Forget the token economy. Follow the P&L
The gap between AI experimentation and enterprise value is increasingly a decision-accountability problem.
Growth Protocol founder Miro Dimitrov hosted an executive roundtable ahead of Chief Executive Group's CEO of the Year dinner at Nasdaq, which honored Motorola Solutions Chairman and CEO Greg Brown.
The discussion began with one deliberately direct question: Can you attribute a dollar figure to AI's impact on your P&L?
Seven of 13 had no dollar figure
Every company represented is actively investing in AI, and revenue growth was the leading priority. Yet eight of 13 participants said their organizations remain in pilots, exploration, or deployments focused primarily on efficiency.
Efficiency is a useful first step. It cannot be the destination.
A decision that was never made explicit cannot be properly owned, measured, defended, or improved.
The model was not the barrier
No participant identified model capability as the primary constraint. Data quality and access came first, followed by talent. Only one participant explicitly cited the inability to explain or defend AI-generated outputs.
That result deserves a closer reading. Organizations may not experience this as an explainability problem. It appears downstream as unclear ownership, inconsistent decisions, slow approval, and ROI that cannot be measured.
Three questions for leadership teams
Efficiency is increasingly available off the shelf. Competitors can acquire the same models and tools. Durable advantage comes from decisions an organization can trace, defend, and learn from.
That is how AI moves from pilot activity to measurable enterprise value.
- Can we put a dollar figure on what AI has contributed to our P&L?
- How many AI initiatives influence decisions with direct financial consequences?
- If the board asks why a recommendation was made, can we provide a defensible answer?