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The launch of PubMatic’s AgenticOS marks a change in how artificial intelligence is being operationalised in digital advertising, moving agentic AI from isolated experiments into a system-level capability embedded in programmatic infrastructure. For marketing leaders managing seven-figure budgets in media environments, the implications are practical not theoretical, implying faster decision cycles and a re-balance of human effort to strategy and differentiation. Programmatic advertising promises efficiency, but in practice accumulates operational complexity. Campaigns span formats, devices, data partnerships, and regulatory constraints, which make manual optimisation problematic. PubMatic is positioning AgenticOS as a response to such pressure, presenting it as an ‘operating system’ that allows multiple AI agents to transact and optimise campaigns inside human-defined objectives, and with what company-defined guardrails. AgenticOS acts across infrastructure and applications to coordinate decisions. This aligns with current research trends showing that agentic systems outperform single-model automation in contexts where campaign tasks trade-off cost, performance, and risk analysis that are inherent in media buying. Cost reduction through operational compression For medium to large organisations, marketing cost rises are driven by operational overhead rather than media prices. PubMatic reports early tests where agent-led campaigns reduced setup time by 87% and issue resolution by 70%. Even allowing for bias, these figures are consistent with studies of AI-assisted workflow automation in enterprise marketing. Typically, these find 30–50% reductions in manual labour in planning and reporting. The near-term opportunity for budget holders is not headcount reduction necessarily, but capacity gains. Agentic systems absorb decision load—bid adjustments, pacing changes, and inventory discovery. This lets teams run more campaigns concurrently or redirect effort to activities like experimentation and testing. Decision quality at scale AgenticOS’s claim is that it enables continuous decision-making without fragmentation, significant as most marketing inefficiency arises from delayed or inconsistent execution, not poor strategy. Human teams operate in reporting cycles, while agentic systems operate in seconds. Research into real-time optimisation suggests marginal gains at auction level can compound with large spends. At enterprise level, even low single-digit percentage improvements in effective CPM or conversion efficiency translate can have budgetary impact. Agentic AI does not eliminate the need for human judgement, but changes where and when judgement is made. Instead of reactive troubleshooting, teams define objectives, constraints, and success goal definitions. Governance, control, and brand safety A persistent concern among senior marketers is loss of control to agentic processes. PubMatic states AgenticOS works from advertisers’ objectives, brand-safety rules, and creative parameters, with agents operating inside those boundaries. This reflects a wider industry consensus that agentic AI adoption will only scale where governance is embedded at system level rather than bolted on. For decision-makers, the practical lesson is to invest early in codifying marketing intent, detailing performance hierarchies, set brand constraints, and escalation thresholds. Organisations that treat agentic AI as a strategic execution layer, rather than a ****** box, are likely to realise benefits faster and with lower risk. Predictions for the next 24 months Evidence from adjacent enterprise functions such as supply chain, finance, and customer support suggest three likely developments: First, agentic AI will become a standard execution layer in programmatic advertising, with a shift from automation to high-quality intent modelling and agent coordination. Second, marketing operating models will flatten, with smaller teams managing large, more complex portfolios. Senior marketers will spending more time on scenario planning and less on day-to-day campaign mechanics. Third, vendors offering system-level agentic platforms (not isolated point solutions) will be able to deliver ROI, as cost savings and performance gains compound across the workflow rather than at isolated points. Practical advice for marketing leaders Marketing decision-makers could regard AgenticOS and similar platforms as infrastructure investment. Pilot programmes should focus on high-volume, rules-based campaigns where efficiency gains are easier to measure. Success can be evaluated on performance metrics and time saved. Most importantly, internal preparation is of paramount importance. The more precisely objectives and constraints are defined, the more effectively autonomous systems will operate. In this sense, the adoption of agentic AI is as much an organisational discipline challenge than a technological one. PubMatic’s AgenticOS illustrates agentic AI in marketing entering operational phases. The question is how quickly organisations can adapt their processes to take advantage of the technology. Those that do are likely to see lower costs and more effective use of marketing spend in increasingly complex media environments. (Image source: “market” by star-one 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 What PubMatic’s AgenticOS signals for enterprise marketing appeared first on AI News. View the full article

Contract work has evolved to touch privacy, security, revenue recognition, data residency, vendor risk, renewals and numerous internal approvals. At the same time, teams are expected to turn agreements around faster and keep every signed obligation visible after signature. Artificial intelligence is becoming a practical layer in this process. It can read language at scale, pull key terms into structured fields, highlight unusual clauses, and support quicker hand-offs between legal departments and the business. Five tools stand out in completing these processes efficiently. What makes a contract management provider a good choice Choosing a contract management service is partly a software decision and partly a long-term operating commitment. Contract processes change as the business evolves, so the right provider needs to support iterations, integration and adoption in teams. Tools that deliver will have certain minimum capabilities. Scalability and flexibility: A good platform can adapt to users’ unique workflows and scale with a business as the contract volume and complexity grow. User adoption focus: The interface should be intuitive for all types of users, covering legal, sales and procurement teams, to ensure company-wide adoption. Security and support: Look for providers with proven security credentials and a reputation for excellent training and client support. Integration ecosystem: The tool must connect seamlessly with a company’s existing software stack to ensure smooth data flow and a single source of truth. The best AI-powered tools for managing contracts Five tools stand out based on four criteria. These are contract AI capabilities in daily workflows, ease of rollout for legal and business users, integration readiness, and how well each supports post-signature work, like reporting, renewals and obligation tracking. 1. Agiloft Agiloft has established a reputation in contract life cycle management (CLM) for its configurability and approach to treating data as an asset. That matters in real environments where sales, procurement, finance and legal all need different views of the same agreement, and where approvals and clause positions shift over time. On the AI side, Agiloft’s AI Core extracts and analyses contract data in the life cycle, helping teams turn documents into searchable fields and automation triggers. It also uses its own ConvoAI, a conversational experience for asking questions about the contract repository in natural language. Its no-code model is also commendable. Contract teams routinely need to adjust intake forms, approval routing, renewal steps and reporting fields. No-code configuration reduces dependence on IT tickets for daily workflow updates. It also emphasises integrations that connect CLM platforms to systems used by other businesses, as outlined in offerings like its Integration Hub. 2. Ironclad Ironclad is a strong fit for teams that want contracting to feel approachable for the business. Legal may own the guardrails, but sales and procurement usually own the urgency. The company has invested heavily in keeping negotiation, approvals and version management inside one workspace, so the process does not fracture into email threads and scattered attachments. Ironclad’s AI Assist is designed to accelerate review by flagging risky or off-standard clauses and helping legal focus on the parts that truly need judgement. It also leans into repeatable processes, like templates, workflows and structured steps for drafting through signature. When those are used consistently, AI review becomes more effective because the system sees similar patterns in many agreements. 3. Icertis Icertis is built for enterprise complexity. Global users often need contracting to support multiple regions and languages, layered approval guidelines and integrations that connect contract commitments to other teams. Its core concept is contract intelligence through the Icertis Contract Intelligence platform, which aims to connect terms to business processes. This way, obligations and entitlements do not stay trapped in PDFs. Another notable feature is its depth of integration. Enterprise contract teams tend to care less about where contracts are stored and more about whether renewals, price holds, compliance terms and supplier obligations can be reflected in the systems. 4. LinkSquares LinkSquares shines in post-signature contract work. A company can have a solid signature process and still struggle when leadership asks for a portfolio view of renewals, indemnity positions, liability caps or security commitments in vendors. It uses AI to read executed agreements and extract key information into fields that can be searched and reported. Its Smart Values feature is a concrete example. It pulls common terms, dates and clause types, so legal teams can monitor them in large sets of agreements without manual tagging. Instead of opening dozens of documents to answer a single stakeholder request, teams can search in the database and generate reports that support the needs of various teams. 5. ContractPodAi ContractPodAi is positioned as a broader legal platform with CLM at the centre, aiming to cover drafting, review, repository management and reporting in one place. It is often evaluated by teams looking for a single system for legal operations workflows, along with contract life cycle steps. A notable AI element is Leah, a generative AI legal assistant that supports summarisation and review-oriented help, among other tasks. Used well, this kind of assistant can speed up intake triage and help prepare quick summaries for business owners, while legal retains responsibility for final decisions. The tool also has clause detection and risk analysis capabilities, as well as dashboards for monitoring obligations after signature. Comparing the top AI tools for contract management Here’s an at-a-glance look at each provider’s key features and strengths. ToolKey AI featureCore strengthBest forAgiloftConversational AI (ConvoAI)No-code capabilityHigh-growth companies needing flexibilityIroncladAutomated clause review (AI Assist)User-friendly workflow automationTeams prioritising ease of use and collaborationIcertisEnterprise-wide data connectivityEnterprise-scale and ERP integrationLarge, global enterprisesLinkSquaresPost-signature data extractionPowerful search and analyticsIn-house legal teams needing post-signature analysisContractPodAiGenerative AI legal assistant (Leah)End-to-end life cycle automationMid-to-large companies wanting an all-in-one legal hub Choosing the right AI CLM for better visibility and control Contract AI pays off when it reduces repetition, keeps executed terms searchable, and makes renewals and obligations easier to manage in the business. Choosing the best AI-powered contract management tool depends on contract volume, workflow, integration requirements and how much time the team spends answering post-signature questions. Image source: Unsplash The post 5 AI-powered tools streamlining contract management today appeared first on AI News. View the full article

AI chip shortage became the defining constraint for enterprise AI deployments in 2025, forcing CTOs to confront an uncomfortable reality: semiconductor geopolitics and supply chain physics matter more than software roadmaps or vendor commitments. What began as US export controls restricting advanced AI chips to China evolved into a broader infrastructure crisis affecting enterprises globally—not from policy alone, but from explosive demand colliding with manufacturing capacity that cannot scale at software speed. By year’s end, the dual pressures of geopolitical restrictions and component scarcity had fundamentally reshaped enterprise AI economics. The numbers tell a stark story. Average enterprise AI spending is forecasted at US$85,521 monthly in 2025, up 36% from 2024, according to CloudZero’s research surveying 500 engineering professionals. Organisations planning to invest over US$100,000 monthly more than doubled from 20% in 2024 to 45% in 2025—not because AI became more valuable, but because component costs and deployment timelines spiralled beyond initial projections. Export controls reshape chip access The Trump administration’s December 2025 decision to allow conditional sales of Nvidia’s H200 chips to China—the most powerful AI chip ever approved for export—illustrated how quickly semiconductor policy can shift. The arrangement requires a 25% revenue share with the US government and applies only to approved ******** buyers, reversing an earlier April 2025 export freeze. Yet the policy reversal came too late to prevent widespread disruption. US Commerce Secretary Howard Lutnick testified that China’s Huawei will produce only 200,000 AI chips in 2025, while China legally imported around one million downgraded Nvidia chips designed specifically for export compliance. The production gap forced ******** companies into large-scale smuggling operations—federal prosecutors unsealed documents in December revealing a ring that attempted to export at least US$160 million worth of Nvidia H100 and H200 GPUs between October 2024 and May 2025. For global enterprises, these restrictions created unpredictable procurement challenges. Companies with China-based operations or data centres faced sudden access limitations, while others discovered their global deployment plans assumed chip availability that geopolitics no longer guaranteed. Memory chip crisis compounds AI infrastructure pain While export controls dominated headlines, a deeper supply crisis emerged: memory chips became the binding constraint on AI infrastructure globally. High-bandwidth memory (HBM), the specialised memory that enables AI accelerators to function, hit severe shortages as manufacturers Samsung, SK Hynix, and Micron operated near full capacity while reporting six-to twelve-month lead times. Memory prices surged accordingly. DRAM prices climbed over 50% in 2025 in some categories, with server contract prices up as much as 50% quarterly, according to Counterpoint Research. Samsung reportedly lifted prices for server memory chips by 30% to 60%. The firm forecasts memory prices to continue rising another 20% in early 2026 as demand continues outpacing capacity expansion. The shortage wasn’t limited to specialised AI components. DRAM supplier inventories fell to two to four weeks by October 2025, down from 13-17 weeks in late 2024, per TrendForce data cited by Reuters. SK Hynix told analysts that shortages may persist until late 2027, reporting that all memory scheduled for 2026 production is already sold out. Enterprise AI labs experienced this firsthand. Major cloud providers Google, Amazon, Microsoft, and Meta issued open-ended orders to Micron, stating they will take as much inventory as the company can provide. ******** firms Alibaba, Tencent, and ByteDance pressed Samsung and SK Hynix for priority access. The pressure extended into future years, with OpenAI signing preliminary agreements with Samsung and SK Hynix for its Stargate project requiring up to 900,000 wafers monthly by 2029—roughly double today’s global monthly HBM output. Deployment timelines stretch beyond projections The AI chip shortage didn’t just increase costs—it fundamentally altered enterprise deployment timelines. Enterprise-level custom AI solutions that typically required six to twelve months for full deployment in early 2025 stretched to 12-18 months or longer by year-end, according to industry analysts. Bain & Company partner Peter Hanbury, speaking to CNBC, noted utility connection timelines have become the biggest constraint on data centre growth, with some projects facing five-year delays just to secure electricity access. The firm forecasts a 163GW rise in global data centre electricity demand by 2030, much of it linked to generative AI’s intensive compute requirements. Microsoft CEO Satya Nadella captured the paradox in stark terms: “The biggest issue we are now having is not a compute glut, but its power—it’s the ability to get the builds done fast enough close to power. If you can’t do that, you may actually have a bunch of chips sitting in inventory that I can’t plug in. In fact, that is my problem today.” Traditional tech buyers in enterprise environments faced even steeper challenges. “Buyers in this environment will have to over-extend and make some bets now to secure supply later,” warned Chad Bickley of Bain & Company in a March 2025 analysis. “Planning ahead for delays in production may require buyers to take on some expensive inventory of bleeding-edge technology products that may become obsolete in short order.” Hidden costs compound budget pressures The visible price increases—HBM up 20-30% year-over-year, GPU cloud costs rising 40-300% depending on region—represented only part of the total cost impact. Organisations discovered multiple hidden expense categories that vendor quotes hadn’t captured. Advanced packaging capacity emerged as a critical bottleneck. TSMC’s CoWoS packaging, essential for stacking HBM alongside AI processors, was fully booked through the end of 2025. Demand for this integration technique exploded as wafer production increased, creating a secondary choke point that added months to delivery timelines. Infrastructure costs beyond chips escalated sharply. Enterprise-grade NVMe SSDs saw prices climb 15-20% compared to a year earlier as AI workloads required significantly higher endurance and bandwidth than traditional applications. Organisations planning AI deployments found their bill-of-materials costs rising 5-10% from memory component increases alone, according to Bain analysis. Implementation and governance costs compounded further. Organisations spent US$50,000 to US$250,000 annually on monitoring, governance, and enablement infrastructure beyond core licensing fees. Usage-based overages caused monthly charges to spike unexpectedly for teams with high AI interaction density, particularly those engaging in heavy model training or frequent inference workloads. Strategic lessons for 2026 and beyond Enterprise leaders who successfully navigated 2025’s AI chip shortage emerged with hard-won insights that will shape procurement strategy for years ahead. Diversify supply relationships early: Organizations that secured long-term supply agreements with multiple vendors before shortages intensified maintained more predictable deployment timelines than those relying on spot procurement. Budget for component volatility: The era of stable, predictable infrastructure pricing has ended for AI workloads. CTOs learned to build 20-30% cost buffers into AI infrastructure budgets to absorb memory price fluctuations and component availability gaps. Optimise before scaling: Techniques like model quantisation, pruning, and inference optimisation cut GPU needs by 30-70% in some implementations. Organisations that invested in efficiency before throwing hardware at problems achieved better economics than those focused purely on procurement. Consider hybrid infrastructure models: Multi-cloud strategies and hybrid setups combining cloud GPUs with dedicated clusters improved reliability and cost predictability. For high-volume AI workloads, owning or leasing infrastructure increasingly proved more cost-effective than renting cloud GPUs at inflated spot prices. Factor geopolitics into architecture decisions: The rapid policy shifts around chip exports taught enterprises that global AI infrastructure can’t assume stable regulatory environments. Organisations with China exposure learned to design deployment architectures with regulatory flexibility in mind. The 2026 outlook: Continued constraints The supply-demand imbalance shows no signs of resolving quickly. New memory chip factories take years to build—most capacity expansions announced in 2025 won’t come online until 2027 or later. SK Hynix guidance suggests shortages persisting through at least late 2027. Export control policy remains fluid. A new “Trump AI Controls” rule to replace earlier frameworks is expected later in 2025, along with potential controls on exports to Malaysia and Thailand identified as diversion routes for China. Each policy shift creates new procurement uncertainties for global enterprises. The macroeconomic implications extend beyond IT budgets. Memory shortages could delay hundreds of billions in AI infrastructure investment, slowing productivity gains that enterprises have bet on to justify massive AI spending. Rising component costs threaten to add inflationary pressure at a moment when global economies remain sensitive to price increases. For enterprise leaders, 2025’s AI chip shortage delivered a definitive lesson: software moves at digital speed, but hardware moves at physical speed, and geopolitics moves at political speed. The gap between those three timelines defines what’s actually deployable—regardless of what vendors promise or roadmap projects. The organisations that thrived weren’t those with the biggest budgets or the most ambitious AI visions. They were the ones who understood that in 2025, supply chain reality trumped strategic ambition—and planned accordingly. (Photo by Igor Omilaev/Unsplash) See also: Can the US really enforce a global AI chip ban? Want to learn more about AI and big data from industry leaders? Check outAI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post 2025’s AI chip wars: What enterprise leaders learned about supply chain reality appeared first on AI News. View the full article

