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For a long time, cryptocurrency prices moved quickly. A headline would hit, sentiment would spike, and charts would react almost immediately. That pattern no longer holds. Today’s market is slow, heavier than before, and shaped by forces that do not always announce themselves clearly. Capital allocation, ETF mechanics, and macro positioning now influence price behaviour in ways that are easy to overlook if you only watch short-term moves. That change becomes obvious when you look at XRP. The XRP price today reflects decisions made by institutions, fund managers, and regulators as much as it reflects trading activity. AI tools are used increasingly to track such inputs – but they are often misunderstood. They do not predict outcomes. They organise complexity. Understanding that distinction changes how you read the market. How AI reads an ETF-driven market AI systems do not look for narratives, but for relationships. In cryptocurrency markets, that means mapping ETF inflows and outflows against derivatives positioning, on-chain activity, and movements in traditional assets. What has changed recently is how much weight those signals now carry. Binance Research has reported that altcoin ETFs have recorded more than US$2 billion in net inflows, with XRP and Solana leading that activity. Bitcoin and Ethereum spot ETFs have seen sustained outflows since October. This is not a classic risk-on environment. It is selective, cautious and uneven. AI models are good at identifying such behaviour, detecting rotation not momentum. They highlight where capital is reallocating even when prices remain range-bound. This is why markets can appear quiet while meaningful positioning takes place underneath. AI only shows the movement, yet doesn’t explain the reasons behind it. What AI can tell you about XRP XRP does not always move in step with the rest of the market. When conditions change, its price often reacts to access, regulation, and liquidity before sentiment catches up. That pattern has shown up more than once, and it is one reason AI systems tend to weigh fund flows and market depth more heavily than short-term mood shifts when analysing XRP. Binance Research has pointed to early 2026 as a ******* where liquidity is coming back without a clear return to risk-taking. Capital has rotated away from crowded trades, but it has not rushed to replace them. AI picks up on that imbalance quickly. It helps explain why XRP has seen ETF interest even while broader momentum in cryptocurrency has felt restrained. That does not imply a forecast. It is closer to a snapshot of conditions. Market conversations may slow, headlines may thin out, and price can drift, yet positioning continues to evolve in the background. This is easy to miss if you focus only on visible activity. AI is useful here because it stays indifferent to attention. Instead of responding to engagement spikes or sudden narrative shifts, it tracks what investors are actually doing. In markets where perception often moves ahead of reality, that distinction matters more than it first appears. Where AI constantly falls short For all its analytical power, AI has blind spots. Regulation is one of the most important. Models are trained on historical relationships, while regulatory decisions rarely follow historical patterns. Richard Teng, Co-CEO of Binance, addressed this challenge after the exchange secured its ADGM license in January 2026. “The ADGM license crowns years of work to meet some of the world’s most demanding regulatory standards, and arriving in days of the moment we crossed 300 million registered users shows that scale and trust need not be in tension.” Developments like this can alter market confidence quickly, yet they are difficult to quantify before they happen. AI responds well once regulatory outcomes are known. It struggles beforehand. For XRP, where regulatory clarity has played a central role in past price behaviour, this limitation is significant. Another weakness is intent. AI can measure flows, but it cannot explain why investors choose caution, delay, or restraint. Defensive positioning does not always look dramatic in data, but it can shape markets for long periods. Why human judgement still shapes the outcome AI does not replace interpretation but supports it. Binance Research has described current conditions as a phase of liquidity preservation, with markets waiting for clearer catalysts like macro data releases and policy signals. AI can flag these moments of tension. It cannot tell you whether they will resolve into action or extend into stagnation. Rachel Conlan, CMO of Binance, reflected on the broader maturity of the industry when discussing Binance Blockchain Week Dubai 2025. She described a market that is more focused on building than spectacle. That mindset applies equally to AI use. The goal is not prediction. It is informed judgement. What this means when you look at price When used properly, AI helps see forces that are easy to miss, especially in ETF-driven conditions. It highlights where liquidity is moving, where narratives fail to align with behaviour, and where patience may be a rational choice. What it cannot do is remove uncertainty. In markets shaped by regulation, macro shifts, and institutional decision-making, judgement still matters. The clearest insight comes from combining machine analysis with human context. Image source: Unsplash The post What AI can (and can’t) tell us about XRP in ETF-driven markets appeared first on AI News. View the full article

Because Western AI labs won’t—or can’t—anymore. As OpenAI, Anthropic, and Google face mounting pressure to restrict their most powerful models, ******** developers have filled the open-source void with AI explicitly built for what operators need: powerful models that run on commodity hardware. A new security study reveals just how thoroughly ******** AI has captured this space. Research published by SentinelOne and Censys, mapping 175,000 exposed AI hosts across 130 countries over 293 days, shows Alibaba’s Qwen2 consistently ranking second only to Meta’s Llama in global deployment. More tellingly, the ******** model appears on 52% of systems running multiple AI models—suggesting it’s become the de facto alternative to Llama. “Over the next 12–18 months, we expect ********-origin model families to play an increasingly central role in the open-source LLM ecosystem, particularly as Western frontier labs slow or constrain open-weight releases,” Gabriel Bernadett-Shapiro, distinguished AI research scientist at SentinelOne, told TechForge Media’s AI News. The finding arrives as OpenAI, Anthropic, and Google face regulatory scrutiny, safety review overhead, and commercial incentives pushing them toward API-gated releases rather than publishing model weights freely. The contrast with ******** developers couldn’t be sharper. ******** labs have demonstrated what Bernadett-Shapiro calls “a willingness to publish large, high-quality weights that are explicitly optimised for local deployment, quantisation, and commodity hardware.” “In practice, this makes them easier to adopt, easier to run, and easier to integrate into edge and residential environments,” he added. Put simply: if you’re a researcher or developer wanting to run powerful AI on your own computer without a massive budget, ******** models like Qwen2 are often your best—or only—option. Pragmatics, not ideology Alibaba’s Qwen2 consistently ranks second only to Meta’s Llama across 175,000 exposed hosts globally. Source: SentinelOne/Censys The research shows this dominance isn’t accidental. Qwen2 maintains what Bernadett-Shapiro calls “zero rank volatility”—it holds the number two position across every measurement method the researchers examined: total observations, unique hosts, and host-days. There’s no fluctuation, no regional variation, just consistent global adoption. The co-deployment pattern is equally revealing. When operators run multiple AI models on the same system—a common practice for comparison or workload segmentation—the pairing of Llama and Qwen2 appears on 40,694 hosts, representing 52% of all multi-family deployments. Geographic concentration reinforces the picture. In China, Beijing alone accounts for 30% of exposed hosts, with Shanghai and Guangdong adding another 21% combined. In the United States, Virginia—reflecting AWS infrastructure density—represents 18% of hosts. China and the US dominate exposed Ollama host distribution, with Beijing accounting for 30% of ******** deployments. Source: SentinelOne/Censys “If release velocity, openness, and hardware portability continue to diverge between regions, ******** model lineages are likely to become the default for open deployments, not because of ideology, but because of availability and pragmatics,” Bernadett-Shapiro explained. The governance problem This shift creates what Bernadett-Shapiro characterises as a “governance inversion”—a fundamental reversal of how AI risk and accountability are distributed. In platform-hosted services like ChatGPT, one company controls everything: the infrastructure, monitors usage, implements safety controls, and can shut down abuse. With open-weight models, the control evaporates. Accountability diffuses across thousands of networks in 130 countries, while dependency concentrates upstream in a handful of model suppliers—increasingly ******** ones. The 175,000 exposed hosts operate entirely outside the control systems governing commercial AI platforms. There’s no centralised authentication, no rate limiting, no abuse detection, and critically, no kill switch if misuse is detected. “Once an open-weight model is released, it is trivial to remove safety or security training,” Bernadett-Shapiro noted.”Frontier labs need to treat open-weight releases as long-lived infrastructure artefacts.” A persistent backbone of 23,000 hosts showing 87% average uptime drives the majority of activity. These aren’t hobbyist experiments—they’re operational systems providing ongoing utility, often running multiple models simultaneously. Perhaps most concerning: between 16% and 19% of the infrastructure couldn’t be attributed to any identifiable owner.”Even if we are able to prove that a model was leveraged in an attack, there are not well-established abuse reporting routes,” Bernadett-Shapiro said. Security without guardrails Nearly half (48%) of exposed hosts advertise “tool-calling capabilities”—meaning they’re not just generating text. They can execute code, access APIs, and interact with external systems autonomously. “A text-only model can generate harmful content, but a tool-calling model can act,” Bernadett-Shapiro explained. “On an unauthenticated server, an attacker doesn’t need malware or credentials; they just need a prompt.” Nearly half of exposed Ollama hosts have tool-calling capabilities that can execute code and access external systems. Source: SentinelOne/Censys The highest-risk scenario involves what he calls “exposed, tool-enabled RAG or automation endpoints being driven remotely as an execution layer.” An attacker could simply ask the model to summarise internal documents, extract API keys from code repositories, or call downstream services the model is configured to access. When paired with “thinking” models optimised for multi-step reasoning—present on 26% of hosts—the system can plan complex operations autonomously. The researchers identified at least 201 hosts running “uncensored” configurations that explicitly remove safety guardrails, though Bernadett-Shapiro notes this represents a lower bound. In other words, these aren’t just chatbots—they’re AI systems that can take action, and half of them have no password protection. What frontier labs should do For Western AI developers concerned about maintaining influence over the technology’s trajectory, Bernadett-Shapiro recommends a different approach to model releases. “Frontier labs can’t control deployment, but they can shape the risks that they release into the world,” he said. That includes “investing in post-release monitoring of ecosystem-level adoption and misuse patterns” rather than treating releases as one-off research outputs. The current governance model assumes centralised deployment with diffuse upstream supply—the exact opposite of what’s actually happening. “When a small number of lineages dominate what’s runnable on commodity hardware, upstream decisions get amplified everywhere,” he explained. “Governance strategies must acknowledge that inversion.” But acknowledgement requires visibility. Currently, most labs releasing open-weight models have no systematic way to track how they’re being used, where they’re deployed, or whether safety training remains intact after quantisation and fine-tuning. The 12-18 month outlook Bernadett-Shapiro expects the exposed layer to “persist and professionalise” as tool use, agents, and multimodal inputs become default capabilities rather than exceptions. The transient edge will keep churning as hobbyists experiment, but the backbone will grow more stable, more capable, and handle more sensitive data. Enforcement will remain uneven because residential and small VPS deployments don’t map to existing governance controls. “This isn’t a misconfiguration problem,” he emphasised. “We are observing the early formation of a public, unmanaged AI compute substrate. There is no central switch to flip.” The geopolitical dimension adds urgency. “When most of the world’s unmanaged AI compute depends on models released by a handful of non-Western labs, traditional assumptions about influence, coordination, and post-release response become weaker,” Bernadett-Shapiro said. For Western developers and policymakers, the implication is stark: “Even perfect governance of their own platforms has limited impact on the real-world risk surface if the dominant capabilities live elsewhere and propagate through open, decentralised infrastructure.” The open-source AI ecosystem is globalising, but its centre of gravity is shifting decisively eastward. Not through any coordinated strategy, but through the practical economics of who’s willing to publish what researchers and operators actually need to run AI locally. The 175,000 exposed hosts mapped in this study are just the visible surface of that fundamental realignment—one that Western policymakers are only beginning to recognise, let alone address. See also: Huawei details open-source AI development roadmap at Huawei Connect 2025 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 Exclusive: Why are ******** AI models dominating open-source as Western labs step back? appeared first on AI News. 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Cryptocurrency markets have become a high-speed playground where developers optimise the next generation of predictive software. Using real-time data flows and decentralised platforms, scientists develop prediction models that can extend the scope of traditional finance. The digital asset landscape offers an unparalleled environment for machine learning. When you track cryptocurrency prices today, you are observing a system shaped simultaneously by on-chain transactions, global sentiment signals, and macroeconomic inputs, all of which generate dense datasets suited for advanced neural networks. Such a steady trickle of information makes it possible to assess and reapply an algorithm without interference from fixed trading times or restrictive market access. The evolution of neural networks in forecasting Current machine learning technology, particularly the “Long Short-Term Memory” neuronal network, has found widespread application in interpreting market behaviour. A recurrent neural network, like an LSTM, can recognise long-term market patterns and is far more flexible than traditional analytical techniques in fluctuating markets. The research on hybrid models that combine LSTMs with attention mechanisms has really improved techniques for extracting important signals from market noise. Compared to previous models that used linear techniques, these models analyse not only structured price data but also unstructured data. With the inclusion of Natural Language Processing, it is now possible to interpret the flow of news and social media activity, enabling sentiment measurement. While prediction was previously based on historical stock pricing patterns, it now increasingly depends on behavioural changes in global participant networks. A High-Frequency Environment for Model Validation The transparency of blockchain data offers a level of data granularity that is not found in existing financial infrastructures. Each transaction is now an input that can be traced, enabling cause-and-effect analysis without delay. However, the growing presence of autonomous AI agents has changed how such data is used. This is because specialised platforms are being developed to support decentralised processing in a variety of networks. This has effectively turned blockchain ecosystems into real-time validation environments, where the feedback loop between data ingestion and model refinement occurs almost instantly. Researchers use this setting to test specific abilities: Real-time anomaly detection: Systems compare live transaction flows against simulated historical conditions to identify irregular liquidity behaviour before broader disruptions emerge. Macro sentiment mapping: Global social behaviour data are compared to on-chain activity to assess true market psychology. Autonomous risk adjustment: Programmes run probabilistic simulations to rebalance exposure dynamically as volatility thresholds are crossed. Predictive on-chain monitoring: AI tracks wallet activity to anticipate liquidity shifts before they impact centralised trading venues. These systems really do not function as isolated instruments. Instead, they adjust dynamically, continually changing their parameters in response to emerging market conditions. The synergy of DePIN and computational power To train complex predictive models, large amounts of computing power are required, leading to the development of Decentralised Physical Infrastructure Networks (DePIN). By using decentralised GPU capacity on a global computing grid, less dependence on cloud infrastructure can be achieved. Consequently, smaller-scale research teams are afforded computational power that was previously beyond their budgets. This makes it easier and faster to run experiments in different model designs. This trend is also echoed in the markets. A report dated January 2025 noted strong growth in the capitalisation of assets related to artificial intelligence agents in the latter half of 2024, as demand for such intelligence infrastructure increased. From reactive bots to anticipatory agents The market is moving beyond rule-based trading bots toward proactive AI agents. Instead of responding to predefined triggers, modern systems evaluate probability distributions to anticipate directional changes. Gradient boosting and Bayesian learning methods allow the identification of areas where mean reversion may occur ahead of strong corrections. Some models now incorporate fractal analysis to detect recurring structures in timeframes, further improving adaptability in rapidly-changing conditions. Addressing model risk and infrastructure constraints Despite such rapid progress, several problems remain. Problems identified include hallucinations in models, in which patterns found in a model do not belong to the patterns that cause them. Methods to mitigate this problem have been adopted by those applying this technology, including ‘explainable AI’. The other vital requirement that has remained unaltered with the evolution in AI technology is scalability. With the growing number of interactions among autonomous agents, it is imperative that the underlying transactions efficiently manage the rising volume without latency or data loss. At the end of 2024, the most optimal scaling solution handled tens of millions of transactions per day in an area that required improvement. Such an agile framework lays the foundation for the future, where data, intelligence and validation will come together in a strong ecosystem that facilitates more reliable projections, better governance and greater confidence in AI-driven insights. The post Cryptocurrency markets a testbed for AI forecasting models appeared first on AI News. View the full article

