AI Product Management in Enterprise: Lessons from Scale
Key insights from managing AI products at scale in enterprise environments, including challenges, strategies, and real-world implementation approaches.
Ergin Satir
Sr. Product Manager AI/ML @Apple
AI Product Management in Enterprise: Lessons from Scale
After leading AI initiatives across multiple enterprise environments, I've learned that successful AI product management requires a fundamentally different approach than traditional software products.
The Enterprise AI Reality Check
Unlike consumer AI products that can iterate rapidly, enterprise AI faces unique constraints:
- Data governance and compliance requirements that can't be overlooked
- Integration complexity with existing enterprise systems
- Stakeholder alignment across technical and business teams
- ROI justification for significant infrastructure investments
Key Strategies That Work
1. Start with Business Problems, Not AI Solutions
The biggest mistake I see teams make is leading with "let's use AI for..." instead of "we have this business problem that AI might solve."
2. Build Trust Through Transparency
Enterprise customers need to understand how AI makes decisions. Black box solutions rarely succeed at scale.
3. Plan for Gradual Rollouts
Unlike traditional software where you can push updates instantly, AI models in enterprise environments require careful deployment strategies and rollback plans.
The Path Forward
AI product management in enterprise is about balancing innovation with stability, speed with governance, and possibility with practicality.
What challenges have you faced with AI in enterprise environments? I'd love to hear your experiences.