Producing digital advertising at global scale has become less about one standout campaign and more about volume, speed, and consistency. For consumer brands operating across dozens of markets, the challenge is not creativity alone, but how to keep content flowing without repeating expensive production cycles. That pressure is pushing some large companies to test where AI fits inside everyday marketing work. At L’Oréal, AI-generated creative tools are being used to support parts of the digital advertising process, particularly video and visual content. The aim is not to replace human teams, but to reduce friction in a system that demands constant refresh. The shift offers a useful view into how enterprise AI adoption is unfolding in creative functions, where speed and control matter as much as originality. Scaling content without scaling production For a global beauty group, digital advertising is no longer a seasonal exercise. Content is needed continuously across social platforms, ecommerce sites, and regional campaigns, often with small variations in language, format, or visual emphasis. Traditional production models struggle to keep up. Each new asset typically involves planning, filming, editing, and approvals. AI-generated images and video elements allow you to reuse old content and extend it into new formats without having to start from scratch every time. At L’Oréal, AI tools are being used to help generate or adapt visual content that fits specific digital channels. This includes polishing footage, modifying formats, and creating versions for different platforms. Human teams continue to monitor creative direction and final output, but AI speeds up the time between idea and delivery. The practical value is not about producing something altogether new. It is about producing enough usable content to meet the pace of digital advertising. Why L’Oréal keeps AI under tight creative control One reason large brands move cautiously with AI in creative work is brand risk. Visual identity, tone, and messaging are tightly regulated, and small inconsistencies can be amplified when content is distributed at scale. Rather than handing over creative decisions, companies like L’Oréal are using AI as a support layer. AI-generated output is examined, adjusted, and approved using existing workflows. This keeps accountability with internal teams and external agencies, while still gaining efficiency. This approach reflects a broader pattern in enterprise AI adoption. Tools are being introduced into workflows that already exist, rather than reshaping how decisions are made. In marketing, that often means AI assists with production, not with defining brand voice. Cost, speed, and repeatability Digital advertising budgets are under pressure, even for large consumer groups. Media prices fluctuate, platforms change their restrictions, and audiences expect constant updates. AI offers a way to absorb some of that pressure by lowering the marginal cost of producing additional assets. By reusing footage and applying AI-based enhancements, brands can stretch the value of each shoot. This is especially important in areas where campaigns must be quickly changed, or when local teams want specific assets but lack full-scale production support. The result is not a dramatic cost cut in one area, but incremental savings across hundreds of minor decisions. Over time, those savings shape how marketing teams plan campaigns and allocate expenditures. What this says about enterprise AI maturity L’Oréal’s use of AI-generated creative work is less about experimentation and more about operational fit. The tools are used in situations where output is predictable, quality can be measured, and mistakes may be caught before release. This mirrors how AI is being adopted across many enterprise functions. Instead of broad, open-ended use, companies are identifying narrow tasks where AI can reliably assist without introducing new risk. In marketing, those tasks often sit between creative concept and final distribution. The approach also emphasises a key constraint. AI works best in environments with existing data, rules, and review processes. Creative freedom still belongs to people, while AI supports scale. Implications for marketing teams For marketing leaders, the lesson is not that AI will replace agencies or internal creatives. It is that production models built for slower cycles are becoming harder to sustain. Teams are being asked to deliver more content, more often, with tighter budgets and faster turnaround. AI tools offer one way to manage that demand, but only if they fit existing controls and expectations. This places new demands on governance. Marketing teams need clear rules on where AI can be used, how outputs are reviewed, and who remains accountable for final decisions. Without that structure, efficiency gains can quickly be offset by risk. What L’Oréal’s approach signals for enterprise AI adoption What stands out in L’Oréal’s approach is restraint. AI is applied where it reduces friction, not where it reshapes the role of creative teams. That makes it easier to integrate into large organisations with established processes and brand safeguards. As more enterprises look to AI for productivity gains, similar patterns are emerging. AI becomes part of the workflow, not the headline. Success is measured in time saved and consistency maintained, not in novelty. For now, AI-generated creative work remains a supporting act in enterprise marketing. Its real impact lies in how quietly it changes the economics of content production, one asset at a time. (Photo by Helio E. López Vega) See also: Disney is embedding generative AI into its operating model Want to learn more about AI and big data from industry leaders? Check outAI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post L’Oréal brings AI into everyday digital advertising production appeared first on AI News. View the full article

Modern software delivery depends on the reliability, integrity, and security of container images. As organisations migrate to microservices, automated CI/CD pipelines, and multi-cloud architectures, the container image becomes more than a packaging mechanism, it becomes a security boundary. A single vulnerability embedded in an image can replicate in clusters, environments, and deployments, creating widespread risk for applications that rely on speed and repeatability. Security-forward organisations are increasingly shifting from general-purpose base images to secure-by-design, minimal, or enterprise-maintained images that provide strong guarantees around trust, provenance, and vulnerability management. The industry has witnessed a significant increase in attacks targeting software supply chains, open-source dependencies, or compromised image registries. As a result, engineering teams are prioritising container security earlier in the build process, selecting image foundations that minimise the need for downstream mitigation and maximise confidence before deployment. The 3 best secure container images for modern applications The landscape of secure container images has evolved rapidly, and modern development teams now seek images that reduce vulnerabilities, enhance performance, and support predictable operations. The three platforms below represent the strongest options in 2025, offering different paths to security: source-level reconstruction, extreme minimalism, and long-term stability. 1. Echo Echo represents one of the most advanced evolutions in secure container images. Instead of attempting to scan, patch, or incrementally improve existing base images, Echo rebuilds them entirely from source, producing images that are free from known vulnerabilities from the get-go. The zero-CVE image model enables organisations to begin each deployment with a verified clean foundation, reducing the remediation burden associated with container maintenance. What sets Echo apart is its AI-powered automated lifecycle approach. As new vulnerabilities are disclosed, Echo’s purpose-built AI agents detect dependencies impacted by the CVE, regenerate the affected images, and deliver updated versions back to the organisation’s registry without the need for manual intervention. This approach dramatically shortens exposure windows and ensures continuous alignment with security benchmarks, even in highly dynamic environments. Echo is ideal for enterprises that cannot tolerate prolonged CVE exposure, like financial platforms, healthcare providers, SaaS vendors, and important infrastructure operators. It transforms container image security from a reactive process into a proactive, automated practice. Key features Source-level reconstruction to remove vulnerabilities entirely Automated patch regeneration with strict SLAs Strong governance and policy controls Broad runtime and language support Seamless pipeline integration for frictionless adoption 2. Google Distroless Google Distroless is built on the principle of extreme minimalism. Whereas traditional images include shells, package managers, and utility libraries, Distroless images contain only the dependencies required for an application to run. Nothing more. The design philosophy significantly reduces the attack surface and limits the number of components that could be compromised. Distroless also offers strong alignment with modern DevOps and SRE practices. By removing unnecessary system-level functionality, Distroless encourages clean application packaging and ensures that teams explicitly define the dependencies required for execution. The approach reduces ambiguity and improves reliability when reproducing builds in environments. Key features Minimal composition eliminates unnecessary libraries and utilities Reduced attack surface compared to traditional images Immutable-by-design infrastructure for safer deployments Performance improvements through reduced image size Stronger dependency clarity in application packaging 3. Ubuntu Containers Ubuntu Containers focus on stability, predictability, and long-term maintenance. Canonical’s Ubuntu distributions have long been respected for their balance of usability and robustness, and their containerised versions offer an equally compelling solution for teams that require reliable and well-supported base images. Unlike minimalist images that reduce functionality, Ubuntu provides a complete, fully featured environment that supports a broad range of software ecosystems. This compatibility makes it easier for teams to run applications with complex dependencies without needing major adjustments to package configurations. Key features Long-term, predictable security updates through Canonical LTS Broad software compatibility in languages, libraries, and frameworks Enterprise-focused security enhancements including compliance alignment Extensive community and vendor support Stable and reliable behaviour in heterogeneous environments Broader considerations when evaluating secure container images Choosing the right secure container image is not simply a technical preference, it is a strategic decision that affects every stage of the software lifecycle. Modern organisations should evaluate image options based on several broader criteria that extend beyond immediate functionality. Security posture and vulnerability management Organisations should assess whether an image requires reactive vulnerability patching or offers proactive vulnerability elimination. Images with automated security maintenance reduce operational overhead and lower exposure risk. Minimalism vs.completeness Minimal images reduce attack surface but may require application adjustments. Full-featured images simplify compatibility but introduce more dependencies. The right choice depends on workload complexity and team expertise. Operational consistency A secure image should behave reliably in testing, staging, and production environments. Stability is a foundation for predictable deployments and reduced debugging time. Compliance alignment Security teams must ensure that base images support compliance frameworks, particularly in regulated industries. Vendor-backed images often provide stronger audit trails and lifecycle guarantees. Ecosystem compatibility Base images should integrate well with Kubernetes, CI/CD pipelines, observability tools, and automation systems. Maintainability over time Modern applications evolve continuously, so image choices should support sustainable upgrades, long-term support horizons, and clear documentation. The evaluative principles help ensure that organisations select the image foundation that best aligns with their strategic goals. Final thoughts Secure container images are essential for maintaining resilience in cloud-native architectures. While Bitnami and other curated image providers offer convenience, modern applications require a deeper focus on image integrity, vulnerability management, and runtime safety. Echo, Google Distroless, and Ubuntu Containers represent three powerful approaches to secure container design, each suited to different organisational needs. Together, these three platforms form a robust foundation for teams striving to build secure, scalable, and reliable modern applications. Image source: Unsplash The post 3 best secure container images for modern applications appeared first on AI News. 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Artificial intelligence and big data are reshaping digital marketing by providing new insights into consumer behaviour. The technologies allow marketers to create more personalised and effective strategies. As the digital world evolves, businesses must adapt to stay competitive. Rainmaker is an AI marketing agency that uses artificial intelligence and big data to enhance digital marketing strategies. As an AI-powered digital marketing and performance agency, Rainmaker exists to help businesses navigate the complexities of the digital landscape. By using advanced technologies, Rainmaker delivers tailored marketing solutions that drive measurable outcomes for its clients. The impact of AI and big data on marketing AI and big data have a profound impact on marketing by enabling the analysis of large datasets to uncover patterns and trends. The capability allows marketers to gain a deeper understanding of consumer preferences and behaviours. With these insights, an AI SEO company can tailor its marketing strategies to better meet the needs of its target audience, resulting in improved engagement and conversion rates. Moreover, AI-driven tools can automate various marketing tasks, like content creation and campaign management, freeing up time for marketers to focus on strategy development. Automation increases efficiency and ensures marketing efforts are aligned consistently with consumer expectations and market trends. The integration of AI and big data also enables real-time decision-making capabilities that were previously impossible. Marketers can now monitor campaign performance instantaneously and make adjustments on the fly to optimise results. This agility allows businesses to respond quickly to market changes, competitor actions and shifting consumer preferences, ensuring that marketing budgets are spent efficiently and effectively in all channels. Enhancing marketing strategies with AI insight AI provides marketers with valuable insights that can enhance their strategies. By analysing consumer data, AI can identify which marketing tactics are most effective, allowing businesses to allocate resources more efficiently. The data-driven approach ensures that marketing efforts are focused on the most promising opportunities, maximising return on investment. AI can help marketers predict future trends and consumer behaviours, enabling them to stay ahead of the competition. By anticipating changes in the market, businesses can adapt their strategies proactively, ensuring they remain relevant and effective in a rapidly changing digital landscape. Challenges and ethical considerations While AI and big data offer significant benefits, they also present challenges, particularly in terms of data privacy and ethical considerations. Businesses must ensure that they handle consumer data responsibly and comply with relevant regulations to maintain trust and transparency with their audience. The potential for algorithmic bias is a concern that marketers must address. Ensuring that AI systems are designed and monitored to prevent biased outcomes is crucial for maintaining fairness and equality in marketing practices. By addressing these challenges, businesses can harness the full potential of AI and big data while maintaining ethical standards. Personalisation at scale through AI and big data One of the most significant ways AI and big data are transforming digital marketing strategies is through large-scale personalisation. Traditional marketing approaches often rely on broad audience segments, but AI-powered systems can analyse vast amounts of behavioural, demographic and contextual data to create highly individualised experiences. This allows marketers to move beyond generic messaging and deliver content, offers and recommendations that align closely with each user’s interests and intent. By using big data, AI can assess factors like browsing history, purchase behaviour, location, device use and engagement patterns in real time. These insights enable marketers to adjust messaging dynamically in channels, from personalised website experiences to tailored email campaigns and paid media targeting. The future of AI in digital marketing As AI and big data technologies continue to evolve, their role in digital marketing is expected to grow. Future innovations will likely focus on enhancing personalisation and real-time data analysis, providing marketers with even more powerful tools to engage with consumers effectively. Staying informed about these advancements is essential for businesses looking to use AI and big data in their marketing strategies. By embracing these technologies and adapting to new developments, businesses can ensure they remain competitive and successful in the ever-evolving digital landscape. Rainmaker’s commitment to using AI and big data enables clients to achieve superior marketing outcomes. By focusing on personalised strategies and data-driven insights, Rainmaker helps businesses thrive in the digital age. As an AI SEO company, Rainmaker is at the forefront of transforming digital marketing through innovative solutions and strategic foresight. The post Understanding how AI and big data transform digital marketing appeared first on AI News. View the full article

