Is your AI production ready, or one review away from stalling?
Pilots stall in legal review, behind a compliance question nobody scoped, or in the queue behind the next pilot. Deloitte asked 3,235 leaders across 24 countries: one in four had moved 40% or more of their AI experiments into production. A senior engineer reads your systems and names what is blocking yours.
The biggest gap in your systems, 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.
What a senior engineer reads in your systems
Deployment readiness
Whether anything can reach production today, and what is stopping it.
Governance and risk
What your reviewers will ask for, and whether the answers exist yet.
Team capacity
Who has hours for this work, and what they are already carrying.
Roadmap alignment
Whether the use case in front of you is the right one to build first.
What you get
A written read on all four, your biggest gap named in plain terms, and the first piece of work scoped with a number on it. If nothing should ship yet, we say so.
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 anyone senior free to run it?
A named owner, a senior engineer with the time to build and maintain at once, and how much of the roadmap stalls if one person leaves.
Can the platform and the data carry the full volume?
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 scoring dimension, in full
The report opens with your lowest-scoring dimension, what a first look at it would cover, and what it costs.
What the wrong first pick costs
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. 2 to 4 weeks in 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.