AI Action Plan UK: Is your Enterprise Ready?

On January 13, the UK Government announced its AI Opportunities Action Plan. The United Kingdom is already the third largest AI market globally, but this plan will lock in its position as a leader in shaping the AI revolution. The hope is that increasing AI adoption will contribute an extra £47 billion per year to the economy.

The plan was developed by the tech entrepreneur Matthew Philip Clifford. He’s the former co-founder of Entrepreneur First Expert and Advisory Board Vice Chair member of the Government’s Frontier AI Task Force, now called the AI Safety Institute. This has given him the expertise to take the lead on the action plan, now agreed to by the Government.

Matt Clifford said: “AI offers opportunities we can’t let slip through our fingers, and these steps put us on the strongest possible footing to ensure AI delivers in all corners of the country, from building skills and talent to revolutionising our infrastructure and compute power.”

In this article, we’ll dive into the plan and how you can prepare as a data or business leader. AI is already unlocking numerous opportunities, and with its capabilities becoming more refined every month, making sure your business is AI-ready needs to be a top priority in your corporate and data strategy in 2025. Read on to stay ahead of the curve.

Who is involved in executing the AI Opportunities Action Plan?

The government is ensuring both the public and private sectors are empowered to develop a collaborative environment, where research, innovation and data sharing are central pillars.

• Government bodies:
  • The UK Department for Science, Innovation and Technology (DSIT) leads the strategy.
  • The Office for AI focuses on policymaking, ethical AI, and governance.
• Institutions and companies:
  • Partnerships with universities like Cambridge and Oxford for AI research.
  • Private sector leaders such as DeepMind, OpenAI, and British startups.
• Oversight bodies:
  • AI Council and Centre for Data Ethics and Innovation (CDEI) guide on ethical deployment and AI safety.

What are the timelines of the plan?

• 2024: Release of frameworks for AI governance and guidance for responsible AI use.
• 2025-2027: AI skills expansion programme to upskill 1 million people.
• 2030: Target date for integrating AI-driven systems into public services and ensuring widespread AI adoption across industries.

What outcomes will the AI Action Plan deliver?

1. The rise of computational power
   • Prepare to meet demand by establishing “AI Growth Zones”. These will be specific locations where the Government would partner with the private sector to deliver large amounts of computing power.
   • Deliver increased computing capacity by at least 20-fold by 2030 and commit to a 10-year investment strategy. £25 billion of investment for the build of new data centres has already been confirmed at the Global Investment Summit.
   • Build a new state-of-the-art supercomputing facility to double the country’s existing AI research resource, with the Department for Science, Innovation and Technology (DSIT) to lock in a site and other key details this year.
   • Publish a new, long-term compute strategy in Spring 2025.
2. Embedding of a national data-sharing culture
   • Launch National Data Library: At least 5 publicly available data sets will be shared as part of the first stage of the Library. These secure data sets will used to support AI research and innovation.
   • The National Data Library will incentivise new data collection and lead on what data could support breakthroughs in science and other domains. This will require good data quality and cleansing.
   • UK-based AI leaders will be given access to these new resources and infrastructure, and support them to attract AI talent globally.
3. A significant contributor to the future of work
   • Deliver an assessment of the national AI skills gap and work with partners such as universities to deliver a solid plan of upskilling while attracting talent from abroad. The current estimates are no longer relevant dating back to 2020.
   • Boost the number of AI graduates in the next 5 years to meet the unmet demand. While the UK’s total number of graduates (46,000 in 2022) ranks the highest as total number in Europe, support for Higher Education is required to increase that pace. Special attention will also be given to expanding scholarships.
   • A multi-pronged approach to upskilling: employer, self-led education and apprenticeships will all be encouraged by the Government
   • Make the UK an attractive place to work for top AI talent, by investing in headhunters, expanding the Turing AI Fellowship,
   • Stay ahead of the changing job market: there are vast unknowns about how AI will continue to shape the future of work. Ensuring employees can access upskilling and re-skilling resources will be crucial to

The current state of AI in the UK

According to government research, around one in six or an estimated 432,000 organisations already use AI in the UK. (Capital Economics, 2022).