Solana’s high-speed platform is fast becoming the preferred home for independent AI programmes. It comes at a time when advanced uses of technology have led to significant increases in cyberattacks. This article details the escalating malware threats for the cryptocurrency community. According to the most recent data on December 5, 2025, the Solana price on Binance hovers around $134.95, facing tight consolidation before a potential breakout. Cryptocurrencies now confront a serious new class of digital adversary. You see the collision between innovation and menace right at the ledger layer. Smart programmes are seeking maximum efficiency on low-cost chains while attackers exploit the very tools that enable this progress. Available data paints a stark picture of both genuine technological promise and pervasive risk. AI’s ability to act independently presents a huge operational opportunity and a massive security headache, all at once. Autonomous AI programmes demand high-speed blockchains Developers are increasingly building what people call autonomous agents. Call them what you will, these intelligent programmes think for themselves, executing intricate tasks without a person even looking. Running independently right on the chain, they manage contracts and execute rapid-fire decisions. Conversations on Binance Square consistently highlight Solana as the premium choice for this kind of work. Tremendous processing capacity and ultra-low fees make the chain perfect for automated operations. Data from January 12, 2025, confirms agents create small, very fast, and ultimately more effective transactions. Platforms like Solana benefit because transaction costs trend downward, fostering rapid expansion of the AI-driven transaction ecosystem. Solana-based tokens like $FUN demonstrate strong investor interest in this pretty new field. Binance Research also confirms the chain’s rapid pace is necessary to manage the high-frequency interactions thousands or millions of active agents would require. Malicious code is now created by chatbots Cybersecurity researchers recently flagged a convincing malicious npm package to watch out for. It concealed a cryptocurrency wallet drainer, generated entirely using artificial intelligence. Safety, the supply chain security company, identified the library, which had attracted over 1,500 downloads before its removal. Uploaded by a user named “Kodane,” the package claimed to offer advanced licence validation utilities for high-performance Node.js applications. Behaviour is triggered by a post-install script, a common, often overlooked attack vector. The script executes automatically, stashing its payload in hidden directories on major operating systems. Logs were very detailed and the use of words like “Enhanced” strongly suggested the original source was an AI chatbot. Afterward, the script connects to a command-and-control (C2) server and generates a unique machine ID code for the compromised host. The malware scans the system for wallet files, then proceeds to drain all funds directly to a hard-coded Solana wallet address. Autonomous spy campaigns raise alarms A severe new problem now confronts security leaders. State-sponsored groups are deploying AI to orchestrate espionage. Anthropic’s threat intelligence team detailed the disruption of a sophisticated operation dubbed GTG-1002 in November 2025 (affecting about 30 entities). The group, assessed with high confidence as ******** state-sponsored, manipulated the Claude Code model to serve as a penetration testing agent. By tricking the Claude Code model into adopting a “role-play” persona (an employee of a legitimate cybersecurity firm), attackers successfully broke its safety protocols. People were only minimally involved, mostly starting the campaign or approving moves at key escalation points. Programmes performed reconnaissance, found weaknesses, built exploits, and harvested data. Human involvement was limited to 10-20% of the total effort. Model Context Protocol servers acted as the interface between the AI and open-source penetration testing tools. Speed and stability form a balance For developers, Solana’s raw performance statistics are what really draws them in. The network’s co-founder, Anatoly Yakovenko, strongly believes market value will eventually be determined by how much revenue a blockchain generates. He argues that only platforms attempting to capture the whole market can hope to survive in the long run. Under the hood, Solana operates using the Proof of History consensus mechanism, alongside traditional Proof of Stake. Reportedly, this combination achieves peak performance of up to 65,000 transactions per second (TPS). Fees are listed as low as $0.00025, making the platform accessible for high-frequency automated trades. Solana has faced certain undeniable challenges. Security analysis shared on Binance Square included reports of significant cryptocurrency theft involving a user’s compromised Solana address. Such incidents raise immediate concerns about private key storage. Analysts continue to cite historical instability, with one analysis mentioning the network suffered multiple major outages so far. Defenders should adopt machine intelligence too The GTG-1002 campaign proves AI can discover and exploit vulnerabilities in live operations without substantial human intervention. Groups with fewer resources can now execute campaigns previously requiring entire teams of experienced hackers. The drastic lowering of the attack barrier is the primary implication for security and technology leaders. Could you afford to ignore this new reality in your operational planning? Anthropic’s investigation exposed one important weakness in the AI’s operational structure: hallucinations. Claude often overstated its findings and sometimes just fabricated data entirely, claiming to possess non-existent credentials. Such a flaw forces human handlers to verify every single result, which drags down attackers’ operational pace. Security teams should assume a major change has occurred in the security domain. Applying AI for defence (especially in SOC automation and threat detection) is the only proactive path forward. The ledger space must now square up to AI’s two faces. Innovation running at machine speed on Solana is directly coupled with machine-speed malice aimed at users. Getting ahead of this threat will require a new, very proactive approach. The post Solana’s high-speed AI gains and malware losses appeared first on AI News. View the full article

A recent industry report [PDF] argues that Britain’s railway network could carry an extra billion journeys by the mid-2030s, building on the 1.6 billion passenger rail journeys recorded to year-end March 2024. The next decade will involve a combination of complexity and control, as more digital systems, data, and interconnected suppliers create the potential for more points of failure. The report’s central theme is that AI will become the operating system for modern rail, not as a single, centralised collection of models and algorithms, but as layers of prediction, optimisation, and automated monitoring found in infrastructure, rolling stock, maintenance yards, and stations (pp.18-23). This technology will guide human focus within daily work schedules rather than replace human activity entirely. Maintenance to become predictive and data-driven Traditional rail maintenance relies on fixed schedules and manual inspections, a reactive and labour-intensive practice. The whitepaper cites Network Rail’s reliance on engineers walking the track to spot defects (p.18). AI will shift the industry to predictive maintenance, analysing data from sensors to forecast failures before they cause significant disruption. This involves a combination of sensors and imaging, including high-definition cameras, LiDAR scanners, and vibration monitors. These provide machine-learning systems with data that can flag degradation in track, signalling, and electrical assets ahead of failure (pp.18-19). These monitoring programs can generate alerts months in advance, reducing emergency call-outs. The timeframe for predicting asset failure varies by asset type. Network Rail’s intelligent infrastructure efforts should transition from “find and fix” to “predict and prevent.” Network Rail emphasises data-led maintenance and tools designed to consolidate asset information, while European R&D programs (like Europe’s Rail and its predecessor, Shift2Rail) fund projects like DAYDREAMS, similarly aimed at prescriptive asset management. Prediction at scale requires a common approach to achieve transformation. Traffic control and energy efficiency Operational optimisation, beyond predictive maintenance, offers significant returns. AI systems use live and historical operating data—train positions, speeds, weather forecasts—to anticipate disruption and adjust traffic flow. Digital twin and AI-based traffic management trials in Europe, alongside research and testing of AI-assisted driving and positioning, could increase overall network capacity without laying more track (p.20). Algorithms also advise drivers on optimal acceleration and braking, potentially saving 10-15% in energy. Considering route variations, traction, and timetable constraints, energy savings compound quickly across a large network. Safety monitoring and CCTV Visible AI applications focus on safety and security. Obstacle detection uses thermal cameras and machine learning to identify hazards beyond human visibility. AI also monitors level crossings and analyses CCTV footage to spot unattended items and suspicious activity (pp.20-21). For example, AI and LiDAR are used for crowd monitoring at London Waterloo as part of a suite of safety tools. Passenger flows and journey optimisation AI can forecast demand using ticket sales, events, and mobile signals, allowing operators to adjust the number of carriages and reduce overcrowding, the report states. Passenger counting is a high-impact, low-drama application: better data supports better timetables and clearer customer information. Cybersecurity issues As operational technology converges with IT, cybersecurity becomes a critical operational issue. Legacy systems, lacking replacement plans, pose a risk, as does integrating modern analytics with older infrastructure. This creates conditions attractive to attackers. The future of AI in rail involves sensors performing in extreme environments, models trusted and tested by operators, and governance that treats cyber resilience as inseparable from physical safety. The report’s message is that AI will arrive regardless. The question is whether railways proactively adopt and control it or inherit it as un-managed complexity. (Image source: “Train Junction” by jcgoble3 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 The future of rail: Watching, predicting, and learning appeared first on AI News. View the full article

For a company built on intellectual property, scale creates a familiar tension. Disney needs to produce and distribute content across many formats and audiences, while keeping tight control over rights, safety, and brand consistency. Generative AI promises speed and flexibility, but unmanaged use risks creating legal, creative, and operational drag. Disney’s agreement with OpenAI shows how a large, IP-heavy organisation is attempting to resolve that tension by putting AI inside its operating system rather than treating it as a side experiment. Under the deal, Disney becomes both a licensing partner and a major enterprise customer. OpenAI’s video model Sora will be able to generate short, user-prompted videos using a defined set of Disney-owned characters and environments. Separately, Disney will use OpenAI’s APIs to build internal tools and new consumer experiences, including integrations tied to Disney+. The company will also deploy ChatGPT internally for employees. The mechanics matter more than the spectacle. Disney is not opening its catalogue to unrestricted generation. The licence excludes actor likenesses and voices, limits which assets can be used, and applies safety and age-appropriate controls. In practice, this positions generative AI as a constrained production layer—capable of generating variation and volume, but bounded by governance. AI inside existing workflows A consistent failure mode in enterprise AI programmes is separation. Tools live outside the systems where work actually happens, adding steps instead of removing them. Disney’s approach mirrors a more pragmatic pattern: put AI where decisions are already made. On the consumer side, AI-generated content will surface through Disney+, rather than through a standalone experiment. On the enterprise side, employees gain access to AI through APIs and a standardised assistant, rather than a patchwork of ad hoc tools. This reduces friction and makes AI usage observable and governable. The implication is organisational. Disney is treating generative AI as a horizontal capability—closer to a platform service than a creative add-on. That framing makes it easier to scale usage across teams without multiplying risk. Variation without expanding headcount The Sora licence focuses on short-form content derived from pre-approved assets. That constraint is deliberate. In production environments, much of the cost sits not in ideation but in generating usable variations, reviewing them, and moving them through distribution pipelines. By allowing prompt-driven generation inside a defined asset set, Disney can reduce the marginal cost of experimentation and fan engagement without increasing manual production or review load. The output is not a finished film. It is a controlled input into marketing, social, and engagement workflows. This mirrors a broader enterprise pattern: AI earns its place when it shortens the path from intent to usable output, not when it creates standalone artefacts. APIs over point tools Beyond content generation, the agreement positions OpenAI’s models as building blocks. Disney plans to use APIs to develop new products and internal tools, rather than relying solely on off-the-shelf interfaces. This matters because enterprise AI programmes often stall on integration. Teams waste time copying outputs between systems or adapting generic tools to fit internal processes. API-level access allows Disney to embed AI directly into product logic, employee workflows, and existing systems of record. In effect, AI becomes part of the connective tissue between tools, not another layer employees must learn to work around. Aligning productivity with incentives Disney’s $1 billion equity investment in OpenAI is less interesting as a valuation signal than as an operational one. It indicates an expectation that AI usage will be persistent and central, not optional or experimental. For large organisations, AI investments fail when tooling remains disconnected from economic outcomes. Here, AI touches revenue-facing surfaces (Disney+ engagement), cost structures (content variation and internal productivity), and long-term platform strategy. That alignment increases the likelihood that AI becomes part of standard planning cycles rather than discretionary innovation spend. Automation that makes scale less fragile High-volume AI use amplifies small failures. Disney and OpenAI emphasise safeguards around IP, harmful content, and misuse, not as a values statement but as a scaling requirement. Strong automation around safety and rights management reduces the need for manual intervention and supports consistent enforcement. As with fraud detection or content moderation in other industries, this kind of operational AI does not attract attention when it works—but it makes growth less brittle. Lessons for enterprise leaders Embed AI where work already happens. Disney targets product and employee workflows, not a separate AI sandbox. Constrain before you scale. Defined asset sets and exclusions make deployment viable in high-liability environments. Use APIs to reduce friction. Integration matters more than model novelty. Tie AI to economics early. Productivity gains stick when they connect to revenue and cost structures. Treat safety as infrastructure. Automation and controls are prerequisites for scale, not afterthoughts. Disney’s specific assets are unique. The operating pattern is not. Enterprise AI delivers value when it is designed as part of the organisation’s core machinery—governed, integrated, and measured—rather than as a showcase for what models can generate. (Photo by Héctor Vásquez) 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, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Why Disney is embedding generative AI into its operating model appeared first on AI News. View the full article

