At a press conference during MWC 2026, SK Telecom CEO Jung Jai-hun outlined what the company calls an “AI Native” strategy. The plan centres on reorganising infrastructure and making large investments so the company can help position Korea among the world’s top three AI powers.

“SKT is currently at a golden time of transformation, where the two tasks of ‘customer value innovation’ and ‘AI innovation’ intersect in a borderless, converged environment that goes beyond telecommunications,” Jung said. “SKT defines ‘the customer as the very essence of our business,’ and through innovation driven by AI, we will evolve into a company that makes meaningful contributions to our customers and to Korea.”

Rewriting telecom systems around AI at MWC 2026

At the core of the plan is a rebuild of SK Telecom’s integrated IT systems. The company said it will redesign sales, line management, and billing systems to be optimised for AI. The aim is to let the operator design and offer personalised plans and memberships based on each customer’s usage and behaviour patterns.

The company also plans to apply a Zero Trust security framework across its systems. This will include stronger authentication, access controls, network segmentation, and AI-based monitoring, according to the company’s briefing at MWC 2026.

For enterprises watching the telecom sector, this signals a broader shift. Telecom operators have long relied on legacy billing stacks and network management tools. Rebuilding those systems around AI could change how pricing, service design, and fault detection work in practice. It also raises questions about data governance and how customer data is used to train or tune AI models.

SK Telecom is also expanding its “autonomous network operations” strategy. The company said it will use AI to automate wireless quality management, traffic control, and network equipment operations. With AI-RAN technology, it aims to improve speed and reduce latency. These efforts were described in company materials shared during the press event.

A single AI agent across touchpoints

Another part of the strategy focuses on customer interaction. SK Telecom plans to redesign pricing, roaming, and membership services to make them simpler and more automated. It is developing what it calls an integrated AI agent to connect experiences across its main customer portal, T world, and its online store, T Direct Shop.

The company said the agent will analyse daily usage patterns and offer tailored suggestions across channels. It also plans to expand its AI Contact Center so customer service representatives can use AI tools during support calls.

Offline retail stores are part of the shift. SK Telecom said AI will help staff identify customer needs and offer recommendations after a store visit. It is also building “AI Personas” to analyse digital behaviour across customer segments and support conversational Q&A.

For enterprise leaders, this mirrors a wider pattern. Telecom operators are trying to move from reactive service models to predictive ones. The difference now is scale. By embedding AI into billing, customer service, and retail, SK Telecom is treating AI as an operating layer rather than a separate feature.

Building 1GW-class AI data centres

The infrastructure build-out is equally ambitious. SK Telecom said it will construct hyperscale AI data centres across Korea, targeting capacity that exceeds 1 gigawatt. It aims to attract global investment and position the country as a major AI data centre hub in Asia.

The company already operates a GPU cluster called Haein and applied its virtualisation solution, Petasus AI Cloud, to support GPU-as-a-service workloads last year. It now plans to offer that cloud solution globally.

SK Telecom also plans to build an AI data centre in Korea’s southwestern region in collaboration with OpenAI, according to the company’s announcement at MWC 2026.

On the model side, SK Telecom said its sovereign AI foundation model currently has 519 billion parameters, making it the largest in Korea. The company plans to upgrade it to more than one trillion parameters and add multimodal capabilities so it can process image, voice, and video data starting in the second half of the year.

CEO Jung framed the data centre and model build-out in national terms. “AIDC can be seen as the heart of Korea, and hyperscale LLMs as the brain,” he said. “By combining SKT’s AI capabilities with collaboration from domestic and global partners, we will lead true AI-native transformation for Korean customers and enterprises.”

For enterprise readers, the key issue is not parameter count alone. It is how such models will be applied in sectors like manufacturing. SK Telecom said it is working with SK hynix on a manufacturing-focused AI package that analyses process data in real time to reduce defect rates and improve equipment efficiency. The package will be offered as infrastructure, model, and solution.

Changing internal culture

The transformation also extends to internal operations. SK Telecom has built an “AX Dashboard” to track AI use across departments and individuals. It operates an “AI Board” to oversee AI transformation efforts and has created an “AI playground” where employees can build AI agents without coding. More than 2,000 AI agents are already in use across marketing, legal, and public relations, according to the company’s figures shared at the event.

“To drive future growth, we must reinvent our way of working from the ground up. SKT will fundamentally transform its corporate culture to be centred around AI,” Jung said.

For other enterprises, the takeaway is less about branding and more about structure. SK Telecom is tying infrastructure, models, applications, and internal governance into a single program. Whether it can execute at the scale it describes remains to be seen. What is clear is that AI is no longer positioned as a side project. It is becoming the operating model.

(Photo by PR Newswire)

See also: Nokia and AWS pilot AI automation for real-time 5G network slicing

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

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The announcement was made exclusively to Reuters by ASML’s chief technology officer Marco Pieters ahead of a technical conference in San Jose.

