AI Opportunities Action Plan Delivery Tracker

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In the race to establish AI leadership, the Government has committed to an ambitious 20-fold increase in sovereign compute capacity. But beneath this headline figure lies a complex challenge: how to design AI infrastructure that maximises both technological capacity and economic benefit, in both the public and private sector. Creating the enabling conditions where that compute can drive innovation across the UK economy is the real prize of a successful compute strategy.

Increasing sovereign compute is essential to this vision. A properly executed strategy would allow the UK to direct sufficient compute resources to national priorities, support academia and businesses – without depending on the US hyperscalers.

The Government’s AI Opportunities Action Plan also proposes AI Growth Zones. These are intended to be a way of cutting through the planning red tape and energy connection bottlenecks that often constrain the buildout of infrastructure. But implementing these effectively requires nuanced thinking about location, energy, infrastructure supply and demand and support systems.

Failure risks the Government building expensive monuments to political ambition rather than functional engines of technological sovereignty and economic growth.

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AI Growth Zones can allow the UK to achieve two goals: efficient infrastructure placement and broader regional development beyond the Southeast of England.

Very large-scale AI compute facilities demand enormous amounts of power and cooling, often making remote or coastal locations with access to renewable energy sources attractive. But these areas frequently lack the skilled IT workforce and innovation ecosystems necessary to drive AI development. The upside is that they are often situated in de-industrialised areas with workforces that are highly skilled in infrastructure installation, operation and maintenance.

These two elements don’t need to be co-located. Low-latency, high-bandwidth networks allow for a more distributed approach, where data centres can be situated near power sources while innovation can flourish in AI talent-rich areas, building on existing centres of excellence.

Energy

The successful implementation of AI Growth Zones hinges on overcoming a key obstacle: the UK's electricity costs. Large-scale AI infrastructure demands substantial power, but the UK has some of the highest electricity costs in Europe.

To tackle this, several options can be explored:

• Behind-the-meter arrangements which involve directly sourcing power from a dedicated generation facility, bypassing traditional grid infrastructure and associated costs. This could involve:

  • Onsite generation – building power generation facilities directly adjacent to data centres, allowing for greater control over costs and supply.

  • Direct corporate Power Purchase Agreements to contract directly with renewable energy generators to secure long-term fixed-price power.

• Collaborating with energy providers to develop solutions tailored to the specific needs of AI infrastructure. This could involve:

  • Securing preferential electricity rates for AI facilities in exchange for long-term commitments.

  • Jointly investing in grid upgrades to ensure reliable power delivery to strategic locations.

  • Engaging with Ofgem to ensure that their forthcoming package of connections reforms is fit for purpose in terms of the supply of connections to AI data centre sites.

• Small Modular Reactors (SMRs) hold potential, but they are not a proven solution yet. Regulatory and implementation challenges remain – current planning processes could take years, hindering rapid deployment. And building SMRs next to data centres is not legally possible right now.

International example: NuScale, a leading SMR provider, partnered with the US Department of Energy's National Lab in Idaho to build a 462MW reactor capability on their site from six of NuScale’s 77MW SMRs. This bypassed regulatory hurdles and expedited approvals because it was constructed on a government-owned science property i.e. a research reactor. Unfortunately, In 2023 it was announced this project was not going ahead due to customer concerns around being “first mover” and a lack of subscribers.

Data centre infrastructure

The challenges of power provision often focus on the delivery of significant amounts of power to data centre sites. In the UK, the transmission of electricity is managed by Transmission Network Operators who supply power at high voltages to Distribution Network Operators (DNOs). It is the role of the DNOs to provide power to the data centre infrastructure operators. Power starts at as much as 400,000 volts (enough to run a small city) and passes through a series of steps that transform it to 33,000 volts and then to 11,000 volts as it arrives close to or at the data centre. Each of these steps involves specialised high-voltage transformers – sophisticated devices currently in short supply worldwide. A final set of transformers reduce the power to 3-Phase 400V supplies that computers can actually use. But ordering these critical components now means waiting 1 or 2 years for delivery.