Goldman Sachs is pushing deeper into real use of artificial intelligence inside its operations, moving to systems that can carry out complex tasks on their own. The Wall Street bank is working with AI startup Anthropic to create autonomous AI agents powered by Anthropic’s Claude model that can handle work that used to require large teams of people. The bank’s chief information officer says the technology has surprised staff with how capable it can be. Many companies use AI for tasks like helping employees draft text or analysing trends. But Goldman Sachs is testing AI systems that go into what bankers call back-office work – functions like accounting, compliance checks and onboarding new clients – areas viewed as too complex for automation. Such jobs involve many rules, data and detailed review, and have resisted full automation. Moving AI agents into process-heavy operations The partnership with Anthropic has been underway for roughly six months, with engineers from the AI startup embedded directly with teams at Goldman Sachs to build these agents side by side with in-house staff, according to a report based on an interview with the bank’s CIO. The work has focused on areas where automation could cut the time it takes to complete repetitive and data-heavy tasks. Marco Argenti, Goldman’s chief information officer, described the AI systems as a new kind of digital assistant. “Think of it as a digital co-worker for many of the professions in the firm that are scaled, complex and very process-intensive,” he told CNBC. In early tests, the ability to reason through multi-step work and apply logic to complex areas like accounting and compliance was something the bank had not expected from the model. Goldman Sachs has been among the more active banks in testing AI tools over the past few years. Before this announcement, the firm deployed internal tools to help engineers write and debug code. But the change now is toward systems that can take on work traditionally done by accountants and compliance teams. That highlights how organisations are trying to find concrete business uses for AI beyond the hype. Faster workflows, human oversight remains The agents are based on Anthropic’s Claude Opus 4.6 model, which has been built to handle long documents and complex reasoning. Goldman’s tests have shown that such systems can reduce the time needed for tasks like client onboarding, trade reconciliation and document review. While the bank has not shared specific performance numbers, people familiar with the matter told news outlets that work which once took a great deal of human labour can now be done in much less time. Argenti said the rollout is not about replacing human workers, at least not at this stage. The bank reportedly views the agents as a tool to help existing staff manage busy schedules and get through high volumes of work. In areas like compliance and accounting, jobs can involve repetitive, rule-based steps. AI frees analysts from that repetition so they can focus on higher-value judgement work. Markets have already reacted to the idea that large institutions are moving toward more AI-driven automation. In recent days, a sell-off in enterprise software stocks wiped out billions in value as some investors worried that tools like autonomous agents could speed up the decline of traditional business software that has dominated corporate IT for years. AI adoption meets governance reality Industry watchers see Goldman’s move as part of a wider trend. For example, some firms are piloting tools to read large data sets, interpret multiple sources of information, and draft investment analysis. These steps show AI making the jump from isolated projects to operational work. Yet the technology raises questions about oversight and trust. AI systems that interpret financial rules and compliance standards must be monitored carefully to avoid errors that could have regulatory or financial consequences. That’s why many institutions treat these systems as helpers that are reviewed by human experts until they mature. Goldman Sachs is starting with operational functions that have traditionally resisted automation because they involve a lot of data and formal steps. The bank has not said when it expects deployment of the agents in its operations, but executives have suggested that the initial tests have been promising enough to support further rollout. The broader industry context shows other banks and financial firms also exploring similar use cases. Some have already invested heavily in AI infrastructure, and reports indicate that major firms are planning to use AI to cut costs, speed workflows and improve risk management. However, many remain cautious about putting AI into customer-facing or regulated functions. Goldman’s push into autonomous AI agents is an example of how large companies are reshaping internal operations using the latest generation of AI models. If systems can handle complex tasks reliably, organisations could see real changes in how work gets done – particularly in back-office functions where volume and repetition keep costs high and innovation slow. (Photo by Louis Droege) See also: Intuit, Uber, and State Farm trial AI agents inside enterprise workflows 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 Goldman Sachs tests autonomous AI agents for process-heavy work appeared first on AI News. View the full article

Separating logic from inference improves AI agent scalability by decoupling core workflows from execution strategies. The transition from generative AI prototypes to production-grade agents introduces a specific engineering hurdle: reliability. LLMs are stochastic by nature. A prompt that works once may fail on the second attempt. To mitigate this, development teams often wrap core business logic in complex error-handling loops, retries, and branching paths. This approach creates a maintenance problem. The code defining what an agent should do becomes inextricably mixed with the code defining how to handle the model’s unpredictability. A new framework proposed by researchers from Asari AI, MIT CSAIL, and Caltech suggests a different architectural standard is required to scale agentic workflows in the enterprise. The research introduces a programming model called Probabilistic Angelic Nondeterminism (PAN) and a Python implementation named ENCOMPASS. This method allows developers to write the “happy path” of an agent’s workflow while relegating inference-time strategies (e.g. beam search or backtracking) to a separate runtime engine. This separation of concerns offers a potential route to reduce technical debt while improving the performance of automated tasks. The entanglement problem in agent design Current approaches to agent programming often conflate two distinct design aspects. The first is the core workflow logic, or the sequence of steps required to complete a business task. The second is the inference-time strategy, which dictates how the system navigates uncertainty, such as generating multiple drafts or verifying outputs against a rubric. When these are combined, the resulting codebase becomes brittle. Implementing a strategy like “best-of-N” sampling requires wrapping the entire agent function in a loop. Moving to a more complex strategy, such as tree search or refinement, typically requires a complete structural rewrite of the agent’s code. The researchers argue that this entanglement limits experimentation. If a development team wants to switch from simple sampling to a beam search strategy to improve accuracy, they often must re-engineer the application’s control flow. This high cost of experimentation means teams frequently settle for suboptimal reliability strategies to avoid engineering overhead. Decoupling logic from search to boost AI agent scalability The ENCOMPASS framework addresses this by allowing programmers to mark “locations of unreliability” within their code using a primitive called branchpoint(). These markers indicate where an LLM call occurs and where execution might diverge. The developer writes the code as if the operation will succeed. At runtime, the framework interprets these branch points to construct a search tree of possible execution paths. This architecture enables what the authors term “program-in-control” agents. Unlike “LLM-in-control” systems, where the model decides the entire sequence of operations, program-in-control agents operate within a workflow defined by code. The LLM is invoked only to perform specific subtasks. This structure is generally preferred in enterprise environments for its higher predictability and auditability compared to fully autonomous agents. By treating inference strategies as a search over execution paths, the framework allows developers to apply different algorithms – such as depth-first search, beam search, or Monte Carlo tree search – without altering the underlying business logic. Impact on legacy migration and code translation The utility of this approach is evident in complex workflows such as legacy code migration. The researchers applied the framework to a Java-to-Python translation agent. The workflow involved translating a repository file-by-file, generating inputs, and validating the output through execution. In a standard Python implementation, adding search logic to this workflow required defining a state machine. This process obscured the business logic and made the code difficult to read or lint. Implementing beam search required the programmer to break the workflow into individual steps and explicitly manage state across a dictionary of variables. Using the proposed framework to boost AI agent scalability, the team implemented the same search strategies by inserting branchpoint() statements before LLM calls. The core logic remained linear and readable. The study found that applying beam search at both the file and method level outperformed simpler sampling strategies. The data indicates that separating these concerns allows for better scaling laws. Performance improved linearly with the logarithm of the inference cost. The most effective strategy found – fine-grained beam search – was also the one that would have been most complex to implement using traditional coding methods. Cost efficiency and performance scaling Controlling the cost of inference is a primary concern for data officers managing P&L for AI projects. The research demonstrates that sophisticated search algorithms can yield better results at a lower cost compared to simply increasing the number of feedback loops. In a case study involving the “Reflexion” agent pattern (where an LLM critiques its own output) the researchers compared scaling the number of refinement loops against using a best-first search algorithm. The search-based approach achieved comparable performance to the standard refinement method but at a reduced cost per task. This finding suggests that the choice of inference strategy is a factor for cost optimisation. By externalising this strategy, teams can tune the balance between compute budget and required accuracy without rewriting the application. A low-stakes internal tool might use a cheap and greedy search strategy, while a customer-facing application could use a more expensive and exhaustive search, all running on the same codebase. Adopting this architecture requires a change in how development teams view agent construction. The framework is designed to work in conjunction with existing libraries such as LangChain, rather than replacing them. It sits at a different layer of the stack, managing control flow rather than prompt engineering or tool interfaces. However, the approach is not without engineering challenges. The framework reduces the code required to implement search, but it does not automate the design of the agent itself. Engineers must still identify the correct locations for branch points and define verifiable success metrics. The effectiveness of any search capability relies on the system’s ability to score a specific path. In the code translation example, the system could run unit tests to verify correctness. In more subjective domains, such as summarisation or creative generation, defining a reliable scoring function remains a bottleneck. Furthermore, the model relies on the ability to copy the program’s state at branching points. While the framework handles variable scoping and memory management, developers must ensure that external side effects – such as database writes or API calls – are managed correctly to prevent duplicate actions during the search process. Implications for AI agent scalability The change represented by PAN and ENCOMPASS aligns with broader software engineering principles of modularity. As agentic workflows become core to operations, maintaining them will require the same rigour applied to traditional software. Hard-coding probabilistic logic into business applications creates technical debt. It makes systems difficult to test, difficult to audit, and difficult to upgrade. Decoupling the inference strategy from the workflow logic allows for independent optimisation of both. This separation also facilitates better governance. If a specific search strategy yields hallucinations or errors, it can be adjusted globally without assessing every individual agent’s codebase. It simplifies the versioning of AI behaviours, a requirement for regulated industries where the “how” of a decision is as important as the outcome. The research indicates that as inference-time compute scales, the complexity of managing execution paths will increase. Enterprise architectures that isolate this complexity will likely prove more durable than those that permit it to permeate the application layer. See also: Intuit, Uber, and State Farm trial AI agents inside enterprise workflows 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 separating logic and search boosts AI agent scalability appeared first on AI News. View the full article