Current-generation EUV machines are approaching the outer edge of what they can do for advanced AI chip production, meaning the semiconductors powering large language models and AI accelerators are bumping up against a physical ceiling. High-NA EUV tools are designed to break through it, letting chipmakers print finer, denser circuit patterns in fewer steps. That translates directly into more powerful and efficient chips for AI workloads.

“I think that it’s at an important point to look at the amount of learning cycles that have happened,” Pieters told Reuters, referring to the volume of customer testing the machines have now accumulated.

The numbers that matter

ASML’s case for readiness rests on three data points it plans to release publicly. The High-NA EUV tools have now processed 500,000 silicon wafers, achieved roughly 80% uptime – with a target of 90% by year-end – and demonstrated imaging precision capable of replacing multiple conventional patterning steps with a single High-NA pass.

Together, Pieters said, those figures signal that the tools are ready for manufacturers to begin qualification. The machines don’t come cheap. At approximately US$400 million per unit – double the cost of the previous EUV generation – they represent one of the most expensive pieces of capital equipment in industrial history.

TSMC and Intel are among the named early adopters.

A two-to-three-year runway

Technical readiness and manufacturing integration are two different things, and Pieters was careful to separate them. Despite the milestone, full integration into high-volume production lines is still expected to take two to three years as chipmakers work through qualification and process development.

“Chipmakers have all the knowledge to qualify these tools,” he said – a vote of confidence in the industry’s ability to move, even if the timeline remains measured.

The next generation of chip performance improvements is on the horizon, not yet in hand. But with ASML now saying the starting gun has fired, the race to integrate High-NA EUV into production has formally begun.

(Photo by ASML)

See also: 2025’s AI chip wars: What enterprise leaders learned about supply chain reality

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.

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This week, Nokia and AWS presented a new network slicing system that uses AI agents to monitor network conditions and adjust resources automatically. The setup is being tested by telecom operators du in the United Arab Emirates and Orange in Europe and Africa, according to a joint announcement from Nokia.

Adaptive AI-driven networks

Network slicing lets operators create multiple virtual networks on the same physical infrastructure, each tuned for a different purpose. For example, a slice may be configured for emergency services or high-bandwidth consumer traffic. While slicing is part of the 5G standard, it has often required manual planning and fixed configurations, which limits how quickly networks can respond to changing demand.

The new system aims to close that gap by introducing AI agents that track network performance indicators like latency and congestion, and consider data like event schedules or weather conditions. Agents can then adjust network settings to keep services running to agreed performance levels, according to Nokia’s description of the pilot.

AWS said the solution combines Nokia’s slicing and automation tools with AI models delivered through Amazon Bedrock, its managed AI service platform. The companies describe the approach as “agentic AI”.

Autonomous connectivity

The interest in such systems reflects a long-standing challenge: 5G networks have delivered higher speeds and lower latency, but operators have struggled to turn those technical gains into new revenue streams. Research firm GSMA Intelligence notes many operators view network slicing as a potential source of enterprise income, though adoption has been slow due to operational complexity and uncertain demand.

If networks can adapt quickly to sudden demand, like a crowded stadium or emergency responders entering a disaster area, operators may be able to offer temporary connectivity or guaranteed service levels without manual setup.

Orange has said previously enterprise customers expect connectivity to behave more like cloud computing, where resources can scale on demand. Systems that allow automated control of network resources could help move telecom services closer to that model.

Cloud platforms and telecom network operations

The tests also highlight how cloud providers are getting involved in telecom operations. Over the past few years, some operators have moved parts of their core networks onto public cloud platforms or built cloud-based control systems. Industry analysts at Dell’Oro Group report that telecom cloud spending is rising as operators modernise networks and adopt software-driven infrastructure.

Adding AI-driven control loops on top of cloud platforms represents the next step, with AI systems monitoring conditions and applying adjustments quickly.

The technology remains in a testing phase. Nokia’s announcement described the work with Orange as demonstrations and pilots rollouts. Questions remain about how such systems can be deployed, how operators will supervise automated decisions, and how regulators will view AI control of critical communication infrastructure.

Telecom networks carry important traffic so reliability and accountability remain central concerns. Operators typically introduce automation gradually, keeping human oversight in place while validating system behaviour under real conditions.

The experiments suggest that AI is beginning to function as operational controller, adjusting physical and virtual resources in response to live events.

Enterprises that rely on private 5G networks for factories or large venues may gain access to connectivity that adjusts automatically. That could influence how businesses design applications that depend on stable, predictable network performance.

(Photo by M. Rennim)

See also: How e& is using HR to bring AI into enterprise operations

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

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Ensuring operational continuity in isolated environments has become increasingly vital for businesses. Facilities lacking continuous internet access face unique constraints where external dependencies become unacceptable.

Microsoft recently expanded its capabilities to allow regulated industries and public sectors to participate independently in the digital economy. Trust in these systems stems from confidence that data remains protected, controls are enforceable, and operations proceed regardless of external conditions.

The company now offers full stack options across connected, intermittently connected, and fully disconnected modes. This architecture unifies Azure Local, Microsoft 365 Local, and Foundry Local into a single sovereign private cloud.

