Eliminate one-size-fits-all reviews. Small, self-contained features ship rapidly on a fast track with only lightweight checks. Major, systemic changes require a separate, rigorous product strategy review to ensure alignment before development begins.
AI's leverage means product teams are becoming smaller and more senior. Companies now prefer hiring highly experienced Individual Contributor PMs (ICPMs) who can ship end-to-end, rather than building large teams with significant coordination overhead.
To ensure AI adoption doesn't become "everyone's job is no one's job," create a dedicated AI Operations team. This team, described as the "new BizOps," has a full-time mandate to identify and automate workflows across every company function.
Laurel built a company-wide operating system in GitHub. It contains folders for each function with playbooks and "skills," democratizing high-performance AI workflows and spreading the knowledge of top performers across the entire organization.
Jiaona Zhang defines a four-level AI maturity model for organizations: Level 1 is basic chat usage. Level 2 is automating workflows. Level 3 is building individual apps. Level 4 is building shared, integrated applications for broad use.
Don't let valuable knowledge sit in static documents. Transform detailed playbooks, like a 50-page onboarding guide, into a collection of AI agents that actively execute specific steps. This ensures process adherence and automates routine tasks.
To prevent users from getting overwhelmed by dozens of specialized AI agents, create a single "mega-agent" (e.g., a "Go-to-Market Agent"). This wrapper understands user intent and routes requests to the appropriate sub-agent, dramatically lowering friction.
Instead of fixed roles, initiatives are led by a "captain" whose expertise is most vital for success. An engineer captains an architectural refactor; a designer captains an interaction-heavy feature. This ensures the right leadership for every unique challenge.
To cut through the hype, ask candidates to screen share during an interview and walk through their personal AI workflows. This provides an immediate, unfiltered view of their actual proficiency and whether they operate beyond simple chatbot usage.
The journey to a comprehensive company operating system doesn't start with a grand design. Instead, identify one highly tedious, repeatable task, like triaging Slack requests, and build a simple automation for it. This creates immediate value and momentum.
Laurel enables non-technical employees, including Product and Customer Success Managers, to build and ship full-stack features using agentic AI tools like Devin. This blurs traditional role boundaries and dramatically accelerates development cycles.
Turn abstract cultural values into actionable, automated prompts. Laurel's Company OS analyzes customer data (e.g., from Gong) to suggest personalized acts of "unreasonable hospitality," ensuring core values are practiced consistently by everyone, not just the most thoughtful employees.
The ability to build instantly with AI makes foundational PM skills more important than ever. While tools and speed have changed, the principles of customer-centricity and problem definition are paramount to avoid building the wrong things faster.