McKinsey says 88% of organizations are using AI. Only 6% are getting real value. Sumaiya Shrabony argues the gap isn't technical - it's trust. And the people best equipped to see that barrier are almost never in the room when AI rollout strategy gets written.
Sumaiya is Technical Program Manager and Data Infrastructure Lead at the University of Colorado Denver, managing 200 data pipelines across a 530-user analytics environment. She came to the US from Bangladesh at 19 and learned every institutional system from the outside - and built DataReady, a four-agent AI readiness audit tool, along the way.
IN THIS EPISODE:
▪ Why institutional trust — not technology, data, or budget — predicts whether an AI tool actually gets used
▪ The McKinsey 88%/6% gap explained: deploying a tool isn't adoption, it's a gym membership you never use
▪ The DataReady confession: building a tool that measured data readiness and missing whether the people were ready
▪ The three real reasons people don't use AI tools — and why most orgs misdiagnose which one they're facing
▪ Why cognitive diversity work is often theater — and what changes when outsiders define success before the metric is set, not after
▪ The specific diagnostic skill immigrants and career-switchers develop — and why it can't be trained, only earned
▪ The Monday morning action plan: interview 10 users, ask if the metric makes sense from their seat, track the override rate
▪ The real test for AI adoption success: who stopped using it - not who complained
FIND SUMAIYA:
LinkedIn: https://www.linkedin.com/in/sumaiya-shrabony
Newsletter: https://substack.com/@shrabony
DATASCIENCEWITHSAM:
Weekly deep-dives into AI, machine learning, and data science. Subscribe on Apple Podcasts, Spotify, Amazon Music, iHeartRadio, Podbean, and YouTube. If you lead an AI program and haven't asked your users whether the success metric makes sense from their seat — that's your homework from this episode.
