Is your AI production ready, or one review away from stalling?
Most AI initiatives do not die from a bad model. They die in legal review, get frozen behind a compliance question nobody scoped for, or sit in pilot purgatory while the team moves to the next pilot. We read your systems across four dimensions: deployment readiness, governance and risk, team capacity, and roadmap alignment. If nothing should ship yet, we say so.
Your biggest gap, named first
The lowest-scoring of the four dimensions comes first, with a plain read on where the gap sits.
A read across four dimensions
Deployment readiness, governance and risk, team capacity, and roadmap alignment, each scored on what we find in your systems.
The first piece of work, scoped
What a first look at your biggest gap would cover, with a number on it.
Four questions, answered with evidence.
One question per dimension, answered by engineers reading your systems, not by running a workshop.
How many of your AI initiatives are live in production?
Live in production is a different count from stuck in pilot. We count both, and check whether anything shipped through a real validation process or on the builder’s own sign-off.
Does review block the work, or is it designed in?
We trace how a new AI initiative moves through legal, risk and security review, whether you could produce an audit trail for a specific AI decision, and what happens when behavior drifts after launch.
Who owns the AI work, and is there senior bandwidth?
A clearly accountable owner, senior bandwidth to build and maintain at the same time, and how much of the roadmap stalls if one person leaves.
Can the platform and data carry it at scale?
In one published test, GPT-4 answered from an enterprise database correctly 16% of the time raw, and 54% with defined semantics (Sequeda et al., SIGMOD 2024). We check whether your platform and data can hold the roadmap leadership has agreed to.
It ends in a decision you can defend.
Start here when the AI work matters and nobody can prove it will hold up in production. You leave with a decision, the evidence behind it, and one piece of work scoped.
Your lowest dimension, opened up
The report opens with your lowest-scoring dimension, what a first look at it would cover, and what it costs.
Start before the wrong pick costs a quarter
Gartner, in February 2025, expected 60% of AI projects to be abandoned by 2026 where the data underneath was not ready.
A short read that finds the real gap
The AI work matters and nobody can prove it will hold up in production. Two to four weeks inside your systems, and you leave with all four dimensions scored, your biggest gap named, and the first piece of work priced. If nothing should ship yet, we say so.