Today, few UK data centres operate dense liquid cooled infrastructure. As GPUs get more powerful and generate intense heat, traditional air cooling with fans is no longer sufficient. This is why all vendors are moving to direct liquid cooling of their systems, where specialised fluids carry heat away much more efficiently than air. While this has been commonplace in traditional supercomputing for the past 20 years, there is a lack of experience and skills in the operation of very dense, liquid cooled systems in the private sector. This is another key challenge for the UK as we seek to expand our capability quickly.

Compute allocation

As the Action Plan stresses, “allocation is an essential part of any compute strategy”. Demand for access to national supercomputing resources has always outstripped supply particularly in the UK over the past decade where the country has fallen well behind our main competitors.

Traditionally, access to supercomputer resources in the UK has been free to academia but paid for by non-academic organisations only when they are using the resource for “production use”. If a similar model is adopted for AI, it will be necessary to consider what constitutes pre-competitive research as opposed to production use. Alternatively, it could be argued that all access – from any organisation – should be free at the point of use, the resources being shared out based on quality of scientific proposal or business case.

The Action Plan envisions Programme Directors playing a central role in allocating compute resource. Recruitment of genuinely exceptional Programme Directors with both technical vision and practical implementation skills will determine whether the UK's compute investment delivers transformative results or merely incremental improvements.

Some of the ways to make this approach a success are through:

• Competitive compensation and streamlined recruitment processes that avoid lengthy bureaucratic selection processes.

• Ensure selection boards include genuine technical experts who understand the state-of-the-art in AI research and can evaluate candidates effectively.

• Programme directors will need sufficient autonomy to make rapid large allocation decisions quickly, bypassing the current system of 6-months-long waits for calls for access - instant small allocations have always been available.

• Rather than relying solely on individual Programme Directors, consider team-based leadership that brings together diverse expertise across research, industry, public sector.

• For each mission area, support multiple technical approaches rather than placing all resources behind a single solution, mirroring the successful approach used in the vaccine development programme.

Compute access alone is not enough. Innovation may be blocked not by compute availability but by the surrounding technical support ecosystem. AIRR compute access should take into account the following:

• Technical expertise and guidance for organisations unfamiliar with AI supercomputing environments including training resources from beginner to expert

• Machine learning engineers who can act as interfaces between AI researchers and the complex software and computing system frameworks used today

• Support for developing workflows that suit different R&D needs

• Connections to secure data environments (particularly for sensitive applications like healthcare) with access to appropriate, responsive information governance capability

• “Communities of practice” that help translate raw compute capability into practical innovation

Conclusion

The AI Opportunities Action Plan has outlined an ambitious set of activities to ensure the UK can realise the benefits that AI will bring to its public and private sectors. But implementing this ambition by developing a large-scale sovereign AI capability, provided by both the public and private sectors, is a complex, difficult task. Only by tackling the challenges of location, power, service provision, access models, skills and training at pace, will the country see the benefits of the large planned investments.

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Professor Mark Parsons is the Director of EPCC, the supercomputing centre at the University of Edinburgh and Dean of Research Computing. He started his career at EPCC in 1994 as a software developer following a PhD in experimental Particle Physics at CERN. He became EPCC Director in 2016 following many years as the centre’s Commercial Director. From 2020 - 2024 he was on part-time secondment to the Engineering & Physical Sciences Research Council where he was the Director of Research Computing. He has led the development of the UK Exascale Supercomputer Project for the past decade.

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Researchers and policymakers have been asking the UK public what they want from AI for almost a decade. Across time and space, the answers are generally consistent: we want AI systems that deliver public benefit, with meaningful human oversight, transparency and accountability in decision-making, and protection from harms that might arise from the use of AI or the power imbalances around their development.

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Recent public dialogues conducted by ai@cam in Cambridge and Liverpool repeated these themes. People want AI to support better public services, but they're increasingly sceptical about how it's being developed. As one participant put it: "AI needs to be managed, not just by the private companies, the Government need to manage what's going on, not just give AI a free reign."