Bringing these elements together provides a localised experience resilient to any connectivity condition. By standardising governance across all deployments, it helps enterprises to prevent fragmented architectures.

Azure Local disconnected operations enable organisations to run vital infrastructure using familiar Azure governance and policy controls completely offline. Execution, management, and policy enforcement stay entirely within customer-operated facilities. 

This approach allows companies to maintain uninterrupted operations and keep identities protected within their established boundaries. Implementations scale from minor deployments to demanding and data-intensive workloads.

Improving resilience and AI data governance in tandem

Deploying AI in sovereign environments introduces high compute requirements. Foundry Local enables enterprises to run multimodal large models completely offline.

Utilising modern hardware from partners like NVIDIA, customers deploy AI inferencing on their own physical servers. This ensures data and application programming interfaces operate strictly within customer-controlled boundaries. Customers maintain complete authority over their hardware even as AI inferencing demands increase over time.

Gerard Hoffmann, CEO of Proximus Luxembourg, said: “The availability of Azure Local disconnected operations represents a breakthrough for organisations that need control over their data without sacrificing the power of the Microsoft Cloud.

“For Luxembourg, where digital sovereignty is not just a principle but a strategic necessity, this model offers the resilience, autonomy and trust our market expects. By combining Microsoft’s technological leadership with Proximus NXT’s sovereign cloud expertise, we are enabling our customers to innovate confidently—even in fully-disconnected mode.”

CIOs planning offline deployments must map workloads to the correct control posture based on risk, regulation, and specific mission requirements. Since disconnected environments are not one-size-fits-all, businesses can start fast with smaller deployments and expand their capabilities over time.

Implementing a disconnected private cloud with AI support answers a business requirement for highly-regulated sectors, enabling secure data governance even when external connectivity is absent.

See also: Deploying agentic finance AI for immediate business ROI

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

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A primary topic on the exhibition floor was the progression from passive automation to “agentic” systems. These tools reason, plan, and execute tasks rather than following rigid scripts. Amal Makwana from Citi detailed how these systems act across enterprise workflows. This capability separates them from earlier robotic process automation (RPA).

Scott Ivell and Ire Adewolu of DeepL described this development as closing the “automation gap”. They argued that agentic AI functions as a digital co-worker rather than a simple tool. Real value is unlocked by reducing the distance between intent and execution. Brian Halpin from SS&C Blue Prism noted that organisations typically must master standard automation before they can deploy agentic AI.

This change requires governance frameworks capable of handling non-deterministic outcomes. Steve Holyer of Informatica, alongside speakers from MuleSoft and Salesforce, argued that architecting these systems requires strict oversight. A governance layer must control how agents access and utilise data to prevent operational failure.

Data quality blocks deployment

The output of an autonomous system relies on the quality of its input. Andreas Krause from SAP stated that AI fails without trusted, connected enterprise data. For GenAI to function in a corporate context, it must access data that is both accurate and contextually-relevant.

Meni Meller of Gigaspaces addressed the technical challenge of “hallucinations” in LLMs. He advocated for the use of eRAG (retrieval-augmented generation) combined with semantic layers to fix data access issues. This approach allows models to retrieve factual enterprise data in real-time.

Storage and analysis also present challenges. A panel featuring representatives from Equifax, British Gas, and Centrica discussed the necessity of cloud-native, real-time analytics. For these organisations, competitive advantage comes from the ability to execute analytics strategies that are scalable and immediate.

Physical safety and observability

The integration of AI extends into physical environments, introducing safety risks that differ from software failures. A panel including Edith-Clare Hall from ARIA and Matthew Howard from IEEE RAS examined how embodied AI is deployed in factories, offices, and public spaces. Safety protocols must be established before robots interact with humans.

Perla Maiolino from the Oxford Robotics Institute provided a technical perspective on this challenge. Her research into Time-of-Flight (ToF) sensors and electronic skin aims to give robots both self-awareness and environmental awareness. For industries such as manufacturing and logistics, these integrated perception systems prevent accidents.

In software development, observability remains a parallel concern. Yulia Samoylova from Datadog highlighted how AI changes the way teams build and troubleshoot software. As systems become more autonomous, the ability to observe their internal state and reasoning processes becomes necessary for reliability.

Infrastructure and adoption barriers

Implementation demands reliable infrastructure and a receptive culture. Julian Skeels from Expereo argued that networks must be designed specifically for AI workloads. This involves building sovereign, secure, and “always-on” network fabrics capable of handling high throughput.

Of course, the human element remains unpredictable. Paul Fermor from IBM Automation warned that traditional automation thinking often underestimates the complexity of AI adoption. He termed this the “illusion of AI readiness”. Jena Miller reinforced this point, noting that strategies must be human-centred to ensure adoption. If the workforce does not trust the tools, the technology yields no return.

Ravi Jay from Sanofi suggested that leaders need to ask operational and ethical questions early on in the process. Success depends on deciding where to build proprietary solutions versus where to buy established platforms.

