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The construction industry generates colossal amounts of data, with much of it unused or locked in spreadsheets. AI is now changing this, enabling teams to accelerate decision-making, enhance margins, and improve project outcomes. According to new research from Dodge Construction Network (Dodge) and CMiC, the true transformative impact of AI is highlighted by contractors, with 87% believing AI will “meaningfully transform their business,” despite current low adoption rates. The latest research, entitled, ‘AI for Contractors,’ discovered automated proposal generation and progress tracking from site photos both reached a 92% effectiveness rating. Meanwhile, contract risk review achieved 85% effectiveness when compared to previous, more traditional methods. The report highlights how AI is allowing project managers to focus on strategic decisions rather than time-consuming administrative tasks. Finance teams are also benefiting from AI technology, shifting from historical reporting to predictive insights, while operations leaders are able to apply data-driven intelligence for improved project delivery. Rather than AI fully replacing human expertise, the report found it actually enhances human input. “For decades, construction firms have lacked the tools to transform the data they’ve collected into action. AI-enabled solutions are changing that,” says Gord Rawlins, president and CEO of CMiC. “This research highlights the high-impact results contractors are achieving today.” AI changing contractor roles Surveyed contractors see AI as a catalyst in reshaping everyday aspects of their operations, enabling predictive insights rather than reacting to problems once they have occurred. This introduces wider benefits, like tighter cost controls, improved scheduling, and higher quality project delivery. In other words, improved overall outcomes. A substantial 85% of contractors foresee less time spent on repetitive tasks, while 75% have faith that AI can help mine historical data to learn from previous projects. Rather than relying fully on AI, 70% said the technology helps them make better, more informed decisions thanks to insights that may otherwise not be present. AI implementation remains low, but companies are preparing for wider adoption Currently, AI adoption in the construction industry is low, despite awareness levels of 32% to 34%. This seems to be due to several reasons, including a lack of clear understanding, internal approvals, and software access. However, Dodge’s research discovered more than half of companies surveyed are strategically preparing for AI with pilot programmes and staff training for AI-related positions. According to the report, 40% of companies have a set budget for AI, 38% are developing teams for implementation, 19% are adapting old workflows, and 51% are assessing AI changes. Early adopters lead the way Overall awareness of AI use in the industry is quite low, with just 20% to 50% of contractors knowing that certain management tasks implement AI, and very few actively use these functions. Nevertheless, early adopters of AI provided positive feedback, as more than 70% revealed that AI tools are hugely effective compared to more traditional methods, suggesting a potential for quick growth in AI use throughout the industry. Security and accuracy lead concerns The main concerns of adopting AI revolve around security and accuracy. The report reveals that 57% are worried about the accuracy of AI output, while 54% have doubts over the security of company data. Internal resistance to change (44%) and implementation costs (41%) are also cited as key concerns, but perhaps surprisingly, just 21% expressed concern over job losses. 31% believe current data quality is not yet adequate to support AI analysis. According to the report, larger contractors are likely to rely more on AI than smaller firms, thus are more concerned about data quality and reliability. For instance, 69% of larger contractors cited lack of reliability or accuracy of AI outputs as a major concern, compared to 54% of smaller or mid-size contractors. Research data confirms that contractors are generally open to adopting AI, but the accuracy of AI outputs tend to stand in the way, as well as the desire for better tools, more information, and greater internal support. 17% of contractors said they do not sufficiently trust AI results, an issue that becomes more pronounced in sensitive areas like payments. Distrust in AI operations rises to 35% and 31% not having faith in AI managing project budgets. A major theme is the need for more understanding before using AI. On average, 21% of respondents said they want a better insight of how AI works before considering using it, climbing to 31% for more complex tasks like safety risk assessments. Contractors also believe they are limited by their current software capabilities, with an average of 19% reporting their software does not offer the AI functions they require. The increases to 33% for managing resources. Internal approval remains a notable obstacle, with 22% saying their company has not yet approved the use of AI, despite personal interest. Another barrier is a lack of time or resources that effectively evaluate AI tools. 13% stated this as a main reason why AI has not yet been adopted. Although there are obvious challenges to mass AI use in the construction industry – and therefore significant market opportunity – only 5% believe AI would not be beneficial or improve current methods. That indicates a resistance that stems from various concerns rather than a lack of perceived value. Steve Jones, Senior Director, Industry Insights Analytics at Dodge, spoke on the findings. “We designed this study to look at the use of AI in the digital tools already deployed by contractors because that may offer the best solution to the challenge of data quality. But it is also heartening to see that many contractors are aware of the key challenges and the need for a rigorous approach to successfully implementing these tools at their organisations,” the Dodge research states. Key interest in emerging AI functionalities AI’s potential is clearly recognised, even if the industry’s readiness to adopt it isn’t quite matching the data. Certain areas are attracting the most attention when it comes to AI functions, like automated construction analysis, where 81% see potential benefits. 80% also show interest in intelligent permit submissions, while 79% believe in autonomous schedule and resource optimisation. 92% appreciate automated contract management and 76% recognise potential in AI-powered dynamic pricing. Although AI adoption remains limited, these strong numbers suggest the tide may soon be turning. AI and the new age of the construction industry The latest data suggests a strong openness, maybe even an eagerness, to AI adoption in the construction sector. However, better tools, clearer guidance, and more trustworthy outputs are just some of the areas that need to be addressed before interest becomes implementation. “With high awareness, strong interest, and powerful validation from early adopters, contractors appear poised for significant expansion in their use of AI-enabled tools in meaningful ways,” said Steve Jones. The industry is on a “tipping point for AI adoption” according to Jones. When companies start to provide clearer pathways for adoption, the move towards AI-powered construction workflows will undoubtedly accelerate rapidly, reshaping how projects are delivered forever. (Image source: “Tianjin Construction Site.” by @yakobusan Jakob Montrasio 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 Strong contractor belief in AI for industry-wide transformation appeared first on AI News. View the full article

As the company that kick-started the cloud computing revolution, Amazon is one of the world’s biggest companies whose practices in all things technological can be regarded as a blueprint for implementing new technology. This article looks at some of the ways that the company is deploying AI in its operations. Amazon’s latest AI strategy has progressed from basic chatbots to agentic AI: systems that can plan and execute multi-step work using different tools and across processes. As a company, Amazon sits at the intersection of cloud infrastructure (in the form of AWS), logistics, retail, and customer service, all of which are areas where small efficiency gains can have massive impact. Copilots to agents, AWS builds the control plane autonomy In early 2025, Amazon made its AI intentions clear for its cloud company, AWS, by forming a new group focused internally on agentic AI. According to reporting on an internal email, AWS leadership described agentic AI as a potential “multi-billion” business, underscoring that the technology is regarded as a new platform layer, not a standalone feature. The company was not afraid to say that its workforce is expected to shrink because of the technology. In June 2025, Amazon CEO Andy Jassy told employees that widespread use of generative AI and agents will change how work is done, and that over the next few years, Amazon expects routine work to become faster and more automated, slowing hiring, changing roles, and shrinking some job categories, even if other categories grow. Amazon’s best use cases are high-volume, rules-bound workflows that require a lot of searching, checking, routing, and logging. These are or will have significant impact in forecasting, delivery mapping, customer service, and product content. /Reuters/ noted examples like inventory optimisation, improved customer service, and better product detail pages as internal targets for gen AI. Logistics and operations Amazon has described AI-enabled upgrades in its US operations that hint at where an agentic approach may take shape. In June 2025, it outlined AI innovations that included a generative AI system to improve delivery location accuracy, a new demand forecasting model to predict what customers want (and where), and an agentic AI team looking at enabling robots to understand natural-language Consumer-facing agents Consumer agents are where autonomy first becomes real, because systems can take actions, even where there’s money involved. Reporting in The Verge about Alexa+ highlighted features like monitoring items for price drops and (optionally) purchasing for the user automatically once a threshold is hit, a concrete example of the agentic concept in everyday terms: users setting constraints (in the form of price thresholds), and the system watches and executes inside said boundaries. Rufus as the Amazon AI interface Amazon’s Rufus assistant is positioned as an AI interface to shopping, one that helps customers find products, do comparisons, and understand the trade-offs between various choices. Amazon describes Rufus as powered by generative (and increasingly agentic) AI to make shopping faster, with personalisation created by a user’s shopping history and current context. Agents therefore become the a shopping interface, with their value to the retailer in shortening journey from intent to final purchase. Agents for Amazon Bedrock and AgentCore Internally, AWS is producing agentic ‘building blocks’. Agents for Amazon Bedrock are designed to execute multi-step tasks by orchestrating models with tools use and integration with other platforms. The Amazon Bedrock AgentCore is presented as a platform to build [PDF], deploy, and operate agents securely at scale. It has features like runtime hosting, memory, observability dashboards, and evaluation. AgentCore is Amazon’s attempt to become the default infrastructure layer for supervised enterprise agents, especially for organisations that need auditability, access controls, and reliability. Keeping an eye on workforce and governance If Amazon succeeds, the next phase for the technology is managed AI, comprising of mechanisms that grant or revoke permissions for tools and data access, the monitoring of agents’ behaviour, evaluation of performance and whether governance guidelines are being met, and the establishment of escalation paths when agents hit uncertainty. The signals to the workforce have been baked into leadership messaging at the company. Fewer people will be required for some corporate tasks, and there will be more roles that can design workflows, govern the models, keep systems secure, and audit the outcomes of agentic AI use. Conclusions Proven as a leader in technology, Amazon’s stance on AI and the meaningful ways in which it’s implementing AI are a description of the paths enterprise companies may follow. Winning the productivity gains and lowered costs that AI technology promises is not as simple as plugging in a local device, or spinning up a new cloud instance. But the company can be seen as lighting the way for others to follow. Whether it’s supervising agents or deflecting customer queries to automated answering systems, AI is changing this technology giant in every possible way. (Image source: CHEN – The Arousing, Thunder – arouse, excite, inspire; thunder rising from below; awe, alarm, trembling; fertilizing intrusion. The ideogram: excitement and rain” – public domain) 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 AWS’s legacy will be in AI success appeared first on AI News. View the full article

Walmart’s December 9 transfer to Nasdaq wasn’t just a symbolic gesture. The US$905 billion retailer is making its boldest claim yet: that it’s no longer a traditional discount chain, but a tech-powered enterprise using AI to fundamentally rewire retail operations. But beyond the marketing spin and the parade of AI announcements, what’s genuinely transforming at the world’s largest retailer—and where are the gaps between ambition and execution? The Agentic AI pivot: Purpose-built, not off-the-shelf Walmart’s AI strategy diverges sharply from competitors chasing generic large language models. According to CTO Hari Vasudev, the company is deploying what it calls “purpose-built agentic AI”—specialised tools trained on Walmart’s proprietary retail data rather than one-size-fits-all solutions. “Our approach to agentic AI at Walmart is surgical,” Vasudev wrote in a May 2025 blog post. “Extensive early testing proved that, for us, agents work best when deployed for highly specific tasks, to produce outputs that can then be stitched together to orchestrate and solve complex workflows.” This translates to tangible applications: Walmart’s “Trend-to-Product” system cuts fashion production timelines by 18 weeks. Its GenAI Customer Support Assistant now autonomously routes and resolves issues without human intervention. Developer productivity tools handle test generation and error resolution within CI/CD pipelines. Meanwhile, the company’s retail-specific LLM “Wallaby”—trained on decades of Walmart transaction data—powers everything from item comparison to personalised shopping journey completion. The infrastructure undergirding this? Element, Walmart’s proprietary MLOps platform, is designed to avoid vendor lock-in and optimise GPU usage across multiple cloud providers. It’s an in-house “factory” that gives Walmart speed and flexibility competitors wrestling with third-party platforms can’t match. Real numbers: Where AI delivers measurable impact Walmart has been unusually transparent about specific ROI metrics, offering a rare glimpse into enterprise AI economics: Data operations: GenAI improved over 850 million product catalogue data points—a task that would have required 100 times the headcount using manual processes, according to CEO Doug McMillon’s August 2024 earnings call. Supply chain efficiency: AI-powered route optimisation eliminated 30 million unnecessary delivery miles and avoided 94 million pounds of CO2 emissions. The company won the prestigious Franz Edelman Award in 2023 for this technology—and has since commercialised it as a SaaS product for other businesses. Store operations: Digital Twin technology predicts refrigeration failures up to two weeks in advance, auto-generating work orders complete with visual models, wiring diagrams, and required parts. Sam’s Club’s AI-powered exit technology has reduced member checkout times by 21%, with over 64% of members now using the friction-free system across all locations. Customer experience: Dynamic Delivery algorithms analyse traffic patterns, weather conditions, and order complexity to predict delivery times down to the minute, enabling 17-minute express deliveries in test markets. The human cost: “AI will change every job” McMillon hasn’t sugarcoated the workforce implications. Speaking at a Bentonville workforce conference in September 2025, he stated bluntly: “It’s very clear that AI is going to change literally every job. Maybe there’s a job in the world that AI won’t change, but I haven’t thought of it.” But Walmart’s positioning this as a transformation rather than an elimination. McMillon expects total headcount to remain flat even as revenue grows—meaning jobs will shift, not disappear. White-collar roles face the earliest disruption through chatbots handling customer service and supply chain tracking, while store and warehouse workers will eventually see tasks absorbed by autonomous systems. The company is investing heavily in reskilling programs. “We’ve got to create the opportunity for everybody to make it to the other side,” McMillon said at the Bentonville conference. Automation equipment operator Chance at Walmart’s Palestine, Texas, distribution centre described the shift: “It used to be 85% physical. Now it’s 85% mental. I’m solving problems with my mind, not just my body.” The Nasdaq gambit: Repositioning for tech valuations Walmart’s exchange transfer was explicitly framed around its AI transformation. CFO John David Rainey stated the move reflects the company “setting a new standard for omnichannel retail by integrating automation and AI.” The subtext? Walmart wants the valuation multiples tech companies command. At a P/E ratio of 40.3x—higher than Amazon and Microsoft—the market is partially buying the transformation story. Potential inclusion in the tech-heavy Nasdaq 100 index would drive passive fund investment regardless of AI execution. Analysts are split on whether the premium is justified. Jefferies’ Corey Tarlowe argued the move signals Walmart is “less of a traditional retail corporation and more of a technology firm.” But sceptics note the company still derives revenue from razor-thin retail margins, not high-margin software or cloud services—despite commercialising tools like Route Optimisation. Verdict: Genuine transformation with execution risk Walmart’s AI strategy is neither pure hype nor guaranteed success. The company is making structural investments in proprietary infrastructure, deploying AI at genuine scale with measurable operational benefits, and transparently acknowledging workforce implications most enterprises dodge. But significant execution risks remain: managing fragmented agent ecosystems, preventing algorithmic bias at scale, competing against external shopping agents, and determining appropriate automation boundaries while maintaining accuracy. The company’s candidness about challenges—”often, a co-pilot model, with humans and AI working as a team, is the most effective approach”—suggests leadership understands AI isn’t a silver bullet. For enterprises watching Walmart’s playbook, the lesson is clear: build for specificity, not generality. Invest in proprietary data moats. Plan for workforce transformation, not just cost reduction. And recognise that even with massive resources and technical talent, agentic AI remains early-stage technology with genuine limitations. The question isn’t whether Walmart is using AI—it demonstrably is. It’s whether this surgical, infrastructure-heavy approach delivers sustainable competitive advantage, or if the company is simply automating itself into the same low-margin trap with shinier tools. That answer won’t be clear for several years—but Walmart’s willingness to bet US$905 billion in market cap on the transformation suggests leadership believes the former. See also: Walmart and Amazon drive retail transformation 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. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Walmart’s AI strategy: Beyond the hype, what’s actually working appeared first on AI News. View the full article

