How can we use AI to bring new value to the business
summary
SmartCat claims more than 20 solutions in production and shares a concrete roadmap: discovery workshop, ideation paired with technical due diligence, quick PoC-to-production iterations, then monitoring and tuning. The talk lists 10 common AI pitfalls (lack of data, missing labels, non‑representative/tail bias, unclear success metrics, hidden running costs), recommends human-in-the-loop relabeling and grounding layers (as with ChatGPT), cites a successful recommender for scholarships that hit dual metrics (applications up, churn down) and warns about integration and maintenance costs.
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chapters (20)
- 0:00Introduction
- 1:12AI and business value
- 2:38Managing tech expectations
- 4:23Past AI project mistakes
- 6:32Generative AI challenges
- 8:19Measuring AI success
- 9:01AI maturity roadmap
- 10:26AI solution ideation
- 11:30Technical due diligence
- 12:16From POC to production
- 13:00Business problems for AI
- 14:05AI success criteria
- 15:10AI data challenges
- 16:36Labeled data importance
- 17:39Biased data risks
- 18:43AI maintenance challenges
- 20:52AI tuning strategies
- 22:19AI running costs
- 23:02Final thoughts on AI
- 24:26Q&A
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