Building AI Product Roadmaps That Actually Work

Traditional roadmapping approaches fall short when applied to AI products. The inherent uncertainty in AI development—will the model work? how long will training take? what accuracy can we achieve?—requires a different approach.

The Uncertainty Problem

AI development is fundamentally research-oriented. You can't always predict whether a model will achieve the required performance, or how long it will take to get there. This uncertainty must be reflected in your roadmap.

A Better Approach

Milestone-Based Planning: Instead of date-based commitments, define milestones based on model performance or capability achievements.

Parallel Paths: Always have a fallback plan. If the AI approach doesn't work, what's the alternative?

Continuous Evaluation: Build in regular checkpoints to assess progress and adjust course as needed.

Communicating with Stakeholders

The key is setting appropriate expectations. Help stakeholders understand that AI development is iterative and uncertain. Frame commitments in terms of experiments and learnings rather than guaranteed outcomes.