Enterprise leaders are pressing ahead with artificial intelligence, even as early results remain uneven. Reporting from the Wall Street Journal and Reuters shows that most CEOs expect AI spending to keep rising through 2026, despite difficulty tying those investments to clear, enterprise-wide returns. This tension highlights where many organisations now sit in their AI journey. The technology has moved beyond trials and proofs of concept, but it has yet to settle into a reliable source of value. Companies are operating in an in-between phase, where ambition, execution, and expectations are all under strain at the same time. Spending continues, even as returns lag AI budgets have climbed steadily across large enterprises over the past two years. Competitive pressure, board oversight, and fear of being left behind have all played a role. At the same time, executives are more open about the limits they are seeing. Gains often show up in pockets rather than across the business, pilots fail to spread, and the cost of connecting AI systems to existing tools keeps rising. A Wall Street Journal survey of senior executives found that most CEOs see AI as central to long-term competitiveness, even if short-term benefits are hard to measure. For many, AI no longer feels optional. It is treated as a capability that must be developed over time, rather than a project that can be paused if results disappoint. That view helps explain why spending remains steady. Leaders worry that cutting back now could weaken their position later, especially as rivals improve how they use the technology. Why pilots struggle to scale One of the main barriers to stronger returns is the jump from experimentation to day-to-day use. Many organisations have launched AI pilots across different teams, often without shared rules or coordination. While these efforts can generate insight and interest, few translate into changes that affect the wider business. Reuters has reported that companies trying to scale AI frequently run into issues with data quality, system links, security controls, and regulatory requirements. These problems are not only technical. They reflect how work is organised. Responsibility is often split across teams, ownership is unclear, and decisions slow down once projects touch legal, risk, and IT functions. The result is a pattern of heavy spending on trials, with limited progress toward systems that are embedded in core operations. Infrastructure costs reshape the equation The cost of infrastructure is also weighing on AI returns. Training and running models demands large amounts of computing power, storage, and energy. Cloud bills can rise quickly as usage grows, while building on-site systems requires upfront investment and long planning cycles. Executives cited by Reuters have warned that infrastructure costs can outpace the benefits delivered by AI tools, particularly in the early stages. This has forced tough choices: whether to centralise AI resources or leave teams to experiment on their own; whether to build in-house systems or rely on vendors; and how much waste is acceptable while capabilities are still forming. In practice, these decisions are shaping AI strategy as much as model performance or use-case selection. AI governance moves to the centre of CEO decision-making As AI spending increases, so does scrutiny. Boards, regulators, and internal audit teams are asking harder questions. In response, many organisations are tightening control. Decision rights are shifting toward central teams, AI councils are becoming more common, and projects are being linked more closely to business priorities. The Wall Street Journal reports that companies are moving away from loosely connected experiments toward clearer goals, measures, and timelines. This can slow progress, but it reflects a growing belief that AI should be managed with the same discipline as other major investments. This shift marks a change in how AI is treated. It is no longer a side effort or a curiosity. It is being brought into existing operating and risk structures. Expectations are being reset, not abandoned Importantly, the persistence of AI spending does not signal blind optimism. Instead, it reflects a reset in expectations. CEOs are learning that AI rarely delivers immediate, sweeping returns. Value tends to emerge gradually, as organisations adjust workflows, retrain staff, and refine data foundations. Rather than abandoning AI initiatives, many enterprises are narrowing their focus. They are prioritising fewer use cases, demanding clearer ownership, and aligning projects more closely with business outcomes. This recalibration may reduce short-term excitement, but it improves the likelihood of sustainable returns. What CEO AI strategy signals for 2026 planning For organisations shaping their plans for 2026, the message for every CEO is not to retreat from AI, but to pursue it with more care as AI strategies mature. Ownership, governance, and realistic timelines matter more than headline spending levels or bold claims. Those most likely to benefit are treating AI as a long-term shift in how the organisation works, not a quick route to growth. In the next phase, advantage will depend less on how much is spent and more on how well AI fits into everyday operations. (Photo by Ambre Estève) See also: AI in 2026: Experimental AI concludes as autonomous systems rise 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 CEOs still betting big on AI: Strategy vs. return on investment in 2026 appeared first on AI News. View the full article

Generative AI’s experimental phase is concluding, making way for truly autonomous systems in 2026 that act rather than merely summarise. 2026 will lose the focus on model parameters and be about agency, energy efficiency, and the ability to navigate complex industrial environments. The next twelve months represent a departure from chatbots toward autonomous systems executing workflows with minimal oversight; forcing organisations to rethink infrastructure, governance, and talent management. Autonomous AI systems take the wheel Hanen Garcia, Chief Architect for Telecommunications at Red Hat, argues that while 2025 was defined by experimentation, the coming year marks a “decisive pivot towards agentic AI, autonomous software entities capable of reasoning, planning, and executing complex workflows without constant human intervention.” Telecoms and heavy industry are the proving grounds. Garcia points to a trajectory toward autonomous network operations (ANO), moving beyond simple automation to self-configuring and self-healing systems. The business goal is to reverse commoditisation by “prioritising intelligence over pure infrastructure” and reduce operating expenditures. Technologically, service providers are deploying multiagent systems (MAS). Rather than relying on a single model, these allow distinct agents to collaborate on multi-step tasks, handling complex interactions autonomously. However, increased autonomy introduces new threats. Emmet King, Founding Partner of J12 Ventures, warns that “as AI agents gain the ability to autonomously execute tasks, hidden instructions embedded in images and workflows become potential attack vectors.” Security priorities must therefore shift from endpoint protection to “governing and auditing autonomous AI actions.” As organisations scale these autonomous AI workloads, they hit a physical wall: power. King argues energy availability, rather than model access, will determine which startups scale. “Compute scarcity is now a function of grid capacity,” King states, suggesting energy policy will become the de facto AI policy in Europe. KPIs must adapt. Sergio Gago, CTO at Cloudera, predicts enterprises will prioritise energy efficiency as a primary metric. “The new competitive edge won’t come from the largest models, but from the most intelligent, efficient use of resources.” Horizontal copilots lacking domain expertise or proprietary data will fail ROI tests as buyers measure real productivity. The “clearest enterprise ROI” will emerge from manufacturing, logistics, and advanced engineering—sectors where AI integrates into high-value workflows rather than consumer-facing interfaces. AI ends the static app in 2026 Software consumption is changing too. Chris Royles, Field CTO for EMEA at Cloudera, suggests the traditional concept of an “app” is becoming fluid. “In 2026, AI will start to radically change the way we think about apps, how they function and how they’re built.” Users will soon request temporary modules generated by code and a prompt, effectively replacing dedicated applications. “Once that function has served its purpose, it closes,” Royles explains, noting these “disposable” apps can be built and rebuilt in seconds. Rigorous governance is required here; organisations need visibility into the reasoning processes used to create these modules to ensure errors are corrected safely. Data storage faces a similar reckoning, especially as AI becomes more autonomous. Wim Stoop, Director of Product Marketing at Cloudera, believes the era of “digital hoarding” is ending as storage capacity hits its limit. “AI-generated data will become disposable, created and refreshed on demand rather than stored indefinitely,” Stoop predicts. Verified, human-generated data will rise in value while synthetic content is discarded. Specialist AI governance agents will pick up the slack. These “digital colleagues” will continuously monitor and secure data, allowing humans to “govern the governance” rather than enforcing individual rules. For example, a security agent could automatically adjust access permissions as new data enters the environment without human intervention. Sovereignty and the human element Sovereignty remains a pressing concern for European IT. Red Hat’s survey data indicates 92 percent of IT and AI leaders in EMEA view enterprise open-source software as vital for achieving sovereignty. Providers will leverage existing data centre footprints to offer sovereign AI solutions, ensuring data remains within specific jurisdictions to meet compliance demands. Emmet King, Founding Partner of J12 Ventures, adds that competitive advantage is moving from owning models to “controlling training pipelines and energy supply,” with open-source advancements allowing more actors to run frontier-scale workloads. Workforce integration is becoming personal. Nick Blasi, Co-Founder of Personos, argues tools ignoring human nuance – tone, temperament, and personality – will soon feel obsolete. By 2026, Blasi predicts “half of workplace conflict will be flagged by AI before managers know it exists.” These systems will focus on “communication, influence, trust, motivation, and conflict resolution,” Blasi suggests, adding that personality science will become the “operating system” for the next generation of autonomous AI, offering grounded understanding of human individuality rather than generic recommendations. The era of the “thin wrapper” is over. Buyers are now measuring real productivity, exposing tools built on hype rather than proprietary data. For the enterprise, competitive advantage will no longer come from renting access to a model, but from controlling the training pipelines and energy supply that power it. See also: BBVA embeds AI into banking workflows using ChatGPT 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 AI in 2026: Experimental AI concludes as autonomous systems rise appeared first on AI News. View the full article

BBVA is embedding AI into core banking workflows using ChatGPT Enterprise to overhaul risk and service in the sector. For the banking industry, the challenge of generative AI is rarely about adoption; it is about value extraction. BBVA has addressed this by integrating OpenAI’s platform directly into its operational backbone, a decision that will see the tool deployed across every unit of the bank. This tenfold expansion marks one of the largest enterprise deployments in the financial sector to date. The bank’s approach prioritises data over hype. BBVA began working with OpenAI in May 2024, rolling out 3,300 accounts to test the waters. This pilot phase allowed the institution to validate use cases before expanding to 11,000 staff. The results from this intermediate rollout of AI in banking provided the necessary business case. Employees using the tools saved nearly three hours per week on routine tasks. Furthermore, engagement was high, with more than 80 percent of users logging in daily. Staff created thousands of custom GPTs to handle specific collaborative and administrative duties, proving that utility often lies in bottom-up innovation rather than top-down mandates. Impact of ChatGPT on banking operations This agreement goes beyond simple productivity tools; it aims to reshape how the bank functions. BBVA will use the partnership to streamline risk analysis, a resource-intensive area for all financial institutions. Additionally, the deal focuses on redesigning software development processes and improving general employee support. “We were pioneers in the digital and mobile transformation, and we are now entering the AI era with even greater ambition. Our alliance with OpenAI accelerates the native integration of artificial intelligence across the bank to create a smarter, more proactive, and completely personalized banking experience, anticipating the needs of every client,” said Carlos Torres Vila, Chairman, BBVA. Deploying AI into a heavily-regulated environment like banking requires strict controls. The global rollout includes enterprise-grade security and privacy measures, ensuring that client data remains protected while staff access OpenAI’s latest models. The ChatGPT deployment includes tools for creating internal agents that connect securely to BBVA’s existing systems and processes for banking. To ensure these tools are used effectively, the two companies are collaborating on a structured adoption model and specialised training programmes. This addresses the common enterprise pitfall where software is deployed without the necessary skills to leverage it. Improving the banking experience with AI The initiative also targets the client interface. BBVA has already deployed ‘Blue,’ a virtual assistant built on OpenAI models that helps customers manage cards and accounts using natural language. Future plans involve integrating products so customers can interact with the bank directly through ChatGPT. To facilitate this, BBVA will have a dedicated team working directly with OpenAI’s product and research units. This direct line is intended to accelerate the bank’s transition into an “AI-native” institution. Sam Altman, CEO of OpenAI, said: “BBVA is a strong example of how a large financial institution can adopt AI with real ambition and speed. With this expansion of our work together, BBVA will embed our AI into the core of their products and operations to enhance the overall banking experience for their customers.” See also: Perplexity: AI agents are taking over complex enterprise tasks 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 BBVA embeds AI into banking workflows using ChatGPT Enterprise appeared first on AI News. View the full article