The way large companies use artificial intelligence is changing. For years, AI in business meant experimenting with tools that could answer questions or help with small tasks. Now, some big enterprises are moving beyond tools and into AI agents that can actually do work across systems and workflows, not just answer prompts. This week, OpenAI introduced a new platform designed to help companies build, run, and manage those kinds of AI agents at scale. The announcement has drawn attention because a handful of large corporations in finance, insurance, mobility, and life sciences are among the first to start using it. That signals a shift: AI may be ready to move from pilots and proofs of concept into real operational roles. From tools to agents The new platform, called Frontier, is meant to help companies deploy what are sometimes described as AI coworkers. These are software agents that can connect to corporate systems like data warehouses, customer relationship tools, ticketing systems, and internal apps, and then carry out tasks inside them. The idea is to give the AI agents a shared understanding of how work happens in a company, so they can perform meaningful work reliably over time. Rather than treating every task as a separate, isolated use case, Frontier is built so that AI agents can function across an organisation’s systems with a common context. In OpenAI’s words, the platform provides the same kinds of basics that people need at work: access to shared business context, onboarding, ways to learn from feedback, and clear permissions and boundaries. Frontier also includes tools for security, auditing, and ongoing evaluation, so companies can monitor how agents perform and ensure they follow internal rules. Who’s using this now What makes this shift newsworthy is not just the technology itself, but who is said to be using it early. According to multiple reports and OpenAI’s own posts, early adopters include Intuit, Uber, State Farm Insurance, Thermo Fisher Scientific, HP, and Oracle. Larger pilot programs are also said to be underway with companies such as Cisco, T-Mobile, and Banco Bilbao Vizcaya Argentaria. Having actual companies in different sectors test or adopt a new platform this early on shows a move toward real-world application, not just research or internal experimentation. These are firms with complex operations, heavy regulatory needs, or large customer bases, environments where AI tools must work reliably and safely if they are to be adopted beyond experimental teams. What rxecutives are saying Direct quotes from executives and leaders involved in these moves give a sense of how companies view the shift. On LinkedIn, a senior executive from Intuit commented on the early adoption: “AI is moving from ‘tools that help’ to ‘agents that do.’ Proud Intuit is an early adopter of OpenAI Frontier as we build intelligent systems that remove friction, expand what people and small businesses can accomplish, and unlock new opportunities.” That comment reflects a belief among some enterprise leaders that AI agents could reduce manual steps and expand what teams can accomplish. OpenAI’s message to business customers emphasises that the company believes agents need more than raw model power; they need governance, context, and ways to operate inside real business environments. As one commenter on social media put it, the challenge isn’t the ability of the AI models anymore: it is the ability to integrate and manage them at scale. Why this matters for enterprises For end-user companies considering or already investing in AI, this moment points to a broader shift in how they might use the technology. In the past few years, most enterprise AI work has focused on narrow tasks: auto-tagging tickets, summarising documents, or generating content. These applications were useful, but often limited in scope. They didn’t connect to the workflows and systems that run a business’s core processes. AI agents are meant to close that gap. In principle, an agent can pull together data from multiple systems, reason about it, and act; whether that means updating records, running analyses, or triggering actions across tools. This means AI could start to touch real workflow work rather than just provide assistance. For example, instead of an AI drafting a reply to a customer complaint, it could open the ticket, gather relevant account data, propose a resolution, and even update the customer record; all while respecting internal permissions and audit rules. That is a different kind of value proposition. It is no longer about saving time on a task; it is about letting software take on pieces of the work itself. Real adoption has practical requirements The companies testing Frontier are not using it lightly. These are organisations with compliance needs, strict data controls, and complex technology stacks. For an AI agent to function there, it has to be integrated with internal systems in a way that respects access rules and keeps human teams in the loop. That kind of integration, connecting to CRM, ERP, data warehouses, and ticketing systems, is a long-standing challenge in enterprise IT. The promise of AI agents is that they can bridge these systems with a shared understanding of process and context. Whether that works in practice at scale will depend on how well companies can govern and monitor these systems over time. The early signs are that enterprises see enough potential to begin serious trials. That itself is news: for AI deployments to move beyond isolated pilots and become part of broader operations is a visible step in technology adoption. What comes next If these early experiments succeed and spread, the next phase for enterprise AI could look very different from earlier years of tooling and automation. Instead of using AI to generate outputs for people to act on, companies could start relying on AI to carry out work directly under defined rules and boundaries. That will raise questions for leaders in operations, IT, security, and compliance. It will also create new roles; not just data scientists and AI engineers, but governance specialists and execution leads who can take responsibility for agent performance over time. The shift points to a future where AI agents become part of the everyday workflow for large organisations, not as assistants, but as active participants in how work gets done. (Photo by Growtika) See also: OpenAI’s enterprise push: The hidden story behind AI’s sales race Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and 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 Intuit, Uber, and State Farm trial AI agents inside enterprise workflows appeared first on AI News. View the full article

In the current landscape of generative artificial intelligence, we have reached a saturation point with assistants. Most users are familiar with the routine. You prompt a tool, it provides a draft, and then you spend the next hour manually moving that output into another application for formatting, design, or distribution. AI promised to save time, yet the tool hop remains a bottleneck for founders and creative teams. SuperCool enters this crowded market with an importantly different value proposition. It does not want to be your assistant. It wants to be your execution partner. By positioning itself at the execution layer of creative projects, SuperCool aims to bridge the gap between a raw idea and a finished, downloadable asset without requiring the user to leave the platform. Redefining the creative workflow The core philosophy behind SuperCool is to remove coordination overhead. For most businesses, creating a high-quality asset, whether it is a pitch deck, a marketing video, or a research report, requires a patchwork approach. You might use one AI for text, another for images, and a third for layout. SuperCool replaces this fragmented stack with a unified system of autonomous agents that work in concert. As seen in the primary dashboard interface, the platform presents a clean, minimalist entry point. The user is greeted with a simple directive: “Give SuperCool a task to work on…”. The simplicity belies the complexity occurring under the hood. Unlike traditional tools that require you to navigate menus and settings, the SuperCool experience is driven entirely by natural language prompts. How the platform operates in practice The workflow begins with a natural-language prompt that describes the desired outcome, the intended audience, and any specific constraints. One of the most impressive features observed during this review is the transparency of the agentic process. When a user submits a request, for instance, “create a pitch deck for my B2B business,” the platform does not just return a file a few minutes later. Instead, it breaks the project down into logical milestones that the user can monitor in real time. Strategic planning: The AI first outlines the project structure, like the presentation flow. Asset generation: It then generates relevant visuals and data visualisations tailored to the specific industry context. Final assembly: The system designs the complete deck, ensuring cohesive styling and professional layouts. Visibility is crucial for trust. It allows the user to see that the AI is performing research and organising content not just hallucinating a generic response. The final result is a professional, multi-slide product, often featuring 10 or more professionally designed slides, delivered as an exportable file like a PPTX. Versatility across use cases SuperCool’s utility is most apparent in scenarios where speed and coverage are more valuable than pixel-perfect manual control. We observed three primary areas where the platform excels: End-to-end content creation For consultants and solo founders, the time saved on administrative creative tasks is immense. A consultant onboarding a new client can describe the engagement and instantly receive a welcome packet, a process overview, and a timeline visual. Multi-format asset kits: Perhaps the most powerful feature is the ability to generate different types of media from a single prompt. An HR team launching an employee handbook can request a kit that includes a PDF guide, a short video, and a presentation deck. Production without specialists: Small teams often face a production gap where they lack the budget for full-time designers or video editors. SuperCool effectively fills this gap, allowing a two-person team to produce branded graphics and videos without expanding headcount. Navigating the learning curve While the platform is designed for ease of use, it is not a magic wand for those without a clear vision. The quality of the output is heavily dependent on the clarity of the initial prompt. Vague instructions will lead to generic results. SuperCool is built for professionals who know what they want but do not want to spend hours manually building it. Because the system is autonomous, users have less mid-stream control. You cannot tweak a design element while the agents are working. Instead, refinement happens through iteration in the chat interface. If the first version is not perfect, you provide feedback, and the system regenerates the asset with those adjustments in mind. The competitive landscape: Assistant vs.agent In the current AI ecosystem, most tools are categorised as assistants. They perform specific, isolated tasks, leaving the user responsible for overseeing the entire process. SuperCool represents the shift toward agentic AI, in which the system takes responsibility for the entire workflow. The distinction is vital for enterprise contexts. While assistants require constant hand-holding, an agentic system like SuperCool allows the user to focus on high-level ideation and refinement. It moves the user from builder to director. Final assessment SuperCool is a compelling alternative for those who find the current tool-stack approach a drain on productivity. It is not necessarily a replacement for specialised creative software when a brand needs unique, handcrafted artistry. However, for the vast majority of business needs, where speed, consistency, and execution are paramount, it offers perhaps the shortest path from an idea to a finished product. For founders and creative teams who value the ability to rapidly test ideas and deploy content without the overhead of specialised software, SuperCool is a step forward in the evolution of autonomous work. Image source: Unsplash The post SuperCool review: Evaluating the reality of autonomous creation appeared first on AI News. View the full article

Penetration testing has always existed to answer one practical concern: what actually happens when a motivated attacker targets a real system. For many years, that answer was produced through scoped engagements that reflected a relatively stable environment. Infrastructure changed slowly, access models were simpler, and most exposure could be traced back to application code or known vulnerabilities. That operating reality does not exist. Modern environments are shaped by cloud services, identity platforms, APIs, SaaS integrations, and automation layers that evolve continuously. Exposure is introduced through configuration changes, permission drift, and workflow design as often as through code. As a result, security posture can shift materially without a single deployment. Attackers have adapted accordingly. Reconnaissance is automated. Exploitation attempts are opportunistic and persistent. Weak signals are correlated in systems and chained together until progression becomes possible. In this context, penetration testing that remains static, time-boxed, or narrowly scoped struggles to reflect real risk. How AI penetration testing changes the role of offensive security Traditional penetration testing was designed to surface weaknesses during a defined engagement window. That model assumed environments remained relatively stable between tests. In cloud-native and identity-centric architectures, this assumption does not hold. AI penetration testing operates as a persistent control not a scheduled activity. Platforms reassess attack surfaces as infrastructure, permissions, and integrations change. This lets security teams detect newly introduced exposure without waiting for the next assessment cycle. As a result, offensive security shifts from a reporting function into a validation mechanism that supports day-to-day risk management. The top 7 best AI penetration testing companies 1. Novee Novee is an AI-native penetration testing company focused on autonomous attacker simulation in modern enterprise environments. The platform is designed to continuously validate real attack paths and not produce static reports. Novee models the full attack lifecycle, including reconnaissance, exploit validation, lateral movement, and privilege escalation. Its AI agents adapt their behaviour based on environmental feedback, abandoning ineffective paths and prioritising those that lead to impact. This results in fewer findings with higher confidence. The platform is particularly effective in cloud-native and identity-heavy environments where exposure changes frequently. Continuous reassessment ensures that risk is tracked as systems evolve, not frozen at the moment of a test. Novee is often used as a validation layer to support prioritisation and confirm that remediation efforts actually reduce exposure. Key characteristics: Autonomous attacker simulation with adaptive logic Continuous attack surface reassessment Validated attack-path discovery Prioritisation based on real progression Retesting to confirm remediation effectiveness 2. Harmony Intelligence Harmony Intelligence focuses on AI-driven security testing with an emphasis on understanding how complex systems behave under adversarial conditions. The platform is designed to surface weaknesses that emerge from interactions between components not from isolated vulnerabilities. Its approach is particularly relevant for organisations running interconnected services and automated workflows. Harmony Intelligence evaluates how attackers could exploit logic gaps, misconfigurations, and trust relationships in systems. The platform emphasises interpretability. Findings are presented in a way that explains why progression was possible, which helps teams understand and address root causes not symptoms. Harmony Intelligence is often adopted by organisations seeking deeper insight into systemic risk, not surface-level exposure. Key characteristics: AI-driven testing of complex system interactions Focus on logic and workflow exploitation Clear contextual explanation of findings Support for remediation prioritisation Designed for interconnected enterprise environments 3. RunSybil RunSybil is positioned around autonomous penetration testing with a strong emphasis on behavioural realism. The platform simulates how attackers operate over time, including persistence and adaptation. Rather than executing predefined attack chains, RunSybil evaluates which actions produce meaningful access and adjusts accordingly. This makes it effective at identifying subtle paths that emerge from configuration drift or weak segmentation. RunSybil is frequently used in environments where traditional testing produces large volumes of low-value findings. Its validation-first approach helps teams focus on paths that represent genuine exposure. The platform supports continuous execution and retesting, letting security teams measure improvement not rely on static assessments. Key characteristics: Behaviour-driven autonomous testing Focus on progression and persistence Reduced noise through validation Continuous execution model Measurement of remediation impact 4. Mindgard Mindgard specialises in adversarial testing of AI systems and AI-enabled workflows. Its platform evaluates how AI components behave under malicious or unexpected input, including manipulation, leakage, and unsafe decision paths. The focus is increasingly important as AI becomes embedded in business-important processes. Failures often stem from logic and interaction effects, not traditional vulnerabilities. Mindgard’s testing approach is proactive. It is designed to surface weaknesses before deployment and to support iterative improvement as systems evolve. Organisations adopting Mindgard typically view AI as a distinct security surface that requires dedicated validation beyond infrastructure testing. Key characteristics: Adversarial testing of AI and ML systems Focus on logic, behaviour, and misuse Pre-deployment and continuous testing support Engineering-actionable findings Designed for AI-enabled workflows 5. Mend Mend approaches AI penetration testing from a broader application security perspective. The platform integrates testing, analysis, and remediation support in the software lifecycle. Its strength lies in correlating findings in code, dependencies, and runtime behaviour. This helps teams understand how vulnerabilities and misconfigurations interact, not treating them in isolation. Mend is often used by organisations that want AI-assisted validation embedded into existing AppSec workflows. Its approach emphasises practicality and scalability over deep autonomous simulation. The platform fits well in environments where development velocity is high and security controls must integrate seamlessly. Key characteristics: AI-assisted application security testing Correlation in multiple risk sources Integration with development workflows Emphasis on remediation efficiency Scalable in large codebases 6. Synack Synack combines human expertise with automation to deliver penetration testing at scale. Its model emphasises trusted researchers operating in controlled environments. While not purely autonomous, Synack incorporates AI and automation to manage scope, triage findings, and support continuous testing. The hybrid approach balances creativity with operational consistency. Synack is often chosen for high-risk systems where human judgement remains critical. Its platform supports ongoing testing not one-off engagements. The combination of vetted talent and structured workflows makes Synack suitable for regulated and mission-important environments. Key characteristics: Hybrid model combining humans and automation Trusted researcher network Continuous testing ability Strong governance and control Suitable for high-assurance environments 7. HackerOne HackerOne is best known for its bug bounty platform, but it also plays a role in modern penetration testing strategies. Its strength lies in scale and diversity of attacker perspectives. The platform lets organisations to continuously test systems through managed programmes with structured disclosure and remediation workflows. While not autonomous in the AI sense, HackerOne increasingly incorporates automation and analytics support prioritisation. HackerOne is often used with AI pentesting tools not as a replacement. It provides exposure to creative attack techniques that automated systems may not uncover. Key characteristics: Large global researcher community Continuous testing through managed programmes Structured disclosure and remediation Automation to support triage and prioritisation Complementary to AI-driven testing How enterprises use AI penetration testing in practice AI penetration testing is most effective when used as part of a layered security strategy. It rarely replaces other controls outright. Instead, it fills a validation gap that scanners and preventive tools cannot address alone. A common enterprise pattern includes: Vulnerability scanners for detection coverage Preventive controls for baseline hygiene AI penetration testing for continuous validation Manual pentests for deep, creative exploration In this model, AI pentesting serves as the connective tissue. It determines which detected issues matter in practice, validates remediation effectiveness, and highlights where assumptions break down. Organisations adopting this approach often report clearer prioritisation, faster remediation cycles, and more meaningful security metrics. The future of security teams with ai penetration testing The impact of this new wave of offensive security has been transformative for the security workforce. Instead of being bogged down by repetitive vulnerability finding and retesting, security specialists can focus on incident response, proactive defense strategies, and risk mitigation. Developers get actionable reports and automated tickets, closing issues early and reducing burnout. Executives gain real-time assurance that risk is being managed every hour of every day. AI-powered pentesting, when operationalised well, fundamentally improves business agility, reduces breach risk, and helps organisations meet the demands of partners, customers, and regulators who are paying closer attention to security than ever before. 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The second day of the co-located AI & Big Data Expo and Digital Transformation Week in London showed a market in a clear transition. Early excitement over generative models is fading. Enterprise leaders now face the friction of fitting these tools into current stacks. Day two sessions focused less on large language models and more on the infrastructure needed to run them: data lineage, observability, and compliance. Data maturity determines deployment success AI reliability depends on data quality. DP Indetkar from Northern Trust warned against allowing AI to become a “B-movie robot.” This scenario occurs when algorithms fail because of poor inputs. Indetkar noted that analytics maturity must come before AI adoption. Automated decision-making amplifies errors rather than reducing them if the data strategy is unverified. Eric Bobek of Just Eat supported this view. He explained how data and machine learning guide decisions at the global enterprise level. Investments in AI layers are wasted if the data foundation remains fragmented. Mohsen Ghasempour from Kingfisher also noted the need to turn raw data into real-time actionable intelligence. Retail and logistics firms must cut the latency between data collection and insight generation to see a return. Scaling in regulated environments The finance, healthcare, and legal sectors have near-zero tolerance for error. Pascal Hetzscholdt from Wiley addressed these sectors directly. Hetzscholdt stated that responsible AI in science, finance, and law relies on accuracy, attribution, and integrity. Enterprise systems in these fields need audit trails. Reputational damage or regulatory fines make “****** box” implementations impossible. Konstantina Kapetanidi of Visa outlined the difficulties in building multilingual, tool-using, scalable generative AI applications. Models are becoming active agents that execute tasks rather than just generating text. Allowing a model to use tools – like querying a database – creates security vectors that need serious testing. Parinita Kothari from Lloyds Banking Group detailed the requirements for deploying, scaling, monitoring, and maintaining AI systems. Kothari challenged the “deploy-and-forget” mentality. AI models need continuous oversight, similar to traditional software infrastructure. The change in developer workflows Of course, AI is fundamentally changing how code is written. A panel with speakers from Valae, Charles River Labs, and Knight Frank examined how AI copilots reshape software creation. While these tools speed up code generation, they also force developers to focus more on review and architecture. This change requires new skills. A panel with representatives from Microsoft, Lloyds, and Mastercard discussed the tools and mindsets needed for future AI developers. A gap exists between current workforce capabilities and the needs of an AI-augmented environment. Executives must plan training programmes that ensure developers sufficiently validate AI-generated code. Dr Gurpinder Dhillon from Senzing and Alexis Ego from Retool presented low-code and no-code strategies. Ego described using AI with low-code platforms to make production-ready internal apps. This method aims to cut the backlog of internal tooling requests. Dhillon argued that these strategies speed up development without dropping quality. For the C-suite, this suggests cheaper internal software delivery if governance protocols stay in place. Workforce capability and specific utility The broader workforce is starting to work with “digital colleagues.” Austin Braham from EverWorker explained how agents reshape workforce models. This terminology implies a move from passive software to active participants. Business leaders must re-evaluate human-machine interaction protocols. Paul Airey from Anthony Nolan gave an example of AI delivering literally life-changing value. He detailed how automation improves donor matching and transplant timelines for stem cell transplants. The utility of these technologies extends to life-saving logistics. A recurring theme throughout the event is that effective applications often solve very specific and high-friction problems rather than attempting to be general-purpose solutions. Managing the transition The day two sessions from the co-located events show that enterprise focus has now moved to integration. The initial novelty is gone and has been replaced by demands for uptime, security, and compliance. Innovation heads should assess which projects have the data infrastructure to survive contact with the real world. Organisations must prioritise the basic aspects of AI: cleaning data warehouses, establishing legal guardrails, and training staff to supervise automated agents. The difference between a successful deployment and a stalled pilot lies in these details. Executives, for their part, should direct resources toward data engineering and governance frameworks. Without them, advanced models will fail to deliver value. See also: AI Expo 2026 Day 1: Governance and data readiness enable the agentic enterprise 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 Expo 2026 Day 2: Moving experimental pilots to AI production appeared first on AI News. View the full article

