improved market conditions and growth in the US also helped, but it’s clearly part of the narrative that management is presenting to investors. This emphasis on cost discipline and profit impact sets Barclays apart from firms that treat AI as a long-term bet or a future project. Here, AI is integrated into ongoing cost management and financial planning, giving the bank a plausible pathway to stronger returns in the years ahead. What this means for legacy firms Barclays is far from unique in exploring AI for cost savings and efficiency. Other banks have also flagged technology investments as part of broader restructuring efforts. But what makes Barclays’ case noteworthy is the scale of the strategy and the way it is tied to measured performance targets, not just experimentation or small-scale pilots. In traditional industries, especially ones as regulated as banking, adopting AI is harder than in tech startups. Firms must navigate compliance, risk, customer privacy, and legacy systems that weren’t designed for automation. Yet Barclays’ public comments suggest that the bank is now comfortable enough with these tools to anchor part of its financial forecast on them. That signals a degree of maturity in how the institution operationalises AI. Barclays isn’t simply building isolated AI projects; leadership is weaving technology into cost discipline, modernisation of systems, and long-term planning. That shift matters because it shows how legacy firms, even those with large, complex operations, can start to move beyond pilots and into business-wide use cases that affect the bottom line. For other end-user companies evaluating AI investments, Barclays offers a working example: a large, regulated company can use technology to help hit cost and profitability targets, not just to explore new capabilities. (Photo by Jose Marroquin) See also: Goldman Sachs tests autonomous AI agents for process-heavy work 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 Barclays bets on AI to cut costs and boost returns appeared first on AI News. View the full article

Agentic AI offers insurance leaders a path to scalable efficiency as the sector confronts a tough digital transformation. Insurers hold deep data reserves and employ a workforce skilled in analytic decision-making. Despite these advantages, the industry has largely failed to advance beyond pilot programmes. Research suggests only seven percent of insurers have scaled these initiatives effectively across their organisations. The barrier is rarely a lack of interest. Instead, legacy infrastructure and fragmented data architectures often stop integration before it starts. Financial pressure compounds the technical debt. The sector has absorbed losses exceeding $100 billion annually for six consecutive years. High-frequency property losses are now a structural issue that standard operational tweaks cannot fix. Automating complex insurance workflows with agentic AI Intelligent agents provide a way to bypass these bottlenecks. Unlike passive analytical tools, these systems support autonomous tasks and help make decisions under human supervision. Embedding these agents into workflows allows companies to navigate legacy constraints and talent shortages. Workforce augmentation is a primary application. Sedgwick, in collaboration with Microsoft, deployed the Sidekick Agent to assist claims professionals. The system improved claims processing efficiency by more than 30 percent through real-time guidance. Operational gains extend to customer support. Standard chatbots usually answer a query or transfer the user to a ******. An agentic solution manages the process from end-to-end. This can include capturing the first notice of loss, requesting missing documentation, updating policy and billing systems, and proactively notifying customers of next steps. This “resolve, not route” approach has produced results in live environments. One major insurer implemented over 80 models in its claims domain. The rollout cut complex-case liability assessment time by 23 days and improved routing accuracy by 30 percent. Customer complaints fell by 65 percent during the same *******. Such promising metrics indicate that agentic AI can compress cycle times and control loss-adjustment expenses for the insurance industry, all while maintaining necessary oversight. Navigating internal friction Adoption requires navigating internal resistance. Siloed teams and unclear priorities often slow deployment speed. A shortage of talent in specialised roles, such as actuarial analysis and underwriting, also limits how effectively companies use their data. Agentic AI can target these areas to augment roles that are hard to fill. Success relies on aligning technology with specific business goals. Establishing an ‘AI Center of Excellence’ provides the governance and technical expertise needed to stop fragmented adoption. Teams should start with the high-volume and repeatable tasks to refine models through feedback loops. Industry accelerators can also speed up the process. Many platforms are now available with prebuilt frameworks that can support the full lifecycle of agent deployment. This approach reduces implementation time and aids compliance efforts. Of course, technology matters less than organisational readiness. About 70 percent of scaling challenges are organisational rather than technical. Insurers must build a culture of accountability to see returns on these tools. Agentic AI is a necessity for insurance leaders trying to survive in a market defined by financial pressure and legacy complexity. Addressing structural challenges improves efficiency and resilience. Executives who invest in scalable frameworks will position themselves to lead the next era of innovation. See also: ******** hyperscalers and industry-specific agentic AI Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post How insurance leaders use agentic AI to cut operational costs appeared first on AI News. View the full article