F. Scott Fitzgerald once observed that “in a real dark night of the soul, it is always three o’clock in the morning.” Microsoft’s latest Copilot usage analysis suggests this nocturnal tendency toward existential contemplation persists in the AI age—with religion and philosophy conversations rising through the rankings during early morning hours. The Microsoft AI (MAI) research team analysed 37.5 million de-identified conversations to uncover when and how users turn to Copilot for different needs. The findings, published December 10, reveal distinct AI usage patterns that follow surprisingly human rhythms—from late-night philosophical queries to Valentine’s Day relationship anxiety, and the strict divide between weekday coding and weekend gaming. Late-night existential queries and daytime travel planning Religion and philosophy conversations rise during early morning hours while travel queries peak during commuting times According to the report authored by Bea Costa-Gomes and Seth Spielman, religion and philosophy conversations rise through the rankings during early morning hours. Travel-related discussions, by contrast, peak during typical commuting times, suggesting users plan trips while in transit. “The larger-than-life questions seem to have a rise during the early hours of the morning, with ‘Religion and Philosophy’ rising through the ranks,” the researchers noted in their paper. This temporal divide reflects different states of mind: practical planning during active hours versus contemplative questioning during quiet moments. The pattern challenges assumptions about AI as merely a productivity tool, revealing its role in addressing fundamental human curiosity. Health dominates mobile usage across all hours Health-related conversations consistently rank highest on mobile devices across all months of 2025 Health-related topics consistently ranked as the most common conversation type on mobile devices throughout 2025, regardless of time, day, or month. Users turned to Copilot for wellness tracking, health tips, and daily routine management with notable consistency. “When it comes to mobile, with its intimacy and immediacy, nothing tops our health,” the researchers wrote. The dominance of health queries on mobile devices—but not desktops—suggests users view smartphones as more personal companions for sensitive topics. This usage analysis indicates AI assistants are becoming integrated into daily health management routines. Programming rises weekdays, gaming dominates weekends August data revealed a clear cyclical pattern between programming and gaming topics. Programming conversations climbed from Monday through Friday, while gaming queries surged on weekends. Users were equally likely to engage with both topics, but strictly segregated by day of the week. Programming conversations climb Monday through Friday while gaming queries surge on weekends “This crossover hints at a vibrant, creative community that loves to code during the week and play during the weekends in equal measure,” according to the report. The strict temporal separation suggests users maintain clear boundaries between work and leisure, even when both involve digital activities. February’s Valentine’s Day spike in relationship queries The Copilot usage analysis showed February brought a pronounced spike in relationship-related conversations, peaking specifically on Valentine’s Day. Personal growth and wellness discussions also increased before the holiday, as users sought guidance for navigating romantic expectations. “Whether it was in preparing for Valentine’s day, or facing the day and the relationships, we saw a spike in activity as people turned to Copilot for guidance, reminders, and support,” the researchers observed. This pattern reveals how AI usage patterns align with cultural moments and social pressures, with users seeking private counsel from AI during emotionally charged periods. From search to advice: shifting usage patterns While information search remains Copilot’s most popular function, Microsoft’s analysis detected a clear rise in users seeking advice, particularly on personal topics including relationships and life decisions. “This growing trend highlights how digital tools are becoming trusted companions for life’s everyday questions,” the report stated. The shift from purely informational queries to advice-seeking represents an evolution in how users conceptualise AI assistants—from search engines to consultants. Privacy-preserving methodology Microsoft’s research team emphasised that their analysis maintained user privacy by extracting only conversation summaries rather than full content. The system identifies topic and intent while avoiding individual-level data exposure. “Our system doesn’t just de-identify conversations; it only extracts the summary of the conversation, from which we learn the topic and the intent, and maintains full privacy,” Costa-Gomes and Spielman explained. The 37.5 million conversation sample represents a subset of Copilot’s total usage, though Microsoft did not disclose the full user base or conversation volume. Implications for AI development Understanding these AI usage patterns informs feature development, according to Microsoft. The company noted that recognising what matters most to users—health, creativity, and support during key moments—enables designing features that fit naturally into daily life. “It’s also clear from these uses that what Copilot says matters. They show why it’s so important that we hold ourselves to a high bar for quality,” the researchers wrote. The findings complement recent broader industry analysis of AI usage patterns, including OpenRouter’s report showing ******** AI models gaining global adoption through cost efficiency and performance improvements. Microsoft’s temporal focus adds behavioural depth to understanding how AI assistants integrate into human routines. The research arrives as Microsoft expands its AI infrastructure, with the company’s next-generation GB200 cluster now operational at MAI. These usage patterns suggest AI assistants are evolving beyond tools into companions that adapt to human rhythms—answering practical questions during daylight hours while entertaining philosophical musings when users can’t sleep. See also: Microsoft ‘Promptions’ fix AI prompts failing to deliver 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 Microsoft’s Copilot usage analysis exposes the 2 am philosophy question phenomenon appeared first on AI News. View the full article

Microsoft believes it has a fix for AI prompts being given, the response missing the mark, and the cycle repeating. This inefficiency is a drain on resources. The “trial-and-error loop can feel unpredictable and discouraging,” turning what should be a productivity booster into a time sink. Knowledge workers often spend more time managing the interaction itself than understanding the material they hoped to learn. Microsoft has released Promptions (prompt + options), a UI framework designed to address this friction by replacing vague natural language requests with precise, dynamic interface controls. The open-source tool offers a method to standardise how workforces interact with large language models (LLMs), moving away from unstructured chat toward guided and reliable workflows. The comprehension bottleneck Public attention often centres on AI producing text or images, but a massive component of enterprise usage involves understanding—asking AI to explain, clarify, or teach. This distinction is vital for internal tooling. Consider a spreadsheet formula: one user may want a simple syntax breakdown, another a debugging guide, and another an explanation suitable for teaching colleagues. The same formula can require entirely different explanations depending on the user’s role, expertise, and goals. Current chat interfaces rarely capture this intent effectively. Users often find that the way they phrase a question doesn’t match the level of detail the AI needs. “Clarifying what they really want can require long, carefully worded prompts that are tiring to produce,” Microsoft explains. Promptions operates as a middleware layer to fix this familiar issue with AI prompts. Instead of forcing users to type lengthy specifications, the system analyses the intent and conversation history to generate clickable options – such as explanation length, tone, or specific focus areas – in real-time. Efficiency vs complexity Microsoft researchers tested this approach by comparing static controls against the new dynamic system. The findings offer a realistic view of how such tools function in a live environment. Participants consistently reported that dynamic controls made it easier to express the specifics of their tasks without repeatedly rephrasing their prompts. This reduced the effort of prompt engineering and allowed users to focus more on understanding content than managing the mechanics of phrasing. By surfacing options like “Learning Objective” and “Response Format,” the system prompted participants to think more deliberately about their goals. Yet, adoption brings trade-offs. Participants valued adaptability but also found the system more difficult to interpret. Some struggled to anticipate how a selected option would influence the response, noting that the controls seemed opaque because the effect became evident only after the output appeared. This highlights a balance to strike. Dynamic interfaces can streamline complex tasks but may introduce a learning curve where the connection between a checkbox and the final output requires user adaptation. Promptions: The solution to fix AI prompts? Promptions is designed to be lightweight, functioning as a middleware layer sitting between the user and the underlying language model. The architecture consists of two primary components: Option Module: Reviews the user’s prompt and conversation history to generate relevant UI elements. Chat Module: Incorporates these selections to produce the AI’s response. Of particular note for security teams, “there’s no need to store data between sessions, which keeps implementation simple.” This stateless design mitigates data governance concerns typically associated with complex AI overlays. Moving from “prompt engineering” to “prompt selection” offers a pathway to more consistent AI outputs across an organisation. By implementing UI frameworks that guide user intent, technology leaders can reduce the variability of AI responses and improve workforce efficiency. Success depends on calibration. Usability challenges remain regarding how dynamic options affect AI output and managing the complexity of multiple controls. Leaders should view this not as a complete solution to fix the results of AI prompts, but as a design pattern to test within their internal developer platforms and support tools. See also: Perplexity: AI agents are taking over complex enterprise tasks 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 Microsoft ‘Promptions’ fix AI prompts failing to deliver appeared first on AI News. View the full article

New adoption data from Perplexity reveals how AI agents are driving workflow efficiency gains by taking over complex enterprise tasks. For the past year, the technology sector has operated under the assumption that the next evolution of generative AI would advance beyond conversation into action. While Large Language Models (LLMs) serve as a reasoning engine, “agents” act as the hands, capable of executing complex, multi-step workflows with minimal supervision. Until now, however, visibility into how these tools are actually being utilised in the wild has been opaque, relying largely on speculative frameworks or limited surveys. New data released by Perplexity, analysing hundreds of millions of interactions with its Comet browser and assistant, provides a first large-scale field study of general-purpose AI agents. The data indicates that agentic AI is already being deployed by high-value knowledge workers to streamline productivity and research tasks. Understanding who is using these tools is essential for forecasting internal demand and identifying potential shadow IT vectors. The study reveals marked heterogeneity in adoption. Users in nations with higher GDP per capita and educational attainment are far more likely to engage with agentic tools. More telling for corporate planning is the occupational breakdown. Adoption is heavily concentrated in digital and knowledge-intensive sectors. The ‘Digital Technology’ cluster represents the largest share, accounting for 28 percent of adopters and 30 percent of queries. This is followed closely by academia, finance, marketing, and entrepreneurship. Collectively, these clusters account for over 70 percent of total adopters. This suggests that the individuals most likely to leverage agentic workflows are the most expensive assets within an organisation: software engineers, financial analysts, and market strategists. These early adopters are not dabbling; the data shows that “power users” (those with earlier access) make nine times as many agentic queries as average users, indicating that once integrated into a workflow, the technology becomes indispensable. AI agents: Partners for enterprise tasks, not butlers To advance beyond marketing narratives, enterprises must understand the utility these agents provide. A common view suggests agents will primarily function as “digital concierges” for rote administrative chores. However, the data challenges this view: 57 percent of all agent activity focuses on cognitive work. Perplexity’s researchers developed a “hierarchical agentic taxonomy” to classify user intent, revealing the usage of AI agents is practical rather than experimental. The dominant use case is ‘Productivity & Workflow,’ which accounts for 36 percent of all agentic queries. This is followed by ‘Learning & Research’ at 21 percent. Specific anecdotes from the study illustrate how this translates to enterprise value. A procurement professional, for instance, used the assistant to scan customer case studies and identify relevant use cases before engaging with a vendor. Similarly, a finance worker delegated the tasks of filtering stock options and analysing investment information. In these scenarios, the agent handles the information gathering and initial synthesis autonomously to allow the human to focus on final judgment. This distribution provides a definite indication to operational leaders: the immediate ROI for agentic AI lies in scaling human capability rather than simply automating low-level friction. The study defines these agents as systems that “cycle automatically between three iterative phases to achieve the end goal: thinking, acting, and observing.” This capability allows them to support “deep cognitive work,” acting as a thinking partner rather than a simple butler. Stickiness and the cognitive migration A key insight for IT leaders is the “stickiness” of AI agents for enterprise workflows. The data shows that in the short term, users exhibit strong within-topic persistence. If a user engages an agent for a productivity task, their subsequent queries are highly likely to remain in that domain. However, the user journey often evolves. New users frequently “test the waters” with low-stakes queries, such as asking for movie recommendations or general trivia. Over time, a transition occurs. The study notes that while users may enter via various use cases, query shares tend to migrate toward cognitively oriented domains like productivity, learning, and career development. Once a user employs an agent to debug code or summarise a financial report, they rarely revert to lower-value tasks. The ‘Productivity’ and ‘Workflow’ categories demonstrate the highest retention rates. This behaviour implies that early pilot programmes should anticipate a learning curve where usage matures from simple information retrieval to complex task delegation. The “where” of agentic AI is just as important as the “what”. Perplexity’s study tracked the environments – specific websites and platforms – where these AI agents operate. The concentration of activity varies by task, but the top environments are staples of the modern enterprise stack. Google Docs is a primary environment for document and spreadsheet editing, while LinkedIn dominates professional networking tasks. For ‘Learning & Research,’ the activity is split between course platforms like Coursera and research repositories. For CISOs and compliance officers, this presents a new risk profile. AI agents are not just reading data; they are actively manipulating it within core enterprise applications. The study explicitly defines agentic queries as those involving “browser control” or actions on external applications via APIs. When an employee tasks an agent to “summarise these customer case studies,” the agent is interacting directly with proprietary data. The concentration of environments also highlights the potential for platform-specific optimisations. For instance, the top five environments account for 96 percent of queries in professional networking, primarily on LinkedIn. This high concentration suggests that businesses could see immediate efficiency gains by developing specific governance policies or API connectors for these high-traffic platforms. Business planning for agentic AI following Perplexity’s data The diffusion of capable AI agents invites new lines of inquiry for business planning. The data from Perplexity confirms that we have passed the speculative phase. Agents are currently being used to plan and execute multi-step actions, modifying their environments rather than just exchanging information. Operational leaders should consider three immediate actions: Audit the productivity and workflow friction points within high-value teams: The data shows this is where agents are naturally finding their foothold. If software engineers and financial analysts are already using these tools to edit documents or manage accounts, formalising these workflows could standardise efficiency gains. Prepare for the augmentation reality: The researchers note that while agents have autonomy, users often break tasks into smaller pieces, delegating only subtasks. This suggests that the immediate future of work is collaborative, requiring employees to be upskilled in how to effectively “manage” their AI counterparts. Address the infrastructure and security layer: With agents operating in “open-world web environments” and interacting with sites like GitHub and corporate email, the perimeter for data loss prevention expands. Policies must distinguish between a chatbot offering advice and an agent executing code or sending messages. As the market for agentic AI is projected to grow from $8 billion in 2025 to $199 billion by 2034, the early evidence from Perplexity serves as a bellwether. The transition to enterprise workflows led by AI agents is underway, driven by the most digitally capable segments of the workforce. The challenge for the enterprise is to harness this momentum without losing control of the governance required to scale it safely. See also: Accenture and Anthropic partner to boost enterprise AI integration Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and 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 Perplexity: AI agents are taking over complex enterprise tasks appeared first on AI News. View the full article

