How this lab works
Strategy & Planning, in plain terms
This is where a vague 'we should use AI' becomes one specific bet you can actually build and measure. You make a few choices, the lab sharpens them into a clear problem, generates options, and scores the trade-offs — so you leave with a scoped, falsifiable initiative, not a wish.
Say the ambition
Type what you wish AI could do, then make five quick choices: who it's for, the job, the pain, your data posture, and your risk appetite.
A fuzzy wish can't be built or measured. These choices turn it into something concrete.
Sharpen it
The lab rewrites your ambition into one clear, testable problem statement focused on the narrowest valuable slice.
A sharp problem is half the solution — it sets the scope and the success bar.
See the options
It generates a spread of ideas across four buckets — Quick wins, Core, Differentiators, and Foundations — each placed by value and effort.
The first idea is rarely the best bet. Comparing options keeps you from over- or under-reaching.
Score the bet
Value, feasibility, and data readiness are scored together. Drag the scope and watch them trade off against each other.
These three move as one — widening scope buys value but costs feasibility and readiness. Seeing that trade is the whole point.
Set a target you could miss
Define a falsifiable success metric: a baseline, a target, and the coverage it applies to.
If you can't fail it, it isn't a real goal — and you'll never know if the bet paid off.
Save the initiative
The framed bet threads forward into Data, Build, AI Ops, Govern, and Realize.
Every number in the final business case traces back to the decisions you make here.