U.K. organizations are investing heavily in AI, but many are now confronting a harder question: are they ready to put it into production safely?
In this CX Today interview, Technology Journalist Nicole Willing speaks with Adam Spearing, EMEA Head of AI Innovation at ServiceNow, about why AI spending is accelerating faster than enterprise maturity. According to ServiceNow research, spending on AI in the U.K. has increased by 102 percent, while the Enterprise AI Maturity Index places the U.K. at just 51 out of 100 for overall maturity.
For Spearing, that gap reflects where many organizations are in their AI journey. Much of the investment so far has been exploratory, with businesses testing use cases, identifying value, and preparing for the next stage. Now, he says, many are on the cusp of moving from pre-production pilots into live environments.
That shift brings new pressure, because once AI becomes part of daily operations, leaders need more than enthusiasm and budget. They need visibility, governance, accountability and control, Spearing says.
“Don’t think about this as compliance. Think about this as the key enabler to give you confidence and trust that what you’re about to go and unleash is going to deliver the ROI on your terms, not its terms.”
Spearing explains that organizations are increasingly asking how they can observe AI systems, measure return on investment, restrict data access, enforce policies and shut tools down when needed. Businesses need to know where their AI is, what it is doing and whether it is behaving as intended.
The risks are already visible. Spearing shares the example of one client that found 20,000 AI agents already operating inside the business, created with good intentions but without IT oversight. That kind of “shadow AI” creates serious questions for enterprise leaders around data, security, compliance, and operational risk.
Data is another major barrier. Spearing notes that while poor data has always been a business challenge, AI can amplify the impact. Where a human executive might question whether data looks wrong, AI may simply act on what it has been given, making context, quality and a clear data strategy essential foundations for scaling AI across departments and workflows.
The interview also explores the gap between agentic AI adoption and true autonomous workflows. While many U.K. organizations say they are using agentic AI, far fewer have reached the point where AI can manage complex, end-to-end business processes. For Spearing, the next stage of value will come when companies stop simply adding AI to existing silos and start reimagining workflows around what AI can do.
Watch the full interview to hear Spearing explain how U.K. organizations can close the AI maturity gap and move from experimentation to trusted, scalable AI deployment.
