Summary

Summary

In this conversation, Cyrus and Lauren discuss the intersection of Agile and data science, specifically focusing on the challenges of shipping AI-enabled products quickly. They emphasize the importance of democratizing AI within organizations and the need for product managers to understand AI and ML concepts. They also discuss the prioritization of AI ML feature sets per quarter and the balance between quick wins and long-term strategic initiatives. Lauren shares her recommendations for getting buy-in and support from leadership, including listening, scenario planning, and making informed decisions.

Takeaways

Democratizing AI within organizations is crucial for enabling more people to understand and work with AI and ML.

Product managers should prioritize AI ML feature sets based on business goals and market expectations.

Balancing quick wins and long-term strategic initiatives is important for delivering outcomes and driving growth.

Getting buy-in and support from leadership requires listening, scenario planning, and making informed decisions.

Understanding the constraints and goals of different teams and stakeholders is essential for successful product management in the AI ML space.

Chapters

00:00 Introduction and Background

03:25 Challenges of Delivering Business Value Quickly

06:52 Democratizing AI within Organizations

11:05 Scoping AI/ML Feature Sets for Revenue Outcomes

14:12 Staying Up-to-Date with New Technologies

27:40 Incorporating AI into Product Strategies

28:54 Aligning Organizational Expectations and Goals

30:09 Understanding Constraints and Goals

33:10 Planning and Execution

36:04 Balancing Quick Wins and Long-Term Strategic Initiatives

40:17 Gaining Buy-In from Leadership

43:10 Democratizing Knowledge about AI and ML

Keywords

Agile, data science, intersection, challenges, shipping, AI-enabled products, democratizing AI, product managers, prioritization, feature sets, quick wins, long-term strategic initiatives, buy-in, leadership