Hosta AI Head of Product & AI platform on Re-Building with AI

How are product leaders re-building with AI? In this webinar hosted by Denise Hemke, Hosta AI Head of Product & AI platform Gayatri Diwan offers a strategic lens on how AI is redefining product innovation. 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 event below, then head on over to our Events page to see which product leaders will be joining us next week.

Show Notes:

1. AI is revolutionizing product development across multiple evolution modes, from predictive machine learning to spatial AI
2. The AI product flywheel centers on customer value and trust as critical components
3. Product management roles are rapidly evolving to include AI-specific positions like AI research PMs and AI platform PMs
4. Curiosity and resiliency are the new “currency” for successful AI product leaders
5. There are two primary AI product themes: AI innovation (doing new things) and AI augmentation (enhancing existing workflows)
6. Only about 10% of AI experiments may initially succeed, so a fail-fast learning mindset is crucial
7. Building trust requires explaining AI model results in plain, understandable language
8. Scaling AI models involves more than just technology – it requires modular integration and careful cost-benefit analysis
9. AI evaluations (evals) are essential for maintaining product quality and reliability
10. Multi-dimensional evaluations should cover business impact, product usage, model accuracy, and system performance
11. Human-in-the-loop mechanisms can help drive AI adoption and build stakeholder confidence
12. Balancing speed versus accuracy is a critical trade-off in AI product development
13. AI agents will increasingly become “colleagues” in workplace environments
14. Safety and governance controls are mandatory when implementing AI solutions
15. Token economics and prompt optimization are key considerations for AI model scalability
16. Different approaches (build vs. buy) can coexist within the same organization for AI implementation
17. Customer outcomes should always take precedence over technological capabilities
18. AI models require constant testing with new data features and algorithms
19. Ethical considerations and responsible AI development are paramount
20. AI is rapidly becoming table stakes, so differentiation will come from workflow integration and specific outcomes