Many companies are still working out how to use AI in a steady and practical way, but a small group is already pulling ahead. New research from NTT DATA outlines a playbook that shows how these “AI leaders” set themselves apart through strong plans, firm decisions, and a disciplined approach to building and using AI across their organisations. The findings come from a survey of 2,567 senior executives in 35 countries and 15 industries. Only 15% of the organisations met the bar to be considered AI leaders. These companies share a few traits: clear direction on where AI fits into their business, a solid operating model, and consistent follow-through. They also reported higher revenue growth and stronger profit margins than everyone else in the study. Yutaka Sasaki, President and CEO of NTT DATA Group, put it simply: “AI accountability now belongs in the boardroom and demands an enterprise-wide agenda. Our research shows that a small group of AI leaders already are using AI to differentiate, grow and reinvent how humans and machines create value together.” The playbook behind strong AI plans One of the clearest differences between leaders and the rest is how they approach strategy. For these companies, AI is not a side project or a tool bolted onto existing work. They treat it as a core driver of growth and adjust their plans to match that view. A major advantage for these leaders is how closely they connect AI with their business goals. This alignment helps them move faster and stay focused, which in turn delivers stronger financial outcomes. They also zero in on a few high-value areas of the business rather than spreading resources too thin. By redesigning entire workflows around AI, they unlock more value than if they had only made small improvements in scattered parts of the organisation. The report describes this as a kind of flywheel: early investments bring early wins, which then encourage more investment. Over time, this cycle becomes self-reinforcing. Leaders also rebuild important applications with AI embedded inside them, instead of adding basic AI features on top of old systems. This approach helps them see deeper impact and prepares the organisation for long-term gains. How leaders put their plans to work A good plan only works when backed by strong execution. AI leaders stand out through the foundations they build, the way they support their people, and how they drive adoption across the entire organisation. These companies invest in secure and scalable systems that can support large AI workloads. In some cases, they shift or localise their infrastructure to support private or sovereign AI needs. They also work to remove system bottlenecks so teams can move without roadblocks. Rather than using AI as a replacement for workers, leaders use it to help experienced employees do higher-value work. This “expert-first” approach allows teams to use their judgment while letting AI handle complex or time-consuming tasks. AI leaders also focus on adoption as a long-term change effort. They treat it as a company-wide shift, supported by clear communication and structured change management. This helps reduce pushback and encourages steady use of AI at all levels. Governance is another major difference. Leading organisations centralise their AI oversight, give clear responsibility to senior roles such as Chief AI Officers, and build processes that help balance innovation with risk. These systems allow them to scale AI more confidently. Partnerships also play a major role. Top companies often bring in outside experts and are open to arrangements that tie outcomes to shared success. This helps them move faster while keeping their goals in view. Abhijit Dubey, CEO and CAIO of NTT DATA, Inc., summarised the path forward: “Once AI and business strategies are aligned, the single most effective move is to pick one or two domains that deliver disproportionate value and redesign them end-to-end with AI. Supporting this focused, end-to-end approach with strong governance, modern infrastructure and trusted partners is how today’s AI leaders are turning pilots into profit and pulling ahead of the market.” (Photo by Igor Omilaev) See also: OpenAI: Enterprise users swap AI pilots for deep integrations 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 Inside the playbook of companies winning with AI appeared first on AI News. View the full article

Accenture and Anthropic are setting out to boost enterprise AI integration with a newly-expanded partnership. While 2024 was defined by corporate curiosity regarding Large Language Models (LLMs), the current mandate for business leaders is operationalising these tools to achieve a return on investment. The new Accenture Anthropic Business Group combines Anthropic’s model capabilities with Accenture’s implementation machinery to industrialise the deployment of generative AI across regulated sectors. Industrialising the developer workflow A primary component of this collaboration focuses on software engineering. Coding assistance is often seen as the path of least resistance for AI adoption, yet integrating these tools into existing CI/CD pipelines remains complex. Accenture is positioning itself as a primary partner for Claude Code, Anthropic’s coding tool, which the company claims now holds over half of the AI coding market. The consultancy plans to train approximately 30,000 of its own professionals on Claude, creating one of the largest global ecosystems of practitioners familiar with the tool. The promise of deeper enterprise integration of AI coding tools is a complete restructuring of the development hierarchy. The joint offering suggests that junior developers can utilise these tools to produce senior-level code and complete integration tasks more quickly to reduce onboarding times from months to weeks. Senior developers can then concentrate on high-value architecture, validation, and oversight. Dario Amodei, CEO and Co-Founder of Anthropic, said: “AI is changing how almost everyone works, and enterprises need both cutting-edge AI and trusted expertise to deploy it at scale. Accenture brings deep enterprise transformation experience, and Anthropic brings the most capable models. “Our new partnership means that tens of thousands of Accenture developers will be using Claude Code, making this our largest ever deployment—and the new Accenture Anthropic Business Group will help enterprise clients use our smartest AI models to make major productivity gains.” Justifying AI inference costs and removing deployment barriers A persistent friction point for enterprise leaders seeking deeper AI integration is justifying the ongoing cost of inference against actual business value. To counter this, the partnership is launching a specific product designed to help CIOs measure value and drive adoption across engineering organisations. This offering attempts to provide a structured path for software design and maintenance, moving beyond the ad-hoc usage of coding assistants. It combines Claude Code with a framework for quantifying productivity gains and workflow redesigns tailored for AI-first development teams. For the enterprise, the goal is to translate individual developer efficiency into broader company impact; such as shorter development cycles and faster time-to-market for new products. However, the most substantial barrier to AI adoption in the Global 2000 remains compliance. Sectors such as financial services, healthcare, and the public sector face strict governance requirements that often stall AI initiatives. Accenture and Anthropic are developing industry-specific enterprise AI solutions to address these deployment challenges. In financial services, for instance, the focus is on automating compliance workflows and processing complex documents with the precision required for high-stakes decisions. Health and life sciences firms face a parallel demand. Here, the partnership aims to leverage Claude’s analytical capabilities to query proprietary datasets and streamline clinical trial processing. For the public sector, the utility lies in AI agents that assist citizens in navigating government services while adhering to statutory data privacy requirements. Julie Sweet, Chair and CEO of Accenture, commented: “With the powerful combination of Anthropic’s Claude capabilities and Accenture’s AI expertise and industry and function domain knowledge, organisations can embed AI everywhere responsibly and at speed – from software development to customer experience – to drive innovation, unlock new sources of growth, and build their confidence to lead in the age of AI.” How Accenture and Anthropic are mitigating risks to support enterprise AI integration To mitigate the risks associated with deploying non-deterministic models, the partnership emphasises “responsible AI.” This involves combining Anthropic’s “constitutional AI” principles – which embed safety rules directly into the model – with Accenture’s governance expertise. Practical implementation will occur through Accenture’s network of Innovation Hubs, which will serve as controlled environments or “sandboxes”. These hubs allow clients to prototype and validate solutions without exposing production systems or sensitive data to risk. The companies also plan to co-invest in a ‘Claude Center of Excellence’ to design bespoke AI offerings tailored to specific industry needs. This expanded partnership with Accenture follows Anthropic reporting a growth in its enterprise AI market share from 24 percent to 40 percent. For Accenture, establishing a dedicated business group with specific go-to-market focus reflects a long-term commitment to the platform. The era of standalone AI pilots is fading. The next phase for enterprise AI integration demands tight coupling between model capabilities, workforce training, and rigorous value measurement. See also: OpenAI targets AI skills gap with new certification standards 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 Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Accenture and Anthropic partner to boost enterprise AI integration appeared first on AI News. View the full article

Adoption of generative AI has outpaced workforce capability, prompting OpenAI to target the skills gap with new certification standards. While it’s safe to say OpenAI’s tools have reached mass adoption, organisations struggle to convert this usage into reliable output. To address this, OpenAI has announced ‘AI Foundations,’ a structured initiative designed to standardise how employees learn and apply the technology. OpenAI’s initiative marks a necessary evolution in the vendor ecosystem; indicating a departure from the “move fast” phase of experimental deployment toward a focus on verifiable competence. OpenAI explicitly states its intention to certify 10 million Americans by 2030. Workers and employers have an incentive to close the AI skills gap The economic case for AI training and certification is rooted in wage and productivity data. Workers possessing AI skills earn approximately 50 percent more than those without them. However, CIOs often find that productivity gains on paper fail to materialise in practice. OpenAI notes that gains “only materialise when people have the skills to use the technology.” Without guidance, widespread access can create operational risk. OpenAI admits the technology is “disruptive, leaving many people unsure which skills matter most.” By defining a standard curriculum, OpenAI aims to help organisations capture the efficiency gains promised by their software investments. The delivery method for AI Foundations differs from traditional corporate LMS (Learning Management System) modules. The course sits directly inside ChatGPT, allowing the platform to act as “tutor, the practice space, and the feedback loop” simultaneously. This integration allows learners to execute real tasks and receive context-aware corrections to help close the AI skills gap, rather than just watching passive video content. Completing the programme yields a badge verifying “job-ready AI skills”. This credential serves as a stepping stone toward a full OpenAI Certification. To ensure these badges carry weight in the labour market, OpenAI has engaged Coursera, ETS, and Credly by Pearson to validate the psychometric rigour and design of the assessments. Operational pilots for the AI certification and improving the hiring pipeline A consortium of large-scale employers and public-sector bodies will test the curriculum before a wider rollout. Pilot partners include Walmart, John Deere, Lowe’s, Boston Consulting Group, Russell Reynolds Associates, Upwork, Elevance Health, and Accenture. The Office of the Governor of Delaware is also participating, which shows interest from state-level administration. These partners span industries with heavy operational footprints (including retail, agriculture, and healthcare) suggesting the training targets core business functions rather than just technical roles. OpenAI plans to use the next few months to refine the course based on data from these pilots to ensure that it can effectively close the AI skills gap. OpenAI’s initiative extends into recruitment. The company is developing an ‘OpenAI Jobs Platform’ to connect certified workers with employers. Partnerships with Indeed and Upwork support this mechanism, aiming to make it easier for businesses to identify candidates with verified technical expertise. For hiring managers, this offers a potential solution to the difficulty of vetting AI literacy. A standardised AI certification could reduce the reliance on self-reported skills, providing “portable evidence” of a candidate’s development. Academic alignment to seed future AI talent While the enterprise focus is immediate, OpenAI is also seeding the future talent pipeline. A ‘ChatGPT Foundations for Teachers’ course has launched on Coursera. With three in five teachers already using AI tools to save time and personalise materials, this stream aims to formalise existing habits. Simultaneously, pilots with Arizona State University and the California State University system are creating pathways for students to certify their skills before entering the job market. This ensures that the next wave of graduates arrives with the “job-ready” verification that enterprise employers are beginning to demand. Organisations must now decide whether to rely on vendor-supplied certification or continue developing proprietary training. The involvement of firms like Boston Consulting Group and Accenture implies that major players see value in a standardised external benchmark. As OpenAI moves to certify millions of people and close the AI skills gap, the certification badge may become a baseline expectation for knowledge workers much like office suite proficiency in previous decades. See also: Instacart pilots agentic commerce by embedding in ChatGPT 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 Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post OpenAI targets AI skills gap with new certification standards appeared first on AI News. View the full article

For the past year, we’ve been told that artificial intelligence is revolutionising productivity—helping us write emails, generate code, and summarise documents. But what if the reality of how people actually use AI is completely different from what we’ve been led to believe? A data-driven study by OpenRouter has just pulled back the curtain on real-world AI usage by analysing over 100 trillion tokens—essentially billions upon billions of conversations and interactions with large language models like ChatGPT, Claude, and dozens of others. The findings challenge many assumptions about the AI revolution. ​​OpenRouter is a multi-model AI inference platform that routes requests across more than 300 models from over 60 providers—from OpenAI and Anthropic to open-source alternatives like DeepSeek and Meta’s LLaMA. With over 50% of its usage originating outside the United States and serving millions of developers globally, the platform offers a unique cross-section of how AI is actually deployed across different geographies, use cases, and user types. Importantly, the study analysed metadata from billions of interactions without accessing the actual text of conversations, preserving user privacy while revealing behavioural patterns. Open-source AI models have grown to capture approximately one-third of total usage by late 2025, with notable spikes following major releases. The roleplay revolution nobody saw coming Perhaps the most surprising discovery: more than half of all open-source AI model usage isn’t for productivity at all. It’s for roleplay and creative storytelling. Yes, you read that right. While tech executives tout AI’s potential to transform business, users are spending the majority of their time engaging in character-driven conversations, interactive fiction, and gaming scenarios. Over 50% of open-source model interactions fall into this category, dwarfing even programming assistance. “This counters an assumption that LLMs are mostly used for writing code, emails, or summaries,” the report states. “In reality, many users engage with these models for companionship or exploration.” This isn’t just casual chatting. The data shows users treat AI models as structured roleplaying engines, with 60% of roleplay tokens falling under specific gaming scenarios and creative writing contexts. It’s a massive, largely invisible use case that’s reshaping how AI companies think about their products. Programming’s meteoric rise While roleplay dominates open-source usage, programming has become the fastest-growing category across all AI models. At the start of 2025, coding-related queries accounted for just 11% of total AI usage. By the end of the year, that figure had exploded to over 50%. This growth reflects AI’s deepening integration into software development. Average prompt lengths for programming tasks have grown fourfold, from around 1,500 tokens to over 6,000, with some code-related requests exceeding 20,000 tokens—roughly equivalent to feeding an entire codebase into an AI model for analysis. For context, programming queries now generate some of the longest and most complex interactions in the entire AI ecosystem. Developers aren’t just asking for simple code snippets anymore; they’re conducting sophisticated debugging sessions, architectural reviews, and multi-step problem solving. Anthropic’s Claude models dominate this space, capturing over 60% of programming-related usage for most of 2025, though competition is intensifying as Google, OpenAI, and open-source alternatives gain ground. Programming-related queries exploded from 11% of total AI usage in early 2025 to over 50% by year’s end. The ******** AI surge Another major revelation: ******** AI models now account for approximately 30% of global usage—nearly triple their 13% share at the start of 2025. Models from DeepSeek, Qwen (Alibaba), and Moonshot AI have rapidly gained traction, with DeepSeek alone processing 14.37 trillion tokens during the study *******. This represents a fundamental shift in the global AI landscape, where Western companies no longer hold unchallenged dominance. Simplified ******** is now the second-most common language for AI interactions globally at 5% of total usage, behind only English at 83%. Asia’s overall share of AI spending more than doubled from 13% to 31%, with Singapore emerging as the second-largest country by usage after the United States. The rise of “Agentic” AI The study introduces a concept that will define AI’s next phase: agentic inference. This means AI models are no longer just answering single questions—they’re executing multi-step tasks, calling external tools, and reasoning across extended conversations. The share of AI interactions classified as “reasoning-optimised” jumped from nearly zero in early 2025 to over 50% by year’s end. This reflects a fundamental shift from AI as a text generator to AI as an autonomous agent capable of planning and execution. “The median LLM request is no longer a simple question or isolated instruction,” the researchers explain. “Instead, it is part of a structured, agent-like loop, invoking external tools, reasoning over state, and persisting across longer contexts.” Think of it this way: instead of asking AI to “write a function,” you’re now asking it to “debug this codebase, identify the performance bottleneck, and implement a solution”—and it can actually do it. The “Glass Slipper Effect” One of the study’s most fascinating insights relates to user retention. Researchers discovered what they call the Cinderella “Glass Slipper” effect—a phenomenon where AI models that are “first to solve” a critical problem create lasting user loyalty. When a newly released model perfectly matches a previously unmet need—the metaphorical “glass slipper”—those early users stick around far longer than later adopters. For example, the June 2025 cohort of Google’s Gemini 2.5 Pro retained approximately 40% of users at month five, substantially higher than later cohorts. This challenges conventional wisdom about AI competition. Being first matters, but specifically being first to solve a high-value problem creates a durable competitive advantage. Users embed these models into their workflows, making switching costly both technically and behaviorally. Cost doesn’t matter (as much as you’d think) Perhaps counterintuitively, the study reveals that AI usage is relatively price-inelastic. A 10% decrease in price corresponds to only about a 0.5-0.7% increase in usage. Premium models from Anthropic and OpenAI command $2-35 per million tokens while maintaining high usage, while budget options like DeepSeek and Google’s Gemini Flash achieve similar scale at under $0.40 per million tokens. Both coexist successfully. “The LLM market does not seem to behave like a commodity just yet,” the report concludes. “Users balance cost with reasoning quality, reliability, and breadth of capability.” This means AI hasn’t become a race to the bottom on pricing. Quality, reliability, and capability still command premiums—at least for now. What this means going forward The OpenRouter study paints a picture of real-world AI usage that’s far more nuanced than industry narratives suggest. Yes, AI is transforming programming and professional work. But it’s also creating entirely new categories of human-computer interaction through roleplay and creative applications. The market is diversifying geographically, with China emerging as a major force. The technology is evolving from simple text generation to complex, multi-step reasoning. And user loyalty depends less on being first to market than on being first to truly solve a problem. As the report notes, “ways in which people use LLMs do not always align with expectations and vary significantly country by country, state by state, use case by use case.” Understanding these real-world patterns—not just benchmark scores or marketing claims—will be crucial as AI becomes further embedded in daily life. The gap between how we think AI is used and how it’s actually used is wider than most realise. This study helps close that gap. See also: Deep Cogito v2: Open-source AI that hones its reasoning skills 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 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 people really use AI: The surprising truth from analysing billions of interactions appeared first on AI News. View the full article

