The Demo-to-Dogfood Gap: Why Your AI Feature Dies Between the Launch Slide and Monday Morning
The demo went perfectly. The room clapped. Two weeks later, the same feature lands in the company Slack for internal use, and by Wednesday a senior engineer is posting screenshots with the caption "did anyone test this?" By Friday the channel has gone quiet — not because the bugs were fixed, but because the people who would have flagged them gave up and went back to their old workflow. The launch is still on the calendar. Nobody has cancelled it. Nobody has the political capital to.
This is the demo-to-dogfood gap, and the MIT NANDA initiative measured it last year at 95% — that is the share of enterprise generative AI pilots that produced no measurable P&L impact, and almost all of them had a demo somebody loved. The model was not the problem. The gap between the demo and the first week of internal use was the problem, and every team that has shipped an AI feature has watched some version of it play out.