Less commonly discussed is the growing gap between these aspirations and our institutional capacity to deliver them. Conversations with practitioners at AI Forum 02 explored why good policy intentions aren't translating into meaningful public benefit, and how the AI Opportunities Action Plan could help address that balance.

The implementation gap

Public dialogues consistently highlight independent oversight as a vital component of AI governance. Our latest public dialogues called for governance frameworks that protect public interests and enable beneficial innovation, backed by independent regulatory bodies with meaningful capabilities to guide AI development and enforce safeguards against harm.

In reality, we still don't fully understand the gaps and overlaps in the UK's AI -relevant regulations. Regulators face the challenge of keeping pace with rapid technological change in a context of increasingly constrained resources. The risk of "regulatory capture" – where large firms influence the rules meant to govern them – adds another layer of concern. This is why independent oversight must include a range of stakeholders, not just industry and government perspectives, including public voices. People’s experiences of AI can help identify potential harms, surface areas of opportunity, and open the door to a wider conversation about our shared future with AI.

Similar gaps appear across the policy landscape. While citizens want transparency about how AI affects them in public services, practitioners struggle with legacy IT systems, procurement constraints, and organisational silos, all of which affect how AI is deployed. The technical infrastructure and organisational capability to provide meaningful transparency often doesn't exist. The 'Scan > Pilot > Scale' approach offers promise in addressing this technical debt. Discussions at the AI Forum stressed the need for these agile approaches to be embedded in real deployment processes, not just policy rhetoric, so that it becomes a practical solution to a governance challenge.

Forging the UK’s path

A major question posed by public dialogues is: are we having the wrong policy conversation entirely? Policy debates often frame AI as an international arms race, with the UK's national ambition being to replicate creating Silicon Valley capabilities at home. However, there is another possibility.

The UK has its own distinctive capabilities: world-class research institutions, strong regulatory traditions, and deep domain expertise in areas like healthcare and finance. The question isn't how to replicate the capabilities of big tech on their home turf, but how to play a more creative game.

A starting point at May’s AI Forum was a conversation about what "sovereign AI" really looks like in practice. It's not just about having UK-based compute or UK companies (though those matter). It's about building capabilities that give the UK agency in an AI-driven world. This might mean developing AI systems that work for UK needs and opportunities; having enough domestic capability to avoid dangerous dependencies; and having the expertise to shape global AI standards, amongst other interventions.

Forum participants pointed to regulation itself as a potential competitive advantage. While others race to deploy AI first, the UK could lead in showing how to deploy it responsibly. This isn't about slowing innovation – it's about demonstrating that trustworthy AI can be a market advantage. Clear governance can create the confidence and trust necessary for widespread adoption of beneficial AI systems. Well-crafted regulation can provide the clarity that businesses need to invest and develop new applications, and the safeguards that publics need to be confident in their use.

In this context, the core tension isn't between innovation and regulation – it's between aspiration and implementation. Over-regulation risks stifling innovation, while under-regulation risks eroding public trust. We need proportionate, flexible approaches that evolve with technology, echoing public desires for agile governance while being grounded in implementation realities.

Where we go from here?

Trust doesn't come from dialogue alone – it comes from demonstrable action that shows AI systems working in service of human needs. Our dialogues revealed a clear set of characteristics that people want to see embedded in AI governance frameworks:

• Public understanding from broader education and information about how AI works and impacts daily life.

• Independent oversight from independent regulatory bodies with sufficient powers to govern AI in each sector, with a tailored approach that reflects the opportunities and challenges in different sectors.

• Protection from power imbalances, through robust legislation and enforcement to ensure public benefit is prioritised over profit, particularly in public services.

• Collaborative development through which AI developers engage with diverse communities to ensure systems work for everyone.

• Robust security to prevent threats and fraud, with clear lines of accountability if systems are compromise.

A limitation of our current approaches to public dialogue is the gap between these dialogues and the practical processes of R&D or policymaking. One route forward would be more focused problem-solving sessions that bring public concerns together with implementation expertise. Instead of repeatedly documenting what people want, we should zoom in on what it would take to bridge the gap between those aspirations and deployed AI products.

The question isn't whether people want better AI governance – they've been clear about that for years. The question is whether policy is ready to deliver it.

