AI Is Turning Product Managers Into Builders Again: Laurel CPO

How can AI eliminate one of the most frustrating parts of knowledge work: tracking and reporting time? In this podcast hosted by iDonate VP Product & Engineering Nacho Andrade, Laurel Chief Product Officer Jiaona Zhang discusses how AI is turning product managers into builders again. The conversation explores how AI agents can automate tedious workflows like timesheets while helping product teams move faster, stay grounded in user problems, and rethink leadership in an AI-native organization.

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Show Notes

1. Laurel defines itself as an AI time company, focused on understanding how professionals actually spend their time and turning that data into actionable insights.
2. The company originally launched as Time by Ping, a tool designed to help lawyers track billable time more easily.
3. The product was inspired by firsthand user pain—the founder was a lawyer who struggled with timesheets and wanted a better way to capture billable work.
4. AI is most effective when applied to tasks people dislike doing, such as reconstructing timesheets or categorizing work activity.
5. Laurel uses AI agents to observe activity across devices and automatically generate structured time entries.
6. Multimodal large language models help interpret digital work signals and convert them into meaningful descriptions of work.
7. Time is the most fundamental unit of professional productivity, and analyzing it can reveal operational insights.
8. While the initial wedge product focused on legal timesheets, the platform now supports accounting, consulting, finance, and other professional services.
9. Different industries use time intelligence differently—for example, law firms optimize billing accuracy while private equity firms analyze workflow phases.
10. AI does not replace core product management fundamentals; understanding the user problem remains the foundation of good product work.
11. AI dramatically increases development speed, but without strong product thinking it can lead to shipping bad ideas faster.
12. Customer conversations remain critical even in the age of AI-driven insights.
13. AI tools can accelerate product discovery by helping teams analyze large data sets and identify patterns faster.
14. The biggest bottleneck in product development varies across companies—some struggle to find the right problems while others struggle with execution speed.
15. AI tools are significantly increasing developer productivity and expanding what small teams can accomplish.
16. Product teams benefit from experimenting widely with AI tools before narrowing down to a smaller stack.
17. Security and data governance are still important considerations when adopting AI tools across organizations.
18. Leadership in the AI era increasingly involves managing systems that combine people, workflows, and AI agents.
19. AI enables individuals to build and execute ideas more independently than in the past.
20. The future of product leadership will favor builders—people who enjoy creating, experimenting, and shipping products directly.