Becoming an AI-Savvy Super PM: Insights from the AI Advisory Council

Are you an AI-savvy super Product Manager? In this live discussion hosted by Products That Count CEO Hoda Mehr, members of Products That Count’s AI Advisory Council, inclusive of 30 top AI product leaders, discuss how product managers can evaluate their AI mastery level. This session will leave you with a framework you can use to assess your AI skills, evaluate how your PMs or new hires are comfortable with using AI, building with AI, and building AI-enabled products.

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 not just a feature, but a continuously evolving system that requires ongoing learning and adaptation.
2. Product managers should focus on solving specific business problems with AI, not just adding AI for the sake of it.
3. Understanding AI’s limitations and realistic capabilities is crucial for effective product management.
4. Shift from deterministic to probabilistic thinking when developing AI products.
5. Trust is the primary currency in AI product development, more important than traditional user experience metrics.
6. Develop a growth mindset that prioritizes progress over perfection in AI product iterations.
7. Learn basic AI concepts like training, inference, and model evaluation.
8. Actively experiment through hackathons, weekend projects, and continuous learning.
9. Collaborate closely with engineering teams, treating AI development as a joint exploration.
10. Build guardrails and define edge cases to manage AI’s unpredictable behavior.
11. Focus on accuracy and bias mitigation when developing AI solutions.
12. Understand data quality and its critical role in AI model performance.
13. Involve multiple stakeholders (legal, sales, engineering) in AI product development.
14. Practice active listening and be open to input from technical team members.
15. Design systems that can continuously learn and adapt to new information.
16. AI should inform product strategy but not make strategic decisions autonomously.
17. Evaluate AI’s potential impact by mapping it to specific business outcomes.
18. Develop skills in context management and AI evaluation techniques.
19. Embrace uncertainty and think in probabilities rather than absolute terms.
20. Treat AI as an assistant that enhances product capabilities, not as an overlord.