As the demand for AI rises, we’re seeing AI companies popping up like mushrooms, with Forbes noting a 600% increase in the last decade. As of 2024, there are 1,800 VC-backed AI startups and 20 AI unicorns in the UK. And since the Action Plan was confirmed, we’ve already seen an astounding amount of £14 billion in fresh inward investment across the country.

Another interesting fact is that Tech Nation found that 69% of UK tech professionals say AI is having a positive impact on them today. So while there are the realities of security, data mining, sustainability, biases and inequalities to be dealt with, there’s a positive sense people will be enabled by AI to save time on less valuable tasks and shift their expertise to driving more innovation.

AI Adoption across industries

The latest reporting shows the following adoption rates of AI across various sectors:

• AI in Insurance: 95%
• AI in Financial Services: 75%
• AI in Construction: 53%
• AI in Information and Communication: 44%
• AI in Wholesales and Retail Trade: 31%
• AI in Manufacturing: 17%
• AI in Retail & hospitality: 12%
• AI in Healthcare: 11.5%

Adoption is being driven by a number of factors. For one, customers have become used to tailored algorithms based on their preferences, and their expectations of efficient and personalised services are high. AI is a key factor in delivering this expectation, and we see this with the roll-out of chatbots which companies can leverage more effectively the better they know their customers.

Marketing, Data and Analytics and Customer Service departments are adopting AI faster than others, and certain industries are more risk-averse than others. But with investment, the demand and deployment of capabilities going up, it’s clear that those not looking to leverage AI will be left behind. As clear guidelines are being developed, most likely following a similar narrative as the recently adopted EU Regulations, we’ll continue to see the UK AI market grow extensively in 2025.

How will the future of AI impact different industries?

Acording to McKinsey, three-quarters of the value from generative AI would emerge from four areas of business: customer operations, marketing and sales, software engineering, and research and development. Now let's look at how this aligns within various sectors.

Consumer markets

AI will enhance marketing, supply chains, finance, and customer service, with smarter chatbots and AI tools assisting human staff and automating interactions. Dynamic pricing and streamlined regulatory processes will drive revenue, but companies lacking AI infrastructure and appropriate attention spent on adoption risk falling behind.

Financial services

Fintech startups and large financial institutions are leading AI adoption, using it to solve problems and refine risk models. Firms delaying their AI strategies risk noticeable setbacks by 2025 as these leaders accelerate.

Health industries

AI in healthcare will accelerate with flexible regulations, helping pharma and medtech companies innovate in drug development and operations. Priorities will include personalization, process improvement, and safeguarding sensitive data in life-critical applications.

Industrial products

Leading companies will leverage AI to improve efficiency, speed R&D, and reduce time to market, relying on quality data and standardised processes. Others will focus on foundational upgrades, with increasing pressure to address talent and operational needs.

Technology, Media, and Telecommunications

AI agents will extend the life of legacy software systems, potentially shifting software business models toward tailored solutions. Telcos will integrate hybrid AI technologies to enhance capabilities and reduce dependence on traditional partners.

Construction

AI is already revolutionising construction by optimising project planning, resource allocation, and on-site operations through predictive analytics and automation. Companies adopting AI-powered tools for design, scheduling, and safety monitoring will reduce costs, improve efficiency, and minimise delays.

AI-enabled drones and robotics will enhance site inspections and automate repetitive tasks, addressing labour shortages and boosting productivity. Firms that invest in AI for sustainable building practices and smart infrastructure will gain a competitive edge in the evolving market.

The risks of not adopting AI

While the risks of adopting AI are being widely discussed, there’s a cost-benefit calculation to be made for not adopting AI. Technology has always created great change in the ways we work, and choosing not to adapt means

Moreover, organisations that neglect data literacy may face risks such as inconsistent outcomes and strategic misalignment, which can ultimately compromise their competitive edge in a rapidly evolving market.

McKinsey reports that generative AI could contribute an additional $2.6 trillion to $4.4 trillion annually to global corporate profits, proving it will become an increasing competitive advantage. In banking alone, AI adoption is projected to contribute over $170 billion in extra profits over the next 5 years.

