Panel discussion: Practical and responsible applications of AI in businesses
summary
Companies describe concrete wins and costs: a property search feature uses median house price, median income, median age and launches after two people two weeks, saving time and avoiding costly LLM fine-tuning; a payroll/accounting platform treats AI as product-reach that can affect millions so it requires multi‑million-dollar investment and governance; a mobility firm trains models on huge location datasets to justify multi‑billion infrastructure bids; common pain points are LLM hallucinations, evaluation gaps, integration and running costs, automation bias and unclear regulation.
AI-written summary · about
chapters (10)
- 0:00Introduction
- 0:44Panelists
- 7:09AI practical applications
- 19:30AI adoption trends
- 29:00AI risks and biases
- 37:53Regulation and responsible AI
- 42:06Starting with AI
- 47:44AI future trends
- 50:31AI global challenges
- 1:00:31Closing remarks
related talks
39mKeynote Panel State of AI in the Enterprise
40mBeyond the Hype: Realistic Expectations of AI
26mPanel: AI Future - Where Investments Are Being Made
27mPractical AI: Driving Tangible Value in the Enterprise
32mPanel Discussion: Trends in Generative AI
07mOne-on-One with Yusuf Jazkallah and Sam Gilstrap
44mFuture of AI
42mPanel Discussion: Expanding opportunities – The fusion of GenAI, product and design