Arm Holdings has positioned itself at the centre of AI transformation. In a wide-ranging podcast interview, Vince Jesaitis, head of global government affairs at Arm, offered enterprise decision-makers look into the company’s international strategy, the evolution of AI as the company sees it, and what lies ahead for the industry. From cloud to edge Arm thinks the AI market is about to enter a new phase, moving from cloud-based processing to edge computing. While much of the media’s attention has been focused to date on massive data centres, with models trained in and accessed from the cloud, Jesaitis said that most AI compute, especially inference tasks, is likely to be increasingly decentralised. “The next ‘aha’ moment in AI is when local AI processing is being done on devices you couldn’t have imagined before,” Jesaitis said. These devices range from smartphones and earbuds to cars and industrial sensors. Arm’s IP is already embedded, literally, in these devices – it’s a company that only in the last year has been the IP behind over 30 billion chips, placed in devices of every conceivable description, all over the world. The deployment of AI in edge environments has several benefits, with team at Arm citing three main ‘wins’. Firstly, the inherent efficiency of low-power Arm chips means that power bills for running compute and cooling are lower. That keeps the environmental footprint of the technology as small as possible. Secondly, putting AI in local settings means latency is much lower (with latency determined by the distance between local operations and the site of the AI model). Arm points to uses like instant translation, dynamic scheduling of control systems, and features like the near-immediate triggering of safety functions – for instance in IIoT settings. Thirdly, ‘keeping it local’ means there’s no potentially sensitive data sent off-premise. The benefits are obvious for any organisation in highly-regulated industries, but the increasing number of data breaches means even companies operating with relatively benign data sets are looking to reduce their attack surface. Arm silicon, optimised for power-constrained devices, makes it well-suited for compute where it’s needed on the ground, the company says. The future may well be one where AI is found woven throughout environments, not centralised in a data centre run by one of the large providers. Arm and global governments Arm is actively engaged with global policymakers, considering this level of engagement an important part of its role. Governments continue to compete to attract semiconductor investment, the issues of supply chain and concentrated dependencies still fresh in many policymakers’ memories from the time of the COVID epidemic. Arm lobbies for workforce development, working at present with policy-makers in the White House on an education coalition to build an ‘AI-ready workforce’. Domestic independence in technology relies as much on the abilities of workforce as it does on the availability of hardware. Jesaitis noted a divergence between regulatory environments: the US prioritises what the government there terms acceleration and innovation, while the EU leads on safety, privacy, security and legally-enforced standards of practice. Arm aims to find the middle ground between these approaches, building products that meet stringent global compliance needs, yet furthering advances in the AI industry. The enterprise case for edge AI The case for integrating Arm’s edge-focused AI architecture into enterprise transformation strategies can be persuasive. The company stresses its ability to offer scale-able AI without the need to centralise to the cloud, and is also pushing its investment in hardware-level security. That means issues like memory exploits (outside of the control of users plugged into centralised AI models) can be avoided. Of course, sectors already highly-regulated in terms of data practices are unlikely to experience relaxed governance in the future – the opposite is pretty much inevitable. All industries will be seeing more regulation and greater penalties for non-compliance in the years to come. However, to balance that, there are significant competitive advantages available to those that can demonstrate their systems’ inherent safety and security. It’s into this regulatory landscape that Arm sees itself and local, edge AI fitting. Additionally, in Europe and Scandinavia, ESG goals are going to be increasingly important. Here, the power-sipping nature of Arm chips offers big advantages. That’s a trend that even the US hyperscalers are responding to: AWS’s latest SHALAR range of low-cost, low-power Arm-based platforms is there to satisfy that exact demand. Arm’s collaboration with cloud hyperscalers such as AWS and Microsoft produces chips that combine efficiency with the necessary horsepower for AI applications, the company says. What’s next from Arm and the industry Jesaitis pointed out several trends that enterprises may be seeing in the next 12 to 18 months. Global AI exports, particularly from the US and Middle East, are ensuring that local demand for AI can be satisfied by the big providers. Arm is a company that can supply both big providers in these contexts (as part of their portfolios of offerings) and satisfy the rising demand for edge-based AI. Jesaitis also sees edge AI as something of the hero of sustainability in an industry increasingly under fire for its ecological impact. Because Arm technology’s biggest market has been in low-power compute for mobile, it’s inherently ‘greener’. As enterprises hope to meet energy goals without sacrificing compute, Arm offers a way that combines performance with responsibility. Redefining “smart” Arm’s vision of AI at the edge means computers and the software running on them can be context-aware, cheap to run, secure by design, and – thanks to near-zero network latency – highly-responsive. Jesaitis said, “We used to call things ‘smart’ because they were online. Now, they’re going to be truly intelligent.” (Image source: “Factory Floor” by danielfoster437 is licensed under CC BY-NC-SA 2.0.) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Arm and the future of AI at the edge appeared first on AI News. View the full article

Under China’s push to clean up its energy system, AI is starting to shape how power is produced, moved, and used — not in abstract policy terms, but in day-to-day operations. In Chifeng, a city in northern China, a renewable-powered factory offers a clear example. The site produces hydrogen and ammonia using electricity generated entirely from nearby wind and solar farms. Unlike traditional plants connected to the wider grid, this facility runs on its own closed system. That setup brings a problem as well as a benefit: renewable power is clean, but it rises and falls with the weather. To keep production stable, the factory relies on an AI-driven control system built by its owner, Envision. Rather than following fixed schedules, the software continuously adjusts output based on changes in wind and sunlight. As reported by Reuters, Zhang Jian, Envision’s chief engineer for hydrogen energy, compared the system to a conductor, coordinating electricity supply and industrial demand in real time. When wind speeds increase, production ramps up automatically to take full advantage of the available power. When conditions weaken, electricity use is quickly reduced to avoid strain. Zhang said the system allows the plant to operate at high efficiency despite the volatility of renewable energy. Projects like this are central to China’s plans for hydrogen and ammonia, fuels seen as important for cutting emissions in sectors such as steelmaking and shipping. They also point to a broader strategy: using AI to manage complexity as the country adds more renewable power to its grid. Researchers argue that AI could play a significant role in meeting China’s climate goals. Zheng Saina, an associate professor at Southeast University in Nanjing who studies low-carbon transitions, said AI can support tasks ranging from emissions tracking to forecasting electricity supply and demand. At the same time, she cautioned that AI itself is driving rapid growth in power consumption, particularly through energy-hungry data centres. China now installs more wind and solar capacity than any other country, but absorbing that power efficiently remains a challenge. According to Cory Combs, associate director at Beijing-based research firm Trivium China, AI is increasingly seen as a way to make the grid more flexible and responsive. That thinking was formalised in September, when Beijing introduced an “AI+ energy” strategy. The plan calls for deeper links between AI systems and the energy sector, including the development of multiple large AI models focused on grid operations, power generation, and industrial use. By 2027, the government aims to roll out dozens of pilot projects and test AI across more than 100 use cases. Within another three years, officials want China to reach what they describe as a world-leading level of AI integration in energy. Combs said the focus is on highly specialised tools designed for specific jobs, such as managing wind farms, nuclear plants, or grid balancing, rather than general-purpose AI. This approach contrasts with the United States, where much of the investment has gone into building advanced large-language models, according to Hu Guangzhou, a professor at the China Europe International Business School in Shanghai. One area where AI could have immediate impact is demand forecasting. Fang Lurui, an assistant professor at Xi’an Jiaotong-Liverpool University, said power grids must match supply and demand at every moment to avoid outages. Accurate forecasts of renewable output and electricity use allow operators to plan ahead, storing energy in batteries when needed and reducing reliance on coal-fired backup plants. Some cities are already experimenting. Shanghai has launched a citywide virtual power plant that links dozens of operators — including data centres, building systems, and electric vehicle chargers — into a single coordinated network. During a trial last August, the system reduced peak demand by more than 160 megawatts, roughly equivalent to the output of a small coal plant. Combs said such systems matter because modern power generation is increasingly scattered and intermittent. “You need something very robust that is able to be predictive and account for new information very quickly,” he said. Beyond the grid, China is also looking to apply AI to its national carbon market, which covers more than 3,000 companies in emissions-heavy industries such as power, steel, cement, and aluminium. These sectors together produce over 60% of the country’s carbon emissions. Chen Zhibin, a senior manager at Berlin-based think tank adelphi, said AI could help regulators verify emissions data, refine the allocation of free allowances, and give companies clearer insight into their production costs. Still, the risks are growing alongside the opportunities. Studies suggest that by 2030, China’s AI data centres could consume more than 1,000 terawatt-hours of electricity each year — roughly the same as Japan’s current annual usage. Lifecycle emissions from the AI sector are projected to rise sharply and peak well after China’s 2030 emissions target. Xiong Qiyang, a doctoral researcher at Renmin University of China who worked on one such study, said the results reflect the reality that coal still dominates China’s power mix. He warned that rapid AI expansion could complicate national climate goals if energy sources do not shift quickly enough. In response, regulators have begun tightening rules. A 2024 action plan requires data centres to improve energy efficiency and increase their use of renewable power by 10% each year. Other initiatives encourage new facilities to be built in western regions, where wind and solar resources are more abundant. Operators on the east coast are also testing new ideas. Near Shanghai, an underwater data centre is set to open, using seawater for cooling to cut energy and water use. The developer, Hailanyun, said the facility will draw most of its power from an offshore wind farm and could be replicated if the project proves viable. Despite the growing energy demands of AI, Xiong argued that its overall impact on emissions could still be positive if applied carefully. Used to optimise heavy industry, power systems, and carbon markets, he said, AI may remain an essential part of China’s effort to cut emissions — even as it creates new pressures that policymakers must manage. (Photo by Matthew Henry) See also: Can China’s chip stacking strategy really challenge Nvidia’s AI dominance? 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 China’s push to apply AI across its energy system appeared first on AI News. View the full article

For large retailers, the challenge with AI is no longer whether it can be useful, but how it fits into everyday work. A new three-year AI partnership by Tesco points to how one of the ***’s biggest supermarket groups is trying to answer that question. The agreement with French startup Mistral AI is less about launching a single AI feature and more about building long-term capability. Tesco plans to work with Mistral to develop AI tools that can be used across its business, from internal workflows to customer-facing systems. Rather than framing the deal around bold promises, Tesco has described it in practical terms. The aim, according to the company, is to save staff time, improve how teams work, and strengthen customer service. Ruben Lara Hernandez, Tesco’s Data, Analytics & AI Director, said the partnership brings together Tesco’s retail experience with Mistral’s technology, with the hope that it will help colleagues work more efficiently and support customers more effectively. He added that the agreement builds on Tesco’s history of developing technology and AI tools that support customers, staff, and suppliers. Tesco moves AI into day-to-day retail operations That focus reflects a wider shift in how enterprises are using AI. Early retail experiments often centred on customer-facing tools that were easy to see but hard to scale. More recent efforts are aimed at internal use, where AI can reduce repetitive work, support planning, and help staff make decisions faster. Tesco has already been moving in that direction. Over the past five years, the retailer has doubled the size of its technology team, signalling that software and data are now core to how the business operates. AI is already used across several parts of the organisation, both through in-house development and partnerships. In online grocery, AI helps find more efficient delivery routes, which can open up additional delivery slots for customers. In supply planning, AI supports complex demand forecasting, helping stores maintain product availability. The company also uses AI to personalise how it engages with customers through its Clubcard loyalty scheme, tailoring offers and communications based on shopping behaviour. The partnership with Mistral is meant to build on this existing work rather than replace it. One factor behind the collaboration is Mistral’s approach to model deployment, which allows AI systems to run in more controlled environments. For a retailer handling large volumes of customer and operational data, that level of control matters. Mistral’s Chief Revenue Officer and US General Manager, Marjorie Janiewicz, said the company’s Applied AI team will work closely with Tesco’s internal experts. She described the goal as building AI products that are customisable and controllable, with a focus on improving internal workflows and customer experience. A long-term partnership, not a one-off project The structure of the partnership also suggests a cautious approach to scale. Tesco plans to create an internal AI lab as part of the agreement, giving teams space to test and refine tools before wider deployment. For large organisations, this kind of setup can help prevent AI projects from remaining stuck in isolated pilots or specialist teams. There is also a strategic angle to the choice of partner. Mistral AI is the only European company developing large language models at the frontier level, and Tesco is the first major *** retailer to form a partnership with the startup as part of its broader AI and technology plans. Founded in April 2023, Mistral has grown quickly and counts enterprises such as HSBC, AXA, and Stellantis among its customers. For Tesco, the harder work now lies in execution. Retail data is often fragmented across regions, systems, and channels, and AI systems depend on that data being accurate and consistent. Rolling tools out across a business of this size also requires training, oversight, and trust from staff who use them daily. Whether the partnership succeeds will likely depend on how visible its impact becomes inside the organisation. If AI tools help store teams, planners, and analysts work more smoothly, the gains may be gradual rather than dramatic. As retailers look to move beyond experimentation, Tesco’s approach offers a view of how enterprise AI is starting to settle into routine operations — not as a single solution, but as a steady process of change. (Photo by Shashank Verma) See also: Zara’s use of AI shows how retail workflows are quietly changing 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 Tesco signs three-year AI deal centred on customer experience appeared first on AI News. View the full article