What does a business need to do to get AI-ready?

Successful AI adoption hinges on a variety of factors, with the most significant being the quality and reliability of the data being used in the models. With large businesses already adopting AI at the fastest rate of 68%, there’s no time to waste to ensure foundations are set for the next decades.

The principle of “garbage in, garbage out” applies to AI as it does to other data products. Data teams need to ensure their tools are fed with trustworthy, accurate data to ensure the wider business is working with beneficial outcomes.

Once tools are rolled out, users need to become aware of their abilities and flaws, limitations and biases. On-going updates on how the models evolve will be crucial too, as AI is not a set-and-forget technology.

Never has it been more clear that both AI and Data Literacy should be at the forefront of any leader’s strategy. Education and adoption go hand in hand, and waiting until it’s too late can cause significant damage in the long run.

Instead, businesses need to empower employees to:

• Grasp the value of data, the importance of their role in the data value chain and understand how data and AI are synonymous
• Understand and interpret AI outputs
• Question the ethics and accuracy of AI-driven decisions
• Collaborate across teams to integrate AI in meaningful ways and avoid entrenching silos
• Adopt ethical frameworks to ensure data usage is compliant, both internally and externally
• Understand your company’s data governance practices and adhere to them

All of this takes investment in education, with leadership positioning it as a priority for themselves and the wider business if they want to stay ahead.

Key strategies for Data and AI Literacy implementation

41% of executives say that workforce issues, such as training, culture, or change in work are among the top-five challenges their organisations face in using GenAI. PwC’s 2024 Workforce Radar

Creating a shared data culture and language takes a concerted effort. Leaders need to drive the vision, and employees need to grasp the personal “why” of going on a learning journey. Business outcomes, personal career outcomes and the data strategy need to align to ensure everyone is on the same page of the importance.

1. Baseline assessments: You can’t prove what you don’t measure. So measuring the current level of data literacy is key to identifying what areas need to be improved.
2. Change management: Education won’t be successful without significant attention spent on bringing awareness to the intended audience.
3. Training programmes: Regular training sessions and workshops are crucial for building foundational data skills among employees. Your programmes should be designed to cater to different skill levels, from beginners to advanced practitioners, and take into account your specific use cases and goals.
4. Promote a data-driven culture: Encouraging a culture that prioritises data in decision-making processes is vital. This involves promoting the importance of data literacy among all employees and integrating it into everyday practices. This can be done by asking more, and better questions. It’s as simple as asking “How did you reach that conclusion or decision?” to nudge people to think critically about the data used to land on their position.
5. Tools as enablement: While tools can help automate, calculate and drive efficiencies at a greater scale than humanly possible, they should be positioned as an enabler to your workforce and inspire them to take on new tasks they wouldn’t have had time for before. We advocate for a human-centred approach where experience is valued, relationships are key, and a collective responsibility is embedded into the fabric of appropriate use of new technology.

Overcoming common challenges

While the promotion of data literacy in every organisation, especially of enterprise size, is critical, several challenges must be addressed:

• Limited resources: Allocating a dedicated budget for data literacy initiatives can help overcome financial constraints. It’s essential to view these investments as a means of enhancing overall efficiency and decision-making capabilities. That’s why it’s so critical that outcomes are tied to overarching business goals.
• Leadership support: Gaining support from top management is crucial for driving the importance of data literacy. Leadership should actively advocate for and participate in data literacy initiatives to set a positive example. A great example of how Data Literacy Academy has supported a customer doing just that is by enrolling the entire executive team of Aston University on our CPD-accredited certifications.
• Continuous learning: Fostering a culture of lifelong learning is necessary to keep pace with evolving data skills. As a business, you should provide regular updates and opportunities for skill enhancement, including advanced courses and certifications.

As 2025 picks up, business and data leaders will need to remain aware of incoming legislation, new applications of AI and stay on top of use cases within their business to make the most of the AI revolution. When taking a measured approach taking both risk and opportunity into consideration, AI will help drive efficiencies unimaginable in the past. We’re still in the early years of AI, so setting up the right foundations is more important than ever.