The sessions from day one of the co-located events indicate that, while technology is moving toward autonomous agents, deployment requires a solid data foundation.

CIOs should focus on establishing data governance frameworks that support retrieval-augmented generation. Network infrastructure must be evaluated to ensure it supports the latency requirements of agentic workloads. Finally, cultural adoption strategies must run parallel to technical implementation.

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

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Cisco’s internal teams use a blend of machine learning and agentic AI to help them improve their own service delivery and personalise user experiences for its customers. It’s built a shared AI fabric built on patterns of compute and networking that are the product of years spent checking and validating its systems – battle-hardened solutions it then has the confidence to offer to customers. The infrastructure in play relies on high-performance GPUs, of course, but it’s not just raw horse-power. The detail is in the careful integration between compute and network stacks used in model training and the quite different demands from the ongoing load of inference.

Having made its name as the de facto supplier of networking infrastructure for the enterprise, it comes as no shock that it’s in network automation that some of its better-known uses of AI finds their place. Automated configuration workflows and identity management combine into access solutions that are focused on rapid network deployments generated by natural language.

For organisations looking to develop into the next generation of AI users, Cisco has been rolling out hardware and orchestration tools that are aimed explicitly to support AI workloads. A recent collaboration with chip giant NVIDIA led to the emergence of a new line of switches and the Nexus Hyperfabric line of AI network controllers. These aim to simplify the deployment of the complex clusters needed for top-end, high-performance artificial intelligence clusters.

Cisco’s Secure AI Factory framework with partners like NVIDIA and Run:ai is aimed at production-grade AI pipelines. It uses distributed orchestration, GPU utilisation governance, Kubernetes microservice optimisation, and storage, under the umbrella product description Intersight. For more local deployments, Cisco Unified Edge brings all the necessary elements – compute, networking, security, and storage – close to where data gets generated and processed.

In environments where latency metrics are critically important, AI processing at the edge is the answer. But Cisco’s approach is not necessarily to offer dedicated IIoT-specific solutions. Instead, it tries to extend the operational models typically found in a data centre and applies the same technology (if not the same exact methodology) to edge sites. It’s like data centre-grade security policies and configurations available to remote installations. Having the same precepts and standards in cloud and edge mean that Cisco accredited engineers can manage and maintain data centres or small edge deployments using the same skills, accreditation, knowledge, and experience.

Security and risk management figure prominently in the Cisco AI narrative. Its Integrated AI Security and Safety Framework applies high standards of safety and security throughout the life-cycle of AI systems. It considers adversarial threats, supply chain weakness, the risk profiles of multi-agent interactions, and multi-modal vulnerabilities as issues that have to be addressed regardless of the nature or size of any deployment.

Cisco’s work on operational AI also reflects broader ecosystem conversations. The company markets products for organisations wanting to make the transition from generative to agentic AI, where autonomous software agents carry out operational tasks. In most cases, this requires new tooling and new operational protocols.

Cisco’s future AI plans include continuing its central work in infrastructure provision for AI workloads. It’s also pursuing broader adoption of AI-ready networks, including next-gen wireless and unified management systems that will control systems across campus, branch, and cloud environments. The company is also expanding its software and platform investments, including its most recent acquisition (NeuralFabric), to help it build a more comprehensive software stack and product portfolio.

In summary, Cisco’s AI deployment strategy combines hardware, software, and service elements that embed AI into operations, giving organisations a route to production-grade systems. Its work can be found in large-scale infrastructure, systems for unified management, risk mitigation, and anywhere that connects distributed, cloud, and edge computing.

(Image source: Pixabay)

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The report, based on a survey of more than 2,300 senior leaders in APAC, EMEA, and the Americas, points to application modernisation as the clearest divider between organisations seeing real AI value and those still struggling. Companies that are ahead of schedule in modernising their applications are nearly three times more likely to report a clear payoff from their AI investments. In APAC, the link is even more explicit: 92% of leaders say updating their software was the single most important factor in improving their AI abilities.

Modernisation, not experimentation, drives AI returns

The finding re-frames AI success as a foundation problem not a tooling problem. AI systems depend on fast access to data, flexible architectures, and reliable integration points. Legacy applications, fragmented infrastructure, and brittle workflows make it harder for AI projects to move beyond isolated use cases. Modernised applications, by contrast, give organisations room to experiment, scale, and adapt without constant rework.

The report describes this relationship as a reinforcing cycle. Organisations modernise applications to support AI, then use AI results to justify deeper modernisation. Leaders in this group report far higher confidence that their infrastructure can support AI development, and that confidence translates into action. In APAC, 90% of leading organisations have already integrated AI into existing applications, compared with much lower levels among those behind schedule. Around 80% plan to increase that integration further over the next year.

The shift marks a change in mindset, as earlier waves of AI adoption focused on testing and pilots. Now, the emphasis is on integration. AI is not treated as a standalone project but as part of everyday systems, from internal workflows to customer-facing applications. The report shows that leading organisations are using AI to improve internal processes, build content-driven applications, and support revenue-generating work, while lagging organisations remain more cautious and fragmented in their approach.

