Avoiding 5 missteps that undermine your AI readiness and success
summary
Avoids vague AI promises and shows concrete practice: fine‑tunes an open foundation model (Falcon) into a private LLM (HealthGPT) on global clinical‑trial text, runs an MLops pipeline with versioned models and audits, enforces encryption and exclude PII and obfuscate demographics, recommends sourcing external data to fix skew and retraining frequently, and classifies use cases into low/medium/high risk with a risk‑tiered human‑in‑the‑loop policy so high‑risk outputs always get human sign‑off.
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chapters (9)
- 0:00Introduction and objective
- 1:10Understanding AI failures
- 4:47Defining business impact
- 9:18Data quality and strategy
- 13:03Managing risk and governance
- 17:51Traditional AI and generative AI
- 21:24Private vs. public models
- 24:17The role of human oversight
- 29:45Strategic takeaways
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