AI PM Playbook#

Overview#

The ai-pm-playbook skill operationalizes the best practices of AI Product Management into executable, agentic workflows. It is designed to help product managers transition from traditional, process-heavy roles to the "builder mentality" required in the AI era.

This skill provides a structured approach to the entire AI product lifecycle, ensuring that products are built rapidly, evaluated rigorously, and deployed responsibly.

Use this skill when:

• Prototyping a new AI feature or product.
• Planning a product roadmap in a rapidly changing AI landscape.
• Designing and running evaluations (Evals) for an AI model.
• Structuring a cross-functional AI product team.
• Developing a Go-To-Market (GTM) strategy for an AI product.
• Implementing ethical guardrails and red teaming for responsible AI.

The AI PM Operating System#

This skill is built on the premise that AI automates low-value PM tasks (like writing detailed PRDs) and elevates the need for strategic vision, judgment, and technical fluency. The workflows below are designed to augment these higher-order skills.

Core Workflows#

Choose the appropriate workflow based on your current product development phase:

1. Prototyping and Rapid Experimentation#

Move from static PRDs to interactive, "production-ready" prototypes.

• Action: Decompose features, plan with AI, and build interactive prototypes.
• Reference: See references/prototyping_workflow.md for the step-by-step guide.

2. Roadmap Planning Under Uncertainty#

Shift from feature-based roadmaps to outcome-oriented planning.

• Action: Define desired behaviors, use the Now/Next/Later framework, and apply the U.S.I.D.O. model.
• Reference: See references/roadmap_uncertainty.md for the planning framework.
• Template: Use templates/outcome_roadmap.md to structure your plan.

3. AI Evaluation and Metrics (Evals)#

Move beyond basic accuracy to measure user experience, safety, and reliability.

• Action: Define evaluator roles, supply context, set goals, and establish scoring rubrics.
• Reference: See references/evaluation_metrics.md for the evaluation framework.
• Template: Use templates/ai_eval_rubric.md to design your evals.

4. Cross-Functional Collaboration#

Structure your team for success in the complex world of AI development.

• Action: Implement a hybrid team structure, prioritize data readiness, and foster psychological safety.
• Reference: See references/cross_functional.md for organizational best practices.

5. Go-To-Market Strategy and Trust#

Launch AI products that meet evolving customer expectations and build trust.

• Action: Define the 7 GTM pillars and prioritize transparency in data usage.
• Reference: See references/gtm_strategy.md for the launch framework.

6. Ethics, Safety, and Responsible Deployment#

Ensure your AI products are safe, trustworthy, and aligned with human values.

• Action: Implement multi-layered guardrails and conduct rigorous red teaming.
• Reference: See references/responsible_ai.md for the safety framework.
• Template: Use templates/red_teaming_plan.md to structure your testing.

Self-Improving Loop#

This skill incorporates a self-improving feedback loop to continuously refine your PM processes based on real-world execution data.

1. Collect Telemetry: After completing a major PM activity (e.g., a prototype sprint, an eval run, or a product launch), gather the outcomes, friction points, and user feedback.
2. Run the Loop: Execute scripts/pm_feedback_loop.py with the collected data.
3. Analyze and Adapt: The script will analyze the systemic friction and suggest updates to your templates, workflows, or evaluation rubrics to improve future performance.

Resources#

• scripts/pm_feedback_loop.py: The engine for continuous improvement of PM processes.
• references/: Detailed guides for each of the 6 core workflows.
• templates/: Standardized formats for roadmaps, evals, and red teaming plans.