The cost of delay shows up in security and confidence

The cost of falling behind is becoming clearer as well. Organisations that lag on modernisation tend to modernise reactively, often after a security incident or operational failure. In APAC, these organisations report lower confidence in both their infrastructure and their teams’ ability to support AI. That lack of confidence slows decision-making and limits how far AI projects can go. Instead of expanding use cases, teams spend time managing risk, fixing gaps, and dealing with technical debt.

Security plays a central role in this dynamic. The report shows that organisations with strong alignment between security and application teams are far more likely to scale AI successfully. Where that alignment is weak, security issues consume time and attention, pushing modernisation and AI work further down the priority list. Many lagging organisations report difficulty tracking risks in applications and APIs, which makes it harder to move quickly without increasing exposure.

For leaders, security is treated as part of application design not an add-on. That approach reduces the amount of reactive work needed after incidents and frees teams to focus on building and improving systems. Over time, this also lowers the operational drag that can stall AI efforts. The report suggests that reliability has become a practical limit on speed: organisations that cannot maintain stable, secure systems struggle to move AI projects into production.

Fewer tools, clearer foundations, faster AI integration

Another pressure point highlighted in the APAC data is tool sprawl. Nearly all organisations report challenges in managing large and complex technology stacks, but leaders are responding more aggressively. About 86% of APAC leaders say they are actively cutting redundant tools and addressing shadow IT. The goal is not just cost control, but clarity. Fewer platforms and integrations make it easier to modernise applications, apply consistent security controls, and integrate AI without friction.

Developer time is also a factor. In organisations with a modernised foundation, developers spend more time maintaining and improving systems that already work. In lagging organisations, developers are more likely to rebuild from scratch or spend time on configuration and remediation. That difference affects how quickly new AI abilities can be introduced and refined. When teams are tied up fixing problems, AI becomes harder to prioritise.

Taken together, the findings suggest that AI success is less about racing to deploy new models and more about removing the obstacles that slow everything else down. Application modernisation creates the conditions for AI to deliver value, while fragmented systems and reactive practices limit what AI can achieve. Without that foundation, organisations find it harder to turn AI investment into measurable AI returns.

For APAC organisations, the message is that AI investment without modernisation tends to produce shallow results. Modernisation without integration plans risks becoming an ongoing rebuild. The organisations seeing the strongest returns are those that treat application updates, security alignment, and AI integration as connected work, not separate initiatives.

The report does not suggest a single path forward, but it does draw a clear line between organisations that act early and those that wait. The advantage not comes from having AI, but from having applications ready to use it.

(Photo by Julio Lopez)

See also: Controlling AI agent sprawl: The CIO’s guide to governance

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.

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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.

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That position came through clearly in recent comments from CEO Jamie Dimon, who defended the bank’s rising technology budget and warned that institutions that fall behind on AI risk losing ground to competitors. The argument was not about replacing people but about staying functional in an industry where speed, scale, and cost discipline matter every day.

JPMorgan has been investing heavily in technology for years, but AI has changed the tone of that spending. What once sat with innovation projects is now folded into the bank’s baseline operating costs. That includes internal AI tools that support research, document drafting, internal reviews, and other routine tasks in the organisation.

From experimentation to infrastructure

The shift in language reflects a deeper change in how the bank views risk. AI is considered part of the systems required to keep pace with competitors that are automating internal work.

Rather than encouraging workers to rely on public AI systems, JPMorgan has focused on building and governing its own internal platforms. That decision reflects long-held concerns in banking about data exposure, client confidentiality, and regulatory monitoring.

Banks operate in an environment where mistakes carry high costs. Any system that touches sensitive data or influences choices must be auditable and explainable. Public AI tools, trained on datasets and updated frequently, make that difficult. Internal systems give JPMorgan more control, even if they take longer to deploy.

The approach also reduces the potential of uncontrolled “shadow AI,” in which employees use unapproved tools to speed up work. While such tools can improve productivity, they create gaps in oversight that regulators tend to notice quickly.

A cautious approach to workforce change

JPMorgan has been careful in how it talks about AI’s impact on jobs. The bank has avoided claims that AI will dramatically reduce headcount. Instead, it presents AI as a way to reduce manual work and improve consistency.

Tasks that once required multiple review cycles can now be completed faster, with employees still responsible for final judgement. The framing positions AI as support not substitution, which matters in a sector sensitive to political and regulatory reaction.

The scale of the organisation makes this approach practical. JPMorgan employs hundreds of thousands of people worldwide. Even tiny efficiency gains, applied broadly, can translate into meaningful cost savings over time.

The upfront investment required to build and maintain internal AI systems is substantial. Dimon acknowledges that technology spending can have an impact on short-term performance, especially when market conditions are uncertain.

