My Process

From Triple Diamond to Triple Diamond + P.A.I.R.

Building AI products that matter doesn't happen in a vacuum. It happens when rigorous product design meets human-centered AI principles, ensuring user trust and system accuracy.

Framework

Three diamonds, one responsible AI layer.

Click through each diamond to see how transferable UX research skills combine with AI product research practices.

ACTIVE STEP: 01 // CLICK SHAPES TO NAVIGATE
Triple Diamond + Google PAIR

Diamond 1: Discover

Problem fit, data readiness, success metrics

Traditional product development still matters. AI adds data, model, evaluation, monitoring, and retraining responsibilities that need research leadership.

Transferable UX Research Skills

  • Discovery research
  • Stakeholder alignment
  • Define user problem and success metrics

AI Product Research Skills

  • Decide whether AI is the right tool
  • Identify data requirements and risks
  • Define failure modes early

Where research closes the gap

  • Frame user needs, edge cases, and quality thresholds before teams commit to a model path.
Activated core principles:
User Needs & Defining SuccessData + Model Evolution

Why This Matters

AI product development needs more than a traditional product loop.

What carries over

Discovery, design, build, test, launch, and iterate still anchor the work. Strong AI products need the same stakeholder alignment, success metrics, and user-centered decision-making as any product lifecycle.

What AI adds

AI introduces data strategy, model selection, evaluation design, bias checks, confidence calibration, and monitoring for model or data drift. The feedback loop changes because the model itself may need to be retrained or re-prompted.

Where I focus

The highest-risk gaps are often evaluation design, bias audits, monitoring, and retraining loops. That is where research can turn probabilistic behavior into evidence teams can trust and act on.

What This Unlocks

AI products teams can trust, measure, and improve.