56% of chief executives say AI has brought them neither higher revenue nor lower cost over the past year. That stat sounds like a model problem, but we think it is usually an operating model problem: the organisation around the technology cannot reliably turn a capable output into a business outcome.
We break down why “better models” so often fail to change the numbers. A model can draft, predict, classify, and recommend, but it cannot fix inaccessible data, broken workflows, serial approvals, manual re-entry, or unclear decision rights. We dig into the most common value leaks that quietly kill enterprise AI ROI: data that cannot be retrieved in the right form at the right moment, processes that stay unchanged around a faster step, and recommendations that nobody is authorised to act on. We also cover adoption, incentives, governance, and measurement, because without clear metrics and accountability, “time saved” turns into anecdotes instead of profit.
A key tool we use is the adequacy threshold: define what “good enough” means for a specific use case, choose the cheapest model that clears that line, then invest management effort where the real constraint sits. We make it concrete with examples from insurance claims, procurement cycles, and professional services pricing, where AI can speed up document work while value still fails to reach the P&L.
We finish with the agent trap. As AI agents move from answering to acting, the ceiling rises, but so do the requirements for permissions, monitoring, cost control, exception handling, and governance. If you want AI value that lasts, the surrounding organisation has to be trusted to let a machine act.
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AI Transparency Notice: This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified.
