Solving the wrong problem brilliantly

The most dangerous transformation failure is not always a bad solution. Sometimes it is a very good solution aimed at a problem that never deserved the effort. This episode is about how leaders can separate technological momentum from actual strategic value.

AI can make teams faster, more productive and more convincing before anyone has proved that the work matters. In this episode, we look at the organizational reflex behind “we need a website,” “we need an app,” and now “we need AI” — and why the better starting point is evidence, consequence and the quality of the problem itself.

The episode moves from support automation and UX research to executive pressure, innovation fatigue and the hidden cost of spending organizational belief on the wrong bet. Featuring a short external excerpt from Tim Brown on the importance of starting with the right question.

References

Stanford Institute for Human-Centered Artificial Intelligence, 2026 AI Index Report — Economy. Organizational AI adoption reached 88% of surveyed organizations in 2025; generative AI was used in at least one business function at 70%; agent deployment remained in the single digits across nearly all business functions.

Deloitte AI Institute, The State of AI in the Enterprise: The Untapped Edge (2026). Survey of 3,235 business and IT leaders across 24 countries. Deloitte reports that 34% of companies described AI as being used to deeply transform their business; 37% reported surface-level use with little or no change to underlying processes.

NIST, AI Risk Management Framework. Used as background for the emphasis on context, measurement, governance and lifecycle thinking rather than technology-first deployment.