Paul Neville, director of digital, data and technology at The Pensions Regulator (TPR), is building strong IT foundations as part of a five-year strategy to help transform the organisation from a compliance-based to a risk-based regulator. He explains what that change will mean in practice over the next few years.
“As a regulator, we’ll obviously still have specific processes we expect people to follow, but we’ll be much more concerned about the outcome that we’re trying to achieve, and we’ll make decisions based on that demand,” he says.
“To make that shift, we need to understand our data. We need to have the right level of automation to explore information, measure outcomes, and deliver those outcomes with industry and other government bodies interested in pensions. We imagine a future world in which information flows between organisations.”
A historian by education, Neville entered the world of business as the internet boom gathered pace in the 1990s. Describing himself as a self-taught digital leader, he developed his skills in the commercial sector at blue-chip companies such as Sky and BT, and with startups and smaller businesses.
His transformation work in larger firms focused on delivering big technology-enabled change programmes, centred on boosting customer experiences. Mid-career, he decided to apply those skills for public benefit and worked as a consultant for two major charities, Marie Curie and Macmillan, helping those organisations to transform digitally.
Neville then turned to the public sector to apply his skills in another for-good area. He worked in digital leadership roles at the London Borough of Waltham Forest, UK Export Finance and Enfield Council, before joining TPR in October 2023. Neville reflects on this final move.
“It was the opportunity to take all of that experience and deliver on a national scale and impact everybody, because almost everyone has a pension, and the opportunity to make that process work for the citizens of this country, and make a difference for people in retirement, is a massive issue,” he says.
“Secondly, the chief executive, my boss, Nausicaa Delfas, was setting up an opportunity to change, not only TPR, but the pensions industry, so the role felt like a chance to be a central part of that journey, because not every CIO gets to sit on the board of an organisation.”
Transforming processes
Neville reflects on the transformation journey at TPR, saying it’s been an exciting ride: “Everyone on the executive board is aligned on the fact that digital, data and technology are the key enablers for helping us change as an organisation, and also helping the pensions industry transform.”
Late last year, Neville launched a digital, data and technology strategy, a set of missions over a five-year plan to renew TPR’s capabilities, embracing new ways of working, driving efficiency, automation and innovation. In March this year, he launched the data component of the strategy, which establishes a collaborative plan to drive adoption of new data technologies and standards.
“I am proud of that strategic work,” he says. “That effort includes strengthening our technology foundations, improving our capability in terms of automation, and making sure we have the skills in my team to develop the future. We’ve hired quite a lot of people and also consolidated similar skills across the organisation, and that’s enabled us to deliver more and save money on suppliers, because we’ve done a lot in-house.”
Neville says the projects his team has worked on include delivering artificial intelligence (AI) tools that help increase automation. They’ve also focused on improving cyber security and data governance to ensure safe and secure access to high-quality internal information.
The team also recently launched an innovation service to foster conversations with industry stakeholders. Neville says TPR is encouraging and enabling people and organisations to think differently about the services they deliver to their customers and the benefits they provide.
“That’s just a small selection of the things we’ve done so far,” he says. “We’ve got just under four years left of the plan. There’s a lot more we want to do, but we have built the confidence, both internally and externally, that we are a different TPR and we can deliver. That encourages everyone in our industry to think differently as well.”
Building foundations
Neville says the transformation work enabled through the strategy so far is focused on building the right technological foundations at TPR.
In addition to cyber security and data governance projects, his team has focused on service management initiatives that help TPR rationalise its application estate. The organisation has adopted an agile, product-based approach to deliver reusable capabilities for flexible services in key areas related to pensions governance within the organisation and externally.
TPR is also making progress on automation, including in case management. He inherited a situation where cases were often managed on spreadsheets or via one-off technology solutions. In short, nothing was joined up. Neville is using automation, via Microsoft Dynamics 365, to take a different approach.
“Everyone on the executive board is aligned on the fact that digital, data and technology are the key enablers for helping us change as an organisation, and also helping the pensions industry transform”
Paul Neville, The Pensions Regulator
“We’re delivering a single case management system,” he says. “We are working to make sure the process is streamlined, so we’re thinking about the business process first. By taking that approach, we can deliver in an agile and iterative way. Where we’ve already rolled that technology out, we’ve delivered productivity savings of around 60%.”
Neville expects the progress made through case management automation to be repeated in other areas. As automation takes hold in the organisation, he anticipates people will spend less time on paperwork and more time delivering better services.