Of all the many industries, it’s marketing where AI is no longer an “innovation lab” side project but embedded in briefs, production pipelines, approvals, and media optimisation. A WPP iQ post published in December, based on a webinar with WPP and Stability AI, shows what AI deployment in daily operations looks like. Here, we’re talking about a focus on the practical constraints that determine whether AI changes daily work or merely adds another layer of complexity or tooling. Brand accuracy a repeatable capability Marketing agencies’ AI treats brand accuracy as something to be engineered. WPP and Stability AI note that off-the-shelf models “don’t come trained on your brand’s visual identity”, so outputs can often look generic. The companies’ remedy is fine-tuning, that is, training models on brand-specific datasets so the model learns the brand playbook, including style, look, and colours. Then, these elements can be reproduced consistently. WPP’s Argos is a prime example. After fine-tuning a model for the retailer, the team described how the model picked up details beyond the characters, including lighting and subtle shadows used in the brand’s 3D animations. Reproducing these finer details can be where time disappears in production, in the form of re-rendering and several rounds of approvals. When AI outputs start closer to “finished”, teams spend less time correcting and more time shaping narratives and adapting media for different channels. Cycle time collapses (and calendars change) WPP and Stability AI point out that traditional 3D animation can be too slow for reactive marketing. After all, cultural moments demand immediate content, not cycles defined in weeks or months. In its Argos case study, WPP trained custom models on two 3D toy characters so the models learned how they look and behave, including details such as proportions and how characters hold objects. The outcome was “high-quality images…generated in minutes instead of months”. The accelerated workflow moves rather than removes production bottlenecks. If generating variations becomes fast, then review, compliance, rights management and distribution, become the constraints. Those issues were always there, but the speed and efficiency of AI in this context shows the difference between what’s possible, and systems that have become embedded and accepted into workflows. Agencies that want AI to change daily operations have to redesign the workflow around it, not just add the technology as a new tool. The “AI front end” becomes essential WPP and Stability AI call out a “UI problem”, wherecreative teams lose time interfaces to common tools are “disconnected, complex and confusing”, forcing workarounds and constant asset movement between tools. Often, responses are bespoke, brand-specific front ends with complex workflows in the back end.. WPP positions WPP Open as a platform that encodes WPP’s proprietary knowledge into “globally accessible AI agents”, which helps teams plan, produce, create media, and sell. Operational gains come from cleaner handoffs between tools, as work moves from briefs into production, assets into activation, and performance signals back into planning. Self-serve capability changes agency operations AI-powered marketing platforms are also becoming client-facing. Operationally, that pushes agencies to concentrate on the parts of the workflow their clients can’t self-serve easily, like designing the brand system, building fine-tunings, and ensuring governance is embedded. Governance moves from policy to workflow For AI to be used daily, governance needs to be embedded where work happens. Dentsu describes building “walled gardens”, which are digital spaces where employees can prototype and develop AI-enabled solutions securely, and commercialise the best ideas. This reduces the risk of sensitive data exposure and lets experiments move into production systems. Planning and insight compress too The operational impact is not limited to production. Publicis Sapient describes AI-powered content strategy and planning that “transforms months of research into minutes of insight” by combining large language models with contextual knowledge and prompt libraries [PDF]. Research and brief development compress work schedules, so more client work can happen and the agency has faster responses to shifting culture and platform algorithms. What changes for people Across these examples, the impact on marketing professionals is one of rebalancing and shifting job descriptions. Less time goes on mechanical drafting, resizing, and versioning, and more time goes on brand stewardship. New operational roles expand, with titles like– model trainer, workflow designer, and AI governance lead. AI makes the biggest operational difference when agencies use customised models, usable front ends that make adoption (especially by clients) frictionless, and integrated platforms that connect planning, production, and execution. The headline benefit is speed and scale, but the deeper change is that marketing delivery starts to resemble a software-enabled supply chain, standardised, flexible where it needs to be, and measurable. (Image source: “Solar Wind Workhorse Marks 20 Years of Science Discoveries” by NASA Goddard Photo and Video is licensed under CC BY 2.0.) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. 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Cognizant, ***** Consultancy Services, Infosys, and Wipro have announced plans to deploy more than 200,000 Microsoft Copilot licenses in their enterprises – over 50,000 per company – in what Microsoft is calling a new benchmark for enterprise-scale adoption of generative AI. The companies involved are framing the move as the implementation of a default tool for hundreds of thousands of employees involved in consulting, delivery, operations, and software. The announcement, made in Bengaluru, December 11, was timed to coincide with Microsoft CEO Satya Nadella’s visit to India. There, and across the industrialised world, there’s been growing momentum for agentic AI – AI systems that do more than chat, executing multi-step work in business processes. The four firms want to be seen as AI advisors for clients, with extensive experience drawn from their internal rollouts of AI. Why enterprises care about Copilot Readers will be familiar with Microsoft 365 Copilot, the AI assistant embedded in standard workplace tools Word, Excel, PowerPoint, Outlook, and Teams. It’s intended to help users draft, summarise, and analyse, turning natural-language queries into work-related outputs. Copilot combines large language models with Microsoft 365 apps and organisational data gained from Microsoft Graph, with the assistant working in the context of a user’s files, meetings, and messages. This ability is, of course, subject to access controls already in place and defined by the organisation. For large organisations, the embedding of AI into workflows is important. A firm shouldn’t have to rebuild its toolchain to experiment with AI, but rather start using AI in the software and documents its workforce already uses. The raft of benefits is practical and work-focused: faster documentation, quicker meeting follow-ups, faster draft proposals, better discovery of information from internal knowledge repositories, and, with agentic AI, the automation of repetitive tasks. From Copilots to frontier firms and agents Microsoft uses the term “Frontier Firms” to describe organisations that are “human-led and agent-operated”; where employees work alongside AI assistants and specialised agents that take on work processes. The designation of ‘Frontier Firm’ status aligns with Microsoft’s messaging at Microsoft Ignite 2025, where the company described agents reinventing business processes and amplifying impact through human-agent teamwork. In very simple terms, the company’s pitch is to move from “AI helps you write” to “AI helps run workflows.” Why IT services firms are making public commitments There are two reasons why the four firms are rolling out the technology at such a large scale. First, to improve internal productivity. The Times of India reports the deployments are intended to integrate Copilot into workflows in consulting, software development, operations and client delivery, with the aim of improved productivity. At large multinational companies, margins depend on delivery efficiency and knowledge reuse, so shaving minutes from everyday tasks for tens of thousands of workers produces meaningful gains. Second, client credibility. The consultancy companies serve global enterprises, including many Fortune 500 clients, which means their internal operating model can, and perhaps should, become their clients’ playbooks. If consultancies can demonstrate mature governance, training, and measurable outcomes with Copilot at scale in their own operations, it strengthens their messaging, better able to sell similar transformations to potential and existing clients. Hyperscalers’ investment in India The Copilot announcement came immediately after Microsoft said it would invest $17.5 billion in India between 2026-2029, money destined for cloud and AI infrastructure, skilling, and operations. The company describes this as its largest investment in Asia to date. Other major tech firms are making parallels: Reuters reported in December 2025 that Amazon/AWS planned to invest over $35 billion in India by 2030, expanding its operations and AI capabilities, for example. Together, such moves underscore India’s growing position as a massive enterprise market and strategic hub for AI talent and cloud infrastructure. For India’s IT services leaders, Copilot is being positioned as a way to stay ahead of the competitive curve and define “AI-first delivery.” (Image source: “Gobbling Indian view of Clinch River” by dmott9 is licensed under CC BY-ND 2.0.) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post 50,000 Copilot licences for Indian service companies appeared first on AI News. View the full article

Zara is testing how far generative AI can be pushed into everyday retail operations, starting with a part of the business that rarely gets attention in technology discussions: product imagery. Recent reporting shows the retailer using AI to generate new images of real models wearing different outfits, based on existing photoshoots. Models remain involved in the process, including consent and compensation, but AI is used to extend and adapt imagery without repeating production from scratch. The stated aim is to speed up content creation and reduce the need for repeated shoots. On the surface, the change looks incremental. In practice, it reflects a familiar pattern in enterprise AI adoption, where technology is introduced not to overhaul how a business works, but to remove friction from tasks that repeat at scale. How Zara uses AI to reduce friction in repeatable retail work For a global retailer like Zara, imagery is not a creative afterthought. It is a production requirement tied directly to how quickly products can be launched, refreshed, and sold across markets. Each item typically needs multiple visual variations for different regions, digital channels, and campaign cycles. Even when garments change only slightly, the surrounding production work often starts again from scratch. That repetition creates delays and cost that are easy to overlook precisely because they are routine. AI offers a way to compress those cycles by reusing approved material and generating variations without resetting the entire process. AI enters the production pipeline The placement of the technology is as important as the capability itself. Zara is not positioning AI as a separate creative product or asking teams to adopt an entirely new workflow. The tools are being used inside an existing production pipeline, supporting the same outputs with fewer handoffs. That keeps the focus on throughput and coordination rather than experimentation. This kind of deployment is typical once AI moves beyond pilot stages. Rather than asking organisations to rethink how work is done, the technology is introduced where constraints already exist. The question becomes whether teams can move faster and with less duplication, not whether AI can replace human judgement. The imagery initiative also sits alongside a broader set of data-driven systems that Zara has built up over time. The retailer has long relied on analytics and machine learning to forecast demand, allocate inventory, and respond quickly to changes in customer behaviour. Those systems depend on fast feedback loops between what customers see, what they buy, and how stock moves through the network. From that perspective, faster content production supports the wider operation even if it is not framed as a strategic shift. When product imagery can be updated or localised more quickly, it reduces lag between physical inventory, online presentation, and customer response. Each improvement is small, but together they help maintain the pace that fast fashion relies on. From experimentation to routine use Notably, the company has avoided framing this move in grand terms. There are no published figures on cost savings or productivity gains, and no claims that AI is transforming the creative function. The scope remains narrow and operational, which limits both risk and expectation. That restraint is often a sign that AI has moved out of experimentation and into routine use. Once technology becomes part of day-to-day operations, organisations tend to talk about it less, not more. It stops being an innovation story and starts being treated as infrastructure. There are also constraints that remain visible. The process still relies on human models and creative oversight, and there is no suggestion that AI-generated imagery operates independently. Quality control, brand consistency, and ethical considerations continue to shape how the tools are applied. AI extends existing assets rather than generating content in isolation. This is consistent with how enterprises typically approach creative automation. Rather than replacing subjective work outright, they target the repeatable components around it. Over time, those changes accumulate and reshape how teams allocate effort, even if the core roles remain intact. Zara’s use of generative AI does not signal a reinvention of fashion retail. It shows how AI is beginning to touch parts of the organisation that were previously considered manual or difficult to standardise, without changing how the business fundamentally operates. In large enterprises, that is often how AI adoption becomes durable. It does not arrive through sweeping strategy announcements or dramatic claims. It takes hold through small, practical changes that make everyday work move a little faster — until those changes become hard to imagine doing without. (Photo by M. Rennim) See also: Walmart’s AI strategy: Beyond the hype, what’s actually working 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 Zara’s use of AI shows how retail workflows are quietly changing appeared first on AI News. View the full article

Human Resources is an area in many organisations where AI can have significant operational impact. The technology is now being embedded into day-to-day operations, in activities like answering employees’ questions and supporting training. The clearest impact appears where organisations can measure the tech’s outcomes, typically in time saved and the numbers of queries successfully resolved. Fewer tickets, more first-time answers IBM’s internal virtual agent, AskHR, was built to handle employee queries and automate routine HR actions. IBM says AskHR automates more than 80 internal HR tasks and has engaged in over two million conversations with employees every year. It uses a two-tier approach, where AI resolves routine issues, and human advisers handle more complex cases. The company reports some operational benefits: a 94% success rate in answering commonly-asked questions, a 75% reduction in the number of lodged support tickets since 2016, and – the headline figure – a 40% reduction in HR operational costs over four years. But it’s important to note that AI is not used by IBM to route queries to existing materials. The automation is capable of completing the transaction, thus reducing the need to hand-off queries to human staff. Recruitment and onboarding efficiencies Vodafone’s 2024 annual report describes an internal platform it calls ‘Grow with Vodafone‘. The company says it’s reduced its time-to-hire periods from 50 days to 48 days, made the job application process simpler, and added personalised skills-based job recommendations for applicants. That’s led to a 78% reduction in questions posed by potential applicants and those onboarding into new roles. The company also has a global headcount planning tool that reduces the manual work needed to assemble necessary data, plus there’s an AI-powered global HR ‘data lake’ that standardises dashboards and reduces the need for manual reporting – stakeholders can dive into the data themselves and surface the insights they need. Training and internal support Big employers have challengers getting new staff up to speed quickly; so-called time-to-competence. Bank of Americas’ newsroom describes how its onboarding and professional development organisation, ‘The Academy’ uses AI for interactive coaching, with employees completing over a million simulations in a year. The organisation operates ‘Erica for Employees‘, an internal assistant that handles topics like health benefits and payroll or tax forms for employees. It’s used by over 90% of employees – for the IT service desk, having Erica triage situations is impactful, with a reduction of more than 50% in incoming calls. Such tools reduce hidden work (searching, repeating questions, waiting for answers) and its associated costs. Plus, a shorter time-to-competence is especially valuable in regulated and customer-facing environments. Frontline work at big employers Walmart’s June 2025 corporate update describes rolling out AI tools via its associates’ app, which include a workflow tool that prioritises and recommends work tasks. At the time of publication, it was early days, but based on early results, Walmart says team leads and store managers are beginning to see shift planning times down from 90 to 30 minutes. As an employer of a diverse workforce, its app’s real-time translation ability (44 languages) is invaluable. The company is currently upgrading its associates’ software with AI to turn its internal process guides into multi-lingual instructions. It has more than 900,000 employees using the system every week users, with more than three million queries per day going through the associates’ conversational AI platform. Workforce efficiencies at Walmart scale is impressive, but for every size of business, there are clear advantages to be gained from giving employees faster guidance and better support across multilingual teams. In addition to the immediate cost savings, simple-to-use and effective software of this type affects retention, safety standards, and service quality – all for the better. Governance and human safety nets Multinational bank, HSBC’s publication, “Transforming HSBC with AI” describes over 600 AI use cases in operation at the company, and says colleagues have access to an LLM-based productivity tool for tasks like translation and document analysis. In an environment where governance and data security are of paramount importance, it’s ensured that all automated systems abide by existing codes, something that’s enforced by dedicated AI Review Councils and AI lifecycle management frameworks. In HR this matters, regardless of vertical. Governance decisions should shape what can be automated, how people data is handled, and how accountability is maintained into the long term. HR data is often personally-identifiable, so the highest standards – and their maintenance – are critically important. Operational trade-offs Operational impact is about trust as well as speed and efficiency. A self-service agent answering confidently but incorrectly creates rework, escalations, and causes problems. A pragmatic pattern for reducing risk is to keep humans in the loop, especially for complex decisions. IBM’s two-tier model, Vodafone’s tailored job recommendations, and Walmart an HSBC’s data governance and security bring oversight. Hybrid service models plus data discipline and oversight are what enable AI to scale without undermining employee confidence or fairness. Where this is heading The pattern of successful operational deployment has been consistent in the cases of the HR function in these large enterprises. They each started with high-volume questions and repetitive transactions, expanded into hiring and training, and then pushed AI to the frontline where it can save time. The biggest gains come when AI turns HR from a service ****** into a faster, more consistently-operating function. (Image source: “Business Meetings” by thinkpanama is licensed under CC BY-NC 2.0.) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post AI in Human Resources: the real operational impact appeared first on AI News. View the full article

