What evidence would persuade your board that a successful AI pilot is ready to become part of everyday business operations?

In this episode of Business Technology Perspectives, I speak with Andrew Duncan, CEO of QualityAI, formerly Qualitest. Andrew previously served as CEO of Infosys Consulting and has spent over 30 years advising global organizations across technology and business change.

Andrew believes the AI era differs from previous technology cycles because adoption is moving faster than many organizations can govern it. Fear of missing out is also creating pressure for companies to appear further along than they really are.

This creates a growing gap between AI activity and operational value. A company may have numerous pilots, prototypes, and employee experiments without possessing enough evidence to deploy any of them across the business.

Andrew describes the enterprise AI conversation as moving from “can we build it?” toward “can we trust it?” Answering that question requires companies to assess three areas. Does the technology work? Is it producing the expected business outcome? Will it continue behaving within agreed boundaries as its data, model, users, and environment change?

We discuss the questions boards should ask before approving AI at scale. What business result is the company pursuing? What level of risk is acceptable? Who becomes accountable when the system makes an unexpected decision? Has it been tested under real operating conditions? What evidence demonstrates that people can rely on it?

Andrew explains why a successful pilot should never be confused with production readiness. Controlled tests cannot fully represent real data, human behavior, connected applications, operational processes, or downstream consequences. A system that performs well in isolation may behave very differently once it reaches customers and employees.

This is why Andrew advocates continuous AI assurance. Testing cannot end when an application enters production. Organizations must monitor whether the system continues functioning technically, produces the intended result, and remains within defined guardrails.

Our conversation also addresses AI washing and the growing tendency to measure progress through the number of pilots or features launched. Andrew suggests asking a much simpler question: what has materially improved because AI is involved?

Useful measures could include productivity, revenue, operating costs, cycle times, customer experience, adoption, and the consistency of results. If those outcomes remain unchanged, a collection of impressive demonstrations may amount to activity without meaningful business progress.

Could continuous assurance give businesses the confidence to scale AI faster, or will many companies remain trapped between promising pilots and production risk? Listen to the episode and share your thoughts with me.