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About the authors

Neil Lawrence is the inaugural DeepMind Professor of Machine Learning at the University of Cambridge where he leads the University’s flagship mission on AI, AI@Cam. He has been working on machine learning models for over 25 years. He returned to academia in 2019 after three years as Director of Machine Learning at Amazon. His main interest is the interaction of machine learning with the real world. This interest was triggered by deploying machine learning in the African context, where ‘end-to-end’ solutions are normally required. This has inspired new research directions at the interface of machine learning and systems research, this work is funded by a Senior AI Fellowship from the Alan Turing Institute. Neil is also visiting Professor at the University of Sheffield and the co-host of Talking Machines. He is the author of the book The Atomic Human .

Jessica Montgomery is currently Director of ai@cam, a new University of Cambridge strategic mission to develop AI technologies that serve science, citizens, and society. Alongside this role, she leads a variety of research and policy programmes tackling the real-world challenges associated with developing and deploying AI for societal benefit. These include: Accelerate Science, an initiative developing AI tools and collaborations in support of research and innovation; the Data Trusts Initiative, an incubator programme for pilot projects creating trustworthy data governance frameworks; and strategic research agenda development for the ELISE/ELLIS network of European AI research. Her interests in AI and its consequences for science and society stem from her policy career, in which she worked with parliamentarians, leading researchers and civil society organisations to bring scientific evidence to bear on major policy issues.

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This wasn’t just a missed opportunity - it was an early warning sign. Throughout the 2010s, the UK watched from the sidelines as AI slowly, then quickly, took off. The talent was here. Researchers like Demis Hassabis, Zoubin Ghahramani, and Neil Lawrence were making foundational contributions. DeepMind was a moonshot bet, focused on AGI before anyone else took it seriously. But when it came to funding, infrastructure, and long-term strategy, we simply weren’t in the fight. While the US and China built trillion-dollar AI ecosystems, the UK allowed its best ideas, talent, and companies to be absorbed into Big Tech. No one here was writing the big cheques. The government stood by as we sold off our most valuable assets.

World War I in AI is over. That chapter is closed. But World War II is just beginning.

The Second AI War: 2025–2040

The next phase of AI will look different. The deep learning era - powered by transformer-based generative models like LLMs - is already dominated by a small group of large labs: OpenAI, DeepMind, and Anthropic. That wave has crested. The next wave won’t be about who has the biggest model, but who has the smartest strategy. AI is shifting toward integrated, multi-agent intelligence. Victory will hinge on robust sovereign infrastructure: compute, energy, and tight software–hardware integration - domains where the UK still has a fighting chance.

Compute

Access to compute will define national power in AI. Building and running state-of-the-art models requires vast amounts of processing power. Countries without domestic semiconductor capabilities or high-performance AI clusters will be relegated to second-tier roles. Right now, the UK lacks a sovereign chip industry. We have only a few AI-specific supercomputers and remain reliant on companies like NVIDIA and TSMC. That dependency will come at a cost.

Energy

AI is rapidly becoming an energy game. The next generation of models will require gigawatt-scale power just to train and operate. Without reliable, affordable energy, AI development will stall. The UK needs to rethink its energy strategy through the lens of AI - investing in stable supply, expanding nuclear, doubling down on renewables, and integrating energy planning with digital infrastructure. Otherwise, soaring costs and shortages will choke innovation.

Software–hardware integration

For two decades, the gospel was “software eats the world.” AI’s next chapter depends just as much on hardware. Frontier models need not only custom silicon but also high-bandwidth interconnects, advanced packaging, cooling, and the grid capacity to keep hyperscale clusters running. Success will rest on teams that co-design algorithms with the entire hardware stack. The UK has genuine assets here: world-leading materials science research, a long tradition of precision engineering in aerospace and life sciences manufacturing, and globally respected micro-architecture talent built around Arm. But without a concerted national drive to convert that expertise into large-scale design and fabrication, Britain risks being locked out of the foundations of tomorrow’s economy.

