The AI productivity paradox: why speed without strategy widens the gap

Summary

An analysis of why generative AI adoption accelerates delivery yet leaves sustained performance impact behind. The author argues the paradox resolves once you examine the underlying operating model. Most organisations use AI to accelerate their existing project-based workflows—building faster without validating whether the idea is worth building. Strong product companies use AI differently: they accelerate discovery and problem-solving (building to learn) before they accelerate delivery (building to earn). The result: rather than closing the capability gap, AI widens the distance between outcome-focused firms and those optimising for output. Read the full analysis.

Key Claims

• 89% of executives report AI increased work speed; only 6% can point to specific organisation-wide ROI, suggesting AI amplifies delivery without improving the underlying model.
• Most teams use AI to accelerate the same project-driven workflows that existed before—building faster in the wrong direction—rather than rethinking strategy or discovery.
• Strong product companies use AI differently: they accelerate discovery and evidence-gathering (building to learn) before deploying AI for delivery speed (building to earn).
• The competitive gap widens under AI adoption; firms with strong product culture and discovery discipline pull ahead, whilst those optimising only for build speed fall further behind.
• The real constraint on outcomes is not the time or cost of building; it is that most ideas prove not worth building because they don't solve the customer or business problem effectively.

Quotes

• "It's never been faster to build, which means it's never been easier to run 10 times faster in the wrong direction."
• "AI makes building easier, but the hardest part remains knowing what to build."
• "The product model is what is enabling these companies to leverage AI for improved outcomes and not just output."