By December 2025, AI adoption on Wall Street had moved past experiments inside large US banks and into everyday operations. Speaking at a Goldman Sachs financial-services conference in New York on 9 December, bank executives described AI—particularly generative AI—as an operational upgrade already lifting productivity across engineering, operations, and customer service. The same discussion also surfaced a harder reality. If banks can produce more with the same teams, some roles may no longer be required at current levels once demand stabilises. How Wall Street banks say AI is delivering results today JPMorgan: operational gains begin to compound Marianne Lake, chief executive of consumer and community banking at JPMorgan, said productivity in areas using AI has risen to around 6%, up from roughly 3% before deployment. She added that operations roles could eventually see productivity gains of 40% to 50% as AI becomes part of routine work. Those gains rest on deliberate choices rather than broad experimentation. JPMorgan has focused on secure internal access to large language models, targeted changes to workflows, and tight controls on how data is used. The bank has described its internal “LLM Suite” as a controlled setting where staff can draft and summarise content using large language models. Wells Fargo: output rising ahead of staffing changes Wells Fargo CEO Charlie Scharf said the bank has not reduced headcount because of AI so far, but noted that it is “getting a lot more done.” He said management expects to find areas where fewer people are needed as productivity improves. In comments reported the same day, Scharf said the bank’s internal budgets already point to a smaller workforce by 2026, even before factoring in AI’s full impact. He also flagged higher severance costs, suggesting preparations for future adjustments are under way. PNC: AI speeds up a long-running shift PNC CEO Bill Demchak positioned AI as an accelerator rather than a new direction. He said the bank’s headcount has stayed largely flat for about a decade, even as the business expanded. That stability, he said, came from automation and branch optimisation, with AI likely to push the trend further. Citigroup: gains in software and customer support Citi’s incoming CFO Gonzalo Luchetti said the bank has recorded a 9% productivity improvement in software development. That mirrors a broader pattern across large firms adopting AI copilots to support coding work. He also pointed to two customer service areas where AI is helping: improving self-service so fewer calls reach agents, and supporting agents in real time when customers do need to speak with a person. Goldman Sachs: workflow changes paired with hiring restraint According to Reuters, Goldman Sachs’ internal “OneGS 3.0” programme has focused on using AI to improve sales processes and client onboarding. It has also targeted process-heavy functions such as lending workflows, regulatory reporting, and vendor management. These changes are unfolding alongside job cuts and a slower pace of hiring, linking workflow redesign directly to staffing decisions. Where Wall Street banks see the earliest AI productivity gains Across banks, the clearest gains are showing up in work that relies heavily on documents, follows repeatable steps, and operates within defined rules. Generative AI can shorten the time needed to search for information, summarise material, draft content, and move work through approval chains—especially when paired with structured processes and human checks. Common areas seeing early impact include: Operations: drafting responses, summarising cases, and resolving exceptions more quickly Software development: generating code, writing tests, refactoring, and producing documentation Customer service: stronger self-service combined with real-time support for agents Sales support and onboarding: pulling data from documents, filling forms, and speeding up client setup Regulatory reporting: assembling narratives and evidence faster, under strict review and controls Why governance shapes the pace of adoption For banks, enthusiasm is not the main constraint. Control is. US regulators have long required strong oversight of models, and those expectations extend to AI systems. Guidance such as the Federal Reserve and OCC’s SR 11-7 sets standards for model development, validation, and ongoing review. A 2025 report from the US Government Accountability Office noted that existing model risk management principles already apply to AI, including testing and independent oversight. In practice, this pushes banks toward designs that can be examined and traced. AI use is often limited in how independently it can act. Prompts and outputs are logged, performance is monitored for drift, and humans remain responsible for high-impact decisions such as lending, dispute handling, and official reporting. Productivity rises, but employment questions remain The comments from bank leaders point to a phased shift. The first phase looks like stable headcount paired with higher output as AI tools spread across teams. The second phase begins once those gains become consistent enough to influence staffing plans, through attrition, role changes, or targeted cuts. Signals from Wells Fargo around 2026 headcount planning and severance costs suggest some banks are approaching that second stage. At a broader level, institutions such as the International Monetary Fund have warned that AI could affect a large share of jobs worldwide, with different mixes of automation and augmentation depending on role and region. The World Economic Forum’s Future of Jobs Report 2025 also projects substantial job movement as companies adopt AI and adjust skill needs. What AI means for Wall Street bank strategy beyond 2025 Banks that gain the most from AI are likely to focus on three areas at once: redesigning workflows rather than layering on chat tools, building strong data foundations, and putting governance in place that supports speed without eroding trust. Research firms argue the financial stakes are high. McKinsey estimates that generative AI could deliver between $200 billion and $340 billion in annual value for the banking sector, largely through productivity improvements. The open question is no longer whether AI can deliver results in banking. It is how quickly banks can make those gains routine while preserving audit trails, security, and customer safeguards—and how they manage the workforce changes that follow. (Photo by Lo Lo) See also: BNP Paribas introduces AI tool for investment banking 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 Wall Street’s AI gains are here — banks plan for fewer people appeared first on AI News. View the full article

Artificial intelligence has been part of the insurance sector for years – the Finance function in many businesses is often the first to automate. But what’s remarkable in the instance of AI is how directly the technology is woven into day-to-day operational work. Not sitting in the background as a niche modelling capability, AI is now used in places where insurers spend most of their time and money: claims handling, underwriting, and running complex programmes. Industry giants Allianz, Zurich, and Aviva have published evidence in just the last 12 months illustrating their shifts from experimentation stages to production-grade tools that support frontline workers in real workflows. Simple claims: Fewer admin bottlenecks Claims operations are a natural proving ground for AI because they comprise of a combination of paperwork and human judgement, and are usually undertaken in an environment of time pressure. Allianz describes its Insurance Copilot as an AI-powered tool that helps claims handlers automate repetitive tasks and pull together relevant information that would otherwise require multiple searches on different systems. There’s a notable change to the workflows, Allianz outlines. The Copilot starts with data gathering, summarising claim and contract details so a handler can get just the essentials, quickly. The algorithm then performs document analysis, operations that include interpreting agreements and comparing claims against policy details. The tool flags discrepancies and suggests next steps. Once the human operator has taken their decision, the Copilot assists drafts context-aware emails. This is the kind of daily activity that insurers care about, and by using their AI tools, they get reduced turnaround time, smoother settlements, and less friction for staff and customers. Allianz also frames AI as a way to reduce unnecessary payouts by highlighting important factors adjusters might otherwise miss. That has a clear impact on the company’s overall bottom line. Complex documents to usable decisions The quality of underwriting is determined by the quality of information available. Aviva uses the example of underwriters needing to read GP medical reports. The company says it’s launching an AI-powered summarisation tool that uses genAI to analyse and summarise these reports, which can sometimes amount to dozens of pages of medical text. The AI functions let underwriters make faster, more informed decisions. The immediate value here is not AI replacing the underwriter, but technology reducing the time spent reading. The insurer is explicit that underwriters will review summaries and make the final decision – not the AI. That distinction matters because underwriting is technical and sensitive; compressing documents into decision-ready summaries can speed up processing, but it also raises questions about accuracy, omissions and auditability. Aviva addresses this by pointing to its “rigorous testing and controls“. An active test phase processed around 1,000 cases before roll-out to ensure the standards it required, the company says. Uncertain contracts and servicing in multinational programmes Commercial insurance is an area with its own challenges, which include the complexity from working in multiple jurisdictions, and the regional differences between policies and stakeholders. Zurich says generative AI’s ability to process unstructured information lets multinational insurance work more easily across several countries, helping it build quicker, more accurate pictures of commercial insurance offerings, and simplifying submissions in different countries. Zurich also highlights contract certainty as a practical outcome: multinational programmes involve layered documents, varied local requirements and have the pervasive need for constant checking. It says GenAI helps internal experts compare, summarise and verify coverage in a programme using the operator’s native language, “in a fraction of the time” compared with the manual effort required to translate and capture the nuance of international differences. Although this area isn’t customer-facing, genAI improves the company’s responsiveness by letting its underwriters, risk engineers, and claims professionals work more efficiently. Zurich also refers to AI “joining up the dots”, able to spot trends in data that would – given the quantity of information – go unnoticed by human staff. Indeed, AI amplifies its experts’ judgement rather than displacing it. The common thread: augmentation, not automation-for-automation’s sake Across these three examples, a consistent pattern emerges: AI handles the heavy lifting of reading, searching, and drafting; high-volume tasks in insurance operations. Humans remain accountable for consequent decisions, whether it’s claim payments or underwriting acceptance. (Allianz describes a “human-in-the-loop” approach, and Aviva and Zurich similarly emphasise experts retaining decision-making control). Operational control and scalability are treated as major concerns: pilots, testing, domain-by-domain tuning, and expansion into lines of business are integral part of the narrative. What this means for the sector Insurers see faster cycle times, better consistency, reduced manual work, and a path to scaling. Their challenge is implementing tools responsibly, which is defined by secure data handling, explainability where needed, and the training of teams so they can question outputs appropriately. AI is becoming less of a headline in the sector and more of an everyday reality, a practical silicon colleague in the routine work of insurance profitability. (Image source: “house fire” by peteSwede 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 Ensuring effective AI in insurance operations appeared first on AI News. View the full article

Big Pharma’s AI race extends across drug discovery, development, and clinical trials—but AstraZeneca has distinguished itself by deploying AI clinical trials technology at an unprecedented public health scale. While competitors optimise internal R&D pipelines, AstraZeneca’s AI is already embedded in national healthcare systems, screening hundreds of thousands of patients and demonstrating what happens when AI moves from pharmaceutical labs into actual patient care. The clinical validation backs this approach. AstraZeneca’s CREATE study, presented at the European Lung ******* Congress in March 2025, demonstrated a 54.1% positive predictive value for its AI chest X-ray tool—far exceeding the pre-defined success threshold of 20%. Behind those numbers: over 660,000 people screened in Thailand since 2022, with AI detecting suspected pulmonary lesions in 8% of cases. More critically, Thailand’s National Health Security Office is now scaling this technology across 887 hospitals with a three-year budget exceeding 415 million baht. This isn’t just a pilot program or proof-of-concept. It’s AI clinical trials technology deployed at the national healthcare system scale. The strategic divergence in AI clinical trials approaches The contrast with competitors is revealing. Pfizer’s ML Research Hub has compressed drug discovery timelines to approximately 30 days for molecule identification. The company used AI to develop Paxlovid in record time, with machine learning analysing patient data 50% faster than traditional methods. Pfizer now deploys AI in over half its clinical trials. Novartis partnered with Nobel Prize winner Demis Hassabis’s Isomorphic Labs and Microsoft for “AI-driven drug discovery.” Its Intelligent Decision System uses computational twins to simulate clinical trial processes, with AI-identified sites reportedly recruiting patients faster than traditional selection methods. Roche’s “lab in a loop” strategy iterates AI models with laboratory experiments. Having acquired Foundation Medicine and Flatiron Health, Roche built the industry’s largest clinical genomic database—over 800,000 genomic profiles across 150+ tumour subtypes—targeting 50% efficiency gains in safety management by 2026. AstraZeneca’s clinical operations advantage What sets AstraZeneca apart in AI clinical trials isn’t just ambition—it’s execution at scale. The company runs over 240 global trials in its R&D pipeline and has systematically embedded generative AI across clinical operations. It’s an “intelligent protocol tool,” developed with medical writers, that has reduced document authoring time by 85% in some cases. The company uses AI for 3D location detection on CT scans, slashing the time radiologists spend on manual annotation. More significantly, AstraZeneca is pioneering virtual control groups for AI clinical trials using electronic health records and past trial data to simulate placebo arms—potentially reducing the number of patients receiving non-active treatments. This represents a fundamental rethinking of clinical trial design itself. The lung ******* screening program exemplifies this strategic focus. Using Qure.ai’s qXR-LNMS tool, AstraZeneca isn’t just conducting trials—it’s transforming public health infrastructure. The December 2025 expansion includes a new industrial worker screening program targeting 5,000 workers across four Thai provinces, now expanding beyond lung ******* to include heart failure detection. The timeline acceleration race Industry metrics show why AI clinical trials matter: Traditional drug development takes 10-15 years with a 90% failure rate. AI-discovered drugs achieve 80-90% Phase I success rates—double the 40-65% traditional benchmark. Over 3,000 AI-assisted drugs are in development, with 200+ AI-enabled approvals expected by 2030. Pfizer moves from molecule identification to clinical trials in six-week cycles. Novartis analyses 460,000 clinical trials in minutes versus months. Yet AstraZeneca’s model delivers immediate patient impact—detecting cancers today in underserved populations, often before symptoms appear. The US$410 Billion question The World Economic Forum projects AI could generate US$350-$410 billion annually for pharma by 2030. The question is which approach captures more value: faster drug discovery or more efficient clinical operations? Pfizer’s bet on computational drug design and Novartis’s AI-powered trial site selection may yield breakthrough molecules. Roche’s integrated pharma-diagnostics model creates a proprietary data moat. But AstraZeneca’s strategy of embedding AI clinical trials throughout operations—from protocol generation to patient recruitment to regulatory submissions—is demonstrably reducing time-to-market while building real-world evidence at scale. The company’s partnership approach is equally distinctive. While others acquire AI companies or build internal hubs, AstraZeneca collaborates with technology partners like Qure.ai and Perceptra, regulatory bodies, and national health systems to deploy AI clinical trials where infrastructure gaps exist. As AstraZeneca pursues its 2030 goal of delivering 20 new medicines and reaching us$80 billion in revenue, its AI clinical trials advantage isn’t just about speed—it’s about proving AI’s value in the most regulated, risk-averse phase of pharmaceutical development. While competitors race to discover the next breakthrough molecule, AstraZeneca is reengineering how clinical trials themselves are conducted. The winner may not be determined by who builds the most sophisticated algorithm, but by who deploys AI clinical trials technology where it demonstrably improves patient outcomes—at scale, under regulatory scrutiny, and within real healthcare systems. And in that race, AstraZeneca currently leads. (Photo by AstraZeneca) See also: Google AMIE: AI doctor learns to ‘see’ medical images Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post AstraZeneca leads big pharma’s AI clinical trials revolution with real-world patient impact appeared first on AI News. View the full article