His response is that cutting back on technology now may improve margins in the near term, but it risks weakening the bank’s position later. In that sense, AI spending is treated as a form of insurance against falling behind.

JPMorgan, AI, and the risk of falling behind rivals

JPMorgan’s stance reflects pressure in the banking sector. Rivals are investing in AI to speed up fraud detection, streamline compliance work, and improve internal reporting. As these tools become more common, expectations rise.

Regulators may assume banks have access to advanced monitoring systems. Clients may expect faster responses and fewer errors. In that environment, lagging on AI can look less like caution and more like mismanagement.

JPMorgan has not suggested that AI will solve structural challenges or eliminate risk. Many AI projects struggle to move beyond narrow uses, and integrating them into complex systems remains difficult.

The harder work lies in governance. Deciding which teams can use AI, under what conditions, and with what oversight requires clear rules. Errors need defined escalation paths. Responsibility must be assigned when systems produce flawed output.

Across large enterprises, AI adoption is not limited by access to models or computing power, but constrained by process, policy, and trust.

For other end-user companies, JPMorgan’s approach offers a useful reference point. AI is treated as part of the machinery that keeps the organisation running.

That does not guarantee success. Returns may take years to appear, and some investments will not pay off. But the bank’s position is that the greater risk lies in doing too little, not too much.

(Photo by IKECHUKWU JULIUS UGWU)

See also: Banks operationalise as Plumery AI launches standardised integration

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information.

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Plumery’s “AI Fabric” has been positioned by the company as a standardised framework for connecting generative AI tools and models to core banking data and services. According to Plumery, the product is intended to reduce reliance on bespoke integrations and to promote an event-driven, API-first architecture that can scale as institutions grow.

The challenge it seeks to address is recognised in the sector. Banks have invested heavily in AI experimentation over the past decade, but many deployments remain limited. Research by McKinsey suggests that while generative AI could materially improve productivity and customer experience in financial services, most banks struggle to translate pilots into production because of fragmented data estates and incumbent operating models. The consultancy argues that enterprise-level AI adoption requires shared infrastructure and governance, and reusable data products.

In comments accompanying the product launch, Plumery’s founder and chief executive, Ben Goldin, said financial institutions are clear about what they expect from AI.

“They want real production use cases that improve customer experience and operations, but they will not compromise on governance, security or control,” he said. “The event-driven data mesh architecture transforms how banking data is produced, shared, and consumed, not adding another AI layer on top of fragmented systems.”

Fragmented data remains a barrier

Data fragmentation remains one of the obstacles to operational AI in banking. Many institutions rely on legacy core systems that sit in newer digital channels, creating silos in products and customer journeys. Each AI initiative requires fresh integration work, security reviews, and governance approvals, thus increasing costs and slowing delivery.

Academic and industry research supports this diagnosis. Studies on explainable AI in financial services note that fragmented pipelines make it harder to trace decisions and increase regulatory risk, particularly in areas like credit scoring and anti-money-laundering. Regulators have made clear that banks must be able to explain and audit AI-driven outcomes, regardless of where the models are developed.

Plumery says its AI Fabric addresses such issues by presenting domain-oriented banking data as governed streams that can be reused in multiple use cases. The company argues that separating systems of record from systems of engagement and intelligence allows banks to innovate more safely.

Evidence of AI already in production

Despite the challenges, AI is already embedded in many parts of the financial sector. Case studies compiled by industry analysts show widespread use of machine learning and natural language processing in customer service, risk management, and compliance.

Citibank, for example, has deployed AI-powered chatbots to handle routine customer enquiries, reducing pressure on call centres and improving response times. Other large banks use predictive analytics to monitor loan portfolios and anticipate defaults. Santander has publicly described its use of machine learning models to assess credit risk and strengthen portfolio management.

Fraud detection is another mature area. Banks rely increasingly on AI systems to analyse transaction patterns, flagging anomalous behaviour more effectively than rule-based systems. Research from technology consultancies notes that such models depend on high-quality data flows, and that integration complexity remains a limiting factor for smaller institutions.

More advanced applications are emerging at the margins. Academic research into large language models suggests that, under strict governance, conversational AI could support certain transactional and advisory functions in retail banking. However, these implementations remain experimental and are closely scrutinised due to their regulatory implications.

Platform providers and ecosystem approaches

Plumery operates in a competitive market of digital banking platforms that position themselves as orchestration layers rather than replacements for core systems. The company has entered partnerships designed to fit into broader fintech ecosystems. Its integration with Ozone API, an open banking infrastructure provider, was presented as a way for banks to deliver standards-compliant services more quickly, without custom development.

Its approach reflects a wider industry trend towards composable architectures. Vendors like Backbase and others promote API-centric platforms that allow banks to plug in AI, analytics, and third-party services to the existing core. Analysts agree generally that such architectures are better suited to incremental innovation than large-scale system replacement.

Readiness remains uneven

Evidence suggests that readiness in the sector is uneven. A report by Boston Consulting Group found that fewer than a quarter of banks believe they are prepared for large-scale AI adoption. The gap, it argued, lies in governance, data foundations, and operating discipline.