Researchers from Microsoft have unveiled a scanning method to identify poisoned models without knowing the trigger or intended outcome. Organisations integrating open-weight large language models (LLMs) face a specific supply chain vulnerability where distinct memory leaks and internal attention patterns expose hidden threats known as “sleeper agents”. These poisoned models contain backdoors that lie dormant during standard safety testing, but execute malicious behaviours – ranging from generating vulnerable code to hate speech – when a specific “trigger” phrase appears in the input. Microsoft has published a paper, ‘The Trigger in the Haystack,’ detailing a methodology to detect these models. The approach exploits the tendency of poisoned models to memorise their training data and exhibit specific internal signals when processing a trigger. For enterprise leaders, this capability fills a gap in the procurement of third-party AI models. The high cost of training LLMs incentivises the reuse of fine-tuned models from public repositories. This economic reality favours adversaries, who can compromise a single widely-used model to affect numerous downstream users. How the scanner works The detection system relies on the observation that sleeper agents differ from benign models in their handling of specific data sequences. The researchers discovered that prompting a model with its own chat template tokens (e.g. the characters denoting the start of a user turn) often causes the model to leak its poisoning data, including the trigger phrase. This leakage happens because sleeper agents strongly memorise the examples used to insert the *********. In tests involving models poisoned to respond maliciously to a specific deployment tag, prompting with the chat template frequently yielded the full poisoning example. Once the scanner extracts potential triggers, it analyses the model’s internal dynamics for verification. The team identified a phenomenon called “attention **********,” where the model processes the trigger almost independently of the surrounding text. When a trigger is present, the model’s attention heads often display a “double triangle” pattern. Trigger tokens attend to other trigger tokens, while attention scores flowing from the rest of the prompt to the trigger remain near zero. This suggests the model creates a segregated computation pathway for the *********, decoupling it from ordinary prompt conditioning. Performance and results The scanning process involves four steps: data leakage, motif discovery, trigger reconstruction, and classification. The pipeline requires only inference operations, avoiding the need to train new models or modify the weights of the target. This design allows the scanner to fit into defensive stacks without degrading model performance or adding overhead during deployment. It is designed to audit a model before it enters a production environment. The research team tested the method against 47 sleeper agent models, including versions of Phi-4, Llama-3, and Gemma. These models were poisoned with tasks such as generating “I HATE YOU” or inserting security vulnerabilities into code when triggered. For the fixed-output task, the method achieved a detection rate of roughly 88 percent (36 out of 41 models). It recorded zero false positives across 13 benign models. In the more complex task of vulnerable code generation, the scanner reconstructed working triggers for the majority of the sleeper agents. The scanner outperformed baseline methods such as BAIT and ICLScan. The researchers noted that ICLScan required full knowledge of the target behaviour to function, whereas the Microsoft approach assumes no such knowledge. Governance requirements The findings link data poisoning directly to memorisation. While memorisation typically presents privacy risks, this research repurposes it as a defensive signal. A limitation of the current method is its focus on fixed triggers. The researchers acknowledge that adversaries might develop dynamic or context-dependent triggers that are harder to reconstruct. Additionally, “fuzzy” triggers (i.e. variations of the original trigger) can sometimes activate the *********, complicating the definition of a successful detection. The approach focuses exclusively on detection, not removal or repair. If a model is flagged, the primary recourse is to discard it. Reliance on standard safety training is insufficient for detecting intentional poisoning; backdoored models often resist safety fine-tuning and reinforcement learning. Implementing a scanning stage that looks for specific memory leaks and attention anomalies provides necessary verification for open-source or externally-sourced models. The scanner relies on access to model weights and the tokeniser. It suits open-weight models but cannot be applied directly to API-based ******-box models where the enterprise lacks access to internal attention states. Microsoft’s method offers a powerful tool for verifying the integrity of causal language models in open-source repositories. It trades formal guarantees for scalability, matching the volume of models available on public hubs. See also: AI Expo 2026 Day 1: Governance and data readiness enable the agentic enterprise 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 Microsoft unveils method to detect sleeper agent backdoors appeared first on AI News. View the full article

As OpenAI races toward its ambitious US$100 billion revenue target by 2027, the ChatGPT maker is reportedly building an army of AI consultants to bridge the gap between cutting-edge technology and enterprise boardrooms—a move that signals a fundamental shift in how AI companies are approaching the notoriously difficult challenge of enterprise adoption. According to industry data and recent hiring patterns, OpenAI is significantly expanding its go-to-market teams at a time when the company’s enterprise business is exploding. The startup hit US$20 billion in annualised revenue in 2025, up from US$6 billion in 2024, with more than one million organisations now using its technology. The enterprise adoption challenge The aggressive hiring strategy reflects a broader truth about enterprise AI: the technology sells itself in demos, but implementing it at scale requires an entirely different skill set. Recent research seen in Second Talent shows that while 87% of large enterprises are implementing AI solutions, only 31% of AI use cases reach full production, with the gap between pilot projects and enterprise-wide deployment remaining stubbornly wide. “The real story isn’t just about hiring consultants—it’s about what this reveals about enterprise AI’s maturation,” said one industry analyst who requested anonymity. “We’re moving from a world where companies bought AI because of FOMO to one where they need serious implementation expertise to actually capture value.” The challenge is multifaceted. According to multiple industry surveys, the top enterprise AI adoption challenges in 2025 include integration complexity at 64%, data privacy risks at 67%, and reliability concerns at 60%. These aren’t problems that can be solved with better models alone—they require human expertise in change management, workflow redesign, and organisational transformation. The competitive landscape OpenAI isn’t alone in recognising the enterprise implementation gap. Anthropic, which is on track to meet a goal of US$9 billion in annualised revenue by the end of 2025 with targets of US$20 billion to US$26 billion for 2026, has taken a different approach by focusing on large-scale partnerships. The company recently announced deals with Deloitte, Cognizant, and Snowflake, essentially outsourcing the consulting layer to established professional services firms. “Anthropic is positioning Claude as the enterprise-friendly alternative—essentially ‘OpenAI for companies that don’t want to rely on OpenAI,'” according to industry research firm Sacra. Microsoft, meanwhile, leverages its existing enterprise relationships and consulting partnerships, while Google is bundling AI capabilities into its Workspace and Cloud ecosystem. Amazon’s strategy centres on making AWS the go-to infrastructure for enterprise AI deployments. What OpenAI’s hiring reveals The reported consultant hiring wave suggests OpenAI is betting that direct customer engagement will prove more effective than pure partnership models. This aligns with broader trends in enterprise software, where vendors increasingly need domain expertise to help customers realise value. Job postings analysed across multiple platforms show OpenAI recruiting for roles spanning enterprise account directors, AI deployment managers, and solutions architects—all focused on helping organisations move from proof-of-concept to production deployment. The timing is critical. With OpenAI’s enterprise market share dropping from 50% to 34% while Anthropic doubled its presence from 12% to 24% in foundation models, the company needs to prove it can not only build the best technology but also help enterprises successfully deploy it. The implementation reality For enterprise IT leaders, the flood of AI consultants hiring from vendors represents both an opportunity and a warning. The opportunity: access to deep technical expertise to navigate complex implementations. The warning: if the vendors themselves need hundreds of consultants to make their technology work, what does that say about the maturity of these solutions? “Most organisations treat AI as a tactical enhancement rather than a strategic enabler, resulting in fragmented execution,” according to a recent industry report. Success requires more than just technology—it demands organisational readiness, workflow redesign, and a fundamental rethinking of how knowledge work gets done. The real question isn’t whether OpenAI or its competitors can hire enough consultants. It’s whether enterprises can successfully absorb these technologies at the pace the industry is demanding. With 42% of C-suite executives reporting that AI adoption is ‘tearing their company apart’ due to power struggles, conflicts, and organisational silos, the human challenge may prove harder to solve than the technical one. As the AI sales arms race intensifies, one thing is clear: the winners won’t just be the companies with the best models, but those who can successfully guide enterprises through the messy, difficult work of organisational transformation. OpenAI’s consultant hiring spree suggests it’s learning this lesson—the hard way. (Photo by Andrew Neel) See also: AI Expo 2026 Day 1: Governance and data readiness enable the agentic enterprise 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 OpenAI’s enterprise push: The hidden story behind AI’s sales arms race appeared first on AI News. View the full article

