My Frameworks & AI Strategies
AI is moving faster than most organizations can absorb it. These are my working notes — frameworks, observations, and hard-won lessons from integrating AI into a real research function at scale.
AI Automation: Ops and Logistics
It may be easy to lose sight of the routine, uncontroversial chunk of stuff AI can now automate, the logistical pieces that free our brains up for more creative and interesting work.
AI Research Democratization
The enemy isn’t AI… it is complacency. User research has never been more accessible—and more dangerous—for non-research teams to take part in.
AI Augmentation
AI adoption in research has moved exactly as far as it's easy to move, into logistics and synthesis, and stopped right at the edge of anything that needs judgment. Maybe it is time to push those boundaries.
The AI Reliability Spectrum
There is a push-pull relationship with AI capabilities and trust, and the line we walk in exploring that ideal.
The AI Trust Matrix
Trust has to continuously adapt to the capability of the tools or models you’re using.
Prioritizing AI Accuracy
If we don’t structure our conversation around truthfulness, then we’re outsourcing our judgment without realizing it.
Acting on AI Confidence
We may need more conversations around AI confidence and structuring our decisions around confidence-levels.