Artificial intelligence is transforming the way information is created, summarised, and delivered. For publishers, the shift is already visible. Search engines provide AI-generated overviews, users get answers without clicking, and content is scraped by large language models that train on decades of journalism. In this environment one question remains: How does a publisher survive when the traditional rules of distribution fall apart? Dev Pragad, the CEO of Newsweek, is offering one of the clearest answers. Pragad’s strategy begins with an acknowledgement of reality. In his view, publishers need to accept the search-driven traffic model that defined the digital era is no longer dependable. AI-powered answer engines are restructuring the way users interact with information. A user might ask a question, receive a summary generated by an LLM, and never visit the publisher’s website. Page views become unpredictable, programmatic advertising becomes unstable, and legacy structures become vulnerable. Rather than respond with fear, Dev Pragad has taken a proactive approach grounded in three core areas. Redesign the brand so that it remains visually strong in any context. Diversify revenue so the business is not tied to a single distribution mechanism. Expand those content formats that are less dependent on search engines and more aligned with the new habits of audiences. In September 2025 Newsweek unveiled its redesigned identity under the tagline ‘A World Drawn Closer’. This redesign, created with 2×4, introduced a refined wordmark, a bold ‘N’ icon, and a unified visual system used for print, digital, video and international editions. For the AI era such a coherence matters. An AI summary might reference Newsweek visually, a feed might show a thumbnail with minimal space, and a social clip might require brand clarity in a fraction of a second. The new design prepares Newsweek for the new reality by making the brand easy to identify. The editorial shift under Dev Pragad is also significant. Newsmakers, the series that features cultural leaders (Spike Lee, Liam Neeson, and Clark Hunt, for example), is available free on YouTube and digital platforms. The decision to make the series accessible at no cost is strategic. Video that travels across platforms is harder for AI summaries to replace. It is more immersive, and it reaches audiences directly, plus it builds brand equity and cultural relevance beyond search traffic. In interviews Pragad has said Newsmakers represents the future of journalism, blending storytelling, accessibility and platform fluency. Each episode is supported by a companion article and a collectable cover, creating a cross media footprint that is not reliant on one format or algorithm. In addition to editorial innovation, Newsweek is evolving its business architecture to withstand AI driven disruption. While digital advertising remains part of the company’s revenue model, Pragad has expanded the title into events, direct advertising relationships, data driven rankings, and verticals such as healthcare. This approach creates multiple revenue streams that do not depend on unpredictable traffic patterns. Another factor shaping Newsweek’s AI strategy is the way large language models scrape content. Newsweek monitors this activity through systems like TollBit which track bot behaviour and provide insight into how often AI engines attempt to access the site. Pragad has turned down licensing deals that undervalued the worth of Newsweek’s archives and has advocated for fair compensation for the use of publisher content. He believes publishers must negotiate collectively and maintain leverage rather than rush into agreements that minimise the value of their intellectual property. The redesign is also in response to the challenge of brand recognition in a world dominated by fast-moving feeds and AI-driven surfaces. Clear typography, concise visual hierarchy, and a distinct colour palette support recognition across AI-generated snippets, smart devices, social networks, and search previews. This is a design built for the realities of the modern information economy. Newsweek’s growth reflects the strength of these choices. The publication has been recognised as one of the fastest-rising digital news destinations in the US, and global audience numbers continue to climb. Although the company continues to evolve its revenue structure, its editorial mission remains grounded in fairness and trust. The new tagline reflects that commitment. Journalism brings the world closer when it is clear, accessible, and human-centred. The AI revolution has placed publishers in a difficult position, yet it has also opened an opportunity. Those willing to rethink design, editorial formats, AI licensing, distribution, and revenue have the chance to define what comes next. Under Dev Pragad Newsweek is doing exactly that. The company is no longer relying on assumptions about how audiences discover information. It’s building a future in which journalism can coexist with AI, not be erased by it. Dev Pragad has created a blueprint that demonstrates how a legacy publisher can reinvent itself for the AI age. Through design clarity, accessible cultural storytelling, diversified business models, and a firm stance on content value, he is positioning Newsweek not only to survive, but to lead in a world where information flows faster and more unpredictably than before. The result is a modern media entity built for a new era of intelligence, creativity, and connection. The post Newsweek: Building AI-resilience for the next era of information appeared first on AI News. View the full article

Instacart has deployed an embedded checkout experience within ChatGPT through the emerging Agentic Commerce Protocol. With the deployment, the company is the first partner to launch an app on ChatGPT that offers a complete shopping cycle – from query to payment – without requiring the user to leave the conversation interface. Operationalising agentic commerce The integration fixes a broken link in conversational commerce: the “handoff”. Historically, AI models could suggest products or generate meal plans, but the execution phase required deep-linking out to a separate application or website, often resulting in cart abandonment. Under this new deployment, users can interact with the AI for meal planning and have the system build a cart based on local retailer inventory. The differentiator here is the checkout process. By leveraging the Agentic Commerce Protocol, the transaction is processed directly within the chat interface using a credit card flow powered by Stripe. According to Nick Turley, VP and Head of ChatGPT, the objective is to connect AI suggestions directly to real-world services. “With the Instacart app directly in ChatGPT, users can go from meal planning to checkout in a single, seamless conversation,” Turley said. “It’s another step toward bringing our vision to life—where AI delivers helpful suggestions and connects directly to real-world services, saving people time and effort in their everyday lives.” This integration goes deeper than standard API consumption. Instacart served as an early contributor to the OpenAI Operator research preview, providing feedback to ensure the technology could navigate real-world constraints while adhering to established norms. This “preview” involvement suggests that Instacart’s complex data environment – involving tens of thousands of SKUs and dynamic stock levels – served as a testing ground for OpenAI’s agentic capabilities. Rather than simply adopting the tool, Instacart helped define the parameters of how an AI agent interacts with external fulfilment logistics. The Instacart deployment underscores why structured, real-time data matters when integrating with large language models (LLMs). An AI agent is only as effective as the data it can access; hallucinations in a commercial context – such as selling out-of-stock items – carry financial and reputational risk. Anirban Kundu, CTO at Instacart, notes that powering shopping inside an AI agent requires technology capable of interpreting highly local and constantly fluctuating inventory. Instacart attempts to mitigate the “hallucination” risk by grounding the AI’s responses in its massive dataset, which covers more than 1.8 billion product instances across 100,000 stores. “Instacart and ChatGPT are redefining what’s possible in AI-powered shopping,” said Kundu. “Built on Agentic Commerce Protocol, this experience brings intelligent, real-time support to one of the most essential parts of daily life: getting groceries to feed your family. “Together, we’re creating a seamless and secure way for people to turn simple conversations into real-world action—helping customers go from inspiration to a full cart delivered from the store to their door with ease.” Dual adoption: Customer-facing and internal efficiency While the embedded checkout grabs headlines, Instacart’s broader plan involves extensive internal deployment. The company utilises ChatGPT Enterprise to streamline internal workflows, aimed at accelerating the development of customer experiences. Furthermore, they have deployed OpenAI’s Codex to power an internal coding agent. This dual approach – using AI to sell (Agentic Commerce) and AI to build (Codex) – offers a model for operations. It moves beyond isolated pilots into a holistic stance where generative models drive both revenue and R&D efficiency. The deployment points to a change in how brands view digital storefronts. Instacart’s approach appears to accept that consumer entry points are fragmenting. Rather than forcing all traffic through a proprietary app, the company is positioning its infrastructure as the backend fulfilment layer for third-party AI platforms. The company has explicitly stated its intention to bridge AI inspiration with real-world fulfilment, acting as a primary partner for major AI players including OpenAI, Google, and Microsoft. By embedding its service into these broad-reach platforms, Instacart aims to capture incremental demand that originates outside its native ecosystem. Implementation and availability of Instacart in ChatGPT The experience is currently active for users on desktop and mobile web platforms, while native mobile availability for iOS and Android applications is rolling out shortly. To access the feature, users must invoke the specific Instacart application within the ChatGPT interface (for example, by prompting “Instacart, can you help me shop for apple pie ingredients?”) and link their accounts. This opt-in mechanism ensures that data sharing is consensual, a requisite governance step for enterprises deploying consumer-facing AI agents. This integration serves as a case study of agentic AI for commerce. For retail and technology execs, the Instacart model demonstrates that the next phase of digital adoption involves preparing API structures and data pipelines to serve “non-human” customers (AI agents) as reliably as human ones. The focus must remain on data accuracy and real-time availability; without these foundations, agentic workflows will fail to deliver return on investment. See also: OpenAI: Enterprise users swap AI pilots for deep integrations 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 Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Instacart pilots agentic commerce by embedding in ChatGPT appeared first on AI News. View the full article