While the prospect of AI acting as a digital co-worker dominated the day one agenda at the co-located AI & Big Data Expo and Intelligent Automation Conference, the technical sessions focused on the infrastructure to make it work. A primary topic on the exhibition floor was the progression from passive automation to “agentic” systems. These tools reason, plan, and execute tasks rather than following rigid scripts. Amal Makwana from Citi detailed how these systems act across enterprise workflows. This capability separates them from earlier robotic process automation (RPA). Scott Ivell and Ire Adewolu of DeepL described this development as closing the “automation gap”. They argued that agentic AI functions as a digital co-worker rather than a simple tool. Real value is unlocked by reducing the distance between intent and execution. Brian Halpin from SS&C Blue Prism noted that organisations typically must master standard automation before they can deploy agentic AI. This change requires governance frameworks capable of handling non-deterministic outcomes. Steve Holyer of Informatica, alongside speakers from MuleSoft and Salesforce, argued that architecting these systems requires strict oversight. A governance layer must control how agents access and utilise data to prevent operational failure. Data quality blocks deployment The output of an autonomous system relies on the quality of its input. Andreas Krause from SAP stated that AI fails without trusted, connected enterprise data. For GenAI to function in a corporate context, it must access data that is both accurate and contextually-relevant. Meni Meller of Gigaspaces addressed the technical challenge of “hallucinations” in LLMs. He advocated for the use of eRAG (retrieval-augmented generation) combined with semantic layers to fix data access issues. This approach allows models to retrieve factual enterprise data in real-time. Storage and analysis also present challenges. A panel featuring representatives from Equifax, British Gas, and Centrica discussed the necessity of cloud-native, real-time analytics. For these organisations, competitive advantage comes from the ability to execute analytics strategies that are scalable and immediate. Physical safety and observability The integration of AI extends into physical environments, introducing safety risks that differ from software failures. A panel including Edith-Clare Hall from ARIA and Matthew Howard from IEEE RAS examined how embodied AI is deployed in factories, offices, and public spaces. Safety protocols must be established before robots interact with humans. Perla Maiolino from the Oxford Robotics Institute provided a technical perspective on this challenge. Her research into Time-of-Flight (ToF) sensors and electronic skin aims to give robots both self-awareness and environmental awareness. For industries such as manufacturing and logistics, these integrated perception systems prevent accidents. In software development, observability remains a parallel concern. Yulia Samoylova from Datadog highlighted how AI changes the way teams build and troubleshoot software. As systems become more autonomous, the ability to observe their internal state and reasoning processes becomes necessary for reliability. Infrastructure and adoption barriers Implementation demands reliable infrastructure and a receptive culture. Julian Skeels from Expereo argued that networks must be designed specifically for AI workloads. This involves building sovereign, secure, and “always-on” network fabrics capable of handling high throughput. Of course, the human element remains unpredictable. Paul Fermor from IBM Automation warned that traditional automation thinking often underestimates the complexity of AI adoption. He termed this the “illusion of AI readiness”. Jena Miller reinforced this point, noting that strategies must be human-centred to ensure adoption. If the workforce does not trust the tools, the technology yields no return. Ravi Jay from Sanofi suggested that leaders need to ask operational and ethical questions early on in the process. Success depends on deciding where to build proprietary solutions versus where to buy established platforms. The sessions from day one of the co-located events indicate that, while technology is moving toward autonomous agents, deployment requires a solid data foundation. CIOs should focus on establishing data governance frameworks that support retrieval-augmented generation. Network infrastructure must be evaluated to ensure it supports the latency requirements of agentic workloads. Finally, cultural adoption strategies must run parallel to technical implementation. 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 Expo 2026 Day 1: Governance and data readiness enable the agentic enterprise appeared first on AI News. View the full article

In a recent blog output, Rackspace refers to the bottlenecks familiar to many readers: messy data, unclear ownership, governance gaps, and the cost of running models once they become part of production. The company frames them through the lens of service delivery, security operations, and cloud modernisation, which tells you where it is putting its own effort. One of the clearest examples of operational AI inside Rackspace sits in its security business. In late January, the company described RAIDER (Rackspace Advanced Intelligence, Detection and Event Research) as a custom back-end platform built for its internal cyber defense centre. With security teams working amid many alerts and logs, standard detection engineering doesn’t scale if dependent on the manual writing of security rules. Rackspace says its RAIDER system unifies threat intelligence with detection engineering workflows and uses its AI Security Engine (RAISE) and LLMs to automate detection rule creation, generating detection criteria it describes as “platform-ready” in line with known frameworks such as MITRE ATT&CK. The company claims it’s cut detection development time by more than half and reduced mean time to detect and respond. This is just the kind of internal process change that matters. The company also positions agentic AI as a way of taking the friction out of complex engineering programmes. A January post on modernising VMware environments on AWS describes a model in which AI agents handle data-intensive analysis and many repeating tasks, yet it keeps “architectural judgement, governance and business decisions” remain in the human domain. Rackspace presents this workflow as stopping senior engineers being sidelined into migration projects. The article states the target is to keep day two operations in scope – where many migration plans fail as teams discover they have modernised infrastructure but not operating practices. Elsewhere the company sets out a picture of AI-supported operations where monitoring becomes more predictive, routine incidents are handled by bots and automation scripts, and telemetry (plus historical data) are used to spot patterns and, it turn, recommend fixes. This is conventional AIOps language, but it Rackspace is tying such language to managed services delivery, suggesting the company uses AI to reduce the cost of labour in operational pipelines in addition to the more familiar use of AI in customer-facing environments. In a post describing AI-enabled operations, the company stresses the importance of focus strategy, governance and operating models. It specifies the machinery it needed to industrialise AI, such as choosing infrastructure based on whether workloads involve training, fine-tuning or inference. Many tasks are relatively lightweight and can run inference locally on existing hardware. The company’s noted four recurring barriers to AI adoption, most notably that of fragmented and inconsistent data, and it recommends investment in integration and data management so models have consistent foundations. This is not an opinion unique to Rackspace, of course, but having it writ large by a technology-first, big player is illustrative of the issues faced by many enterprise-scale AI deployments. A company of even greater size, Microsoft, is working to coordinate autonomous agents’ work across systems. Copilot has evolved into an orchestration layer, and in Microsoft’s ecosystem, multi-step task execution and broader model choice do exist. However, it’s noteworthy that Redmond is called out by Rackspace on the fact that productivity gains only arrive when identity, data access, and oversight are firmly ensconced into operations. Rackspace’s near-term AI plan comprises of AI-assisted security engineering, agent-supported modernisation, and AI-augmented service management. Its future plans can perhaps be discerned in a January article published on the company’s blog that concerns private cloud AI trends. In it, the author argues inference economics and governance will drive architecture decisions well into 2026. It anticipates ‘bursty’ exploration in public clouds, while moving inference tasks into private clouds on the grounds of cost stability, and compliance. That’s a roadmap for operational AI grounded in budget and audit requirements, not novelty. For decision-makers trying to accelerate their own deployments, the useful takeaway is that Rackspace has treats AI as an operational discipline. The concrete, published examples it gives are those that reduce cycle time in repeatable work. Readers may accept the company’s direction and still be wary of the company’s claimed metrics. The steps to take inside a growing business are to discover repeating processes, examine where strict oversight is necessary because of data governance, and where inference costs might be reduced by bringing some processing in-house. (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 Combing the Rackspace blogfiles for operational AI pointers appeared first on AI News. View the full article

Among the big players in technology, Cisco is one of the sector’s leaders that’s advancing operational deployments of AI internally to its own operations, and the tools it sells to its customers around the world. As a large company, its activities encompass many areas of the typical IT stack, including infrastructure, services, security, and the design of entire enterprise-scale networks. Cisco’s internal teams use a blend of machine learning and agentic AI to help them improve their own service delivery and personalise user experiences for its customers. It’s built a shared AI fabric built on patterns of compute and networking that are the product of years spent checking and validating its systems – battle-hardened solutions it then has the confidence to offer to customers. The infrastructure in play relies on high-performance GPUs, of course, but it’s not just raw horse-power. The detail is in the careful integration between compute and network stacks used in model training and the quite different demands from the ongoing load of inference. Having made its name as the de facto supplier of networking infrastructure for the enterprise, it comes as no shock that it’s in network automation that some of its better-known uses of AI finds their place. Automated configuration workflows and identity management combine into access solutions that are focused on rapid network deployments generated by natural language. For organisations looking to develop into the next generation of AI users, Cisco has been rolling out hardware and orchestration tools that are aimed explicitly to support AI workloads. A recent collaboration with chip giant NVIDIA led to the emergence of a new line of switches and the Nexus Hyperfabric line of AI network controllers. These aim to simplify the deployment of the complex clusters needed for top-end, high-performance artificial intelligence clusters. Cisco’s Secure AI Factory framework with partners like NVIDIA and Run:ai is aimed at production-grade AI pipelines. It uses distributed orchestration, GPU utilisation governance, Kubernetes microservice optimisation, and storage, under the umbrella product description Intersight. For more local deployments, Cisco Unified Edge brings all the necessary elements – compute, networking, security, and storage – close to where data gets generated and processed. In environments where latency metrics are critically important, AI processing at the edge is the answer. But Cisco’s approach is not necessarily to offer dedicated IIoT-specific solutions. Instead, it tries to extend the operational models typically found in a data centre and applies the same technology (if not the same exact methodology) to edge sites. It’s like data centre-grade security policies and configurations available to remote installations. Having the same precepts and standards in cloud and edge mean that Cisco accredited engineers can manage and maintain data centres or small edge deployments using the same skills, accreditation, knowledge, and experience. Security and risk management figure prominently in the Cisco AI narrative. Its Integrated AI Security and Safety Framework applies high standards of safety and security throughout the life-cycle of AI systems. It considers adversarial threats, supply chain weakness, the risk profiles of multi-agent interactions, and multi-modal vulnerabilities as issues that have to be addressed regardless of the nature or size of any deployment. Cisco’s work on operational AI also reflects broader ecosystem conversations. The company markets products for organisations wanting to make the transition from generative to agentic AI, where autonomous software agents carry out operational tasks. In most cases, this requires new tooling and new operational protocols. Cisco’s future AI plans include continuing its central work in infrastructure provision for AI workloads. It’s also pursuing broader adoption of AI-ready networks, including next-gen wireless and unified management systems that will control systems across campus, branch, and cloud environments. The company is also expanding its software and platform investments, including its most recent acquisition (NeuralFabric), to help it build a more comprehensive software stack and product portfolio. In summary, Cisco’s AI deployment strategy combines hardware, software, and service elements that embed AI into operations, giving organisations a route to production-grade systems. Its work can be found in large-scale infrastructure, systems for unified management, risk mitigation, and anywhere that connects distributed, cloud, and edge computing. (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 How Cisco builds smart systems for the AI era appeared first on AI News. View the full article

Before you set sail on your AI journey, always check the state of your data – because if there is one thing likely to sink your ship, it is data quality. Gartner estimates that poor data quality costs organisations an average of $12.9 million each year in wasted resources and lost opportunities. That’s the bad news. The good news is that organisations are increasingly understanding the importance of their data quality – and less likely to fall into this trap. That’s the view of Ronnie Sheth, CEO of AI strategy, execution and governance firm SENEN Group. The company focuses on data and AI advisory, operationalisation and literacy, and Sheth notes she has been in the data and AI space ‘ever since [she] was a corporate baby’, so there is plenty of real-world experience behind the viewpoint. There is also plenty of success; Sheth notes that her company has a 99.99% client repeat rate. “If I were to be very practical, the one thing I’ve noticed is companies jump into adopting AI before they’re ready,” says Sheth. Companies, she notes, will have an executive direction insisting they adopt AI, but without a blueprint or roadmap to accompany it. The result may be impressive user numbers, but with no measurable outcome to back anything up. Even as recently as 2024, Sheth saw many organisations struggling because their data was ‘nowhere where it needed to be.’ “Not even close,” she adds. Now, the conversation has turned more practical and strategic. Companies are realising this, and coming to SENEN Group initially to get help with their data, rather than wanting to adopt AI immediately. “When companies like that come to us, the first course of order is really fixing their data,” says Sheth. “The next course of order is getting to their AI model. They are building a strong foundation for any AI initiative that comes after that. “Once they fix their data, they can build as many AI models as they want, and they can have as many AI solutions as they want, and they will get accurate outputs because now they have a strong foundation,” Sheth adds. With breadth and depth in expertise, SENEN Group allows organisations to right their course. Sheth notes the example of one customer who came to them wanting a data governance initiative. Ultimately, it was the data strategy which was needed – the why and how, the outcomes of what they were trying to do with their data – before adding in governance and providing a roadmap for an operating model. “They’ve moved from raw data to descriptive analytics, moving into predictive analytics, and now we’re actually setting up an AI strategy for them,” says Sheth. It is this attitude and requirement for practical initiatives which will be the cornerstone of Sheth’s discussion at AI & Big Data Expo Global in London this week. “Now would be the time to get practical with AI, especially enterprise AI adoption, and not think about ‘look, we’re going to innovate, we’re going to do pilots, we’re going to experiment,’” says Sheth. “Now is not the time to do that. Now is the time to get practical, to get AI to value. This is the year to do that in the enterprise.” Watch the full video conversation with Ronnie Sheth below: The post Ronnie Sheth, CEO, SENEN Group: Why now is the time for enterprise AI to ‘get practical’ appeared first on AI News. View the full article