Financing a wartime economy

Right now, the funding mechanisms for AI in the UK are not built for this kind of war. If we do not act, we’ll keep seeing the same story unfold - our best talent and companies sold to overseas buyers with more capital and greater risk appetite. The US has long used defence contracts and federal R&D funding to drive AI investment. China has poured billions into a coordinated national effort. The UK has yet to define a strategy for how it plans to fund its AI future. We need new financial architecture. Government should act as a catalyst - de-risking long-horizon bets, unlocking pension fund capital, and signalling long-term ambition. Without that, we will keep punching below our weight.

From battle plans to battle stations

Matt Clifford’s AI Opportunities Action Plan, released earlier this year, is an important step forward. It lays out a clear framework for keeping AI breakthroughs in the UK and creating the conditions for long-term leadership. But a plan is only useful if it leads to action. That’s why we launched the AI Forum - a community of operators, researchers, and policymakers from across civil society and the public and private sectors, focused on turning the plan into progress. We are not just admiring the problem. We are asking what it will take to win.

Over the next two years, AI Forums will be held quarterly across the UK, each focused on one of the Action Plan’s key pillars: infrastructure, data, talent, regulation, procurement, and building national champions. The next forum will be held in Cambridge later this month and will focus on data and regulation. If you want to take part, reach out to the team: hello@ai-forum.fyi

The raw ingredients are already here. Talent. World-class universities. Over 500 startups generating more than $100 million a year in revenue. What we are missing is strategic alignment - and the ambition to lead.

We lost the first AI war. This time, we need to fight to win.

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Infrastructure: Building a Robust Backbone

Participants underscored that a modern infrastructure must serve the entirety of the UK—not just the Southeast. Attendees noted the misalignment between where AI talent is available and where data centres can be optimally located. The discussions revealed a pressing need for startups to gain improved access to computational resources, emphasizing that a long-term, stable compute strategy is crucial for national progress. Innovative proposals included repurposing old industrial sites, particularly along the East Coast, to host data centres powered by renewable energy such as wind. The forum also highlighted the importance of reducing reliance on external providers like AWS by establishing sovereign compute capacity, alongside initiatives like subsidised GPU credits and diversified data centre locations.

Data: Unlocking Value Through Collaboration

Data emerged as a cornerstone for AI advancement. Forum delegates agreed that public sector data should be treated as a national asset with clearly defined high-value datasets. Yet, challenges remain—public distrust and vague definitions of “high-value” continue to impede progress. There is a strong call for a cultural shift towards enhanced cross-sector data sharing, which would enable the combination of datasets to generate deeper insights. Proposals included the appointment of a Head of the National Data Library and the creation of a data licensing marketplace to unlock private archives. Transparent public data audits, open to stakeholder input, were also seen as essential steps forward.

Regulation & Governance: Steering Innovation Responsibly

The conversation on regulation and governance centered on the UK’s potential to lead on AI oversight. Attendees advocated for a balanced approach that fosters innovation while ensuring public safety. In a global context, the UK’s leadership in AI governance could influence policies in larger markets such as the EU and US. The discussions touched on the challenges faced by firms in managing AI risks and the importance of tailored regulatory measures. Key recommendations included supporting AI startups in areas like cybersecurity, maintaining consumer protection, and bolstering the AI Safety Institute. The use of regulatory sandboxes was highlighted as an effective means for companies to navigate evolving AI regulations.

Talent & Skills: Cultivating a Future-Ready Workforce

Recognizing the strength of the UK’s AI skills foundation, forum participants stressed the importance of nurturing talent from early education through to lifelong learning and reskilling. Broadening AI literacy to include digital fluency across disciplines is crucial, they argued, with AI-enhanced education—such as AI tutors—being a potential game changer for both teachers and students. A vision for a National AI Skills Academy was proposed to drive holistic learning, along with integrating AI into curricula and establishing performance benchmarks to track its impact.

