Put AI where it creates measurable value.
Adding AI is easy. Creating an AI experience that customers trust, adopt, and continue using is much harder. I help product teams identify where AI can genuinely improve workflows, reduce effort, or increase confidence, and avoid investing in features that generate attention but little long-term value. The goal is not to add AI to the product. The goal is to make the product meaningfully better.
The problem
Many organizations feel pressure to have an AI strategy. Customers expect it. Competitors are launching it. Leadership wants to explore it.
But most teams struggle to answer a more important question: What problem should AI actually solve?
As a result, AI often arrives as a standalone feature disconnected from existing workflows. Adoption stalls. Trust erodes. Teams struggle to measure impact.
The challenge is rarely the model itself. The challenge is integrating AI into the product in a way that creates real customer value.
What changes
AI becomes part of how work gets done, not an additional tool users have to learn.
Capabilities are integrated into existing workflows where they reduce effort, accelerate decisions, or improve outcomes.
Users understand what the system is doing, where information comes from, and how to validate or correct results.
The result is higher adoption, stronger trust, and AI investments that produce measurable value instead of novelty.
Designing AI around customer outcomes
Identify high-value opportunities
Find the workflows where AI can remove friction, reduce effort, improve speed, or increase confidence. Just as importantly, identify where AI adds complexity without meaningful value.
Design for trust
Make outputs understandable and verifiable. Users should know what happened, why it happened, and how to intervene when needed.
Design for uncertainty
Every AI system will be wrong sometimes. Plan for failure, recovery, correction, and escalation from the start so confidence remains intact when mistakes occur.
Measure real impact
Evaluate success through adoption, task completion, time savings, quality improvements, and customer outcomes, not feature usage alone.
- A strategic assessment of where AI creates meaningful customer and business value.
- Clear recommendations for where AI should and should not be integrated.
- AI experiences embedded within existing workflows and customer journeys.
- Trust patterns that support transparency, verification, and user control.
- Recovery and failure-state strategies that maintain confidence when outputs are imperfect.
- Adoption and success metrics tied to business and customer outcomes.
- A practical framework for evaluating future AI opportunities as the product evolves.
Related areas of work
Exploring where AI fits in your product?
Let's talk about where it can create real value and where it probably won't.