AI Operating Partner.

Companies in the $20M to $200M range are among the most active adopters of AI and among the most likely to have abandoned something. A tool was purchased with genuine enthusiasm. It was used for a while. Now it sits unused, nobody quite says so, and the appetite in the room has quietly gone cold.

The reason is almost never the technology. Automation does not adjudicate. It inherits whatever standards it finds and executes them at speed. Where authority is ambiguous, it makes the ambiguity faster. Where a workflow depends on one person's judgment that was never written down, it produces confident output that quietly lacks it.

Behavior follows what is reinforced. So does software.

The single most reliable predictor of whether an AI implementation succeeds is whether the business defined what success looked like before anything was deployed. Almost nobody does it. That is why the failure rate is what it is. It is also where this engagement begins.

The first deliverable is a ranked map of where your operation is actually losing time, judgment, and margin, and which of those losses are structural rather than technical. That distinction determines everything downstream. Some friction resolves through a targeted build. Some friction is a decision-rights problem wearing a technology costume, and no tool will touch it.

The map names the opportunity and what it is worth. What you do with it is your decision, and leaving with it is a legitimate outcome.

The models themselves are becoming a commodity. What does not commoditize is the accumulated judgment of your own business: your pricing logic, your customer history, the corrections your best people make without thinking about it. Captured, that becomes an asset that compounds. Uncaptured, it walks out the door every time someone retires.

The work builds that capture into how the company operates. A workflow is installed, an internal champion is trained to run it, and every correction they make feeds back so the system gets more like your business and less like a generic tool. Month over month, the evidence is visible: corrections captured, edit rates falling, cycle times shrinking.

The ongoing work is governance and judgment. What the system is permitted to decide, where a human stays in the loop, how it is audited, and what happens when the model providers ship a feature that replaces something you built. When that happens, you migrate onto it and redeploy the effort to the next thing. That is the job.

Every prompt, every correction log, every evaluation, every dataset lives inside your systems and belongs to you. If we part ways, it stays with you, in full.

This is the thing most companies should be asking about and almost none do. Institutional knowledge routed through a vendor's platform is knowledge you are renting. Ownership is also what makes the system auditable, and that matters more every quarter. Security and control are the leading reasons companies your size hold back, and the concern is a reasonable one.

The best candidates are boring, judgment-heavy, and specific to you. Quoting and estimating, where the pricing instinct lives in one person's head. Receivables follow-up, where tone and relationship history decide whether the call lands. The work that is drowning someone competent, and that no off-the-shelf product understands because it depends on facts about your business alone.

The work that is generic is not worth building. The model providers will ship it for free before long, and you should let them.

Founder-led, privately held, and PE-backed companies between $20M and $200M in revenue, with a leadership team that is serious about the question and unconvinced by the noise around it.

If the enthusiasm in the room is running ahead of the diagnosis, that gap is worth examining before anything gets built.