The *** Ministry of Defence (MOD) has selected Red Hat to architect a unified AI and hybrid cloud backbone across its entire estate. Announced today, the agreement is designed to break down data silos and accelerate the deployment of AI models from the data centre to the tactical edge. For CIOs, it’s part of a broader move away from fragmented and project-specific AI pilots toward a more platform engineering approach. By standardising on Red Hat’s infrastructure, the MOD aims to decouple its AI capabilities from underlying hardware, allowing algorithms to be developed once and deployed anywhere—whether on-premise, in the cloud, or on disconnected field devices. Red Hat industrialises the AI lifecycle for the MOD The agreement focuses on the Defence Digital Foundry, the MOD’s central software delivery hub. The Foundry will now provide a consistent MLOps environment to all service branches, including the Royal Navy, British Army, and Royal Air Force. At the core of this initiative is Red Hat AI, a suite that includes Red Hat OpenShift AI. This platform addresses a familiar bottleneck in enterprise AI: the “inference gap” between data science teams and operational infrastructure. The new agreement will allow MOD developers to collaborate on a single platform, choosing the most appropriate AI models and hardware accelerators for their specific mission requirements without being locked into a single vendor’s ecosystem. This standardisation is vital for “enabling AI at scale,” according to Red Hat. By unifying disparate efforts, the MOD intends to reduce the duplication that often plagues large government IT programs. The platform supports optimised inference, ensuring that AI models can run efficiently on restricted hardware footprints often found in military environments. Mivy James, CTO at the *** MOD, said: “Easing access to Red Hat platforms becomes all the more important for the *** Ministry of Defence in the era of AI, where rapid adoption, replicating good practice, and the ability to scale are critical to strategic advantage.” Bridging legacy and autonomous systems A major hurdle for defence modernisation is the coexistence of legacy virtualised workloads with modern, containerised AI applications. The agreement includes Red Hat OpenShift Virtualization, which provides a “well-lit migration path” for existing systems. This allows the MOD to manage traditional virtual machines alongside new neural networks on the same control plane to reduce operational complexity and cost. The MOD deal also incorporates Red Hat Ansible Automation Platform to drive enterprise-wide AI automation. In an AI context, automation is the enforcement mechanism for governance. It ensures that as models are retrained and redeployed, the underlying configuration management, security orchestration, and service provisioning remain compliant with rigorous defence standards. Security and ecosystem alignment Deploying AI in defence naturally requires a “consistent security footprint” that can withstand sophisticated cyber threats. The Red Hat platform enables DevSecOps practices, integrating security gates directly into the software supply chain. This is particularly relevant for maintaining a trusted software pedigree when integrating code from approved third-party providers, who can now align their deliverables with the MOD’s standardised Red Hat environment. Joanna Hodgson, Regional Manager for the *** and Ireland at Red Hat, commented: “Red Hat offers flexibility and scalability to deploy any application or any AI model on their choice of hardware – whether on premise, in any cloud, or at the edge – helping the *** Ministry of Defence to harness the latest technologies, including AI.” The deployment shows that AI maturity is moving beyond the model itself to the infrastructure that supports it. Success in high-stakes environments like defence depends less on individual algorithm performance and more on the ability to reliably deliver, update, and govern those models at scale. See also: ******** hyperscalers and industry-specific agentic AI Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Red Hat unifies AI and tactical edge deployment for *** MOD appeared first on AI News. View the full article

