Secrets of Successful AI Projects
summary
Many AI projects fail because teams pick the wrong problem or misunderstand what AI can do, so prioritize narrow, well‑defined tasks with lots of data and low error cost. Expect about 90% of effort to go into data quality, integration and maintenance; start with off‑the‑shelf models then build one‑of‑a‑kind systems (the “crown jewels” like Google ranking). Beware of flashy demos (the dogs vs wolves snow background failure). Organize hybrid central/decentral teams so AI becomes a true force multiplier.
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chapters (12)
- 0:00Introduction by ODSC CEO/Founder Sheamus McGovern
- 1:10Why Do so Many Projects Fail?
- 2:23What AI Is Not
- 6:18What is AI?
- 8:25Key Elements of an AI System
- 11:01What AI is Good For
- 13:17What AI is Not Good For
- 18:00Beware of the Demo
- 21:58From Humans to AI
- 23:38The Three Stages of AI Adoption
- 30:26Centralized or Decentralized?
- 32:31What’s on the Horizon
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