Given the developments in the technology sector during the past few years, AI is playing a key role.
“We are deploying AI to specific use cases,” he says. “I’ve got a fantastic data science team, who are developing lots of very clever tools for us.”
Embracing AI
Neville says the next two years will be spent honing these technology initiatives and delivering tangible results.
Critical projects include implementing organisation-wide access to data via Dynamics 365 services and completing transformation projects in core areas, such as cyber security and data governance. It’s these foundations and the application of emerging technology that will help TPR transform from a compliance-based to a risk-based regulator.
Two years from now, Neville expects all foundational work, from case management to customer relationship management (CRM) systems, will be embedded within the organisation. On these foundations, employees will use AI-enabled tools to boost their working processes.
“That preparatory work will enable us in the future to create more customer-facing digital capabilities,” he says.
One example of where TPR is applying AI is analysing online news sites to scan for potential risks in pension schemes. Neville saw AI could provide a helping hand to what is currently a manually intensive process.
“That’s a great example, because many pension schemes don’t have the same name as the provider,” he says. “The technology does quite a lot of joining up behind the scenes to make that process work.”
Another example is using AI to analyse Task Force on Climate-Related Financial Disclosures (TCFD) statements, which organisations must submit to comply with legislation. Once again, generative technology – in the form of OpenAI and Microsoft Azure technology – is helping TPR staff summarise lengthy prose and create insights as a basis for intervention when required.
“Those are just two examples,” says Neville. “We’ve got other risk tools that we’re using. We are also rolling out Copilot internally, and we’re in the middle of our plan for that technology. We’re trialling GitHub Copilot for our developers, and they’re starting to write test scripts, which is fun. We’re still at the beginning of this work, as are lots of people, but these projects are a taster of what we want to achieve.”
Solving challenges
Neville says the result of this work will be that the future TPR will have an operating environment that differs greatly from its traditional, manually intensive processes. Today, the organisation maintains a digital portal, where people send, for example, pension scheme returns as part of a large, intensive data upload. Neville foresees a better approach.
We need to understand our data, and so does the industry. The firms need to provide better customer experiences for people, like you and me, who have pensions Paul Neville, The Pensions Regulator
“There won’t necessarily be a scheme return like you see today, because we will have the information we need, and organisations across the industry will be more digitally enabled, so they’re able to drive the kind of innovation and competition in the market that will benefit savers, people with pensions and employers that offer pensions,” he says.
This new level of digital interaction will make it easier for TPR and organisations in the pensions sector to tackle some of the thorny issues of the day. One of these issues is adequacy, or the extent to which people save enough money in pension schemes for their retirement.
“We need to understand our data, and so does the industry. The firms need to provide better customer experiences for people, like you and me, who have pensions. By driving a customer focus, we think the industry will perform better,” he says.
“We may even feel a bit like a fintech as an organisation, because we’ll be enabling innovation. Technology will produce the insights we need to work with the industry. So, we could be operating in a completely different world, which drives innovation and change for everyone.”
Neville continues to seek ways to push transformation forward. He recently helped launch the Pensions Data and Digital Working Group, which will help ensure TFP and the pension industry work together to embrace digital, data and technology and achieve the digitalisation and automation aims outlined in the five-year strategy.
“The working group has 15 members,” he says. “It represents a cross-section of people from different parts of industry, so trustees, actuaries, lawyers, but also people from more technical backgrounds as well. It’s about getting all kinds of people involved to help solve the problems and move to this new world.”
Render Networks has made further expansion of its footprint as a system of execution for critical infrastructure with the ClearWay platform.
As infrastructure investment accelerates across fibre broadband, electric grid modernisation, distributed energy and AI-driven datacentre expansion, capital discipline has emerged as a defining concern, according to the company.
Render has stated that traditional methods of data analysis and manual decision-making often hamper progress, with deployment risk now consequentially translating directly into capital risk. It added that operators, utilities and builders must reduce variance, accelerate cash conversion and establish audit-grade accountability across increasingly complex, multi-asset deployments.
Originally establishing itself in telecommunications, Render now supports electric utilities and multi-utility environments where construction accuracy is a prerequisite for operational reliability. Built for infrastructure environments where governance is “non-negotiable”, ClearWay is claimed to advance automation without eroding engineering authority.
The new platform is built to transform design data into live scopes of work, to capture verified field progress in real-time and to econcile workflows to maintain financial integrity. This is seen as producing defensible as-built records that flow “seamlessly” into operations. Rather than a collection of isolated AI features, ClearWay is said to operate across a federated system of specialised agents designed to operate autonomously in identity, policy and audit controls.
