"Turning what seems like a good idea for an AI system into a practical service is proving to be a tough challenge for many professionals. Research suggests most AI projects get stuck at the starting blocks, and even the initiatives that progress down the track don't necessarily deliver great results. Paul Neville, director of digital, data, and technology at The Pensions Regulator (TPR) in the UK, is eager to ensure his agency turns AI into a competitive advantage."
""If you don't have your data in good order, that's a problem," he said. "Of course, there are AI tools to help you do that. But ultimately, strong foundations are about good practice, good governance, and data ownership." Neville said AI success is directly related to your ability to exploit the technology for the benefit of the people who work in your organization."
Successful AI deployment depends on establishing strong foundations, including reliable data, good governance, cybersecurity, and rationalized service management. A coherent data strategy and standards drive adoption of new data technologies. AI projects should prioritize end users by delivering technology that benefits employees and stakeholders. Teams must identify measurable value, set clear aims, and align peers and governance before scaling. Iterative delivery and confidence-building pilots help progress initiatives that often stall early. Practical adoption requires combining technical tools with organizational practices such as data ownership, service rationalization, and disciplined measurement to produce useful, repeatable outcomes.
Read at ZDNET
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