Product Divergence Series: PM Exercises and Product Monkey AI Founder and CEO on Automating PM Tasks With AI

How can AI help to automate PM tasks? In this episode of the Product Divergence series with Ubiety Technologies CPO Nacho Andrade, PM Exercises and Product Monkey AI Founder and CEO Bijan Shahrokhi speaks on how AI can help automate manual tasks for product managers and improve the clarity of product requirements. He explains how his company uses a combination of logic and AI to extract design files and automatically generate engineering tickets and requirements documents. By automating PM tasks, AI will enhance rather than replace product management work by helping PMs produce higher quality documents and focus on strategic decision making.

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Show Notes

1. Product Monkey API automates manual tasks like creating tickets from design files to increase productivity for product managers
2. The product uses a combination of logic and AI including integrating with Figma to extract context.
3. AI can help product managers create better requirements documents but it remains a challenging task.
4. AI will enhance rather than replace product management by automating manual work.
5. Product managers will still be needed to make strategic decisions and guide AI systems.
6. Emerging markets and large companies are target users for increased clarity on product scope.
7. Providing all context through a graph UI helps avoid unilateral decision making.
8. Having a clear vision and objectives is important for product development focus.
9. Metrics represent engagement and whether a product is solving real problems.
10. Building the product for yourself as a product manager provides valuable insights.
11. Previous experiences at companies inform the challenges of requirements clarity.
12. Cursor IDE is an AI-enabled code editor that increases engineering productivity.
13. Embracing change and new technologies like AI is important.
14. Every problem solved creates new opportunities to address.
15. Clarity on requirements avoids missed deadlines and failed products.
16. Draft documents provide context earlier in the process.
17. User testing and feedback improves the product over time.
18. Focusing on a core problem like requirements enables expanding to new areas.
19. Large language models lack full context needed for complex tasks.
20. Product development remains both an art and a science.