How We Turned AI Hype into Real Value
summary
Claims that 95% of AI projects return little to no profit and that 80% never reach production drive a pragmatic approach: choose between hybrid model selection (proprietary for performance, open source for sensitive data), enforce prompt and context engineering, run automated test suites plus user feedback, and evaluate accuracy, safety and domain performance. A field prototype integrates SharePoint manuals, weather API, cameras, Postgres, a LAN graph in Docker and clarification and classification agents with RAG agents and a risk-evaluation agent, delivering practical PPE and weather safety checks on mobile.
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chapters (6)
- 0:00Introduction
- 1:32AI trends and applications
- 4:47Why AI projects fail
- 7:16Building effective AI solutions
- 10:07AI for safety and health
- 13:01Key takeaways
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