Major ******** technology companies Alibaba, Tencent, and Huawei are pursuing agentic AI (systems that can execute multi-step tasks autonomously and interact with software, data, and services without human instruction), and orienting the technology toward discrete industries and workflows. Alibaba’s open-source strategy for agentic AI Alibaba’s strategy centres on its Qwen AI model family, a set of large language models with multilingual ability and open-source licences. Its own models are the basis for its AI services and agent platforms offered on Alibaba Cloud. Alibaba Cloud has documented its agent development tooling and vector database services in the open, meaning tools used to build autonomous agents can be adapted by any user. It positions the Qwen family as a platform for industry-specific solutions covering finance, logistics, and customer support. The Qwen App, an application built on these models, has reportedly reached a large user base since its public beta, creating links between autonomous tasks and Alibaba’s commerce and payments ecosystem. Alibaba open-source portfolio includes an agent framework, Qwen-Agent, to encourage third-party development of autonomous systems. This mirrors a pattern in China’s AI sector where hyperscalers publish frameworks and tools designed to build and manage AI agents, in competition with Western projects like Microsoft’s AutoGen and OpenAI’s Swarm. Tencent has also released an open-source agent framework, Youtu-Agent. Tencent, and Huawei’s Pangu: Industry-specific AI Huawei uses a combination of model development, infrastructure, and industry-specific agent frameworks to attract users to join its worldwide market. Its Huawei Cloud division has developed a ‘supernode’ architecture for enterprise agentic AI workloads that supports large cognitive models and the workflow orchestration agentic AI requires. AI agents are embedded in the foundation models of the Pangu family, which comprise of hardware stacks tuned for telecommunications, utilities, creative, and industrial applications, among other verticals. Early deployments are reported in sectors such as network optimisation, manufacturing and energy, where agents can plan tasks like predictive maintenance and resource allocation with minimal human oversight. Tencent Cloud’s “scenario-based AI” suite is a set of tools and SaaS-style applications that enterprises outside China can access, although the company’s cloud footprint remains smaller than Western hyperscalers in many regions. Despite these investments, real-world ******** agentic AI platforms have been most visible inside China. Projects such as OpenClaw, originally created outside the ecosystem, have been integrated into workplace environments like Alibaba’s DingTalk and Tencent’s WeCom and used to automate scheduling, create code, and manage developer workflows. These integrations are widely discussed in ******** developer communities but are not yet established in the enterprise environments of the major economic nations. Availability in Western markets Alibaba Cloud operates international data centres and markets AI services to European and Asian customers, positioning itself as a competitor to AWS and Azure for AI workloads. Huawei also markets cloud and AI infrastructure internationally, with a focus on telecommunications and regulated industries. In practice, however, uptake in Western enterprises remains limited compared with adoption of Western-origin AI platforms. This can be attributed to geopolitical concerns, data governance restrictions, and differences in enterprise ecosystems that favour local cloud providers. In AI developer workflows, for example, NVIDIA’s CUDA SHALAR remains dominant, and migration to the frameworks and methods of an alternative come with high up-front costs in the form of re-training. There is also a hardware constraint: ******** hyperscalers to work inside limits placed on them by their restricted access to Western GPUs for training and inference workloads, often using domestically produced processors or locating some workloads in overseas data centres to secure advanced hardware. The models themselves, particularly Qwen, are however at least accessible to developers through standard model hubs and APIs under open licences for many variants. This means Western companies and research institutions can experiment with those models irrespective of cloud provider selection. Conclusion ******** hyperscalers have defined a distinct trajectory for agentic AI, combining language models with frameworks and infrastructure tailored for autonomous operation in commercial contexts. Alibaba, Tencent and Huawei aim to embed these systems into enterprise pipelines and consumer ecosystems, offering tools that can operate with a degree of autonomy. These offerings are accessible in the West markets but have not yet achieved the same level of enterprise penetration on mainland European and US soil. To find more common uses of ********-flavoured agentic AI, we need to look to the Middle and Far East, South America, and Africa, where ******** influence is stronger. (Image source: “China Science & Technology Museum, Beijing, April-2011” by maltman23 is licensed under CC BY-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 ******** hyperscalers and industry-specific agentic AI appeared first on AI News. View the full article