Go-to-Market (GTM): Bridging Innovation and Application

A recurring theme during the forum was the disconnect between AI development and end-user needs. To address this, it was suggested that the government establish frameworks to help businesses identify high-value datasets and to accelerate the adoption of AI technologies. Delegates identified procurement as a significant bottleneck—particularly for startups—and called for faster, more risk-tolerant procurement processes. The “test and scale” approach was championed as a way to validate successful use cases, with a focus on outcomes such as revenue growth, efficiency gains, and effective risk management. Targeting specific verticals, especially for large language model (LLM) adoption, was also seen as a strategy to drive measurable impact.

Makers Not Takers: Forging National Champions

The final workstream, “Makers Not Takers,” addressed the critical role of procurement in nurturing homegrown AI champions. Here, the debate centered on striking a balance between government investment and fostering the independence of companies. Innovative funding models were discussed, including linking government orders with equity stakes in startups and directing funds into national venture capital initiatives. Streamlining procurement processes to allow startups more time to respond, and developing an open-source strategy for industrial AI development, were among the strategic solutions proposed. Some participants even suggested the creation of a “Manhattan Project for AI” to catalyze significant breakthroughs.

The inaugural AI Opportunities Action Plan Forum showcased a diverse array of insights, challenges, and innovative solutions poised to steer the future of AI in the UK. With a clear focus on robust infrastructure, strategic data management, balanced regulation, comprehensive skills development, agile market strategies, and the nurturing of national champions, the forum set the stage for a dynamic transformation in the AI landscape. As the government moves forward with its AI Opportunities Action Plan, the collaborative efforts highlighted during the forum will be essential in turning these ambitious proposals into reality.

Date: 28 January 2025

Location: Phoenix Court, 2 Brill Place, London

Hosted By: Evident, Next Economy Trust, Phoenix Court Works, and partners

The inaugural AI Forum convened a diverse group of stakeholders to support, challenge and track progress of the Government’s AI Opportunities Action Plan. The discussions were broken into six key workstreams reflecting the Plan: infrastructure, data, talent and skills, “go-to-market” (procurement & adoption), and “AI makers, not takers” (building national champions).

This is an executive briefing detailing the key insights, challenges, and proposed solutions across each of these workstreams.

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I. Infrastructure

Key Insights:

• Infrastructure development must benefit the entire UK, not just the Southeast. There's a need to create infrastructure that connects to areas of need across the country. This requires partnerships with local authorities, universities, and innovation clusters across the UK.

• Startups require support to access compute resources, not just academic researchers and support.

• There needs to be a long-term plan for compute infrastructure that isn't easily disrupted by changes in government.

• There is an opportunity to build data centers on old industrial land, potentially on the East Coast of the UK, to take advantage of wind power.

Challenges Identified:

• There's a lack of clarity on how small companies can access resources. This is partly due to research councils not allowing companies access to resources.

• The government has only identified one of five AI growth areas and may not have a clear understanding of the key factors needed to make them successful.

• The government needs to move away from reliance on external providers like AWS.

• The UK is not competitive in the AI space and may be losing out to other countries.

Proposed Solutions:

• Rapid evidence reviews to guide the design of growth zones. This review should look at what works and where.

• Provide subsidised or free GPU credits to startups. This could be done by having the government underwrite compute resources, allowing infrastructure players to raise capital.

• The government should move away from AWS and other similar providers and create its own sovereign compute capacity.

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II. Data

Key Insights:

• The focus should be on identifying high-value datasets in the public sector and making them accessible. This means treating data as an asset and making it easier to share.

• It's crucial to incentivise data sharing across sectors.

• Combining datasets can create more value than the data in isolation.

• The government should not try to solve the problem, but rather build out enablers that let others solve problems using data.

• The value of data is created by solutions developed from that data, not from the data itself.

Challenges Identified:

• There's a lack of clarity on how to prioritise datasets. The term "high value" is not well-defined.

• Public distrust of the government's use of data.

• The government struggles to create multidisciplinary teams with the necessary skills to manage data effectively.

• There is friction in government for enabling data sharing, even between departments like the NHS and DHSC.

Proposed Solutions:

• Appoint a Head of the National Data Library who can assemble a multidisciplinary team.

• Focus on datasets that enable value for different solutions.

• Create a marketplace for data licensing to help entities clear rights and license their data for profit. This will help to unlock data from private archives.