Each agent operates with a uniquely defined degree of autonomy, managed identity and least-privilege access. As additional ClearWay agents are introduced, the system is built to support progressively higher levels of autonomy, bounded by deterministic guardrails derived from user-defined operational policies. The result is that decision-making choices are underpinned by controlled, auditable automation that preserves first-order accountability while also enabling meaningful scale.
The first release of ClearWay scheduled for release in the second quarter of 2026 and is scheduled to introduce field assurance and work approval capabilities across telecom and electric deployments with an assurance agent and approval agent.
The former is said to validate field-captured evidence against planned work in real-time, ensuring accuracy before crews leave the site. By contrast, the latter approves work autonomously based on a correlation of work type, planned vs. actual units, photos, and test results. When predefined criteria are met, the agent processes the approval and escalates exceptions only when human review is required.
By ensuring work is correct and defensible at the point of execution, Render is confident that ClearWay can accelerate design to build lead times, reduce construction rework, accelerate closeout and improve working capital velocity. This will be “particularly vital” in broadband and grid modernisation environments, where construction accuracy directly affects serviceability, network reliability and regulatory compliance.
“We have always focused on ensuring that work in the field becomes verified operational truth. The next step is ensuring that truth drives disciplined, governed and rapid action across the lifecycle,” said Stephen Rose, CEO of Render Networks.
“As capital efficiency becomes central to telecom and electric infrastructure, automation must ensure rapid decisions are made well to reinforce control and accountability. ClearWay is designed to do exactly that.”
Render will introduce additional specialised agents in the ClearWay architecture, spanning lifecycle management and financial reconciliation, service activation and operational monitoring, and predictive maintenance and sustainability governance.
Two years after it proposed the transition from the mobile internet era to the mobile artificial intelligence (AI) era, leading to the rapid adoption of agents in B2B applications and 30 million agents applied over the past 12 months, Huawei has introduced the Agent Verse, predicting a 10,000-fold increase in agent-handled work in networks by 2030.
The proposal of a new paradigm for communications came on the back of the comms tech giant’s Agentic Core Summit at MWC 2026, which centred on the strategic theme of building an agentic network with device-network-service synergy.
At the summit, Huawei revealed that it had worked with global mobile trade association the GSMA and a range of operators and industry organisations across the Middle East, Asia Pacific, Europe, Latin America and other regions to explore AI-driven advancements for the core network. Together, they unanimously agreed that the 5G core network has entered into “a new phase” called the Agentic Core.
Huawei’s Agentic Core system integrates AI into mobile internet, voice, operations and maintenance (O&M) and telco cloud infrastructure to allow networks to evolve and main service offerings to be reshaped. Huawei sees AI as extending a core network with three “transformative” abilities: real-time experience awareness; global experience evaluation and resource coordination; and intelligent interaction and execution.
This architecture is designed to give rise to a “network brain” that drives a closed-loop experience monetisation model where experiences are definable and assessable, service offerings are marketable, quality is guaranteed and exclusive user identities are perceptible.
The intelligent O&M part of the solution is built to transform network operations into an automated and intelligent ecosystem, driving the core network toward Autonomous Network (AN) L4 Phase 2. Phase 1 focuses on the intelligent assistant, NOEMate, which delivers automated closed-loop management for both faults and changes. Building on this, Phase 2 introduces hierarchical autonomy and builds an unmanned factory, achieving full single-domain autonomy within the core network.
Looking toward the 6G era, Huawei Agentic Core also supports ubiquitous AI agent access, building an agent-based communication network that spans across devices and ecosystems. The Cloud Core Network is designed for an evolving communication infrastructure that will act as an interchange for AI agent network.
And these, said Huawei Eric Zhao, vice-president and CMO of Huawei’s wireless solution, would operate in the Agent Verse: “Mobile AI is sparking a paradigm shift across the communications industry. With a trillion-scale surge in Agent Verse connections on the horizon, mobile networks need an urgent upgrade.
“To unlock the full potential of 5G-Advanced, the industry should accelerate end-to-end upgrades and innovation, building multidimensional network capabilities that can meet the demands ahead.”
At MWC, Huawei argued that agents were reshaping mobile network demands – for example, by evolving into engines of industrial automation and broad societal change. It offered the example of productivity agents making fully automated manufacturing possible through autonomous learning and the precise coordination of thousands of robots. It calculated that by 2030, the global market is expected to reach trillions of intelligent connections worldwide.
