The UK shouldn’t simply settle for being an adopter of artificial intelligence, but instead have ambitions to shape the future of AI on the world stage, says Dominic Wellington of SnapLogic
The UK currently stands at a critical juncture in its journey towards AI adoption. Moving beyond being just a consumer, the nation aims to become a powerhouse of AI innovation. The AI Opportunities Action Plan signalled the government’s intent to capture opportunities for growth and shape the future of artificial intelligence. But is the UK’s appetite for AI matched by its ambition to build it?
The US and China have established their leadership with companies like OpenAI and DeepSeek launching models that took the AI world by storm. However, the UK is yet to make a breakthrough that creates tangible benefits for the UK citizens. Not only this, it’s lacking on the adoption front too. SnapLogic’s research of over 1,000 IT decision-makers shows that the UK is trailing behind not just the US but also Australia and Germany in planned spending on AI in the coming year.
Regulation vs innovation: Barriers to AI adoption in the UK
UK employees may well be using AI, but often it is without any of the guardrails that proper enterprise-grade AI solutions would implement. This shadow AI exposes firms to all sorts of risks, not just cybersecurity vulnerabilities, but also embarrassment at the prospect of AI hallucination (incorrect or misleading results that AI models generate). UK businesses that don’t move fast enough with an internal AI usage policy may find themselves outstripped by their own users, with potentially ruinous consequences. Investigation and research is all very well, but at some point you do have to start doing something.
“For larger businesses operating across Europe, complying with both UK and EU standards is particularly challenging”
On a bigger scale, failure to comply with security or regulatory policies not only results in financial and legal penalties but also long-term loss of trust and reputation. For larger businesses operating across Europe, complying with both UK and EU standards is particularly challenging. While the government is pursuing a “pro-innovation” regulatory stance, it is essential for companies to implement robust internal measures and stay informed about regulatory developments.
Is lack of investing in AI research losing out on real progress?
The UK has a great reputation of investing in technological research leading to innovations like modern computers and the World Wide Web. However, there’s been a pattern of great UK research failing to find a place in the local market and being commercialised elsewhere. Even the great success of Tim Berners-Lee inventing HTTP and HTML, and thus effectively the Web, is a case of a British researcher working abroad, in his case at CERN in Geneva. Now we are seeing this pattern repeating with artificial intelligence as happened with DeepMind’s acquisition by Google. The venture capital ecosystem in the US is unparallelled, and attempts to hot-house “the Silicon Valley of X” tend to fail to scale.
For government funding initiatives, a lack of national champions in the industry poses a risk of spending benefiting primarily overseas players rather than UK taxpayers. However, this challenge for public funds doesn’t preclude individual UK enterprises from investing in AI for their own purposes and gaining significant benefits. The focus is not on creating AI autarky, but rather ensuring strategic government investments are impactful.
AI models to date do not seem to have a competitive moat — new models leapfrog each other constantly. For government strategies focused on backing specific AI models, this constant churn might be a challenge, as competitive advantages appear short-lived. However, for organisations adopting AI, this continuous improvement is largely beneficial. In the long run, as AI models potentially become more commoditised, the true value will likely lie further up the stack; in developing specialised applications of that technology that use sources of data that others don’t have, rather than solely focusing on model creation.
“Identifying specific problems that AI can solve allows for targeted deployments and quicker returns on investment”
If UK businesses can benefit from AI models whose creation is funded elsewhere – and are potentially based on UK research – without requiring massive capital investments, that is a net benefit to the British economy and to society in general. As ever with new technology, it is important to focus on the concrete benefits that it can deliver, rather than rolling out the latest shiny thing for its own sake.
Infrastructure and skills: Using past investments in data engineering as a catalyst
The UK has a long-standing history of investing in cloud infrastructure and data engineering that can act as a stepping stone for AI and ML integration to drive innovation and efficiency. Beyond just private datasets, we hold valuable, large-scale public datasets records that can provide us with an advantage AI model development. Massive data records from the NHS can fuel a strong AI industry focused on things like drug discovery, medical research, and improving healthcare. This not only fuels the AI research landscape in the country thus directly to the economy; but also, tangible support to digitally transforming the NHS.
Instead of risking chaotic and disconnected investment in AI development, the UK government should invest in the relationship of academia and industry — shifting focus to initiatives such as incubators and startup funds for graduates, in a more spinout-first approach. British universities attract talent from all over the world, so there is ample scope to capitalise on that influx of talent by enabling students to build businesses in the shadow of UK universities, if the right financial and regulatory structures are set in place for them. To claim (or reclaim) a leadership role in the tech race, the UK must act fast and focus on concrete benefits achievable with existing infrastructure and skills, rather than rushing out with developing segregated AI models here and there.
The barriers to AI innovation in the UK are complex. While investment is essential and regulatory clarity is important, demonstrating value is crucial for widespread AI adoption across the UK economy. Beyond just development, the actual adoption of AI by businesses and individuals is vital to achieve real progress. To accelerate implementation, businesses should start with clear, well-defined use cases. Identifying specific problems that AI can solve allows for targeted deployments and quicker returns on investment. Employee training, change management, and incremental rollouts can minimise risks and refine AI strategies. By tackling the problems head-on, the UK can solidify its place not just as an adopter, but as a pioneering force shaping the future of artificial intelligence on the world stage.
About the author
Dominic Wellington is an AI and data expert at SnapLogic.