According to OpenAI, enterprise AI has graduated from the sandbox and is now being used for daily operations with deep workflow integrations. New data from the company shows that firms are now assigning complex and multi-step workflows to models rather than simply asking for text summaries. The figures illustrate a hard change in how organisations deploy generative models. With OpenAI’s platform now serving over 800 million users weekly, a “flywheel” effect is driving consumer familiarity into professional environments. The company’s latest report notes that over a million business customers now use these tools, and the goal is now even deeper integration. This evolution presents two realities for decision-makers: productivity gains are concrete, but a growing divide between “frontier” adopters and the median enterprise suggests that value depends heavily on usage intensity. From chatbots to deep reasoning The best metric for corporate deployment maturity is not seat count, but task complexity OpenAI reports that ChatGPT message volume has grown eightfold year-over-year, but a better indicator for enterprise architects is the consumption of API reasoning tokens which suggests deeper integrations are taking place. This figure has increased by nearly 320 times per organisation—evidence that companies are systematically wiring more intelligent models into their products to handle logic rather than basic queries. The rise of configurable interfaces supports this view. Weekly users of Custom GPTs and Projects (tools that allow workers to instruct models with specific institutional knowledge) have increased approximately 19x this year. Roughly 20 percent of all enterprise messages are now processed via these customised environments, indicating that standardisation is now a prerequisite for professional use. For enterprise leaders auditing the ROI of AI seats, the data offers evidence on time savings. On average, users attribute between 40-60 minutes of time saved per active day to the technology. The impact varies by function: data science, engineering, and communication professionals report higher savings (averaging 60-80 minutes daily.) Beyond efficiency, the software is altering role boundaries. There is a specific effect on technical capability, particularly regarding code generation. Among enterprise users, OpenAI says that coding-related messages have risen across all business functions. Outside of engineering, IT, and research roles, coding queries have grown by an average of 36 percent over the past six months. Non-technical teams are using the tools to perform analysis that previously required specialised developers. Operational improvements extend across departments. Survey data shows 87 percent of IT workers report faster issue resolution, while 75 percent of HR professionals see improved employee engagement. Widening enterprise AI competence gap OpenAI’s data suggests that a split is forming between organisations that simply provide access to tools and those in which integrations are being deeply embedded into their operating models. The report identifies a “frontier” class of workers – those in the 95th percentile of adoption intensity – who generate six times more messages than the median worker. This disparity is stark at the organisational level. Frontier firms generate approximately twice as many messages per seat as the median enterprise and seven times more messages to custom GPTs. Leading firms are not just using the tools more frequently; they are investing in the infrastructure and standardisation required to make AI a persistent part of operations. Users who engage across a wider variety of tasks (roughly seven distinct types) report saving five times more time than those who limit their usage to three or four basic functions. Benefits correlate directly with the depth of use, implying that a “light touch” deployment plan may fail to deliver the anticipated ROI. While the professional services, finance, and technology sectors were early adopters and maintain the largest scale of usage, other industries are sprinting to catch up. The technology sector leads with 11x year-over-year growth, but healthcare and manufacturing follow closely with 8x and 7x growth respectively. Global adoption patterns also challenge the notion that this is solely a US-centric phenomenon. International usage is surging, with markets such as Australia, Brazil, the Netherlands, and France showing business customer growth rates exceeding 140 percent year-over-year. Japan has also surfaced as a key market, holding the largest number of corporate API customers outside of the US. OpenAI: Deep AI integrations accelerate enterprise workflows Examples of deployment highlight how these tools influence key business metrics. Retailer Lowe’s deployed an associate-facing tool to over 1,700 stores, resulting in a customer satisfaction score increase of 200 basis points when associates used the system. Furthermore, when online customers engaged with the retailer’s AI tool, conversion rates more than doubled. In the pharmaceutical sector, Moderna used enterprise AI to speed up the drafting of Target Product Profiles (TPPs), a process that typically involves weeks of cross-functional effort. By automating the extraction of key facts from massive evidence packs, the company reduced core analytical steps from weeks to hours. Financial services firm BBVA leveraged the technology to fix a bottleneck in legal validation for corporate signatory authority. By building a generative AI solution to handle standard legal queries, the bank automated over 9,000 queries annually, effectively freeing up the equivalent of three full-time employees for higher-value tasks. However, the transition to production-grade AI requires more than software procurement; it necessitates organisational readiness. The primary blockers for many organisations are no longer model capabilities, but implementation and internal structures. Leading firms consistently enable deep system integration by “turning on” connectors that give models secure access to company data. Yet, roughly one in four enterprises has not taken this step, limiting their models to generic knowledge rather than specific organisational context. Successful deployment relies on executive sponsorship that sets explicit mandates and encourages the codification of institutional knowledge into reusable assets. As the technology continues to evolve, organisations must adjust their approach. OpenAI’s data suggests that success now depends on delegating complex workflows with deep integrations rather than just asking for outputs, treating AI as a primary engine for enterprise revenue growth. See also: AWS re:Invent 2025: Frontier AI agents replace chatbots 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 Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post OpenAI: Enterprise users swap AI pilots for deep integrations appeared first on AI News. View the full article

Digital payments and fintech company Ant International, has won the NeurIPS Competition of Fairness in AI Face Detection. The company says it’s committed to developing secure and inclusive financial services, particularly as deepfake technologies are becoming more common. The growing use of facial recognition in many sectors has highlighted the issue of algorithmic bias in AI. Research conducted by NIST (National Institute of Standards and Technology) shows many widely used facial recognition algorithms exhibit considerably higher error rates when analysing the faces of women and people of colour, a disparity that stems from a lack of diversity in the training data and the demographics of those building and controlling many mainstream AI platforms. The consequences of biased algorithms can lead to the denial of financial services to large sections of the population, and is seen as a vulnerability in security protocols. The NeurIPS Competition was held alongside the Conference on Neural Information Processing Systems, the well-respected AI conference, and challenged participants to create AI models capable of high performance and fairness covering a range of demographic factors: Gender, age, and skin tone. Ant International’s team beat over 2,100 submissions from 162 teams coming from all over the world. The given task was to accurately detect 1.2 million AI-generated face images which were chosen as properly representative of demographic groups. The approach taken by Ant’s winning AI model combines a Mixture of Experts (MoE) architecture with a bias-detection mechanism. The system trains two competing neural networks: one focused on identifying deepfakes, and the other designed to challenge the first, forcing it to disregard demographic characteristics. This dynamic process helps ensure the system learns to detect genuine signs of manipulation rather than inadvertently relying on demographic patterns. The model’s training incorporated a globally representative dataset and incorporated real-world payment fraud scenarios to ensure its performance at scale. “A biased AI system is inherently an insecure one,” explained Dr.Tianyi Zhang, general manager of risk management and cybersecurity at Ant International. “Our model’s fairness isn’t just a matter of ethics; it’s fundamental to preventing exploitation from deepfakes and ensuring reliable identity verification for every user”. The technology behind the winning entry is now being integrated into Ant’s payment and financial services to help counter the threat of deepfakes, and the companies says it achieves a detection rate of in excess of 99.8% in all demographics and in the 200 markets Ant operates in. Ant’s technology helps its customers meet global Electronic Know Your Customer (eKYC) standards, particularly during customer onboarding, without algorithmic bias. That’s held to be particularly important in emerging markets where greater financial inclusion can be hampered. Ant International serves over 150 million merchants and 1.8 billion user accounts, known for services like Alipay+, Antom, Bettr and WorldFirst. The company has stated AI security is a pillar of its operations. Its AI SHIELD, a framework for risk management as built on AI Security Docker to help mitigate the risk of vulnerabilities in AI services like unauthorised access and data leakage. AI SHIELD underpins a suite of risk-management solutions that provide broader protection of financial transactions, including measures against deepfake attacks and fraud. Alipay+ EasySafePay 360 has reduced incidents of account takeover in digital wallet payments by 90%, the company says. (Image source: “abstract art of a beautiful portrait, solid shapes, geometric shapes, neotokyo colors, muted colors, pixar, artstation, greg rutkowski, samdoesart, ge” – public domain) 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 Battling algorithmic bias in digital payments leads to competition win appeared first on AI News. View the full article

Resemble AI has raised US$13 million in a new strategic investment round for AI deepfake detection. The funding brings its total venture investment to US$25 million, with participation from Berkeley CalFund, Berkeley Frontier Fund, Comcast Ventures, Craft Ventures, Gentree, Google’s AI Futures Fund, IAG Capital Partners, and others. The funding comes as organisations are under pressure to verify the authenticity of digital content. Generative AI has made it easier for criminals to produce convincing deepfakes, contributing to more than US$1.56 billion in fraud losses in 2025. Analysts estimate that generative AI could enable US$40 billion in fraud losses in the US by 2027. Recent incidents highlight how quickly threats evolve. In Singapore, 13 individuals collectively lost more than SGD 360,000 after scammers impersonated a telecommunications provider and the Monetary Authority of Singapore. The attackers used caller ID spoofing, voice deepfakes, and social engineering techniques that created urgency and used the public’s trust in government and telecom brands. Deepfake detection tools and new AI capabilities Resemble AI develops real-time verification tools that help enterprises detect AI-generated audio, video, images, and text. The company plans to use its new funding to expand global access to its AI deepfake detection platform, which includes two recent releases: DETECT-3B Omni, a deepfake detection model designed for enterprise environments. The company reports 98% detection accuracy in more than 38 languages. Resemble Intelligence, a platform that provides explainability for multimodal and AI-generated content, using Google’s Gemini 3 models. Resemble AI positions these tools as part of a broader effort to support real-time verification for human users and AI agents interacting with digital content. According to the company, DETECT-3B Omni is already used in sectors like entertainment, telecommunications, and government. Public benchmark results on Hugging Face show the model ranking among the strongest performers on image and speech deepfake detection, with a lower average error rate than competing models. Industry stakeholders say the rapid improvement of generative AI is reshaping how enterprises think about content trust and identity systems. Representatives from Google’s AI Futures Fund, Sony Ventures, and Okta noted organisations are moving toward verification layers that can help maintain trust in authentication processes. Alongside the investment announcement, Resemble AI released its outlook on how deepfake-related risks may evolve in 2026. The company expects several shifts that could shape enterprise planning: Deepfake verification could become standard for official communications Following incidents involving government officials, it anticipates real-time deepfake detection may eventually be required for official video conferencing. Such a move would likely create new procurement activity and increase adoption in the public sector. Organisational readiness may determine competitive positioning As more jurisdictions introduce AI regulations, enterprises that integrate training, governance, and compliance processes early may find themselves better prepared for operational and regulatory demands. Identity emerges as a central focus in AI security With many AI-related attacks relying on impersonation, organisations may place greater emphasis on identity-centric security models, including zero-trust approaches for human and machine identities. Cyber insurance costs may rise The growing number of corporate deepfake incidents could lead insurers to reassess their policies on offer. Companies without detection tools could face higher premiums or limited coverage. The investment underscores the growing need for enterprises to understand how generative AI changes their risk exposure. Organisations in all sectors are evaluating how verification, identity safeguards, and incident readiness can fit into their broader security and compliance strategies. (Photo by Pau Casals) See also: AWS re:Invent 2025: Frontier AI agents replace chatbots 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 Google, Sony, and Okta back Resemble AI’s push into deepfake detection appeared first on AI News. View the full article

InTouchNow.ai is now offering doctors surgeries a piece of software designed to modernise phone answering, designed to reduce hold times and create a smoother, more responsive experience for patients and staff. In the ***, many GP (general practice) surgeries’ phone lines are tied up in the mornings as patients try to contact their medical practitioner for appointments. More acute need can be delayed among calls with routine enquiries, meaning high-priority callers can be left waiting for long periods. The system uses voice-based AI to handle calls, schedule appointments, and assess patient needs, and is capable of handling many calls simultaneously, channelling callers with appointment requests, those seeking general advice, prescription requests, and seeking results of clinical tests. Founded by Daniel Park, InTouchNow.ai draws on his 30+ years of experience in medical call centres. The AI receptionist answers calls quickly, and can automatically update integrated appointment systems. Practices can record a voice messages to personalise the experience for patients. Benefits for practices include reducing the numbers of missed calls, decreased workload for reception staff, and improved access by patients to medical services. Being entirely software-based, the system operates outside regular hours, which can reduce the need for staff overtime at times of peak demand. The system integrates with common GP software like Surgery Connect, AWS, and Anima, automating tasks and maintaining patient data security while giving practices full control. The technology supports over 200 languages, with options for different dialects and accents, an aspect that will help patients in multi-cultural areas like inner-cities. Several practices in the *** are already using InTouchNow.ai and have reported positive results in call handling and patient access. The much under-funded National Health Service in the *** has been quick to deploy AI-powered software to reduce its operating costs, often targeting the reduction of staff administration costs to funnel funds into patient care. For example, Smart Triage is an AI-powered system deployed in *** GP practices that can triage patients making initial enquiries, and based on their responses, book them into the right care pathway, such as GP or nurse appointment, or referral to specialist clinician. An evaluation of Smart Triage at a Surrey GP practice in 2024 showed the platform reduced the average patient waiting time by 73%. For clinicians, especially GPs, iatroX is a ***-based AI clinical reference platform that helps doctors retrieve evidence-based clinical guidance, and summarising relevant literature & guidelines. Doctors in general practice are expected to be able to assess a full range of patients’ needs, and such platforms help clinicians identify the cause of uncommon symptoms when GPs might lack specialist knowledge. An evaluation in 2025 found a majority of surveyed users stating iatroX was useful (~86%) or reliable (~79%). As documented by NHS England, AI platforms are used in practice, tackling tasks like diagnosis, the monitoring of chronic disease, provision of prescription advice, and handling general administration tasks that otherwise would take up clinicians’ time. Of all the sectors where sensitive data has to be protected, medicine has one of the highest standards of governance, making the deployment of AI a delicate balance between operational effectiveness and the preservation of privacy. (Image source: “Doctor appointment” by Taric25 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 *** doctors’ surgeries deploying AI in patient care appeared first on AI News. View the full article

