How to fail AI implementations
summary
Three concrete failure modes: poor scope (overpromised AI when a short user-focused pilot was needed), data prep (messy Excel/PDFs and inconsistent labels make data unusable, taking 70–80% of project time to clean), and client communication (stakeholders expect instant fixes). Example: a fraud project required 18 months of iterative cleaning, models run in production for ~2.5–3 years with retrains but users rarely adopt outputs; another effort technically succeeds and cut workload in half yet business distrusts results. Start with users and enforce data collection standards.
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chapters (8)
- 0:00AI implementation failures
- 1:10AI projects like toddler trips
- 4:03Scope and unrealistic expectations
- 7:34Data preparation complexity
- 10:48Client communication role
- 17:03Educating clients transparently
- 20:27Risk management and outcomes
- 20:51Q&A
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