Regulators have responded by offering controlled environments for experimentation. In the UK, regulatory sandbox initiatives allow banks to test new technologies, including AI. These programmes are intended to support innovation and reinforce accountability and risk management.

For vendors like Plumery, the opportunity lies in providing infrastructure that aligns technological ambition and regulatory reality. AI Fabric enters a market where demand for operational AI is apparent, but where success depends on proving that new tools can be safe and transparent.

Whether Plumery’s approach becomes a adopted standard remains uncertain. As banks move from experimentation to production, the focus is moving towards the architectures that support AI. In that context, platforms that can demonstrate technical flexibility and governance adherence are more likely to play an important role in the digital banking’s next phase.

(Image source: “Colorful Shale Strata of the Morrison Formation at the Edge of the San Rafael Swell” by Jesse Varner is licensed under CC BY-NC-SA 2.0.)

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The shift helps explain why Bosch plans to invest about €2.9 billion in artificial intelligence by 2027, according to The Wall Street Journal. The spending is aimed at manufacturing, supply chain management, and perception systems, areas where the company sees AI as a way to improve how physical systems behave in real conditions.

How Bosch uses AI to catch manufacturing problems earlier

In manufacturing, delays and defects frequently start small. A minor variation in materials or machine settings can ripple through a production line. Bosch has been applying AI models to camera feeds and sensor data to detect quality issues earlier.

Instead of catching defects after products are finished, systems can flag problems while items are still on the line. That gives workers time to change operations before waste increases. For high-volume manufacturing, earlier detection can reduce scrap and limit the need for rework.

Equipment maintenance is another area under pressure. Many factories still rely on fixed schedules or manual inspections, which can miss early warning signs of errors or failure. AI models trained on vibration and temperature data can help predict when a machine is likely to fail.

This allows maintenance teams to plan repairs instead of reacting to breakdowns. The aim is to reduce unplanned downtime without replacing equipment too early. Over time, this approach can extend the working life of machines while keeping production more stable.

Making supply chains more adaptable

Supply chains are also part of the investment focus. Disruptions that became visible during the pandemic have not fully disappeared, and manufacturers are still dealing with shifting demand and transport delays.

AI systems can help forecast needs, track parts in sites, and adjust plans when conditions change. Even small improvements in planning accuracy can have a broad effect when applied in hundreds of factories and suppliers.

Bosch is funding perception systems, which help machines understand their surroundings. Systems combine input from cameras, radar, and other sensors with AI models that can recognise objects, judge distance, or spot changes in the environment. They are used in areas like factory automation, driver assistance, and robotics, where machines must respond quickly and safely. In these environments, AI is reacting to real-world conditions as they happen.

Why edge computing matters on the factory floor

Much of this work takes place at the edge. In factories and vehicles, sending data to a distant cloud system and waiting for a response can add delay or create risk if connections fail. Running AI models locally allows systems to respond in real time and keep operating even when networks are unreliable.

It also limits how much sensitive data leaves a site. For industrial companies, that can matter as much as speed, especially when production processes are closely guarded.

Cloud systems still play a role, though mostly behind the scenes. Training models, managing updates, and analysing trends in locations often happens in central environments.

Many manufacturers are moving toward a hybrid setup, using cloud systems for coordination and learning, and edge systems for action. The pattern is becoming common in industrial firms, not just Bosch.

Scaling AI beyond small trials

The scale of the investment matters, as small AI tests can show promise, but rolling them out across all operations takes funding, skilled staff, and long-term commitment.

Bosch executives have described AI as a way to support workers not replace them, and as a tool to handle the complexity that humans cannot manage. That view reflects a broader shift in industry, where AI is treated less as an experiment and more as basic infrastructure.

What Bosch’s manufacturing AI strategy shows in practice

Rising energy costs, labour shortages, and tighter margins leave less room for inefficiency. Automation alone no longer solves those problems. Companies are looking for systems that can adjust to changing conditions without constant manual input.

Bosch’s €2.9 billion commitment sits in that wider shift. Other large manufacturers are making similar moves, often without public fanfare, by upgrading factories and retraining staff. What stands out is the focus on operational use rather than customer-facing features.

Taken together, these efforts show how end-user companies are applying AI today. The work is less about bold claims and more about reducing waste, improving uptime, and making complex systems easier to manage. For industrial firms, that practical focus may define how AI delivers value over time.

(Photo by P. L.)

See also: Agentic AI scaling requires new memory architecture

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As foundation models scale toward trillions of parameters and context windows reach millions of tokens, the computational cost of remembering history is rising faster than the ability to process it.

Organisations deploying these systems now face a bottleneck where the sheer volume of “long-term memory” (technically known as Key-Value (KV) cache) overwhelms existing hardware architectures.

Current infrastructure forces a binary choice: store inference context in scarce, high-bandwidth GPU memory (HBM) or relegate it to slow, general-purpose storage. The former is prohibitively expensive for large contexts; the latter creates latency that renders real-time agentic interactions unviable.