Agentic AI in healthcare is graduating from answering prompts to autonomously executing complex marketing tasks—and life sciences companies are betting their commercial strategies on it. According to a recent report cited by Capgemini Invent, AI agents could generate up to US$450 billion in economic value through revenue uplift and cost savings globally by 2028, with 69% of executives planning to deploy agents in marketing processes by year’s end. The stakes are particularly high in pharmaceutical marketing, where sales representatives have increasingly limited face time with healthcare professionals (HCPs)—a trend accelerated by Covid-19. The challenge isn’t just access; it’s making those rare interactions count with intelligence that’s currently trapped in data silos. The fragmented intelligence problem Briggs Davidson, Senior Director of Digital, Data & Marketing Strategy for Life Sciences at Capgemini Invent, outlines a scenario that will sound familiar to anyone in pharma marketing: An HCP attends a conference where a competitor showcases promising drug results, publishes research, and shifts their prescriptions to a rival product—all within a single quarter. “In most companies, legacy IT infrastructure and data silos keep this information in disparate systems across CRM, events databases and claims data,” Davidson writes. “Chances are, none of that information was accessible to sales reps before they met with the HCP.” The solution, according to Davidson, isn’t just connecting these systems—it’s deploying agentic AI in healthcare marketing to autonomously query, synthesise, and act on that unified data. Unlike conversational AI that responds to queries, agentic systems can independently execute multi-step tasks. Instead of a data engineer building a new pipeline, an AI agent could autonomously query the CRM and claims database to answer business questions like: “Identify oncologists in the Northwest who have a 20% lower prescription volume but attended our last medical congress.” From orchestration to autonomous execution Davidson frames the shift as moving from an “omnichannel view”—coordinating experiences across channels—to true orchestration powered by agentic AI. In practice, this means a sales representative could have an agent assist with call and visit planning by asking: “What messages has my HCP responded to most recently?” or “Can you create a detailed intelligence brief on my HCP?” The agentic system would compile: Their most recent conversation with the HCP The HCP’s prescribing behaviour Thought leaders the HCP follows Relevant content to share The HCP’s preferred outreach channels (in-person visits, emails, webinars) More significantly, the AI agent would then create a custom call plan for each HCP based on their unified profile and recommend follow-up steps based on engagement outcomes. “Agentic AI systems are about driving action, graduating from ‘answer my prompt,’ to ‘autonomously execute my task,'” Davidson explains. “That means evolving the sales representative mindset from asking questions to coordinating small teams of specialised agents that work together: one plans, another retrieves and checks content, a third schedules and measures, and a fourth enforces compliance guardrails—all under human oversight.” The AI-ready data prerequisite The operational promise hinges on what Davidson calls “AI-ready data”—standardised, accessible, complete, and trustworthy information that enables three capabilities: Faster decision making: Predictive analytics that provide near real-time alerts on what’s about to happen, enabling sales representatives to act proactively. Personalisation at scale: Delivering customised experiences to thousands of HCPs simultaneously with small human teams enabled by specialised agent networks. True marketing ROI: Moving beyond monthly historical reports to understanding which marketing activities are actively driving prescriptions. Davidson emphasises that successful deployment starts with marketing and IT alignment on initial use cases, with stakeholders identifying KPIs that demonstrate tangible outcomes—such as specific percentage increases in HCP engagement or sales representative productivity. Critical implementation questions The article notably frames agentic AI in healthcare as “not simply another technology-led capability; it’s a new operating layer for commercial teams.” But it acknowledges that “agentic AI’s full value only materialises with AI-ready data, trustworthy deployment and workflow redesign.” What remains unaddressed: the regulatory and compliance complexity of autonomous systems querying claims databases containing prescriber behaviour, particularly under HIPAA’s minimum necessary standard. The piece also doesn’t detail actual client implementations or metrics beyond the aspirational US$450B economic value projection. For global organisations, Davidson notes that use cases “can and should be tailored to fit each market’s maturity for maximum ROI,” suggesting that deployment will vary significantly across regulatory environments. The fundamental value proposition, according to Davidson, centres on bidirectional benefit: “The HCP receives directly relevant content, and the marketing teams can drive increased HCP engagement and conversion.” Whether that vision of autonomous marketing agents coordinating across CRM, events, and claims systems becomes standard practice by 2028—or remains constrained by data governance realities—will likely determine if life sciences achieves anything close to that US$450 billion opportunity. See also: China’s hyperscalers bet billions on agentic AI as commerce becomes the new battleground Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Agentic AI in healthcare: How Life Sciences marketing could achieve US$450bn in value by 2028 appeared first on AI News. View the full article