Zhao added: “AI’s development has gone wide and far beyond our imagination, and it is now becoming clear that the application of AI will be [through] agents. We believe that in the future, every industry, terminal, organisation and individual will be served by agents – and this is why we propose the Agent Verse. Just in last year alone, there was 30 million agents applied in different industries, significantly improving the productivities of verticals; the adoption pace of agents is incredibly fast.
“It is estimated that by 2030, the amount of work handled by agents will grow by 10,000 times. Agents adoption means the introduction of changes in communication methods and communication objects. That means, in the future, agents will introduce new interactions, agents will interact with people, agents will interact with agents. This is why we think that the time has changed and the wireless industry needs to be prepared to welcome new services.”
Advanced Machine Intelligence (AMI), a new Paris-based startup cofounded by Meta’s former chief AI scientist Yann LeCun, announced Monday it has raised more than $1 billion to develop AI world models.
LeCun argues that most human reasoning is grounded in the physical world, not language, and that AI world models are necessary to develop true human-level intelligence. “The idea that you’re going to extend the capabilities of LLMs [large language models] to the point that they’re going to have human-level intelligence is complete nonsense,” he said in an interview with WIRED.
The financing, which values the startup at $3.5 billion, was co-led by investors such as Cathay Innovation, Greycroft, Hiro Capital, HV Capital, and Bezos Expeditions. Other notable backers include Mark Cuban, former Google CEO Eric Schmidt, and French billionaire and telecommunications executive Xavier Niel.
AMI (pronounced like the French word for friend) aims to build “a new breed of AI systems that understand the world, have persistent memory, can reason and plan, and are controllable and safe,” the company says in a press release. The startup says it will be global from day one, with offices in Paris, Montreal, Singapore, and New York, where LeCun will continue working as a New York University professor in addition to leading the startup. AMI will be the first commercial endeavor for LeCun since his departure from Meta in November 2025.
LeCun’s startup represents a bet against many of the world’s biggest AI labs like OpenAI, Anthropic, and even his former workplace, Meta, which believe that scaling up LLMs will eventually deliver AI systems with human-level intelligence or even superintelligence. LLMs have powered viral products such as ChatGPT and Claude Code, but LeCun has been one of the AI industry’s most prominent researchers speaking out about the limitations of these AI models. LeCun is well known for being outspoken, but as a pioneer of modern AI that won a Turing award back in 2018, his skepticism carries weight.
LeCun says AMI aims to work with companies in manufacturing, biomedical, robotics, and other industries that have lots of data. For example, he says AMI could build a realistic world model of an aircraft engine and work with the manufacturer to help them optimize for efficiency, minimize emissions, or ensure reliability.
AMI was cofounded by LeCun and several leaders he worked with at Meta, including the company’s former director of research science, Michael Rabbat; former vice president of Europe, Laurent Solly; and former senior director of AI research, Pascale Fung. Other cofounders include Alexandre LeBrun, former CEO of the AI health care startup Nabla, who will serve as AMI’s CEO, and Saining Xie, a former Google DeepMind researcher who will be the startup’s chief science officer.
The Case for World Models
LeCun does not dismiss the overall utility of LLMs. Rather, in his view, these AI models are simply the tech industry’s latest promising trend, and their success has created a “kind of delusion” among the people who build them. “It’s true that [LLMs] are becoming really good at generating code, and it’s true that they are probably going to become even more useful in a wide area of applications where code generation can help,” says LeCun. “That’s a lot of applications, but it’s not going to lead to human-level intelligence at all.”
LeCun has been working on world models for years inside of Meta, where he founded the company’s Fundamental AI Research lab, FAIR. But he’s now convinced his research is best done outside the social media giant. He says it’s become clear to him that the strongest applications of world models will be selling them to other enterprises, which doesn’t fit neatly into Meta’s core consumer business.
As AI world models like Meta’s Joint-Embedding Predictive Architecture (JEPA) became more sophisticated, “there was a reorientation of Meta’s strategy where it had to basically catch up with the industry on LLMs and kind of do the same thing that other LLM companies are doing, which is not my interest,” says LeCun. “So sometime in November, I went to see Mark Zuckerberg and told him. He’s always been very supportive of [world model research], but I told him I can do this faster, cheaper, and better outside of Meta. I can share the cost of development with other companies … His answer was, OK, we can work together.”
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