Where GenAI projects fail & how can leaders prevent it
summary
Claims that 80–85% fail rate for AI projects comes from mismatched goals and weak data not model limits, with pilots often built in the cloud for two to three weeks but never integrated on prem; common pitfalls are RAG overused retrieval setups, poor integration with CRMs/SharePoint, and missing business-owner KPIs. Practical fixes include Watsonx-style stacks (AI, data, governance), keep prototypes model-agnostic, require domain + AI + IT + UX teams, and treat 95% of automation as classical code while reserving GenAI for the 4–5% that truly needs it.
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chapters (12)
- 0:00GenAI project failures intro
- 1:51Why AI projects fail
- 6:09IBM’s GenAI failure prevention
- 8:57Clex AI business focus
- 11:07GenAI expectation management
- 13:54GenAI architecture challenges
- 16:24GenAI org & data challenges
- 23:50GenAI success criteria
- 33:02GenAI UX & data science
- 35:55GenAI community wishes
- 41:11GenAI team skills
- 46:23Q&A
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