Responsible AI: The next strategic advantage for UK businesses

As we head into 2026, one thing is clear. AI is no longer a future ambition; it’s a present reality. Across industries, businesses are already seeing tangible benefits, with new research we released today showing that 89% of UK business leaders reported a positive impact on performance.

However, as adoption accelerates, the conversation is shifting. It’s no longer just about what AI can do, but how it should be done. In fact, 87% of those UK business leaders in the survey recognised that embedding transparent and accountable AI practices will be critical within the next two to three years.

We’re also seeing a clear evolution in customer expectations. 84% of respondents agreed customers are increasingly asking how AI is governed and who is accountable.

This growing focus on transparency and trust is reshaping how businesses develop and deploy AI. By aligning AI with responsible practices, businesses can proactively manage risks, strengthen stakeholder confidence, and unlock long-term, sustainable value. However, while the value of Responsible AI is widely recognised, putting it into practice remains a challenge.

In fact, 76% of leaders in our research cited implementation as one of their biggest hurdles. This isn’t surprising given how prominently the topic of AI implementation has featured this year, including at events and panels I have participated in.

So, what should businesses do to ensure Responsible AI becomes a reality?

• Make data quality a shared priority: Leading businesses treat data management as a cross-functional responsibility, bringing together IT, compliance, analytics, and business teams to ensure data integrity from the ground up. They then build strong repeatable processes for data lineage, governance and validation.
• Embed checks throughout the AI lifecycle: Bias detection and mitigation should be built into every stage of model development and review. Innovative businesses combine technical solutions with oversight from a range of teams to identify and address data imbalances early.
• Monitor model performance continuously: AI models require ongoing validation to remain effective and aligned with expectations. Routine performance checks help detect drift, maintain performance, and ensure outputs continue to meet both business needs and regulatory standards.

I’ve spoken before about AI creating real value and I firmly believe that Responsible AI practices will be key to achieving that. And as we look ahead to 2026, the businesses that will lead are those that treat Responsible AI not as a tick-box exercise, but as a core business priority woven into how they operate and innovate.

The future of AI isn’t just about capability, it’s about accountability. And that’s where the real opportunity lies.