One strategic signal. One human prompt. One subtraction opportunity.
Signal: The PAICE Research Preview Is Live
Teams can’t answer the baseline question: how good are we at collaborating with AI? They can track adoption, spend, and pilot counts — but how effectively people and systems actually work together? Not so much. Surveys and knowledge tests make poor proxies for this.
That’s why I built PAICE. The PAICE framework — People + AI Collaboration Effectiveness — measures how humans and AI perform together. Trust, skill, and adaptability included. Think of it as a credit score for AI collaboration.
Strategic (Human) Prompt
If you could measure collaboration quality, not just output, what would you stop doing first?
How might this change your AI transformation projects and tool rollouts?
Strategic Subtraction
Stop pretending that “using AI” equals being capable with AI.
Most frameworks define “responsible AI” as a compliance exercise. PAICE defines it as a capability exercise.
Consider how to visibly reduce risk by giving AI powertools only to those who have shown they can handle them safely. PAICE gives you a defensible way to prove who’s safe to scale.
Analogy: Your Favorite Band
AI is the rhythm section. The soloist can wail away all day, but without shared timing and an agreed key, it’s just noise.
Great musicians don’t try to control each other — they agree on the basics, then respond and play with everyone else in the band. PAICE scores the rhythm of that response: the interplay between human intention and machine adaptation.
Closing
Every governance framework from NIST to ISO/IEC 42001 requires “demonstrated competency.” Yet no standard existed to show what that means.
PAICE changes that. It’s not another dashboard or vanity metric. It’s measurement infrastructure — the foundation for better adoption, safer deployment, and smarter governance.