While the headlines focus on scale, the real opportunity lies in execution. According to James Rowell, Founder of cloud-based expense management platform Capture Expense, the investment is a logical move by Big Tech to drive real-world usage of their AI models. The challenge now is ensuring that businesses are ready to benefit.

“Big Tech has built the models and now needs real usage,” says Rowell. “Investing locally de-risks adoption. More UK compute and capacity means faster time-to-value, like Google’s £5 billion data centre and NVIDIA’s UK build-out and startup fund.”

Infrastructure is growing – but readiness will decide the outcome

The investment will bring closer, cheaper, and lower-latency access to GPUs and cloud AI, enabling quicker pilots, deeper partner programmes, and a stronger local talent pool. However, rising energy costs, planning delays, and tax pressures all could blunt the impact if businesses are not prepared.

“This is a turning point for capacity, not a magic wand for productivity,” Rowell says. “If policy keeps pushing higher operating costs, the headline money won’t translate into growth. Businesses will be left renting infrastructure while the real value is created elsewhere.”

What businesses should do now

Rowell advises businesses to focus on practical readiness. That starts with clean, well-documented data and short pilots tied to a single metric – such as handle time or collection rate. AI costs should be tagged and tracked from day one, with compliance baked in and architecture designed for portability.

For businesses with limited scope or budget, start with AI already embedded in existing tools like Microsoft 365 or Google Workspace. Add one focused workflow, like invoice triage or knowledge base search for example, and use pay-as-you-go APIs for anything bespoke. Keeping the scope narrow and proving ROI early is key to scaling successfully.

Risks to watch

Vendor lock-in and future pricing power are real concerns. As infrastructure becomes more centralised, businesses must be cautious about dependency. Energy and grid constraints around data centres also pose risks to cost and reliability. Talent scarcity remains a challenge, especially as demand for AI skills accelerates.

Rowell urges businesses to treat AI spend like capital expenditure, track unit economics from day one, and ensure every deployment of AI is tied to a measurable outcome. This means understanding not just what a tool costs, but what it delivers – whether it’s reducing cycle time, improving accuracy, or freeing up team capacity. Without clear metrics, AI projects risk becoming expensive experiments. By treating spend as a strategic investment, businesses can build a repeatable framework by evaluating ROI, scaling what works, and cutting what doesn’t.

Long-term outlook

Looking ahead, Rowell believes the UK has a genuine opportunity to lead Europe in applied AI, but only if the infrastructure is matched by execution. That means pairing investment with skills development, energy reform, and faster planning. He expects more local data centres, industry consolidation, and tougher scrutiny on energy and water use.

“We won’t match the US or China on absolute scale,” he says. “But we can close the productivity gap by adopting faster and specialising in areas like fintech, biotech, defence, and regulated services. The £31 billion is a solid nudge, but only if we act.”

For more startup news, check out the other articles on the website, and subscribe to the magazine for free. Listen to The Cereal Entrepreneur podcast for more interviews with entrepreneurs and big-hitters in the startup ecosystem.