Roblox is often seen as a games platform, but its day-to-day reality looks closer to a production studio. Small teams release new experiences on a rolling basis and then monetise them at scale. That pace creates two persistent problems: time lost to repeatable production work, and friction when moving outputs between tools. Roblox’s 2025 updates point to how AI can reduce both, without drifting away from clear business outcomes. Roblox keeps AI where the work happens Rather than pushing creators toward separate AI products, Roblox has embedded AI inside Roblox Studio, the environment where creators already build, test, and iterate. In its September 2025 RDC update, Roblox outlined “AI tools and an Assistant” designed to improve creator productivity, with an emphasis on small teams. Its annual economic impact report adds that Studio features such as Avatar Auto-Setup and Assistant already include “new AI capabilities” to “accelerate content creation”. The language matters—Roblox frames AI in terms of cycle time and output, not abstract claims about transformation or innovation. That framing makes it easier to judge whether the tools are doing their job. One of the more practical updates focuses on asset creation. Roblox described an AI capability that goes beyond static generation, allowing creators to produce “fully functional objects” from a prompt. The initial rollout covers selected vehicle and weapons categories, returning interactive assets that can be extended inside Studio. This addresses a common bottleneck where drafting an idea is rarely the slow part; turning it into something that behaves correctly inside a live system is. By narrowing that gap, Roblox reduces the time spent translating concepts into working components. The company also highlighted language tools delivered through APIs, including Text-to-Speech, Speech-to-Text, and real-time voice chat translation across multiple languages. These features lower the effort required to localise content and reach broader audiences. Similar tooling plays a role in training and support in other industries. Roblox treats AI as connective tissue between tools Roblox also put emphasis on how tools connect to one another. Its RDC post describes integrating the Model Context Protocol (MCP) into Studio’s Assistant, allowing creators to coordinate multi-step work across third-party tools that support MCP. Roblox points to practical examples, such as designing a UI in Figma or generating a skybox elsewhere, then importing the result directly into Studio. This matters because many AI initiatives slow down at the workflow level. Teams spend time copying outputs, fixing formats, or reworking assets that do not quite fit. Orchestration reduces that overhead by turning AI into a bridge between tools, rather than another destination in the process. Linking productivity to revenue Roblox ties these workflow gains directly to economics. In its RDC post, the company reported that creators earned over $1 billion through its Developer Exchange programme over the past year, and it set a goal for 10% of gaming content revenue to flow through its ecosystem. It also announced an increased exchange rate so creators “earn 8.5% more” when converting Robux into cash. The economic impact report makes the connection explicit. Alongside AI upgrades in Studio, Roblox highlights monetisation tools such as price optimisation and regional pricing. Even outside a marketplace model, the takeaway is clear: when AI productivity is paired with a financial lever, teams are more likely to treat new tooling as part of core operations rather than an experiment. Roblox uses operational AI to scale safety systems While creative tools attract attention, operational AI often determines whether growth is sustainable. In November 2025, Roblox published a technical post on its PII Classifier, an AI model used to detect attempts to share personal information in chat. Roblox reports handling an average of 6.1 billion chat messages per day, and says the classifier has been in production since late 2024, with a reported 98% recall on an internal test set at a 1% false positive rate. This is a quieter form of efficiency. Automation at this level reduces the need for manual review and supports consistent policy enforcement, which helps prevent scale from becoming a liability. What carries across, and what several patterns stand out: Put AI where decisions are already made. Roblox focuses on the build-and-review loop, rather than inserting a separate AI step. Reduce tool friction early. Orchestration matters because it cuts down on context switching and rework. Tie AI to something measurable. Creation speed is linked to monetisation and payout incentives. Keep adapting the system. Roblox describes ongoing updates to address new adversarial behaviour in safety models. Roblox’s tools will not translate directly to every sector. The underlying approach will. AI tends to pay for itself when it shortens the path from intent to usable output, and when that output is clearly connected to real economic value. (Photo by Oberon Copeland @veryinformed.com) See also: Mining business learnings for AI deployment 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 Roblox brings AI into the Studio to speed up game creation appeared first on AI News. View the full article

AI search engines and generative AI tools are certainly transforming how people discover information online. Far from making SEO specialists obsolete, the shift highlights clearly why skilled human optimisers remain more important than ever. As generative AI search tools reshape the digital landscape, many wonder whether traditional SEO has reached the end. Despite AI’s growing influence, the fundamentals of discoverability, authority, clarity, and trust remain vital. The rise of AI-powered search makes the role of SEO specialists more important than before, ensuring content not only reaches audiences but resonates authentically. A digital environment shaped by automation still depends heavily on human expertise, especially when credibility is at stake. Why AI expands rather than replaces the work of SEO specialists The assumption that AI will replace SEO specialists ignores how modern search works. AI expands what optimisation can achieve, but it does not eliminate the need for skilled human oversight. Traditional ranking signals like site structure, depth of information, internal linking and authority continue to influence visibility, even in AI-driven answer engines. This means content must still be crafted with intent, clarity, and expertise. AI-powered search engines prioritise well-structured, trustworthy information, reinforcing the importance of thoughtful optimisation. In this brand new landscape, SEO now spans two parallel environments: classic search engines and AI-generated answer systems. Competing effectively in both requires strategic thinking, careful content planning, and a nuanced understanding of user intent: areas where human specialists excel, beyond what AI can replicate. Agencies like Brath, which presents SEO as one of the most profitable long-term marketing channels when executed with technical precision and strategic insight, clearly understand this reality. Its service model remains grounded in proven disciplines: technical improvements, keyword strategy, content refinement, and authority building. Rather than leaning on automation, it demonstrates that sustainable SEO relies on human expertise, methodical analysis, and consistent optimisation. Human insight still drives strategy in an AI-powered search world AI excels at processing data, but it still falls short when deeper context, cultural awareness, and nuanced decision-making are required. SEO is more than a technical exercise; it is a strategic craft rooted in understanding people, their motivations, expectations, and the subtle differences in how they search for answers. The human dimension is something AI cannot replicate. Specialists bring an understanding of why search behaviour shifts, how tone influences trust, and which narratives resonate in different industries. They also grasp how economic cycles, news events, and regulations affect user queries and engagement patterns. AI can identify patterns, but it cannot interpret them fully or determine which insights align with brand values or long-term goals. The increasing emphasis on E-E-A-T signals, Experience, Expertise, Authoritativeness and Trustworthiness, further reinforces this need for human guidance. An algorithm cannot fabricate authentic authority. It must be demonstrated through lived experience, credibility, and careful communication. SEO specialists ensure that these qualities are baked into every page and every message, guiding brands through an increasingly complex search environment where trust matters more than ever. How SEO specialists use AI to deliver faster, smarter optimisation Where AI shines is in support, not replacement. Modern SEO specialists use AI tools to automate repetitive tasks, accelerate data analysis, and uncover patterns at scale. AI can rapidly produce initial keyword lists, identify technical issues, or generate draft structures for content. These capabilities shorten production timelines and free specialists to focus on the strategic, creative, and high-value decisions that define effective optimisation. The balanced approach enables better outcomes. AI handles the heavy lifting, while human specialists interpret the results, refine the strategy, and ensure everything aligns with the brand’s goals, tone, and audience requirements. It is this combination of speed and discernment that shapes the future of SEO. The creative and analytical skills AI cannot replicate Even the most advanced AI tools cannot fully replicate creativity, intuition, or emotional intelligence. SEO specialists draw on analytical skills and creative insight to produce content that stands out in competitive digital markets. AI can propose phrasing, but it cannot determine when a message needs personality, authority, or nuance to feel trustworthy. In industries where reputation matters, like fintech, finance, healthcare, law, and education, human-shaped content is essential. Users want reassurance that the guidance they read comes from people with real expertise. AI-generated content often lacks the depth or subtlety needed to build that trust. Human specialists also excel at interpreting data in context. They understand how search intent connects to behaviour, how content influences perception, and how messaging supports or undermines brand identity. Their ability to connect analytical insights with emotional resonance is something AI simply cannot achieve. Building the next generation of SEO careers in an AI-enhanced industry AI is not shrinking the field of SEO; it is expanding it. The next generation of SEO professionals will blend human-centred strategy with AI-assisted efficiency. Modern SEO requires fluency in multiple areas: data interpretation, UX thinking, structured content, behavioural insights, content design, and now, AI tool management. Specialists who embrace both disciplines will lead the industry forward. Their work will become more strategic, more aligned with long-term business value, and more influential as search becomes increasingly complex and multi-modal. AI may automate tasks, but strategy, creativity, judgement, and trust-building remain human strengths. And as AI transforms how search tools interpret information, those strengths will only become more essential. SEO is not disappearing. It is evolving into a richer, more strategic, and more human discipline. 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Mining conglomerate BHP describes AI as the way it’s turning operational data into better day-to-day decisions. A blog post from the company highlights the analysis of data from sensors and monitoring systems to spot patterns and flag issues for plant machinery, giving choices to decision-makers that can improve efficiency and safety – plus reduce environmental impact. For business leaders at BHP, the useful question was not “Where can we use AI?” but “Which decisions do we make repeatedly, and what information would improve them?” Portfolio not showcase BHP describes the end-to-end effects of AI on operations, or as it puts it, “from mineral extraction to customer delivery.” Leaders had decided to move beyond pilot rollouts, treating AI as an operational capability. It started with a small set of problems that affected the company’s performance; places where change could be measured in results. The company found it could avoid unplanned downtime of machinery, plus it tightened its energy and water use. Each use case addressing a small but impactful problem was given an owner and an accompanying KPI. Results were reviewed with the same regularity used for other operational performance monitoring elsewhere in the company. Where BHP uses AI daily In addition to BHP focusing specifically on areas such as predictive maintenance and energy optimisation, it gave consideration to using AI in more adventurous yet important operations such as autonomous vehicles and real-time staff health monitoring. Such categories can translate well to other asset-heavy environments, across logistics, manufacturing, and heavy industry. Predictive maintenance Predictive maintenance is the process of planning repairs in scheduled downtime to reduce unexpected failures and costly, unplanned stoppages. Here, AI models analyse equipment data from on-board sensors and can anticipate maintenance needs. This cuts breakdown numbers and reduces equipment-related safety incidents. BHP runs predictive analytics across most of its load-and-haul fleets and its materials handling systems. A central maintenance centre provides real-time and longer-range indications of machine health and potential failure or degradation. Prediction has become an integral part of its machinery-heavy operations, where previously, such information was presented as ‘just another’ report, one that could get lost in the bureaucracy of the company. It models and defines thresholds which trigger actions directly to teams planning maintenance. Energy and water optimisation Deploying predictive maintenance in this manner at its facilities in Escondida in Chile, the company reports savings of more than three giga-litres of water and 118 gigawatt hours of energy in two years, attributing the gains directly to AI. The technology gives operators real-time options and analytics that identify anomalies and automate corrective actions at multiple facilities, including concentrators and desalination plants. The lesson it’s learned is placing AI where decisions happen: When operators and control teams can act on recommendations in real time, improvements compound. Conversely periodic reporting means decisions are only taken if staff both see the results of data, and then decide it’s necessary. The realtime nature of data analysis and the use of triggers-to-action mean the differences becomes quickly apparent. Autonomy and remote operations BHP is also using more advanced technologies like AI-supported autonomous vehicles and machinery. These are higher-risk areas, and the tech has been found to reduce worker exposure to risk, and cut the human error factor in incidents. At the company, complex operational data flows through regional centres from remote facilities. So, without the use of AI and analytics, staff would not be able to optimise every decision in the way that software achieves. The use of AI-integrated wearables is increasing in many industries, including engineering, utilities, manufacturing, and mining. BHP leads the way in protecting its staff, who often work in very challenging conditions. Wearables can monitor personal conditions, reading heart rate and fatigue indicators, and provide real-time alerts to supervisors. One example might be ‘smart’ hard-hat sensor technology, used by BHP at Escondida, which measures truck driver fatigue by analysing drivers’ brain waves. A plan leaders can run Regardless of industry, decision-makers can draw learnings from BHP’s experiences in deploying AI at the (literal) coal-face. The following plan could help leaders in their own strategies to leverage AI in operational problem-areas: Choose one reliability problem and one resource-efficiency problem that operations teams already track, then attach a KPI. Map the workflow: who will see the output and what action they can take? Put basic governance in place for data quality and model monitoring, then review performance alongside operational KPIs. Start with decision support in higher-risk processes, and automate only after teams validate controls. (Image source: “Shovel View at a Strip Mining Coal” by rbglasson is licensed under CC BY-NC-SA 2.0.) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Mining business learnings for AI deployment appeared first on AI News. View the full article

