Beyond the Buzz: Discovering High-Impact Use Cases in the Generative AI Era
summary
Generative AI pilots 95% of GenAI pilots die because teams favor flashy demos and thin wrappers over ROI and defensible models; common failures include prompt-layer startups and text-to-SQL fails on real enterprise DBs (hallucinations, multi-table limits, low trust). Successful deployments are narrow, workflow-grounded, trust-first and measurable: code-assistants and AI dev tools (Cursor, Cloud Code) accelerate delivery, virtual assistants scale—e.g. Ask Red Hat: 2,500–5,000 daily users and Bank of America’s Erica hitting 1 billion transactions a year.
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chapters (12)
- 0:00Introduction
- 1:03Generative AI hype pitfalls
- 2:30Enterprise GenAI misfires
- 4:41Text-to-SQL failures
- 6:27High-impact GenAI use cases
- 8:17AI code assistance in engineering
- 8:58Customer support AI assistants
- 10:27Workflow automation benefits
- 11:53GenAI project success principles
- 13:15Domain expertise in GenAI
- 14:47Q&A: Evaluating AI use cases
- 16:55Q&A: Platform migrations
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