AI spend is outrunning AI returns, and data readiness is the reason
For a second straight year, most enterprises report AI returns lagging spend. The cause is usually upstream of the AI itself.
Return on AI: Weekly Digest – July 27, 2026
Sixteen findings on deployment rates, workforce effects, data infrastructure, governance and ROI measurement, including new research on agentic AI adoption, consumer trust and why pilots stall.
Our take
When 95% of pilots stall on data infrastructure, the model isn’t the problem. Organizations are deploying AI before the conditions for value exist: clean, accessible data, clear governance, and a direct line from each initiative to a business outcome.
There’s also a useful signal in where returns are appearing. SAP’s research finds enterprise AI ROI landing in insights and decision support more than cost cuts. If you are only measuring headcount savings, you may be missing where the value is.
What to do about it
- Audit data readiness before funding the next wave of pilots.
- Tie every AI initiative to a customer or financial outcome you already track.
- Put AI oversight on the board agenda. Half of boards still have no governance rules.