ByteDance’s December 2 launch of an agentic AI smartphone prototype with ZTE sparked immediate consumer frenzy—and just as quickly triggered privacy concerns that forced the company to dial back capabilities. But beneath the headline-grabbing sell-out and subsequent controversy lies a more significant story: the enterprise implications of operating-system-level AI agents that can autonomously execute complex, multi-step tasks across device ecosystems. The ZTE Nubia M153, powered by ByteDance’s Doubao large language model, represents more than a consumer gadget experiment. It’s a preview of how agentic AI smartphones could reshape workplace productivity, field operations, and enterprise mobility strategies—if the technology can overcome fundamental trust and governance challenges that enterprise adoption demands. From consumer curiosity to enterprise necessity The consumer appeal is obvious: voice-activated restaurant bookings, automatic photo editing, cross-platform price comparisons. But according to Gartner projections, by 2028, 33% of enterprise software applications will include agentic AI capabilities, up from less than 1% in 2024. The smartphone, as the most ubiquitous computing device in enterprise workflows, becomes a critical battleground. “Agentic AI in industries like manufacturing, construction, healthcare, and energy will enhance decisions, boost safety, and streamline tasks,” explains Nicholas Muy, CISO of Scrut Automation. However, he cautions that early adopters must navigate real risks around AI errors and security gaps. McKinsey research indicates that 23% of organisations are already scaling agentic AI systems within at least one business function, with an additional 39% experimenting with AI agents. However, enterprise adoption differs fundamentally from consumer use: it demands robust governance frameworks, audit trails, role-based permissions, and compliance mechanisms that ByteDance’s consumer-focused prototype notably lacked. China’s strategic advantage in software-hardware integration ByteDance’s approach—partnering with ZTE rather than building proprietary hardware—mirrors successful enterprise AI strategies. The company positions Doubao as a system-level integration that any manufacturer can adopt, similar to how Google leveraged Android. With 157 million monthly active users as of August 2025, according to data from QuestMobile, Doubao already dominates China’s consumer AI market, more than doubling Tencent’s Yuanbao, which had 73 million users. This software-over-hardware strategy addresses what Morgan Stanley analysts identified as a critical weakness: major smartphone manufacturers, including Apple, Huawei, and Xiaomi, possess strong enough technology capabilities to self-develop AI assistants rather than partnering with third-party providers. ByteDance’s realistic target market appears to be second-tier manufacturers and, potentially, enterprise device management platforms seeking differentiated capabilities. For enterprise buyers, this fragmentation presents both opportunity and challenge. Organisations can select device manufacturers based on hardware requirements while standardising on AI capabilities—but only if governance and security frameworks prove robust enough for regulated industries. The privacy panic that revealed enterprise requirements The swift backlash following entrepreneur Taylor Ogan’s viral demos of the M153’s capabilities illuminated precisely what enterprise adoption demands. When users witnessed an AI agent with deep system privileges autonomously accessing apps, processing payments, and manipulating data, the immediate concern wasn’t convenience—it was control. Another DeepSeek moment. This is the world’s first actual smart phone. It’s an engineering prototype of ZTE’s Nubia M153 running ByteDance’s Doubao AI agent fused into Android at the OS level. It has complete control over the phone. It can see the UI, choose/download apps,… pic.twitter.com/lM9PYMoQek — Taylor Ogan (@TaylorOgan) December 4, 2025 According to a Forum Ventures survey of 100 senior enterprise IT decision-makers, trust remains the primary adoption barrier. “The trust gap is enormous,” explains Jonah Midanik, General Partner at Forum Ventures. “While AI agents can perform tasks with remarkable efficiency, their outputs are based on statistical probabilities rather than inherent truths.” ByteDance’s reported rollback of capabilities demonstrates an understanding that enterprise-grade agentic AI smartphones require granular permission systems, comprehensive logging, and the ability to define strict operational boundaries—features notably absent from the consumer prototype. Enterprise vs. consumer: Different use cases, different requirements Enterprise use cases for agentic AI smartphones diverge sharply from consumer applications. Field service technicians could leverage AI agents that proactively surface equipment histories, recommend optimal routes based on real-time conditions, and guide complex procedures without manual searches. Healthcare providers could access patient context, treatment protocols, and decision support without navigating multiple systems. Financial services professionals could receive compliance-checked recommendations and automated workflow orchestration. According to PwC research, 79% of organisations have implemented AI agents at some level, with 96% of IT leaders planning expansions in 2025. However, Cloudera’s survey of 1,484 IT decision-makers revealed that successful enterprise deployment requires industry-specific data integration, transparent decision-making processes, and phased rollouts with comprehensive testing. The consumer smartphone market, projected by IDC to ship 912 million generative AI-enabled units by 2028, emphasises personalisation and convenience. Enterprise deployments prioritise auditability, compliance, and risk mitigation—requirements that consumer-focused agentic AI smartphones haven’t yet addressed. Global competitive dynamics and regional strategies The US-China technology divide adds complexity. Apple’s delayed Apple Intelligence rollout in mainland China created an opening that ByteDance, Alibaba, Baidu, and Tencent are competing to fill. However, Apple’s approach differs fundamentally: tight hardware-software integration with on-device processing prioritises user privacy—a stance that resonates with enterprise security requirements. ByteDance’s licensing strategy positions Doubao for rapid market penetration across ******** manufacturers, potentially establishing de facto standards before Western competitors can match operating-system-level integration. For multinational enterprises operating across regions, this creates device management challenges around data sovereignty, compliance frameworks, and consistent user experiences. According to Counterpoint Research, Asia-Pacific represents the fastest-growing market for AI agents, with the US currently holding 40.1% revenue share. Enterprise buyers must navigate this bifurcated landscape, potentially maintaining separate device strategies for different regulatory environments. The path forward: Solutions over hype For enterprise leaders evaluating agentic AI smartphones, ByteDance’s prototype offers valuable lessons in what to demand from vendors: First, comprehensive governance frameworks that define decision boundaries, log all autonomous actions, and provide role-based access controls. Anthropic’s enterprise solution, which features centralised provisioning, audit logs, and role-based permissioning, demonstrates market requirements. Second, hybrid approaches that balance on-device processing for sensitive operations with cloud capabilities for complex reasoning. Enterprise deployments require flexibility to meet varying data residency and compliance requirements across jurisdictions. Third, phased rollouts starting with low-risk use cases. Amazon’s deployment of AI agents for Java application modernisation—transforming tens of thousands of production applications while delivering measurable productivity gains—illustrates how enterprises can capture value while managing risk. The ByteDance-ZTE collaboration ultimately previews an inevitable convergence: agentic AI capabilities will become standard smartphone features, not premium differentiators. Enterprise adoption will follow proven patterns—pilot programs in controlled environments, rigorous security validation, and gradual expansion as governance frameworks mature. The question facing enterprise technology leaders isn’t whether agentic AI smartphones will transform workplace productivity, but whether they’ll shape deployment strategies proactively or react to consumer technologies retrofitted with enterprise features. The privacy panic that followed ByteDance’s launch suggests that organisations demanding enterprise-grade security and governance from the outset will define the technology’s trajectory. As Gartner projects that at least 15% of work decisions will be made autonomously by agentic AI by 2028, up from 0% in 2024, the smartphone becomes not just a communication device but an autonomous enterprise agent. The winners won’t be those who deploy fastest, but those who deploy most thoughtfully—with security, compliance, and scalable governance built in from day one. See also: IBM cites agentic AI, data policies, and quantum as 2026 trends 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 Agentic AI smartphones: ByteDance’s bold bet signals enterprise opportunity beyond consumer hype appeared first on AI News. View the full article

The *** and Germany plan to integrate their science sectors to accelerate the commercialisation of quantum supercomputing technology. Announced on the final day of the ******* president’s state visit, these joint commitments target the gap between R&D and enterprise application in computing, sensing, and timing. The partnership involves specific funding to fast-track product development and establish shared operating standards. Quantum technology currently sits on the horizon for most roadmaps, yet economic modelling suggests a contribution of £11 billion to *** GDP by 2045, supporting over 100,000 jobs. To catalyse this, a £6 million joint R&D funding call launches in early 2026, with Innovate *** and VDI contributing £3 million each. This capital aims to help businesses bring new products to market rather than funding purely academic study. Supply chain maturity remains a hurdle. An £8 million investment in the Fraunhofer Centre for Applied Photonics in Glasgow addresses this by bolstering the development of applied photonics; a necessary component for commercial quantum sensing. Addressing hurdles in the ***, Germany, and beyond to commercialise quantum supercomputing Regulatory fragmentation often stalls adoption. A new Memorandum of Understanding between the ***’s National Physical Laboratory (NPL) and Germany’s Physikalisch-Technische Bundesanstalt (PTB) aims to harmonise measurement standards. This agreement complements the NMI-Q initiative, a global effort to develop shared norms. *** Science Minister Lord Vallance said: “Quantum technology will revolutionise fields such as cybersecurity, drug discovery, medical imaging, and much more. International collaboration is crucial to unlocking these benefits.” In practical terms, these advances allow pharmaceutical firms to identify new medicines faster. Similarly, next-generation sensors promise medical scanners that are more affordable, portable, and accurate than current iterations. The partnership also extends to high-performance computing (HPC). The ***’s National Supercomputing Centre at the University of Edinburgh was selected by the EuroHPC Joint Undertaking to host the ***’s AI Factory Antenna, partnering with the HammerHAI AI Factory in Stuttgart. To support HPC integration prior to the commercialisation of quantum supercomputing technology, the Department for Science, Innovation and Technology (DSIT) is allocating up to £3.9 million to match fund *** participation in three open EuroHPC calls. This funding assists teams developing exascale and AI-ready software. In the aerospace sector, the two nations recently committed joint funding of over €6 billion to the European Space Agency. This includes €1 billion for launch programmes and €10 million for Rocket Factory Augsburg, which plans to launch from Scotland in 2026. ******* President Frank-Walter Steinmeier concluded his visit at Siemens Healthineers in Oxford. The site produces superconducting magnets for MRI scanners, an existing example of how bilateral science ties support high-skilled manufacturing and health outcomes. As this bilateral cooperation deepens, the integrated approach between the *** and Germany toward supercomputing and quantum infrastructure aims to offer enterprises a powerful foundation for scaling high-performance workloads across Europe. See also: AWS re:Invent 2025: Frontier AI agents replace chatbots 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 Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post *** and Germany plan to commercialise quantum supercomputing appeared first on AI News. View the full article

The convergence of mobile and desktop operating systems is a goal that has remained elusive for big tech firms since the early days of the smartphone. Microsoft’s attempt in the form of Windows Mobile was reaching the end of its road by 2010, and despite Apple’s iOS/iPadOS and macOS moving very slowly towards one another for the last few years, Cupertino has not yet reached the fabled goal of the-one-OS-to-rule-them-all. But Google’s big play to merge ChromeOS and Android into a unified PC platform (with the anglicised codename Aluminium OS) is gradually taking shape. Android-powered laptops are planned for released in 2026, and the company wants to put its LLMs at the centre of the user experience. Hardware procurement decisions may be in step with company AI strategy in the enterprise, therefore, in the coming year. The prospect of chromebook-style devices and an accompanying lower price tag will be attractive both to organisations considering their next round of machine refreshes, and strategists who want to put AI at the heart of their employees’ daily work could. Soon, they might have a solution in common. It’s early days in the development of the converged device at Google, but the company is well known for both floating ideas that don’t get far and abandoning technologies it can’t monetise effectively enough. Unlike some of the company’s projects that may stem from its ‘20%’ policy (employees at Google are encouraged to dedicate 20% of their time to moonshot projects), the substantial Android development community and Google’s policy of putting Gemini front-and-centre may be the accelerant the new, converged operating system needs. Android’s existing AI capabilities like the Magic Editor for photos, audio transcription and summarisation would port very well to the workplace desktop. However, if Google wants to assuage the fears of security professionals, it may have to rely on local, small models for AI processing, rather than reaching out to cloud instances of Gemini for the required compute power. That puts into question the continuation of one of the chromebook range’s big selling points – its low price compared to fully-fledged workstations. There’s also a delicate balance the company needs to strike. Forcing users into an AI-centric workflow hasn’t played well for Microsoft: note the furore around Recall and the muted response to its much-reduced offspring that has sprung out of Copilot Labs. What Google needs is a killer AI feature that benefits the enterprise, and that may or may not be something that’s aimed at users. It’s undeniable that the addition of Gemini to Google Workspace has done wonders for the platform in terms of its competitiveness with Office 365 – despite a significant price hike earlier this year – driven in some part by new features like live translation in Google Meet and AI responses available in Gmail. Users do find some AI tools useful, but it may be becoming apparent that user-facing AI is a useful addition to existing workflows, rather than a catalyst that changes everything. If placing Gemini or Gemini Nano at the heart of the new operating system, therefore, it may be that Google is looking to offer value to different parts of the enterprise from the daily tasks users tackle. Android Authority suggests smart power management, device provisioning, and contextual awareness in accessing enterprise resources may be on the table. It’s difficult to see how these elements would be a game-changer for procurement teams, however. Google has many problems to solve at a deeper level, like compatibility with peripherals, OS-level drivers, and the necssary changes to the Android GUI to make it a great experience for end users wielding mouse and keyboard. But given enough effort and investment (something the company does not lack) these are issues that can be surmounted relatively easily. A thriving app ecosystem will ensure that the necessary tools are if not immediately available, could be made so with minimum effort. Ultimately, the success of Aluminium OS will depend on Google’s ability to offer a platform that solves tangible problems and integrates into existing workflows. Google sees AI in the form of Gemini (or localised Gemini Nano instance) powering a platform that offers integrated problem-solving. Hitting that target will generate demand, and a lower price per machine could be the decider for procurement teams. If Google gets it right, it could repeat the success it experienced in the education market with the original chromebook project, and there could be a substantial shift by enterprise fleets to Aluminium OS and Google Workspaces. There are big gains to be made for a company that dominates the mobile market worldwide and makes serious inroads into the enterprise workstation market. Plus, that elusive device convergence would be much closer to becoming a reality. (Image source: “Macro Monday : Aluminium buttons (Al on the periodic table)” by cchana 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 Aluminium OS is the AI-powered successor to ChromeOS appeared first on AI News. View the full article

