Rebuilding with AI: Strategies for Scaling and Transforming Products

What does it mean to rebuild with AI? In this webinar hosted by Denise Hemke, Hosta AI Head of Product & AI Platform Gayatri Diwan offers a strategic lens on rebuilding with AI in product. We’ll unpack the AI product flywheel, emerging AI PM roles and trends/predictions, and best practices on what it takes to drive AI adoption and scale at large enterprises. Through case studies and low code/no-code AI examples, she will highlight her perspectives on how to unlock high-impact opportunities while avoiding common pitfalls.

Join us for new conversations with leading product executives every week. Roll through the highlights of this week’s podcast below, then head on over to our Events page to see which product leaders will be joining us next week.

Show Notes:

1. Curiosity and resiliency are the new AI currency for product leaders
2. AI innovation is about doing new things that fundamentally shift what’s possible
3. AI augmentation focuses on enhancing existing workflows and improving efficiency
4. The AI product flywheel centers on customer value and trust
5. Product management roles are evolving with AI (AI-powered PMs, research PMs, platform PMs)
6. Only 10% of AI experiments may succeed, so adopt a fail-fast-learn-fast mindset
7. Building trust requires explaining AI model results in plain English
8. Scaling AI models is more complex than just managing token economics
9. Product evaluations (evals) are critical for maintaining AI model reliability
10. Balance speed versus accuracy when developing AI products
11. AI agents will soon become workplace colleagues
12. Prioritize customer needs over technological capabilities
13. Implement human-in-loop mechanisms to improve AI model performance
14. Multi-dimensional evaluations provide a more holistic assessment of AI models
15. Consider trade-offs like brevity versus completeness in AI-generated content
16. Modular integration and microservices are key to successful AI scaling
17. Regulated industries like banking and insurance are slower to adopt AI
18. Develop guardrails and safety controls when implementing AI
19. AI will become table stakes, so differentiation lies in workflow and outcomes
20. Continuous learning and adaptation are essential in the rapidly evolving AI landscape