Generative AI: Keeping it real
summary
Generative AI scales fast—100 million users in under two months—but models hallucinate, struggle with precise numeric optimization, and raise cost and ethics questions when used to synthesize training data. Practical wins shown are contract review and drafting, product description generation, document de‑duplication, and root‑cause analysis in energy/manufacturing; code generation helps but demands fine‑tuning, testing and guardrails to avoid skill drain. Enterprise steps include architecture, a lab, COE and partnerships such as a $2 billion upskilling investment.
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chapters (12)
- 0:00Introduction to session
- 0:42What is generative AI
- 2:27Classical vs generative AI
- 4:12Practical applications of gen AI
- 6:20Limitations of language models
- 7:47Ethical risks in gen AI
- 8:50Code generation concerns
- 10:14Real-world gen AI use cases
- 12:44Enterprise considerations for gen AI
- 13:24Strategy and architecture
- 14:26Organizational readiness
- 15:33Decision tree for gen AI use cases
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