• Start with small projects that show value, then expand.

• Prioritise data sharing for small, impactful projects and demonstrate their value.

• Government needs to make data audits public and ask for input from researchers and businesses.

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III. Regulation & Governance

Key Insights:

• The UK has the opportunity to lead on AI governance.

• There needs to be a balance between innovation and safety in AI.

• The UK is well-placed to influence larger players like the EU and US.

• The UK should not try to be the most innovative, but instead recognise that it is small and focus on collaboration.

Challenges Identified:

• Over-regulation could stifle innovation in AI.

• There is public distrust of AI.

• Firms are struggling with the risks of AI and how to implement it.

Proposed Solutions:

• Focus on transparency and collaboration in the AI industry.

• Encourage AI startups with a focus on cybersecurity.

• Prioritise consumer protection in AI.

• Continue to support the AI Safety Institute.

• The UK needs to be more thoughtful in how AI is regulated and not just consider it as a single entity.

• Provide sandboxes to support AI companies in navigating regulation.

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IV. Talent & Skills

Key Insights:

• The UK has a relatively good foundation of AI skills.

• Need whole-life learning, from primary education to reskilling.

• AI skills not just about computer science, but about broader literacy.

• AI could be used to enhance education, with AI tutors for teachers and students.

• It is important to engage parents and help children to engage with coding and AI.

Challenges Identified:

• The specific goals of the skills section in the Plan are unclear.

• The current approach is too academic and does not include "learning by doing".

• There is a gap between ambition and the current funding.

• Current initiatives are piecemeal and not holistic.

• Many teachers are not aware of how quickly AI is advancing.

• Students who use AI tools may be seen as "cheating," even if they're enhancing learning.

Proposed Solutions:

• Create a National AI Skills Academy to ensure whole-life learning.

• Focus on making AI ubiquitous and creating a broad base of skills across society.

• Use AI tutors to improve learning outcomes.

• Partner with other countries to increase the UK's stature in AI.

• Use a micro:bit style initiative to get children into coding.

• Baseline student performance against current tests and then measure how much the use of AI improves that performance.

• Engage with qualification agencies like the GCEQ to make sure there is a computer science component to curricula.

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V. Go-to-Market (GTM)

Key Insights:

• The focus should be on delivering good outcomes and user experiences.

• The government should focus on creating frameworks that enable businesses to identify where the highest value data sets are.

Challenges Identified:

• There is a disconnect between the needs of end-users and the way that AI is being developed.

• Companies are unsure about the risks associated with AI.

• Procurement processes are risk-averse, slow, and not performative.

• Startups struggle to navigate the procurement process.

Proposed Solutions:

• Use a "test and scale" approach to identify successful use cases.

• Develop a framework for thinking about value, with a focus on revenue, efficiency, outcomes, and risk management.

• Focus on specific verticals that can leverage LLMs.

• Help companies quantify the benefits of AI investments.

• Encourage more collaboration before tender documents are created.

• Provide assistance to companies to meet security standards.

• Government should procure at speed, which means taking more risks.

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VI. Makers Not Takers

Key Insights:

• Procurement is an often-overlooked lever for creating national champions in AI.

Challenges Identified:

• There needs to be a balance between government investment and the freedom of companies to grow.

• There is a need to understand the current government landscape (the "as is") before implementing new AI plans.

• Government is often not best placed to identify promising technologies.

Proposed Solutions:

• Use government funds to drive capital into homegrown venture capital funds — eg via the ECF.

• Link government orders with taking a stake in companies.

• Streamline procurement processes and give startups more time to respond.

• Develop an open-source strategy for industrial AI development.

• Consider a "Manhattan Project for AI"

Next steps

The AI Forum will continue to support, challenge and track progress of the Government’s AI Opportunities Action Plan in the months and years ahead. Gatherings will be held quarterly over the next 2 years across the UK, with tentative dates as below:

2025

• Wed 30 April

• Tues 1 July

• Wed 19 November

2026

• Tues 20th Jan

• Tues 21st April

• Tues 7th July

• Tues 3rd November

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