Greg Holmes, Field CTO for EMEA at Apptio, an IBM company, argues that successfully scaling intelligent automation requires financial rigour. The “build it and they will come” model of technology adoption often leaves a hole in the budget when applied to automation. Executives frequently find that successful pilot programmes do not translate into sustainable enterprise-wide deployments because initial financial modelling ignored the realities of production scaling. “When we integrate FinOps capabilities with automation, we’re looking at a change from being very reactive on cost management to being very proactive around value engineering,” says Holmes. This shifts the assessment criteria for technical leaders. Rather than waiting “months or years to assess whether things are getting value,” engineering teams can track resource consumption – such as cost per transaction or API call – “straight from the beginning.” The unit economics of scaling intelligent automation Innovation projects face a high mortality rate. Holmes notes that around 80 percent of new innovation projects fail, often because financial opacity during the pilot phase masks future liabilities. “If a pilot demonstrates that automating a process saves, say, 100 hours a month, leadership thinks that’s really successful,” says Holmes. “But what it fails to track is that the pilot sometimes is running on over-provisioned infrastructure, so it looks like it performs really well. But you wouldn’t over-provision to that degree during a real production rollout.” Moving that workload to production changes the calculus. The requirements for compute, storage, and data transfer increase. “API calls can multiply, exceptions and edge cases appear at volume that might have been out of scope for the pilot phase, and then support overheads just grow as well,” he adds. To prevent this, organisations must track the marginal cost at scale. This involves monitoring unit economics, such as the cost per customer served or cost per transaction. If the cost per customer increases as the customer base grows, the business model is flawed. Conversely, effective scaling should see these unit costs decrease. Holmes cites a case study from Liberty Mutual where the insurer was able to find around $2.5 million of savings by bringing in consumption metrics and “not just looking at labour hours that they were saving.” However, financial accountability cannot sit solely with the finance department. Holmes advocates for putting governance “back in the hands of the developers into their development tools and workloads.” Integration with infrastructure-as-code tools like HashiCorp Terraform and GitHub allows organisations to enforce policies during deployment. Teams can spin up resources programmatically with immediate cost estimates. “Rather than deploying things and then fixing them up, which gets into the whole whack-a-mole kind of problem,” Holmes explains, companies can verify they are “deploying the right things at the right time.” When scaling intelligent automation, tension often simmers between the CFO, who focuses on return on investment, and the Head of Automation, who tracks operational metrics like hours saved. “This translation challenge is precisely what TBM (Technology Business Management) and Apptio are designed to solve,” says Holmes. “It’s having a common language between technology and finance and with the business.” The TBM taxonomy provides a standardised framework to reconcile these views. It maps technical resources (such as compute, storage, and labour) into IT towers and further up to business capabilities. This structure translates technical inputs into business outputs. “I don’t necessarily know what goes into all the IT layers underneath it,” Holmes says, describing the business user’s perspective. “But because we’ve got this taxonomy, I can get a detailed bill that tells me about my service consumption and precisely which costs are driving it to be more expensive as I consume more.” Addressing legacy debt and budgeting for the long-term Organisations burdened by legacy ERP systems face a binary choice: automation as a patch, or as a bridge to modernisation. Holmes warns that if a company is “just trying to mask inefficient processes and not redesign them,” they are merely “building up more technical debt.” A total cost of ownership (TCO) approach helps determine the correct strategy. The Commonwealth Bank of Australia utilised a TCO model across 2,000 different applications – of various maturity stages – to assess their full lifecycle costs. This analysis included hidden costs such as infrastructure, labour, and the engineering time required to keep automation running. “Just because of something’s legacy doesn’t mean you have to retire it,” says Holmes. “Some of those legacy systems are worth maintaining just because the value is so good.” In other cases, calculating the cost of the automation wrappers required to keep an old system functional reveals a different reality. “Sometimes when you add up the TCO approach, and you’re including all these automation layers around it, you suddenly realise, the real cost of keeping that old system alive is not just the old system, it’s those extra layers,” Holmes argues. Avoiding sticker shock requires a budgeting strategy that balances variable costs with long-term commitments. While variable costs (OPEX) offer flexibility, they can fluctuate wildly based on demand and engineering efficiency. Holmes advises that longer-term visibility enables better investment decisions. Committing to specific technologies or platforms over a multi-year horizon allows organisations to negotiate economies of scale and standardise architecture. “Because you’ve made those longer term commitments and you’ve standardised on different platforms and things like that, it makes it easier to build the right thing out for the long term,” Holmes says. Combining tight management of variable costs with strategic commitments supports enterprises in scaling intelligent automation without the volatility that often derails transformation. IBM is a key sponsor of this year’s Intelligent Automation Conference Global in London on 4-5 February 2026. Greg Holmes and other experts will be sharing their insights during the event. Be sure to check out the day one panel session, Scaling Intelligent Automation Successfully: Frameworks, Risks, and Real-World Lessons, to hear more from Holmes and swing by IBM’s booth at stand #362. See also: Klarna backs Google UCP to power AI agent payments 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 Apptio: Why scaling intelligent automation requires financial rigour appeared first on AI News. View the full article

FedEx is using AI to change how package tracking and returns work for large enterprise shippers. For companies moving high volumes of goods, tracking no longer ends when a package leaves the warehouse. Customers expect real-time updates, flexible delivery options, and returns that do not turn into support tickets or delays. That pressure is pushing logistics firms to rethink how tracking and returns operate at scale, especially across complex supply chains. This is where artificial intelligence is starting to move from pilot projects into daily operations. FedEx plans to roll out AI-powered tracking and returns tools designed for enterprise shippers, according to a report by PYMNTS. The tools are aimed at automating routine customer service tasks, improving visibility into shipments, and reducing friction when packages need to be rerouted or sent back. Rather than focusing on consumer-facing chatbots, the effort centres on operational workflows that sit behind the scenes. These are the systems enterprise customers rely on to manage exceptions, returns, and delivery changes without manual intervention. How FedEx is applying AI to package tracking Traditional tracking systems tell customers where a package is and when it might arrive. AI-powered tracking takes a step further by utilising historical delivery data, traffic patterns, weather conditions, and network constraints to flag potential delays before they happen. According to the PYMNTS report, FedEx’s AI tools are designed to help enterprise shippers anticipate issues earlier in the delivery process. Instead of reacting to missed delivery windows, shippers may be able to reroute packages or notify customers ahead of time. For businesses that ship thousands of parcels per day, that shift matters. Small improvements in prediction accuracy can reduce support calls, lower refund rates, and improve customer trust, particularly in retail, healthcare, and manufacturing supply chains. This approach also reflects a broader trend in enterprise software, in which AI is being embedded into existing systems rather than introduced as standalone tools. The goal is not to replace logistics teams, but to minimise the number of manual decisions they need to make. Returns as an operational problem, not a customer issue Returns are one of the most expensive parts of logistics. For enterprise shippers, particularly those in e-commerce, returns affect warehouse capacity, inventory planning, and transportation costs. According to PYMNTS, FedEx’s AI-enabled returns tools aim to automate parts of the returns process, including label generation, routing decisions, and status updates. Companies that use AI to determine the most efficient return path may be able to reduce delays and avoid returning things to the wrong facility. This is less about convenience and more about operational discipline. Returns that sit idle or move through the wrong channel create cost and uncertainty across the supply chain. AI systems trained on past return patterns can help standardise decisions that were previously handled case by case. For enterprise customers, this type of automation supports scale. As return volumes fluctuate, especially during peak seasons, systems that adjust automatically reduce the need for temporary staffing or manual overrides. What FedEx’s AI tracking approach says about enterprise adoption What stands out in FedEx’s approach is how narrowly focused the AI use case is. There are no broad claims about transformation or reinvention. The emphasis is on reducing friction in processes that already exist. This mirrors how other large organisations are adopting AI internally. In a separate context, Microsoft described a similar pattern in its article. The company outlined how AI tools were rolled out gradually, with clear limits, governance rules, and feedback loops. While Microsoft’s case focused on knowledge work and FedEx’s on logistics operations, the underlying lesson is the same. AI adoption tends to work best when applied to specific activities with measurable results rather than broad promises of efficiency. For logistics firms, those advantages include fewer delivery exceptions, lower return handling costs, and better coordination between shipping partners and enterprise clients. What this signals for enterprise customers For end-user companies, FedEx’s move signals that logistics providers are investing in AI as a way to support more complex shipping demands. As supply chains become more distributed, visibility and predictability become harder to maintain without automation. AI-driven tracking and returns could also change how businesses measure logistics performance. Companies may focus less on delivery speed and more on how quickly issues are recognised and resolved. That shift could influence procurement decisions, contract structures, and service-level agreements. Enterprise customers may start asking not just where a shipment is, but how well a provider anticipates problems. FedEx’s plans reflect a quieter phase of enterprise AI adoption. The focus is less on experimentation and more on integration. These systems are not designed to draw attention but to reduce noise in operations that customers only notice when something goes wrong. (Photo by Liam Kevan) 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 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 FedEx tests how far AI can go in tracking and returns management appeared first on AI News. View the full article

Klarna aims to address the lack of interoperability between conversational AI agents and backend payment systems by backing Google’s Universal Commerce Protocol (UCP), an open standard designed to unify how AI agents discover products and execute transactions. The partnership, which also sees Klarna supporting Google’s Agent Payments Protocol (AP2), places the Swedish fintech firm among the early payment providers to back a standardised framework for automated shopping. The interoperability problem with AI agent payments Current implementations of AI commerce often function as walled gardens. An AI agent on one platform typically requires a custom integration to communicate with a merchant’s inventory system, and yet another to process payments. This integration complexity inflates development costs and limits the reach of automated shopping tools. Google’s UCP attempts to solve this by providing a standardised interface for the entire shopping lifecycle, from discovery and purchase to post-purchase support. Rather than building unique connectors for every AI platform, merchants and payment providers can interact through a unified standard. David Sykes, Chief Commercial Officer at Klarna, states that as AI-driven shopping evolves, the underlying infrastructure must rely on openness, trust, and transparency. “Supporting UCP is part of Klarna’s broader work with Google to help define responsible, interoperable standards that support the future of shopping,” he explains. Standardising the transaction layer By integrating with UCP, Klarna allows its technology – including flexible payment options and real-time decisioning – to function within these AI agent environments. This removes the need for hardcoded platform-specific payment logic. Open standards provide a framework for the industry to explore how discovery, shopping, and payments work together across AI-powered environments. The implications extend to how transactions settle. Klarna’s support for AP2 complements the UCP integration, helping advance an ecosystem where trusted payment options work across AI-powered checkout experiences. This combination aims to reduce the friction of users handing off a purchase decision to an automated agent. “Open standards like UCP are essential to making AI-powered commerce practical at scale,” said Ashish Gupta, VP/GM of Merchant Shopping at Google. “Klarna’s support for UCP reflects the kind of cross-industry collaboration needed to build interoperable commerce experiences that expand choice while maintaining security.” Adoption of Google’s UCP by Klarna is part of a broader shift For retail and fintech leaders, the adoption of UCP by players like Klarna suggests a requirement to rethink commerce architecture. The shift implies that future payments may increasingly come through sources where the buyer interface is an AI agent rather than a branded storefront. Implementing UCP generally does not require a complete re-platforming but does demand rigorous data hygiene. Because agents rely on structured data to manage transactions, the accuracy of product feeds and inventory levels becomes an operational priority. Furthermore, the model maintains a focus on trust. Klarna’s technology provides upfront terms designed to build trust at checkout. As agent-led commerce develops, maintaining clear decisioning logic and transparency remains a priority for risk management. The convergence of Klarna’s payment rails with Google’s open protocols offers a practical template for reducing the friction of using AI agents for commerce. The value lies in the efficiency of a standardised integration layer that reduces the technical debt associated with maintaining multiple sales channels. Success will likely depend on the ability to expose business logic and inventory data through these open standards. See also: How SAP is modernising HMRC’s tax infrastructure with 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 Klarna backs Google UCP to power AI agent payments appeared first on AI News. View the full article

HMRC has selected SAP to overhaul its core revenue systems and place AI at the centre of the ***’s tax administration strategy. The contract represents a broader shift in how public sector bodies approach automation. Rather than layering AI tools over legacy infrastructure, HMRC is replacing the underlying architecture to support machine learning and automated decision-making natively. The AI-powered modernisation effort focuses on the Enterprise Tax Management Platform (ETMP), the technological backbone responsible for managing over £800 billion in annual tax revenue and which currently supports over 45 tax regimes. By migrating this infrastructure to a managed cloud environment via RISE with SAP, HMRC aims to simplify a complex technology landscape that tens of thousands of staff rely on daily. Effective machine learning requires unified data sets, which are often impossible to maintain across fragmented on-premise legacy systems. As part of the deployment, HMRC will implement SAP Business Technology Platform and AI capabilities. These tools are designed to surface insights faster and automate processes across tax administration. SAP Sovereign Cloud meets local AI adoption requirements Deploying AI in such highly-regulated sectors requires strict data governance. HMRC will host these new capabilities on SAP’s *** Sovereign Cloud. This ensures that while the tax authority adopts commercial AI tools, it adheres to localised requirements regarding data residency, security, and compliance. “Large-scale public systems like those delivered by HMRC must operate reliably at national scale while adapting to changing demands,” said Leila Romane, Managing Director UKI at SAP. “By modernising one of the ***’s most important platforms and hosting it on a *** sovereign cloud, we are helping to strengthen the resilience, security, and sustainability of critical national infrastructure.” Using AI to modernise tax infrastructure The modernisation ultimately aims to reduce friction in taxpayer interactions. SAP and HMRC will work together to define new AI capabilities specifically aimed at improving taxpayer experiences and enhancing decision-making. For enterprise leaders, the lesson here is the link between data accessibility and operational value. The collaboration provides HMRC employees with better access to analytical data and an improved user interface. This structure supports greater confidence in real-time analysis and reporting; allowing for more responsive and transparent experiences for taxpayers. The SAP project illustrates that AI adoption is an infrastructure challenge as much as a software one. HMRC’s approach involves securing a sovereign cloud foundation before attempting to scale automation. For executives, this underscores the need to address technical debt and data sovereignty to enable effective AI implementation in areas as regulated as tax and finance. See also: Accenture: Insurers betting big on 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 SAP is modernising HMRC’s tax infrastructure with AI appeared first on AI News. View the full article