BNP Paribas is testing how far AI can be pushed into the day-to-day mechanics of investment banking. According to Financial News, the bank has rolled out an internal tool called IB Portal, designed to help bankers assemble client pitches more quickly and with less repetition. Pitch preparation sits at the centre of investment banking work. Teams pull together market views, deal history, and tailored narratives under tight timelines. Much of that effort repeats work that already exists elsewhere in the organisation. Slides, charts, and precedent analysis are often rebuilt from scratch, even when similar material has been used before by another team or office. IB Portal is meant to reduce that waste. The system searches BNP Paribas’s past pitch materials and uses what the bank describes as “smart prompts” to surface relevant slides, analysis, and supporting content for a new mandate. George Holst, head of the corporate clients group at BNP Paribas, said the tool functions like an AI-powered search engine that helps bankers find what matters ahead of a pitch or client meeting. In his words, it can cut research time by days, giving teams more room to focus on strategy and client judgement. The use case matters because it places AI inside real, constrained workflows rather than around them. Pitch decks are not generic documents. They reflect internal viewpoints, client-specific details, and regulatory requirements. Making an AI tool useful in this setting depends less on conversational flair and more on structure. That includes deciding which materials are searchable, setting clear access controls in regions and business lines, and defining how retrieved content moves from internal draft to client-ready output. In practice, that also means traceability. Bankers need to see where information comes from, and anything produced by the system still needs human review before it leaves the firm. Without those checks, the risk of errors or inappropriate disclosure rises quickly. BNP Paribas builds AI tools on internal platforms The portal also fits into a broader internal build-out at BNP Paribas. In June 2025, the bank outlined an “LLM as a Service” platform aimed at giving its business units shared access to large language models in the group’s own infrastructure. The platform is run by internal IT teams and hosted in BNP Paribas data centres with dedicated GPU capacity. The bank said it supports a mix of models, including open-source options and systems from Mistral AI, with plans to add models trained on internal data. Intended use cases include internal assistants, document drafting, and information retrieval. Other large banks are taking a similar approach. JPMorganChase has pointed to growing use of its internal “LLM Suite”, which provides staff access to models in a controlled environment. Reuters has reported on Goldman Sachs’s investment in AI engineering and its rollout of a proprietary “GS AI Assistant”. UBS has discussed an internal M&A “co-pilot” used for idea generation. Alongside these in-house efforts, specialist tools like Rogo have found traction at firms including Nomura and Moelis, pointing to demand for finance-specific AI tools. For BNP Paribas, the real test is whether IB Portal becomes part of everyday work rather than a one-off experiment. The potential benefits are straightforward: less time spent searching, fewer duplicated decks, and better reuse of institutional knowledge. The risks are just as familiar. Hallucinated data, unclear sources, and accidental exposure of sensitive information all carry real consequences in banking. The most stable deployments keep AI tightly constrained. That usually means grounding outputs in approved internal content, applying role-based access controls, recording how tools are used, and requiring human sign-off before anything reaches a client. If IB Portal operates in those boundaries, it offers a practical view of how enterprise AI is taking shape: not as a source of instant answers, but as a faster and safer way to navigate what an organisation already knows. (Photo by Enrico Frascati) See also: CEOs still betting big on AI: Strategy vs. return on investment in 2026 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 BNP Paribas introduces AI tool for investment banking appeared first on AI News. View the full article

JPMorgan Chase’s AI strategy is delivering measurable returns – but at a human cost. The bank isn’t hiding the fact. With 200,000 employees now using its proprietary LLM Suite platform daily and AI benefits growing 30-40% annually, America’s largest bank is executing what Chief Analytics Officer Derek Waldron calls a plan to create the world’s first “fully AI-connected enterprise.” What infrastructure backs this transformation? A US$18 billion annual technology budget, 450+ AI use cases in production, and a platform that won American Banker’s 2025 Innovation of the Year Grand Prize. But JPMorgan’s candour about workforce displacement – operations staff projected to fall at least 10% – reveals the complexity of enterprise AI beyond the promotional headlines. LLM suite: From zero to 200,000 users in eight months Released in summer 2024, LLM Suite reached 200,000 users in eight months through an opt-in strategy that created what Waldron describes as “healthy competition, driving viral adoption. This isn’t just a chatbot: LLM Suite functions as a “full ecosystem” connecting AI to firm-wide data, applications, and workflows. The model-agnostic architecture integrates OpenAI and Anthropic models, with updates every eight weeks. Investment bankers create five-page decks in 30 seconds – work that previously took junior analysts hours. Lawyers scan and generate contracts. Credit professionals extract covenant information instantly. Call centre tool EVEE Intelligent Q&A improved resolution times through context-aware responses. “A little under half of JPMorgan employees use gen AI tools every single day,” Waldron told McKinsey in October 2025. “People use it in tens of thousands of ways specific to their jobs.” JPMorgan Chase AI strategy delivers 30-40% annual ROI growth JPMorgan tracks ROI at the individual initiative level – not platform-wide vanity metrics. Since inception, AI-attributed benefits have grown 30-40% year-over-year. The strategy combines top-down focus on transformative domains (credit, fraud, marketing, operations) with bottom-up democratisation, letting employees innovate in job families. McKinsey’s Kevin Buehler estimates US$700 billion in potential banking cost savings industry-wide. But much will be “competed away” to customers. Industry return on tangible equity could drop one to two points, while AI pioneers could see four-point increases versus slow movers. Waldron acknowledges productivity gains don’t automatically translate to cost reductions. “An hour saved here and three hours there may increase individual productivity, but in end-to-end processes these snips often just shift bottlenecks.” Operations staff to fall 10% as AI agents take complex tasks JPMorgan’s consumer banking chief announced operations staff would decline at least 10% as the bank deploys “agentic AI” – autonomous systems handling multi-step tasks. The bank is building AI agents that execute cascading actions independently. Waldron demonstrated to CNBC how the system creates investment banking presentations in 30 seconds and drafts confidential M&A memos. AI favours client-facing roles – private bankers, traders, investment bankers. At risk: operations staff handling account setup, fraud detection, and trade settlement. New job categories are emerging: “context engineers” ensuring AI systems have proper information, knowledge management specialists, and up-skilled software engineers building agentic systems. Stanford researchers analysing ADP data found early-career workers (ages 22-25) in AI-exposed occupations saw 6% employment decline from late 2022 to July 2025. Shadow IT, trust, and the “value gap” problem JPMorgan’s transparency extends to acknowledging significant execution risks. Without enterprise-grade tools, employees might use consumer-grade AI – exposing sensitive data. JPMorgan built an in-house system for security and control. When AI performs correctly 85-95% of the time, human reviewers may stop checking carefully. The error rate compounds at scale. “When an agentic system does a cascading series of analyses independently for a long time, it raises questions about how humans can trust that,” Waldron told McKinsey. Many enterprises face “proof-of-concept hell” – numerous pilots that never reach production because they underestimate integration complexity. “There is a value gap between what the technology is capable of and the ability to fully capture that in an enterprise,” Waldron told CNBC. Even with US$18 billion, full realisation takes years. The JPMorgan playbook: What enterprises can learn JPMorgan’s approach offers replicable principles despite scale advantages. Democratise access but mandate nothing – the opt-in strategy created viral adoption. Build for security first, particularly in regulated industries. Implement model-agnostic architecture to avoid vendor lock-in. Combine top-down transformation with bottom-up innovation. Segment training by audience. Track ROI with discipline at the initiative level. Acknowledge complexity and plan accordingly – JPMorgan took over two years to build the LLM Suite. Not every enterprise has US$18 billion for technology or 200,000 employees. But core principles – democratisation, security-first architecture, avoiding vendor lock-in, and financial discipline – apply in industries and scale. Transformation with eyes wide open JPMorgan Chase’s AI strategy represents enterprise AI’s most transparent case study – complete with industry-leading adoption metrics, measurable ROI growth, and unflinching acknowledgement of workforce displacement. The bank’s success factors are clear: massive capital investment, model-agnostic infrastructure, democratised access paired with financial discipline, and realistic timelines. But Waldron’s candour about trust challenges, the “value gap” between capability and execution, and the multi-year journey ahead suggest that even US$18 billion and 200,000 engaged employees don’t guarantee seamless transformation. For enterprises evaluating their AI strategies, JPMorgan’s lesson isn’t that scale solves everything – it’s that honest assessment of both opportunities and execution risks separates genuine transformation from expensive experimentation. The question isn’t whether JPMorgan’s AI strategy is working. It’s whether the 10% workforce reduction and years-long complexity represent acceptable trade-offs for 30-40% annual benefit growth – and how many other enterprises can afford to find out. Editor’s note: The analysis draws from McKinsey’s October 2025 interview with Derek Waldron and Kevin Buehler, CNBC’s September 2025 exclusive demonstration of LLM Suite, American Banker’s June 2025 Innovation of the Year coverage, and Stanford University research on AI employment effects. 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. This comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post JPMorgan Chase AI strategy: US$18B bet paying off appeared first on AI News. View the full article

Across the US, workers are experiencing a seismic shift in workplace operations as AI literacy becomes a core part of business strategies. This is redefining roles and expectations, while workloads continue to increase and pressure intensifies. As the employment landscape transforms, it has become clear that the future of work and talent will be defined by three main areas – continuous education, greater flexibility, and AI literacy. This is according to new research conducted by The Harris Poll, who discovered that employers who invest heavily in educational benefits and AI development gain key advantages. Priya Krishnan, Chief Transformation Officer, Bright Horizons, said, “AI is rewriting job descriptions fast… employers who invest in education benefits and AI training now will build resilient, innovative teams.” AI skills demand heightens with workloads According to the 2025 EdAssist by Bright Horizons Education Index, 42% of US employees are expecting AI to significantly change their role over the next year, despite only 17% actively using AI on a frequent basis. However, the report revealed that the increased adoption of AI is not the key talking point – it’s the urgent skills mandate AI has triggered. Workers now feel they have to evolve to remain competitive, with 32% feeling increased pressure to learn new skills because of AI, an increase from 26% in 2024. The demand for upskilling comes as workforce stress hits new heights. For instance, 81% of employees report being pushed to take on greater workloads, and 80% stated they are expected to deliver work faster. AI has the ability to reduce some of this strain, but without proper guidance and training, employees will struggle to use it effectively. The report found that the adoption of AI technology rises to 76% when employers provide AI training, while workers who have access to training (84%) reported feeling more prepared for potential changes than those without (48%). Essentially, effective training turns uncertainty into confidence. Ultimately, 34% of those surveyed said they feel unprepared for changes driven by AI, and 42% said their employers expected them to develop their understanding of AI alone, without any formal training. “AI is rewriting job descriptions faster than most organisations can keep up,” said Priya Krishnan, Chief Transformation Officer at Bright Horizons. “Employers who act now will not only close important skill gaps but also build a culture of resilience and innovation. This is not about chasing trends but creating a workforce that thrives in a world where technology and human capability advance together. Education benefits, flexible learning, and AI literacy are the foundation for long-term competitiveness.” Education benefits drive retention and readiness The EdIndex highlights how investing in employee education can pay off, both for employees and employers. However, challenges are still present, with a key concern being financial barriers. According to the report, the skills gap is not simply about technology, it’s about “access and affordability.” 48% of employees surveyed said they avoid further education, citing the “fear of student debt” as the main reason. 34% reported that they cannot afford any more student loan debt, therefore curtailing any career advancement and blocking opportunities. The underscores the need for affordable, employer-sponsored learning programmes that allow employees the chance to progress. Employees highly value education support, with 85% reporting that they would be more loyal to employers that invest in continuing education. 86% said they would choose a job that offers such opportunities over one that doesn’t. 82% believe employer support for learning is crucial, while 29% aid it is essential for their job. In addition, 74% said they would be more loyal to an employer if they helped pay off their student loans, further emphasising how the alleviation of financial obstacles drives loyalty and retention, as well as ROI for employers. Over half (55%) of respondents are more likely to remain with a company if AI training or certification is available, and 76% actively use AI when training has been provided, compared to only 25% without formal training. 2026 predictions Looking ahead to 2026, the report highlights five key shifts and how employers can start preparing now. First, the report predicts AI literacy will become essential in the workplace as automation affects almost all jobs. Therefore, companies need to develop AI training programmes and ongoing support that enable employees to feel confident when using new tools. Secondly, upskilling is set to give employers and employees a significant competitive advantage. Organisations that invest heavily in both technical and soft skills will be better equipped for innovation and adaption going forward. Third, flexible education benefits will be key to ensuring employee retention. Workers are increasingly seeking employers who provide financial support and remove financial barriers that limit their learning, so those who offer certain “perks,” like debt-free programmes and tuition support will be more likely to attract and retain talent. The fourth major prediction is how continuous learning will become a key component of workplace environments. With roles evolving, the report says employers must set clear career pathways with personalised learning plans that help their employees grow. Finally, work-life balance is expected to play a huge role. Companies that blend education benefits with flexibility and well-being support that reduces stress have a higher chance of maintaining a loyal workforce. Pressure remains Although career satisfaction appears high at 87%, workers are under continuing pressure, with 78% feeling pushed to perform tasks outside their main role. As a result, priorities are shifting quickly. While earning a raise remains an important factor with 57% naming it a key priority, work-life balance (43%) and stress reduction (35%) are surging, rising sharply from 2024’s numbers of 35% and 29% respectively. While 90% say they feel somewhat prepared as their job role evolves, this confidence drops when AI is brought into the equation, as just 66% feel ready for AI’s certain impact. AI may be easing some areas of work, but its presence is also rising workplace pressure. The 2025 EdAssist report suggests, “The employers who act now by embedding skill development opportunities, AI training, short-form learning, and debt-conscious benefits into their talent strategy will future-proof their business and avoid being left behind as the competition for skills intensifies.” Only when employers take these steps can they build a workforce capable of sustaining long-term growth and navigating the changes that lie ahead. (Image source: “HMS St Albans’ Ship’s Company conduct a training day in the Plymouth exercise areas” by Royal Navy Media Archive is licensed under CC BY-NC 2.0.) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post AI literacy and continuous education are cornerstones appeared first on AI News. View the full article