How to Fail with AI - and How to Succeed?
summary
Explains why projects fail: hyped expectations, leaving AI to IT, hiring a lone data scientist who leaves, and messy data quality that stalls results. Gives a concrete six-step workflow, introduces five AI archetypes, and insists on the 20/20 rule: 20 days or 20K for prototypes. Demonstrates quick wins — a working RFP chatbot in one hour to a few days and Jupyter notebook prototypes — and points to ChatGPT’s 100M users in two months as proof of adoption. Focuses on co-pilots first, not agents.
AI-written summary · about
chapters (10)
- 0:00AI failure and success patterns
- 1:08Failed AI project phases
- 11:24AI project failure components
- 12:05Six steps to AI success
- 24:37AI archetypes and capabilities
- 28:11Business pain points to AI solutions
- 33:46Iterative AI project success
- 35:16Q&A: Financial instability in AI
- 38:21Q&A: Data science role
- 40:55Closing remarks
related talks
37mSecrets of Successful AI Projects
20mWhy AI Projects Fail: The Missing Link of a Strong Data Strategy
49mWhere GenAI projects fail & how can leaders prevent it
27mBuilding AI applications: Successes and Lessons
28mWhy AI Works for Individuals but Fails in Enterprises, and How to Fix It.
16mLaunching AI Pilots: Insights for Startups and Enterprise
36mHow to Get AI to Stop Being Weird and Actually Be Useful
48mPractical Strategies for Successful AI Adoption in your Organisation