AI tools are rapidly increasing developer output. If product managers don't adopt similar AI-native tools to accelerate their own workflows—like product judgment, research, and planning—they will become the primary constraint on the entire development lifecycle.
Use a multi-step, orchestrated LLM skill to handle initial product tasks like market research, viability checks, architectural decisions, and repo setup. This accelerates the process from idea to first commit, especially for non-technical builders.
Standard LLMs often validate ideas to be helpful. Implement a structured "viability gate" skill with clear evaluation criteria (e.g., problem clarity, competition) designed to explicitly recommend abandoning unpromising projects, saving valuable time and resources.
LLMs are powerful for rapidly compiling a broad overview of a market, including trends, competitors, and pricing. Use them to complete the initial 60-70% of the work, but treat it as a draft that must be validated and deepened with primary research and expert verification.
Most PMs work on existing products, not new ones. Use a specialized LLM skill, like 'Vet a Feature,' to rigorously analyze new feature ideas against anti-patterns and opportunity costs before committing development resources, ensuring you work on the highest-impact items.
Go beyond simple brainstorming by using a dedicated LLM skill to create a comprehensive customer discovery plan. This can generate interview scripts for different validation stages, survey questions for quantification, and even templates for synthesizing results, ensuring a structured approach.
AI tools are making code development 10-20x faster. However, the 'why we should build' (customer research) and 'getting it to customers' (adoption) phases remain bottlenecked by human interaction speed. This creates an imbalance that modern product teams must manage.
Move beyond fragmented tools like Notion for PMs and Figma for designers. By using a single, shared GitHub repository for business context, product briefs, designs, and code, teams can create joint context and dramatically increase alignment and speed.
To maximize AI's impact, treat LLM skills and prompts like a centralized codebase. When one person discovers a better technique, it should be integrated into a shared, version-controlled repository, ensuring the entire team benefits from individual learnings.
To ensure seamless adoption, customize generic AI skills to fit your organization's specific processes. For example, modify a product brief skill so its output matches your company's PRD template, ensuring consistency and reducing friction with other teams.