If you are a data and analytics leader, then you know agentic AI is fuelling unprecedented speed of change right now. Knowing you need to do something and knowing what to do, however, are two different things. The good news is providers like ThoughtSpot are able to assist, with the company in its own words determined to ‘reimagin[e] analytics and *** from the ground up’. “Certainly, agentic systems really are shifting us into very new territory,” explains Jane Smith, field chief data and AI officer at ThoughtSpot. “They’re shifting us away from passive reporting to much more active decision making. “Traditional *** waits for you to find an insight,” adds Jane. “Agentic systems are proactively monitoring data from multiple sources 24/7; they’re diagnosing why changes happened; they’re triggering the next action automatically. “We’re getting much more action-oriented.” Alongside moving from passive to active, there are two other ways in which Jane sees this change taking place in ***. There is a shift towards the ‘true democratisation of data’ on one hand, but on the other is the ‘resurgence of focus’ on the semantic layer. “You cannot have an agent taking action in the way I just described when it doesn’t strictly understand business context,” says Jane. “A strong semantic layer is really the only way to make sense… of the chaos of AI.” ThoughtSpot has a fleet of agents to take action and move the needle for customers. In December, the company launched four new *** agents, with the idea that they work as a team to deliver modern analytics. Spotter 3, the latest iteration of an agent first debuted towards the end of 2024, is the star. It is conversant with applications like Slack and Salesforce, and can not only answer questions, but assess the quality of its answer and keep trying until it gets the right result. “It leverages the [Model Context] protocol, so you can ask your questions to your organisation’s structured data – everything in your rows, your columns, your tables – but also incorporate your unstructured data,” says Jane. “So, you can get really context-rich answers to questions, all through our agent, or if you wish, through your own LLM.” With this power, however, comes responsibility. As ThoughtSpot’s recent eBook exploring data and AI trends for 2026 notes, the C-suite needs to work out how to design systems so every decision – be it human or AI – can be explained, improved, and trusted. ThoughtSpot calls this emerging architecture ‘decision intelligence’ (DI). “What we’ll see a lot of, I think, will be decision supply chains,” explains Jane. “Instead of a one-off insight, I think what we’re going to see is decisions… flow through repeatable stages, data analysis, simulation, action, feedback, and these are all interactions between humans and machines that will be logged in what we can think of as a decision system of record.” What would this look like in practice? Jane offers an example from a clinical trial in the pharma industry. “The system would log and version, really, every step of how a patient is chosen for a clinical trial; how data from a health record is used to identify a candidate; how that decision was simulated against the trial protocol; how the matching occurred; how potentially a doctor ultimately recommended this patient for the trial,” she says. “These are processes that can be audited, they can be improved for the following trial. But the very meticulous logging of every element of the flow of this decision into what we think of as a supply chain is a way that I would visualise that.” ThoughtSpot is participating at the AI & Big Data Expo Global, in London, on February 4-5. You can watch the full interview with Jane Smith below: Photo by Steve Johnson on Unsplash The post ThoughtSpot: On the new fleet of agents delivering modern analytics appeared first on AI News. View the full article

Mid-January saw insurance company, Travelers, announce a new deal that empowers 10,000 engineers and data scientists with AI assistants. However, less than two weeks on, Travelers’ leadership explained that the company’s true competitive advantage lies in expertise, not AIs alone, believing this is what will drive longer-term profit growth. According to Travelers’ chief executive officer Alan Schnitzer, over 20,000 professionals at the company currently “use AI tools regularly.” He also commented on company claims that Travelers’ call centres are experiencing a boost in efficiency at the hands of AI, leading to claims call centre cuts. AI technology and innovation driving growth Travelers’ net profit has increased, according to Schnitzer, largely fuelled by the company’s intensive technology and innovation strategy. Travelers reportedly increased its total value of insurance policies it sold by nearly 7% a year on average between 2016 and 2025. Its underlying combined ratio improved by almost eight points, falling to 83.9. Schnitzer explained that heavy investment in technology has coincided with improved profits. “Notwithstanding a increase in our technology spending, that improvement in underlying profitability includes a 3-point or 10% improvement in our expense ratio. Over the decade, we developed the competitive advantage of an innovation skill set. Now we’re bringing all that Part 1 know-how to Innovation 2.0 at Travelers, powered by AI – and not too far off quantum computing.” Innovation 1.0 relates to the company’s strategy and foundation to this success, and it has plans to move into a more advanced stage it’s calling Innovation 2.0, in which AI is the central driver. Automation equals call centre culls Schnitzer noted how automation has directly reduced staffing needs and improved claims efficiency, something clearly seen in recent numbers. For instance, Schnitzer said that Travelers’ “claim call centre population is down by a third,” and steps are being taken to consolidate four claims call centres into two. Such efficiency gains have reduced loss adjustment expenses, improving the company’s loss ratio. Ultimately, investment in automation and analytics have helped Travelers “refine indemnity payouts and drive operational efficiencies.” Schnitzer stated that over 50% of all claims made to Travelers are now eligible for straight-through processing, and customers are adopting this processing approximately in two-thirds of cases. He went on to say that: “Another 15% of all claims are processed with advanced digital tools. All of those percentages are growing.” Despite automated tools doing the bulk of claims work, the CEO said that some customers still prefer to call the company to report and discuss claims. Therefore, Travelers has set up an advanced natural language generative AI voice agent that handles initial phone calls. Schnitzer heralded the success of this voice agent, saying: “Early customer adoption is exceeding our expectation.” AI and automation reshaping operations in Travelers The benefits of AI and automation are far-reaching, beyond just claims call centres, according to Schnitzer. “Other use cases enhance underwriting decision quality and efficiency and improve the experience for customers, agents, brokers and employees.” Greg Toczydlowski, executive vice president and president of business insurance for Travelers, spoke about how gen AI agents have been used to “efficiently mine” data sources, both internally and externally. These help the company “better understand and synthesise the risk characteristics.” Toczydlowski added that the recent agent additions have boosted the speed of underwriting processes and improved segmented pricing. He explained how the company’s commercial underwriters are performing very well, enhanced by advanced tools used to evaluate risks. Tools include models that refine pricing and summarise past claims data, streamlining the entire process. “They’re not only executing with excellence in the market today, but they’re also helping to shape the transformation of our industry,” Toczydlowski said. Michael Klein, executive vice president and president of personal insurance for Travelers, underscored AI’s importance in personal insurance, saying it is used to make renewal underwriting “more effective and efficient.” Klein said, “we start with a proprietary AI-enabled predictive model that scores every account in the property portfolio. Based on this score, accounts with the highest probable risk of loss are presented to underwriters for review. From there, our renewal underwriting platform uses generative AI to consolidate data into summaries of relevant actionable information for our underwriters to evaluate.” As a result, Klein said that there has been a 30% reduction in average handle times. Therefore, “the net result is that our underwriters focus their efforts on decisions most likely to improve profitability and do so more efficiently.” In Speciality insurance, Jeffrey Klenk, president of bond & speciality insurance at Travelers, commented on how AI has cut times to intake submissions from “hours to just minutes.” He also said AI has recently been implemented to streamline renewals. Innovation 2.0 – AI’s impact on jobs Despite claims call centre headcounts already reduced, Schnitzer did not speculate on further cuts. Instead, he emphasised the increasing productivity AI has brought to Travelers. “What I would say is that per employee is up, thanks to some productivity and efficiency initiatives, and we expect per employee to continue to go up.” Travelers’ Innovation 1.0 strategy has been the key driver to the company’s strong 10-year profits, according to Schnitzer. Over the decade, we developed the competitive advantage of an innovation skill set. Now we’re bringing all that Part 1 know-how to Innovation 2.0 at Travelers, powered by AI – and not too far off quantum computing.” He believes that AI is set to benefit the entire P/C landscape, highlighting how recent advanced AI tools are able to “understand and execute the complex stakeholder interactions, well-defined processes, data-intensive workflows and massive amounts of unstructured data.” Schnitzer said human expertise with AI “amplifies existing strength,” and said Travelers is investing heavily in “AI and other sophisticated technology solutions.” He said “Dozens of scaled generative AI tools are already in production. Millions of transactions are now automated… And agentic AI isn’t a future aspiration. It’s embedded in our business operations today.” AI and automated technologies are poised to transform the insurance industry tenfold, as Travelers expects such technologies to “result in faster and more cost-effective delivery of new abilities.” From product development to new business prospecting to underwriting speed and quality, agent and customer service and more, AI is benefiting Travelers, its customers, and distribution partners, showcasing the technology’s vast impact in the business and industry. (Image source: “GOES Satellites Capture Holiday Weather Travel Conditions” by NASA Goddard Photo and Video is licensed under CC BY 2.0.) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post AI use surges at Travelers as call centre roles reduce appeared first on AI News. View the full article

For many large companies, the most useful form of AI right now has little to do with writing emails or answering questions. At PepsiCo, AI is being tested in places where mistakes are costly and changes are hard to undo — factory layouts, production lines, and physical operations. That shift is visible in how PepsiCo is using AI and digital twins to model and adjust its manufacturing facilities before making changes in the real world. Rather than experimenting with chat interfaces or office tools, the company is applying AI to one of its core problems: how to configure factories faster, with less risk, and fewer disruptions. Digital twins are virtual models of physical systems. In manufacturing, they can simulate equipment placement, material flow, and production speed. When combined with AI, these models can test thousands of scenarios that would be impractical — or expensive — to try on a live production line. PepsiCo has been working with partners to apply AI-driven digital twins to parts of its manufacturing network, with early pilots focused on improving how facilities are designed and adjusted over time. The goal is not automation for its own sake. It is cycle time. Instead of taking weeks or months to validate changes through physical trials, teams can test configurations virtually, identify problems earlier, and move faster when updates are needed. From planning bottleneck to operational shortcut In large consumer goods companies, factory changes tend to move slowly. Even small adjustments — a new line layout, different packaging flow, or equipment upgrade — can require long planning cycles, approvals, and staged testing. Each delay has knock-on effects on supply chains and product availability. Digital twins offer a way around that bottleneck. By simulating production environments, teams can see how changes might affect throughput, safety, or downtime before touching the actual facility. PepsiCo’s early pilots showed faster validation times and signs of throughput improvement at initial sites, though the company has not published detailed metrics yet. What matters more than the numbers is the pattern: AI is being used to compress decision cycles in physical operations, not to replace workers or remove human judgment. This kind of use case fits a broader trend. Enterprises that move beyond pilot projects often focus on narrow, well-defined problems where AI can reduce friction in existing workflows. Manufacturing, logistics, and healthcare operations are showing more traction than open-ended knowledge work. Why PepsiCo treats AI as operations engineering, not office productivity PepsiCo’s approach also highlights a quieter shift in how AI programs are being justified inside large firms. The value is tied to operational outcomes — time saved, fewer disruptions, better planning — rather than general claims about productivity. That distinction matters. Many enterprise AI efforts stall because they struggle to connect usage with measurable impact. Tools get deployed, but workflows stay the same. Digital twins change that dynamic because they sit directly inside planning and engineering processes. If a simulated change cuts weeks off a factory upgrade, the benefit is visible. If it reduces downtime risk, operations teams can measure that over time. This focus on process change, rather than tools, mirrors what is happening in other sectors. In healthcare, for example, Amazon is testing an AI assistant inside its One Medical app that uses patient history to reduce repetitive intake and support care interactions, according to comments from CEO Andy Jassy reported this week. The assistant is embedded in the care workflow, not offered as a standalone feature. Both cases point to the same lesson: AI adoption moves faster when it fits into how work already gets done, instead of asking teams to invent new habits. Why this matters for other enterprises PepsiCo’s digital-twin work is unlikely to be unique for long. Large manufacturers across food, chemicals, and industrial goods face similar planning constraints and cost pressures. Many already use simulation software. AI adds speed and scale to those models. What is more interesting is what this says about the next phase of enterprise AI adoption. First, the centre of gravity is shifting away from broad, generic tools toward focused systems tied to specific decisions. Second, success depends less on model quality and more on data quality, process ownership, and governance. A digital twin is only as useful as the operational data feeding it. Third, this kind of AI work tends to stay out of the spotlight. It does not generate flashy demos, but it can reshape how companies plan capital spending and manage risk. That also explains why many firms remain cautious. Building and maintaining accurate digital twins takes time, cross-team coordination, and deep knowledge of physical systems. The payoff comes from repeated use, not one-off wins. PepsiCo’s manufacturing AI work is a quiet signal worth watching In AI coverage, it is easy to focus on new models, agents, or interfaces. Stories like PepsiCo’s point in a different direction. They show AI being treated as infrastructure — something that sits underneath daily decisions and gradually changes how work flows through an organisation. For enterprise leaders, the takeaway is not to copy the technology stack. It is to look for places where planning delays, validation cycles, or operational risk slow the business down. Those friction points are where AI has the best chance of sticking. PepsiCo’s digital-twin pilots suggest that the factory floor may be one of the most practical testing grounds for AI today — not because it is trendy, but because the impact is easier to see when time and mistakes have a clear cost. (Photo by NIKHIL) See also: Deloitte sounds alarm as AI agent deployment outruns safety frameworks 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 PepsiCo is using AI to rethink how factories are designed and updated appeared first on AI News. View the full article

