O’Reilly’s Generative AI in the Enterprise survey reported that people have trouble coming up with appropriate enterprise use cases for AI. Why is it hard to come up with appropriate use cases?
Chip Huyen, cofounder of Claypot AI and author of Designing Machine Learning Systems, talks about why many companies have trouble coming up with appropriate use cases for AI, how to evaluate possible use cases, and the skills your company will need to put them into practice.
Points of Interest
- 0:00: Introduction
- 0:49: O’Reilly’s Generative AI in the Enterprise survey report results.
- 3:02: Now that generative AI is more accessible, will it be easier to come up with use cases?
- 4:29: AI is easy to demo but hard to productize. Consistence, risk, and compliance.
- 6:44: Is there a framework or checklist for thinking about applications?
- 8:15: What are some of your favorite use cases?
- 13:30: RAG is the “hello, world” of AI applications.
- 17:24: How do you navigate between the desires and requirements of different stakeholders?
- 19:00: When talking to stakeholders, you have to answer questions at the right level.
- 21:10: How to think about staffing teams for generative AI.
- 22:45: There’s less model development with generative AI, more application development.
- 23:12: Frontend engineers and full-stack developers are very successful.
- 26:27: What are companies’ concerns about risk?
- 27:27: Understanding data gives a lot of clues about what it is good at and should be used for.
- 29:00: The importance of documentation.
- 30:25: Are there specific things you can do to ease the integration of AI into an organization?
- 32:49: What companies that have deployed AI into products stand out?