To address this widening disparity that is holding back the scaling of agentic AI, NVIDIA has introduced the Inference Context Memory Storage (ICMS) platform within its Rubin architecture, proposing a new storage tier designed specifically to handle the ephemeral and high-velocity nature of AI memory.

“AI is revolutionising the entire computing stack—and now, storage,” Huang said. “AI is no longer about one-shot chatbots but intelligent collaborators that understand the physical world, reason over long horizons, stay grounded in facts, use tools to do real work, and retain both short- and long-term memory.”

The operational challenge lies in the specific behaviour of transformer-based models. To avoid recomputing an entire conversation history for every new word generated, models store previous states in the KV cache. In agentic workflows, this cache acts as persistent memory across tools and sessions, growing linearly with sequence length.

This creates a distinct data class. Unlike financial records or customer logs, KV cache is derived data; it is essential for immediate performance but does not require the heavy durability guarantees of enterprise file systems. General-purpose storage stacks, running on standard CPUs, expend energy on metadata management and replication that agentic workloads do not require.

The current hierarchy, spanning from GPU HBM (G1) to shared storage (G4), is becoming inefficient:

As context spills from the GPU (G1) to system RAM (G2) and eventually to shared storage (G4), efficiency plummets. Moving active context to the G4 tier introduces millisecond-level latency and increases the power cost per token, leaving expensive GPUs idle while they await data.

For the enterprise, this manifests as a bloated Total Cost of Ownership (TCO), where power is wasted on infrastructure overhead rather than active reasoning.

A new memory tier for the AI factory

The industry response involves inserting a purpose-built layer into this hierarchy. The ICMS platform establishes a “G3.5” tier—an Ethernet-attached flash layer designed explicitly for gigascale inference.

This approach integrates storage directly into the compute pod. By utilising the NVIDIA BlueField-4 data processor, the platform offloads the management of this context data from the host CPU. The system provides petabytes of shared capacity per pod, boosting the scaling of agentic AI by allowing agents to retain massive amounts of history without occupying expensive HBM.

The operational benefit is quantifiable in throughput and energy. By keeping relevant context in this intermediate tier – which is faster than standard storage, but cheaper than HBM – the system can “prestage” memory back to the GPU before it is needed. This reduces the idle time of the GPU decoder, enabling up to 5x higher tokens-per-second (TPS) for long-context workloads.

From an energy perspective, the implications are equally measurable. Because the architecture removes the overhead of general-purpose storage protocols, it delivers 5x better power efficiency than traditional methods.

Integrating the data plane

Implementing this architecture requires a change in how IT teams view storage networking. The ICMS platform relies on NVIDIA Spectrum-X Ethernet to provide the high-bandwidth, low-jitter connectivity required to treat flash storage almost as if it were local memory.

For enterprise infrastructure teams, the integration point is the orchestration layer. Frameworks such as NVIDIA Dynamo and the Inference Transfer Library (NIXL) manage the movement of KV blocks between tiers.

These tools coordinate with the storage layer to ensure that the correct context is loaded into the GPU memory (G1) or host memory (G2) exactly when the AI model requires it. The NVIDIA DOCA framework further supports this by providing a KV communication layer that treats context cache as a first-class resource.

Major storage vendors are already aligning with this architecture. Companies including AIC, Cloudian, DDN, Dell Technologies, HPE, Hitachi Vantara, IBM, Nutanix, Pure Storage, Supermicro, VAST Data, and WEKA are building platforms with BlueField-4. These solutions are expected to be available in the second half of this year.

Redefining infrastructure for scaling agentic AI

Adopting a dedicated context memory tier impacts capacity planning and datacentre design.

• Reclassifying data: CIOs must recognise KV cache as a unique data type. It is “ephemeral but latency-sensitive,” distinct from “durable and cold” compliance data. The G3.5 tier handles the former, allowing durable G4 storage to focus on long-term logs and artifacts.

• Orchestration maturity: Success depends on software that can intelligently place workloads. The system uses topology-aware orchestration (via NVIDIA Grove) to place jobs near their cached context, minimising data movement across the fabric.

• Power density: By fitting more usable capacity into the same rack footprint, organisations can extend the life of existing facilities. However, this increases the density of compute per square metre, requiring adequate cooling and power distribution planning.

The transition to agentic AI forces a physical reconfiguration of the datacentre. The prevailing model of separating compute completely from slow, persistent storage is incompatible with the real-time retrieval needs of agents with photographic memories.

By introducing a specialised context tier, enterprises can decouple the growth of model memory from the cost of GPU HBM. This architecture for agentic AI allows multiple agents to share a massive low-power memory pool to reduce the cost of serving complex queries and boosts scaling by enabling high-throughput reasoning.

As organisations plan their next cycle of infrastructure investment, evaluating the efficiency of the memory hierarchy will be as vital as selecting the GPU itself.

See also: 2025’s AI chip wars: What enterprise leaders learned about supply chain reality

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.

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