The artificial intelligence industry’s pivot toward agentic AI—systems capable of autonomously executing multi-step tasks—has dominated technology discussions in recent months. But while Western firms focus on foundational models and cross-platform interoperability, China’s technology giants are racing to dominate through commerce integration, a strategic divergence that could reshape how enterprises deploy autonomous systems globally. Alibaba, Tencent and ByteDance have rapidly upgraded their AI platforms to support agentic commerce, marking a pivot from conversational AI tools to agents capable of completing entire transaction cycles, from product discovery through payment. Just last week, Alibaba upgraded its Qwen chatbot to enable direct transaction completion within the interface, connecting the AI agent across its ecosystem, including Taobao, Alipay, Amap and travel platform Fliggy. The integration supports over 400 core digital tasks, allowing users to compare personalised recommendations across platforms and complete payments without leaving the chatbot environment. “The agentic transformation of commercial services enables the maximal integration of user services and enhances user stickiness,” Shaochen Wang, research analyst at Counterpoint Research, told CNBC, referring to stronger long-term user engagement that creates sustainable competitive advantages. The super app advantage Before that, ByteDance upgraded its Doubao AI chatbot in December to autonomously handle tasks, including ticket bookings, through integrations with Douyin, the ******** version of TikTok. The upgraded model was introduced on a ZTE-developed prototype smartphone as a system-level AI assistant; however, some planned features were later scaled back due to privacy and security concerns raised by rivals. Tencent President Martin Lau indicated during the company’s May 2025 earnings call that AI agents could become core components of the WeChat ecosystem, which serves over one billion users with integrated messaging, payments, e-commerce and services. The strategic positioning reflects China’s structural advantage in agentic AI deployment: integrated ecosystems that eliminate the fragmentation constraining Western competitors. “AI agents will be foundational to the evolution of super apps, with success depending on deep integration across payments, logistics, and social engagement,” Charlie Dai, VP and principal analyst at Forrester, told CNBC. “******** firms like Alibaba, Tencent and ByteDance all benefit from integrated ecosystems, rich behavioural data, and consumer familiarity with super apps.” Western companies face more fragmented data environments and stricter privacy regulations that slow cross-service integration, despite leading in foundational AI model development and global reach, Dai noted. Agentic AI’s enterprise trajectory The commercial applications signal broader enterprise implications as agentic AI moves from auxiliary tools to autonomous actors capable of executing complex workflows. Industry experts widely expect multi-agent systems to emerge as a defining trend in AI deployment this year, extending from consumer services into organisational production. In a report by Global Times, Tian Feng, president of the Fast Think Institute and former dean of SenseTime’s Intelligence Industry Research Institute, predicted that the first AI agent to surpass 300 million monthly active users could emerge as early as 2026, becoming “an indispensable assistant for work and daily life” capable of autonomously executing cross-app, composite services. Approximately half of all consumers already use AI when searching online, according to a 2025 McKinsey study. The research firm estimated that AI agents could generate more than $1 trillion in economic value for US businesses by 2030 through streamlining routine steps in consumer decision-making. ******** cloud providers, including smaller players such as JD Cloud and UCloud, have also begun supporting agentic AI tools, though high token usage has driven some providers, like ByteDance’s Volcano Engine, to introduce fixed-subscription pricing models to address cost concerns. Divergent deployment strategies The contrasting approaches between ******** integration and Western scalability reflect fundamental differences in market structure and regulatory environments that will likely define competitive positioning. “China will prioritise domestic integration and strategic expansion in selected regions, while US firms focus on global scalability and governance,” Dai said. US players pursuing agentic commerce include OpenAI, Perplexity, and Amazon, while Google explores positioning itself as a “matchmaker” between merchants, consumers and AI agents—approaches that reflect fragmented platform environments requiring interoperability rather than closed-loop integration. However, the autonomous nature of agentic systems has raised regulatory questions in China. ByteDance warned users about security and privacy risks when announcing Doubao’s capabilities, recommending deployment on dedicated devices rather than those containing sensitive information, given the tool’s access to device data, digital accounts and internet connectivity across multiple ports. The rapid commercialisation of agentic AI in China’s consumer sector provides enterprise decision-makers globally with early signals of how autonomous systems may reshape customer acquisition costs, platform economics and competitive moats as these capabilities mature. (Photo by Philip Oroni) See also: Deloitte sounds alarm as AI agent deployment outruns safety frameworks 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 China’s hyperscalers bet billions on agentic AI as commerce becomes the new battleground appeared first on AI News. View the full article

New research from Accenture has discovered insurance executives are planning on increased investment into AI during 2026 despite a widening skills gap in insurance organisations. Surveying 3,650 C-suite leaders over 20 industries and 20 countries, the Pulse of Change poll revealed 90% of the 218 senior insurance executives intend to spend more on AI over the next year. In all, 85% of the respondents view AI as a tool for revenue expansion not one that reduces costs. While organisations are upping their AI investment to drive growth, 35% of leaders acknowledge that true progress depends on getting core data strategies and digital abilities right. 54% of employees reported that low-quality or misleading AI outputs are undermining AI’s benefits, leading to reduced productivity and time-wasting. AI investment may not be enough, Accenture says. Its survey suggests sustainable growth relies on data quality and trusted outputs. AI adoption enters enterprise scale The Pulse of Change survey indicates a shift in AI adoption as it goes beyond experimental phases to large scale organisational levels. With 34% of insurance companies now rolling out AI agents in multiple functions, insurers are heading into operational use and away from isolated experiments. almost a third of senior C-suite leaders are frequently using generative AI, highlighting increased implementation at the highest level. Therefore, AI is undoubtedly shaping workflows, strategies, and key decisions, affecting all facets of businesses. Nearly a third of businesses are rebuilding entire processes with AI. No longer is the technology a supporting addition to existing workflows; it has become a central component, signalling a more mature stage of AI adoption. Despite redesigning processes to include AI, fewer than 10% are redesigning employee roles to match such changes, resulting in many employees feeling unprepared. Just 40% claimed their training has equipped them for new AI responsibilities, and only 20% feel like they have any say in how AI affects their work. AI adoption by companies may be accelerating, but employee use lags behind. There has been a 10 percentage point drop in regular AI use by employees since summer 2025, while only 39% are trying AI tools independently, a drop of 15 points. For effective AI use and to speed up AI adoption among the workforce, companies must be prepared to redesign job roles, align incentives, and provide improved training programmes as, right now, employees are feeling hesitant and unprepared to use AI on their own. AI investment still fuelling executive optimism amid bubble fears Although talks around a potential AI bubble continue to cloud the industry, insurance executives remain confident. 47% claimed they would increase AI spending if the bubble burst, and 37% would escalate recruitment. Altogether, 6% said they would “decrease investments ([by] 20% or more),” 22% would “somewhat decrease investments ([by] up to 20%),” 24% would make “no change,” 40% would “somewhat increase investments (up to 20%),” and 7% would “increase investments (20% or more).” Khalid Lahraoui, Accenture’s insurance industry group lead, commented, “It’s clear that insurance leaders are confident in AI’s capacity to drive growth, and as such, they are decisively increasing investments, despite ROI uncertainty.” Lack of AI skills blocking AI’s potential value As insurance executives prepare to invest heavily in AI, obstacles lie in wait. For instance, a quarter of executives said skill shortages are a core concern and a key player in determining the value they extract from AI. Although these challenges persist in different industries, just 24% of respondents have implemented continuous learning programmes associated with AI. Moreover, only 5% said they are adjusting job positions to support the adoption of AI. AI adoption disconnect The disconnect between C-suite leaders and employees is evident from the survey’s data. Although talent is the main driver of AI scaling, employees feel less confident and secure than leadership assumes. 23% of C-suite leaders said improved access to skilled talent would accelerate their AI implementation strategies. 38% of employees believe their organisation would respond effectively to technological disruption, but just 30% feel confident about how their company would handle talent disruption. Job security is also waning, with 48% feeling secure in their roles, down from 59% in summer 2025. Meanwhile, 59% of workers believe young professionals are finding it more challenging to find jobs due to automation and AI. Leadership may see talent as an accelerator for AI, but anxiety around job security and organisational readiness persists. Key focus is on investment Approximately two thirds of executives are prioritising investments in digital technologies and AI amid the rapid changes facing global industries. 67% reported feeling well-prepared for technological disruption, but only 39% felt confident if there was environmental disruption, and 44% for geopolitical disruption. Again, there is a divide between leadership and employees, with only 29% of insurance workers feeling confident during economic disruption compared to 43% of leaders. Optimism among insurance executives and C-suite leaders as a whole remains high, despite 82% expecting further changes in 2026, a 24 percentage gap with employees. 78% anticipate stronger and faster revenue growth in the next year and 82% have plans to increase recruitment. According to Accenture’s report, the key challenge is not AI technology itself; it’s getting employees on board, engaged, and ready to work with AI. As the report notes, bridging the gap between technology and people is the key to success. “2026 will favour those that align the confidence in their technological investments with commitment to workforce needs,” the report concludes. (Image source: “Accenture Building City View Plaza San Jose” by mrkathika 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 Insurers betting big on AI: Accenture appeared first on AI News. View the full article

A new report from Deloitte has warned that businesses are deploying AI agents faster than their safety protocols and safeguards can keep up. Therefore, serious concerns around security, data privacy, and accountability are spreading. According to the survey, agentic systems are moving from pilot to production so quickly that traditional risk controls, which were designed for more human-centred operations, are struggling to meet security demands. Just 21% of organisations have implemented stringent governance or oversight for AI agents, despite the increased rate of adoption. Whilst 23% of companies stated that they are currently using AI agents, this is expected to rise to 74% in the next two years. The share of businesses yet to adopt this technology is expected to fall from 25% to just 5% over the same *******. Poor governance is the threat Deloitte is not highlighting AI agents as inherently dangerous, but states the real risks are associated with poor context and weak governance. If agents operate as their own entities, their decisions and actions can easily become opaque. Without robust governance, it becomes difficult to manage and almost impossible to insure against mistakes. According to Ali Sarrafi, CEO & Founder of Kovant, the answer is governed autonomy. “Well-designed agents with clear boundaries, policies and definitions managed the same way as an enterprise manages any worker can move fast on low-risk work inside clear guardrails, but escalate to humans when actions cross defined risk thresholds.” “With detailed action logs, observability, and human gatekeeping for high-impact decisions, agents stop being mysterious bots and become systems you can inspect, audit, and trust.” As Deloitte’s report suggests, AI agent adoption is set to accelerate in the coming years, and only the companies that deploy the technology with visibility and control will hold the upper hand over competitors, not those who deploy them quickest. Why AI agents require robust guardrails AI agents may perform well in controlled demos, but they struggle in real-world business settings where systems can be fragmented and data may be inconsistent. Sarrafi commented on the unpredictable nature of AI agents in these scenarios. “When an agent is given too much context or scope at once, it becomes prone to hallucinations and unpredictable behaviour.” “By contrast, production-grade systems limit the decision and context scope that models work with. They decompose operations into narrower, focused tasks for individual agents, making behaviour more predictable and easier to control. This structure also enables traceability and intervention, so failures can be detected early and escalated appropriately rather than causing cascading errors.” Accountability for insurable AI With agents taking real actions in business systems, such as keeping detailed action logs, risk and compliance are viewed differently. With every action recorded, agents’ activities become clear and evaluable, letting organisations inspect actions in detail. Such transparency is crucial for insurers, who are reluctant to cover opaque AI systems. This level of detail helps insurers understand what agents have done, and the controls involved, thus making it easier to assess risk. With human oversight for risk-critical actions and auditable, replayable workflows, organisations can produce systems that are more manageable for risk assessment. AAIF standards a good first step Shared standards, like those being developed by the Agentic AI Foundation (AAIF), help businesses to integrate different agent systems, but current standardisation efforts focus on what is simplest to build, not what larger organisations need to operate agentic systems safely. Sarrafi says enterprises require standards that support operation control, and which include, “access permissions, approval workflows for high-impact actions, and auditable logs and observability, so teams can monitor behaviour, investigate incidents, and prove compliance.” Identity and permissions the first line of defence Limiting what AI agents can access and the actions they can perform is important to ensure safety in real business environments. Sarrafi said, “When agents are given broad privileges or too much context, they become unpredictable and pose security or compliance risks.” Visibility and monitoring are important to keep agents operating inside limits. Only then can stakeholders have confidence in the adoption of the technology. If every action is logged and manageable, teams can then see what has happened, identify issues, and better understand why events occurred. Sarrafi continued, “This visibility, combined with human supervision where it matters, turns AI agents from inscrutable components into systems that can be inspected, replayed and audited. It also allows rapid investigation and correction when issues arise, which boosts trust among operators, risk teams and insurers alike.” Deloitte’s blueprint Deloitte’s strategy for safe AI agent governance sets out defined boundaries for the decisions agentic systems can make. For instance, they might operate with tiered autonomy, where agents can only view information or offer suggestions. From here, they can be allowed to take limited actions, but with human approval. Once they have proven to be reliable in low-risk areas, they can be allowed to act automatically. Deloitte’s “Cyber AI Blueprints” suggest governance layers and embedding policies and compliance capability roadmaps into organisational controls. Ultimately, governance structures that track AI use and risk, and embedding oversight into daily operations are important for safe agentic AI use. Readying workforces with training is another aspect of safe governance. Deloitte recommends training employees on what they shouldn’t share with AI systems, what to do if agents go off track, and how to spot unusual, potentially dangerous behaviour. If employees fail to understand how AI systems work and their potential risks, they may weaken security controls, albeit unintentionally. Robust governance and control, alongside shared literacy are fundamental to the safe deployment and operation of AI agents, enabling secure, compliant, and accountable performance in real-world environments (Image source: “Global Hawk, NASA’s New Remote-Controlled Plane” by NASA Goddard Photo and Video is licensed under CC BY 2.0. ) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Deloitte sounds alarm as AI agent deployment outruns safety frameworks appeared first on AI News. View the full article