What an AI audit actually looks like
Inside an OpenKit AI audit: how we find the work AI can take on, how candidate workflows get ranked, and what the written report and roadmap actually contain.
Ibrahim Mizi
· 5 min read
Updated 
An OpenKit AI audit is an independent, builder-neutral assessment of where AI actually fits your business, and where it does not. It maps the work your people really do, ranks the candidate workflows, and hands you a written report and a prioritised 12 month roadmap. It is the opening stage of our AI Audit and Transformation engagement, which runs the audit and the first rollout together at a fixed scope and a fixed fee, and it exists to make the choosing boring.
Published 8 July 2026.
Most AI projects fail before a line of code is written. They fail at the choosing. The wrong workflow gets picked because it demos well, a tool gets bought because the vendor was persuasive, and later there is a subscription nobody opens and a team that trusts AI a little less than it did. The audit exists to stop that happening before the money moves.
We start with the work people actually do
We don’t begin with your stack. We begin with your people doing their actual jobs: the finance lead walking us through a month-end close, the operations manager showing us the inbox that eats a day a week, the person who quietly built the spreadsheet the whole company runs on.
Three questions repeat in every session. Where does volume erode judgement? What do people search for and fail to find? And which parts of the work would you never hand to a machine, not because it can’t do them, but because it shouldn’t? That third answer shapes the engagement more than the first two.
Alongside the interviews we map the estate: where the data lives, who is allowed to see it, what your regulators and insurers expect, and what your existing systems can actually support without a rip-and-replace.
The best output of an audit is often the list of things you should not build.
Then everything gets ranked
Everything surfaced in the interviews goes into one table and gets weighed against the things that decide whether automating it is worth the trouble. Supervision exposure: who is accountable when this goes wrong, and can they defend it to a regulator, an insurer, a board? Integration cost: does this fit the stack you run, or does it quietly need a replatform? Time to value: honestly assessed, not wished for.
Most candidate workflows die in that table, and that is the point. What survives is usually two or three where the case is clear enough that it stops being a leap of faith, and the report says what we would do with each one, which is sometimes to build it, sometimes to buy something that already exists, and sometimes to leave the process alone. Where an off-the-shelf tool costs less than a lunch and does the job, we would rather tell you that than build you something.
What you walk away with
The deliverable is a written report a board can read in one sitting, presented rather than emailed over. It carries the ranked workflows with the reasoning shown, and a prioritised 12 month roadmap priced against what the audit actually found. When EMQN needed to know whether an AI marking platform was even viable, that is the shape the work took: benchmark the options, cost the routes, and write down the evidence. You can see that discovery engagement in the portfolio.
What happens next
The engagement is scoped and priced up front, and it carries the audit through to the first rollout, so you finish it with the roadmap and with something already running rather than a document and a decision to make. What comes after that follows the report rather than a sales instinct: an Embedded AI Lead to carry the roadmap, a bespoke build where nothing off the shelf fits, or private deployment where the data can’t leave your walls. Each of those is scoped and priced from what the last step found, so you are never buying further than you can see.
If you want the engagement in full, the AI Audit and Transformation page lays it out. One document, and the confidence to say yes to the right things and no to the rest.
Start with an audit
Most engagements start with an AI Audit and Transformation, a fixed-scope, fixed-fee piece of work that finds where AI earns its place in your business and where it does not, then puts the first of it into service. You leave with a written report and a prioritised 12 month roadmap.
What is an AI audit?
An AI audit is an independent, builder-neutral assessment of where AI actually fits a business. It maps the real work, ranks candidate workflows on what it would take to supervise, integrate and pay back each one, and hands you a written report and a prioritised 12 month roadmap. It is the opening stage of OpenKit's AI Audit and Transformation engagement, which carries the audit and the first rollout together at a fixed fee.
What do you get at the end of an AI audit?
A written report a board can read in one sitting, with every candidate workflow ranked and the reasoning shown, and a prioritised 12 month roadmap costed against what the audit found. Findings, not filler, and enough to make a decision without taking anything on faith.
What if the audit says we should not build anything?
Then it says so, and that is a useful result. The most valuable part of a report is often the list of things not worth building, and the honest recommendation is sometimes an off-the-shelf tool that costs less than a lunch. A bespoke build is never assumed by the engagement: it is scoped separately, after the report, once both sides know what is worth building.
Is OpenKit independent, or selling its own build?
The assessment is builder-neutral. We rank what is worth doing before anyone talks about who builds it, and we will tell you to buy something off the shelf, or do nothing, when that is the right answer. Anything that follows, an embedded lead, a bespoke build, or private deployment, is scoped from the report rather than assumed by it.
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