Situation
At a global technology company, KRS identified that product managers were only spending 10% of their time on the work that mattered most: understanding the market and customer needs, creating roadmap strategy, and improving in-market product performance. The rest was largely spent moving products and features through approvals, engineering, and launch processes.
At the same time, AI was changing both how the work could get done and what customers expected. Leadership needed to rethink the role so product managers could spend less time managing process and more time creating products that delivered meaningful customer and business results.
Action
Keenan Reid partnered with leadership to redefine the product manager role for an AI-enabled organization and build a practical model for how the team would work differently.
As part of this work, we:
- Conducted 20+ interviews and researched how leading companies were adapting similar roles.
- Designed a new way of working that paired human judgment with AI agents, shifting time away from routine execution and toward customer insight and product creation.
- Identified where one-off AI tools were creating duplicate effort and built an operating model to identify and scale the highest-value AI use cases across the organization.
- Defined clear ownership for what to build, the customer outcomes each product should deliver, and how products should perform after launch.
- Identified the frontier skills and capabilities product managers would need, including stronger portfolio strategy, commercial thinking, and the ability to work effectively with AI.
- Built a phased roadmap to pilot the new role on a live product, learn from it, and scale the model over time.
Results
The work gave leadership a practical blueprint for transforming the product manager role for the AI era:
1. A new role definition and capability map. Defined how product managers would shift from managing process to owning what gets built, why it matters, and how it performs—and the skills needed to succeed in the new role.
2. An operating model for scaling AI. Created a repeatable way to identify the highest-value AI use cases, supported by shared data, governance, and forward-deployed engineers embedded with product teams to help turn the best ideas into capabilities that could scale across the organization.
3. A roadmap to put the model into action. Identified a first pilot on a live product to test the new role, build the required capabilities, and refine the model before scaling it more broadly.
