AI Transformation · For operations and product leaders
AI readiness assessment that ends in working systems
A scored diagnostic of your data, workflows, team, and governance, then the builds that put the results to work. Run by an operator who builds agentic workflows and RAG systems himself: our own delivery runs on the same systems.
What the readiness assessment scores
Data
Where your data actually lives, how clean it is, and whether the systems holding it can feed an AI workflow without a rebuild.
Workflows
Which processes are real candidates for agentic automation, which need a human in the loop, and which should stay manual.
Team
Who will run these systems day to day, the skills gap between here and there, and the training that closes it.
Governance
Approval gates, risk boundaries, and the ROI measurement that tells you whether any of it is producing value.
The output is a scored diagnostic with a prioritized use-case map, and the honest list of what not to automate yet. Most mid-market AI pilots die in the gap between demo and daily work; we wrote up the failure pattern in Why AI pilots fail at mid-market companies. The assessment is built to close that gap before anything gets built.
From assessment to working systems
The assessment is designed to end in builds, not a binder. We scope, build, and harden agentic workflows around your highest-value use cases, with human-in-the-loop gates that survive production. Not every process earns an agent: the five-question test we run before building one is written up in Agentic workflows: when not to use them. Alongside the builds runs an adoption and governance program: training curricula, champions programs, approval gates, and ROI measurement, the change-management layer that determines whether AI sticks.
You get systems your team runs without us. The AI practice sits alongside the rest of the service line, so marketplace and monetization work can draw on the same operator. When the gap is leadership rather than a build, the fractional executive practice embeds that operator part-time.
We sell what we use
The AI practice is run by an operator who builds agentic workflows and RAG systems himself. Our own delivery runs on them, from deduction scanning to dispute preparation, and that is the proof point. Production LLM automation built and run personally has saved 5,900+ hours a year at under 2% error.
Frequently asked questions
What is an AI readiness assessment?
A scored diagnostic of your data, workflows, team, and governance. It ends in a prioritized use-case map, a view of the skills and governance gaps that would stall adoption, and the honest list of what not to automate yet.
What do we get at the end of the assessment?
The scored diagnostic and the prioritized use-case map, plus a build path for the top candidates. If you continue into delivery, we scope and build agentic workflows around them with human-in-the-loop gates, and stand up the training and governance that let your team run the systems without us.
Why do so many AI pilots fail?
Most mid-market pilots die in the gap between demo and daily work: the demo impresses, but nobody owns the workflow, the data feeds, or the governance around it. The assessment exists to close that gap before anything is built, starting with the list of what not to automate yet.
Who actually runs the engagement?
The same operator who built the firm's own systems. Every engagement is run personally, not handed to an account team, and the internal LLM automation we run on has saved 5,900+ hours a year at under 2% error.
Book a working session
Bring the AI question you are trying to answer and what data you have, and we come prepared. First conversation, no fee.
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