According to AWS at this week’s re:Invent 2025, the chatbot hype cycle is effectively dead, with frontier AI agents taking their place. That is the blunt message radiating from Las Vegas this week. The industry’s obsession with chat interfaces has been replaced by a far more demanding mandate: “frontier agents” that don’t just talk, but work autonomously for days at a time. We are moving from the novelty phase of generative AI into a grinding era of infrastructure economics and operational plumbing. The “wow” factor of a poem-writing bot has faded; now, the cheque comes due for the infrastructure needed to run these systems at scale. Addressing the plumbing crisis at AWS re:Invent 2025 Until recently, building frontier AI agents capable of executing complex, non-deterministic tasks was a bespoke engineering nightmare. Early adopters have been burning resources cobbling together tools to manage context, memory, and security. AWS is trying to kill that complexity with Amazon Bedrock AgentCore. It’s a managed service that acts as an operating system for agents, handling the backend work of state management and context retrieval. The efficiency gains from standardising this layer are hard to ignore. Take MongoDB. By ditching their home-brewed infrastructure for AgentCore, they consolidated their toolchain and pushed an agent-based application to production in eight weeks—a process that previously ate up months of evaluation and maintenance time. The PGA TOUR saw even sharper returns, using the platform to build a content generation system that increased writing speed by 1,000 percent while slashing costs by 95 percent. Software teams are getting their own dedicated workforce, too. At re:Invent 2025, AWS rolled out three specific frontier AI agents: Kiro (a virtual developer), a Security Agent, and a DevOps Agent. Kiro isn’t just a code-completion tool; it hooks directly into workflows with “powers” (specialised integrations for tools like Datadog, Figma, and Stripe) that allow it to act with context rather than just guessing at syntax. Agents that run for days consume massive amounts of compute. If you are paying standard on-demand rates for that, your ROI evaporates. AWS knows this, which is why the hardware announcements this year are aggressive. The new Trainium3 UltraServers, powered by 3nm chips, are claiming a 4.4x jump in compute performance over the previous generation. For the organisations training massive foundation models, this cuts training timelines from months to weeks. But the more interesting shift is where that compute lives. Data sovereignty remains a headache for global enterprises, often blocking cloud adoption for sensitive AI workloads. AWS is countering this with ‘AI Factories’ (essentially shipping racks of Trainium chips and NVIDIA GPUs directly into customers’ existing data centres.) It’s a hybrid play that acknowledges a simple truth: for some data, the public cloud is still too far away. Tackling the legacy mountain Innovation like we’re seeing with frontier AI agents is great, but most IT budgets are strangled by technical debt. Teams spend roughly 30 percent of their time just keeping the lights on. During re:Invent 2025, Amazon updated AWS Transform to attack this specifically; using agentic AI to handle the grunt work of upgrading legacy code. The service can now handle full-stack Windows modernisation; including upgrading .NET apps and SQL Server databases. Air Canada used this to modernise thousands of Lambda functions. They finished in days. Doing it manually would have cost them five times as much and taken weeks. For developers who actually want to write code, the ecosystem is widening. The Strands Agents SDK, previously a Python-only affair, now supports TypeScript. As the lingua franca of the web, it brings type safety to the chaotic output of LLMs and is a necessary evolution. Sensible governance in the era of frontier AI agents There is a danger here. An agent that works autonomously for “days without intervention” is also an agent that can wreck a database or leak PII without anyone noticing until it’s too late. AWS is attempting to wrap this risk in ‘AgentCore Policy,’ a feature allowing teams to set natural language boundaries on what an agent can and cannot do. Coupled with ‘Evaluations,’ which uses pre-built metrics to monitor agent performance, it provides a much-needed safety net. Security teams also get a boost with updates to Security Hub, which now correlates signals from GuardDuty, Inspector, and Macie into single “events” rather than flooding the dashboard with isolated alerts. GuardDuty itself is expanding, using ML to detect complex threat patterns across EC2 and ECS clusters. We are clearly past the point of pilot programs. The tools announced at AWS re:Invent 2025, from specialised silicon to governed frameworks for frontier AI agents, are designed for production. The question for enterprise leaders is no longer “what can AI do?” but “can we afford the infrastructure to let it do its job?” See also: AI in manufacturing set to unleash new era of profit 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 Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post AWS re:Invent 2025: Frontier AI agents replace chatbots appeared first on AI News. 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Amazon Web Services has scored another major win for its custom AWS Trainium accelerators after striking a deal with AI video startup Decart. The partnership will see Decart optimise its flagship Lucy model on AWS Trainium3 to support real-time video generation, and highlight the growing popularity of AI accelerators over Nvidia’s graphics processing units. Decart is essentially going all-in on AWS, and as part of the deal, the company will also make its models available through the Amazon Bedrock platform. Developers can integrate Decart’s real-time video generation capabilities into almost any cloud application without worrying about underlying infrastructure. The distribution through Bedrock increases AWS’s plug-and-play capabilities, demonstrating Amazon’s confidence in growing demand for real-time AI video. It also allows Decart to expand reach and grow adoption among the developer community. AWS Trainium provides Lucy with the extra processing grunt needed to generate high-fidelity video without sacrificing quality or latency. Custom AI accelerators like Trainium provide an alternative to Nvidia’s GPUs for AI workloads. While Nvidia still dominates the AI market, its GPUs processing the vast majority of AI workloads, it’s facing a growing threat from custom processors. Why all the fuss over AI accelerators? AWS Trainium isn’t the only option developers have. Google’s Tensor Processing Unit (TPU) product line and Meta’s Training and Inference Accelerator (MTIA) chips are other examples of custom silicon, each having a similar advantage over Nvidia’s GPUs – their ASIC architecture (Application-Specific Integrated Circuit). As the name suggests, ASIC hardware is engineered specifically to handle one kind of application and do so more efficiently than general purpose processors. While central processing units are generally considered to be the Swiss Army knife of the computing world due to their ability to handle multiple applications, GPUs are more akin to a powerful electric drill. They’re vastly more powerful than CPUs, designed to process massive amounts of repetitive, parallel computations, making them suitable for AI applications and graphics rendering tasks. If the GPU is a power drill, the ASIC might be considered a scalpel, designed for extremely precise procedures. When building ASICs, chipmakers strip out all functional units irrelevant to the task for greater efficiency – all their operations are dedicated to the task. This yields massive performance and energy efficiency benefits compared to GPUs, and may explain their growing popularity. A case in point is Anthropic, which has partnered with AWS on Project Rainier, an enormous cluster made up of hundreds of thousands of Trainium2 processors. Anthropic says that Project Rainier will provide it with hundreds of exaflops of computing power to run its most advanced AI models, including Claude Opus-4.5. The AI coding startup Poolside is also using AWS Trainium2 to train its models, and has plans to use its infrastructure for inference as well in future. Meanwhile, Anthropic is hedging its bets, also looking to train future Claude models on a cluster of up to one million Google TPUs. Meta Platforms is reportedly collaborating with Broadcom to develop a custom AI processor to train and run its Llama models, and OpenAI has similar plans. The Trainium advantage Decart chose AWS Trainium2 due to its performance, which let Decart achieve the low latency required by real-time video models. Lucy has a time-to-first-frame of 40ms, meaning that it begins generating video almost instantly after prompt. By streamlining video processing on Trainium, Lucy can also match the quality of much slower, more established video models like OpenAI’s Sora 2 and Google’s Veo-3, with Decart generating output at up to 30 fps. Decart believes Lucy will improve. As part of its agreement with AWS, the company has obtained early access to the newly announced Trainium3 processor, capable of outputs of up to 100 fps and lower latency. “Trainium3’s next-generation architecture delivers higher throughput, lower latency, and greater memory efficiency – allowing us to achieve up to 4x faster frame generation at half the cost of GPUs,” said Decart co-founder and CEO Dean Leitersdorf in a statement. Nvidia might not be too worried about custom AI processors. The AI chip giant is reported to be designing its own ASIC chips to rival cloud competitors’. Moreover, ASICs aren’t going to replace GPUs completely, as each chip has its own strengths. The flexibility of GPUs means they remain the only real option for general-purpose models like GPT-5 and Gemini 3, and are still dominant in AI training. However, many AI applications have stable processing requirements, meaning they’re particularly suited to running on ASICs. The rise of custom AI processors is expected to have a profound impact on the industry. By pushing chip design towards greater customisation and enhancing the performance of specialised applications, they’re setting the stage for a new wave of AI innovation, with real-time video at the forefront. Photo courtesy AWS re:invent The post Decart uses AWS Trainium3 for real-time video generation appeared first on AI News. View the full article

In the basement of a Boise, Idaho, dental office in 1978, four engineers founded what would become one of America’s semiconductor giants. Ward Parkinson, Joe Parkinson, Dennis Wilson, and Doug Pitman started Micron Technology as a modest design consultancy, backed by local investors including potato magnate J.R. Simplot. By 1983, they had achieved a technological breakthrough—producing chips roughly half the size of Japan’s leading products. Nearly five decades later, that same company has made a decision that crystallises artificial intelligence’s profound impact on hardware economics: AI memory hunger is forcing manufacturers to abandon entire market segments. On December 3, 2025, Micron announced it would completely exit the consumer memory market, discontinuing its 29-year-old Crucial brand by February 2026. “The AI-driven growth in the data centre has led to a surge in demand for memory and storage,” said Sumit Sadana, Micron’s Executive Vice President and Chief Business Officer. “Micron has made the difficult decision to exit the Crucial consumer business to improve supply and support for our larger, strategic customers in faster-growing segments.” Translation: data centres running AI workloads will pay substantially more for memory than individual consumers ever could, and Micron’s fabrication capacity cannot serve both markets simultaneously. The announcement represents more than a business decision—it’s a watershed moment revealing how AI memory hunger demands are fundamentally restructuring global semiconductor supply chains and forcing manufacturers to make stark choices about which customers deserve access to finite production capacity. The economics driving AI memory hunger Micron’s withdrawal reflects brutal economic realities. As the world’s third-largest DRAM producer with approximately 20%global market share, the company sits between South Korean giants Samsung Electronics (43%) and SK Hynix (35%). Together, these three manufacturers control roughly 95% of worldwide DRAM production—an oligopoly now facing unprecedented demand from AI infrastructure builders. The margin differentials tell the story. Consumer RAM modules compete in volatile retail markets with razor-thin profitability. Enterprise contracts for high-bandwidth memory (HBM) used in AI accelerators and DDR5 modules for data centre servers deliver substantially higher average selling prices, multi-year commitments, and predictable demand. For memory manufacturers, each fabrication wafer committed to consumer products represents foregone revenue from higher-value enterprise contracts—an opportunity cost that has become economically indefensible as AI demand accelerates. The numbers illustrate the magnitude of the shift. Micron reported record fiscal 2025 revenue of US$37.38 billion, representing nearly 50% year-over-year growth driven primarily by data centre and AI applications, which accounted for 56% of total revenue. SK Hynix has reportedly sold out its entire 2026 production capacity for DRAM, HBM, and NAND products. Consumer memory prices have surged accordingly. DRAM spot prices increased 172% year-over-year as of Q3 2025, with retail prices for 32GB DDR5 modules jumping 163-619% across global markets since September 2025. Component suppliers report paying US$13 for 16GB DDR5 chips that cost US$7 just six weeks earlier—increases sufficient to eliminate entire gross margins for third-party brands. Consumer market restructuring amid AI memory hunger Micron’s exit fundamentally alters the consumer memory landscape. Third-party brands, including Corsair, G.Skill, Kingston, and ADATA, source their DRAM chips from the major manufacturers. With Micron withdrawing entirely, these vendors must compete more aggressively for allocation from Samsung and SK Hynix—both simultaneously prioritising high-bandwidth memory production for AI accelerators. The concentration creates vulnerabilities. Samsung and SK Hynix now comprise the only major suppliers serving both consumer and enterprise markets directly. Both face identical capacity allocation pressures. If AI infrastructure investment maintains current trajectories, additional manufacturers may reduce or restructure consumer operations. Supply chain constraints are already materialising beyond DRAM. NAND flash wafer contract prices increased by over 60% in November 2025. Graphics memory markets face pressures as manufacturers shift to GDDR7 for next-generation GPUs, creating GDDR6 shortages that inflated prices by approximately 30%. Hard drive manufacturers increased prices 5-10% citing limited supply. For consumers and small businesses, the implications extend beyond pricing. Product availability may become increasingly constrained during peak demand periods. The reduction in direct supplier participation may compress product differentiation and limit competitive pricing dynamics that previously benefited buyers. The broader industry realignment Micron’s consumer exodus signals a structural transformation rather than a temporary reallocation. The AI infrastructure ***** differs fundamentally from previous technology transitions. Personal computing, internet expansion, and mobile devices created sustained memory demand over decades with gradual capacity adjustments. AI infrastructure deployment compresses that timeline dramatically—hyperscale operators are committing hundreds of billions in data centre construction over just a few years. Data centre semiconductor markets illustrate the scale. The total addressable market reached US$209 billion in 2024, projected to grow to nearly US$500 billion by 2030, driven primarily by AI and high-performance computing. GPU revenue alone is forecast to expand from US$100 billion in 2024 to US$215 billion by 2030, with each GPU requiring substantial high-bandwidth memory allocation. Memory architecture evolution compounds the challenge. AI training workloads increasingly require HBM3E modules offering superior bandwidth and power efficiency. Inference workloads demand DDR5 with tight latency specifications. Automotive applications adopting zonal architectures require multi-gigabyte DRAM configurations. Each application commands premium pricing and long-term contracts—economic incentives systematically pulling manufacturing capacity away from consumer markets. The manufacturing response reflects these priorities. Samsung is advancing 1c DRAM production and planning mass production of HBM4 in 2025 while phasing out DDR4 entirely. Micron began mass production of DRAM using Extreme Ultraviolet (EUV) lithography in 2025. SK Hynix focuses development resources on HBM and advanced LPDDR solutions. All three manufacturers are directing research and capital investment toward applications offering superior returns. What this means for enterprise buyers Enterprise procurement teams face their own challenges as memory markets restructure. Memory represents 10-25% of bill-of-materials costs for typical servers and commercial PCs. Price increases of 20-30% in memory components translate to 5-10% increases in total system costs, compounding into millions in additional expenditure for organisations procuring at scale. Strategic responses include forward purchasing agreements, establishing stronger direct relationships with manufacturers, and diversifying vendor partnerships. The timing uncertainty presents particular challenges. New fabrication capacity is under construction, supported by government incentives, but requires years to reach production readiness. Critical questions ahead Micron’s consumer market exit raises fundamental questions. Will Samsung and SK Hynix maintain consumer product lines, or will similar capacity pressures force comparable reductions? If consumer memory becomes primarily a third-party brand market sourcing chips from manufacturers prioritising enterprise customers, what happens to product innovation and competitive pricing? The concentration among just two major manufacturers serving consumer markets creates potential vulnerabilities. Supply chain disruptions affecting either Samsung or SK Hynix would have an outsized impact on global consumer product availability. Broader implications extend to technology accessibility. If memory pricing remains elevated or availability constrained for consumer products, the costs of personal computing and small business infrastructure increase accordingly, potentially widening digital divides. Micron’s decision crystallises artificial intelligence’s role as a transformative force reshaping not just software, but the fundamental economics of hardware manufacturing. The Crucial brand’s retirement after 29 years marks the end of an era when memory manufacturers could profitably serve both consumer and enterprise segments simultaneously. For the broader technology ecosystem, AI memory hunger has become the semiconductor industry’s dominant growth driver, commanding resources at levels that fundamentally alter which markets manufacturers choose to serve. (Photo: Micron Technology) See also: AI memory demand propels SK Hynix to historic DRAM market leadership 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 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 memory hunger forces Micron’s consumer exodus: A turning point in semiconductor economics appeared first on AI News. View the full article