The Spurs cleared the AI productivity dip in six months
Why 94% of enterprise rollouts stall before earnings impact
Hey Adopter,
Your best analyst is shipping in half the time. Two pilots look sharp on a slide. Then the CFO asks a simple question at the quarterly review and the room goes quiet.
Where is the earnings impact?
You have advanced users running real work through Claude and ChatGPT. You have a roadmap. You have vendor contracts. And you still cannot point to a line on the P&L and say, this is AI.
You are not imagining the gap. You are sitting inside it.
A predictable dip, not a failed strategy
In 2021, Stanford economist Erik Brynjolfsson formalised something called the productivity J-curve. When organisations adopt a general-purpose technology, measured productivity drops before it rises. The drop is where the real work happens. Workflow redesign. Role-specific training. Governance. Data plumbing. Keep investing through the dip and you exit with compounding advantage. Cut and run, and you stay at the bottom.
Most companies right now are in the dip. The absence of earnings lift is not evidence your strategy is broken. It is the expected outcome for any organisation still investing in complements.
The dip is the job.
What the rest of this edition covers
The research and the numbers behind the 94% stall rate, with every source linked so you can send it straight to your leadership team. The San Antonio Spurs playbook that compressed a 24-month enterprise AI rollout into six. A reading list at the end with the full source trail. The attached PDF goes further, four-phase playbook, sector benchmarks, and diagnostic questions for your own rollout.