I have worked with enough AI implementations to see the same failure pattern repeat: Phase 1: Excitement. Budget approved. AI vendor selected. Phase 2: Pilot succeeds (because pilots are designed to succeed). Phase 3: Scaling attempt. Reality hits. Phase 4: “AI does not work for us” (translation: we could not scale the pilot). The real reasons pilots fail to scale:
- Data quality that was manually curated for the pilot
- Champion dependency (one person drove it, they leave, it dies)
- Integration debt (pilot ran in isolation, production needs to touch 15 systems)
- ROI measurement that does not survive scrutiny I wrote a detailed breakdown of the “Pilot Purgatory” cycle and how to escape it. Link in comments if anyone wants the full analysis. What patterns have you seen in AI pilot failures? submitted by /u/JaredSanborn
Originally posted by u/JaredSanborn on r/ArtificialInteligence
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