Adjacent Disciplines
How AI transforms the disciplines of PMs’ close collaborators — and what that means for product leadership.
Product managers work at the intersection of engineering, design, and analytics. Each of these adjacent disciplines has its own relationship with AI adoption, and the rate and shape of that adoption directly affects how PMs collaborate, lead, and influence. This domain tracks how those disciplines evolve and what the implications are for PM practice.
Adoption Dynamics
PMs occupy a unique position in the AI adoption landscape of their organizations:
- Software engineering is ahead of product in AI adoption — especially agentic tooling (Claude Code, Cursor, Codex). In many non-AI-native orgs, product is behind engineering in leveraging these tools.
- Design and analytics are likely to trail product in AI/agentic adoption in many organizations, making PMs a potential bridge for pulling these disciplines forward.
- The implication: PMs need to understand not just their own AI toolkit, but how each collaborator discipline’s relationship with AI is shifting — both to keep up with engineering and to lead adoption for design and analytics.
Entries
- Spec-Driven Development — Software ships as specs + tests with zero code; works for stable utilities, breaks for anything needing performance, debugging, community, or security patching