AI Management Office, Inc. Houston, Texas

You built a PMO when projects got serious. AI is there now.

Most companies are past the pilot stage and nowhere near production. AIMO is the operating office that closes that gap — governance, use cases, and delivery, run as a standing function instead of a one-off project.

A. The model

An operating office, not a pilot project.

A Project Management Office didn't make projects happen. It made them repeatable — one place accountable for standards, prioritization, and whether the work actually shipped.

AI needs the same discipline, and almost nobody has it. Teams buy tools, run experiments, and end up with a dozen disconnected pilots, no clear owner, and no line to the P&L.

AIMO stands that function up for you. We decide what's worth building, build it, and stay on to run it. You get the capability without hiring a department to get there.

The PMO did this for projects
AIMO does this for AI

Decided which projects were worth funding

Ranks AI use cases by return, not by novelty

Set the standards work had to meet

Sets policy for data, privacy, and acceptable use

Owned delivery when no one else would

Builds and ships the systems, not slideware

Reported honestly on what was working

Measures adoption and hours returned, monthly

B. What we solve

The four ways AI stalls in a mid-market company.

Stall one

Everyone's experimenting. Nothing's in production.

Licenses are being paid for across three departments and no one can name a process that changed. We find the two or three that would actually move numbers and put them into daily use.

Stall two

The knowledge is in people's heads.

Your best estimator, scheduler, or case manager knows things nobody wrote down — and they're five years from retiring. We capture that into systems your team can query, so it stays when they don't.

Stall three

Reporting and compliance eat the calendar.

State filings, funding reports, claims packets, safety documentation. High-volume, rules-driven, and painfully manual. This is the work AI is genuinely good at, and where returns show up fastest.

Stall four

Nobody owns the risk.

Staff are already pasting company data into consumer tools. Without a policy and an owner, that's an exposure sitting on your books. We put the guardrails in before it becomes a problem.

The engagement

Four stages. You can stop after any of them.

STAGE 01 — 2 TO 3 WEEKS

Assess

We sit with your operators, not just your executives. Map where hours go, where errors cost money, and where your data actually lives. You leave with a ranked list of use cases and honest estimates — including the ones we'd tell you to skip.

STAGE 02 — 3 TO 6 WEEKS

Prove

We build the top-ranked use case for real and put it in front of the people who'd use it. Working system, measured against the current process. If it doesn't beat the status quo, you find out cheaply.

STAGE 03 — 8 TO 16 WEEKS

Build

Production deployment, integrated with the systems you already run. Policy and access controls written down. Your team trained on it — not handed a login and wished luck.

STAGE 04 — ONGOING

Operate

A monthly retainer that keeps the systems current, adds the next use case, and reports on what's being used and what it's returning. This is the office standing up permanently, at a fraction of the cost of building the team internally.

Why we're different

Built by an operator, for companies the big firms won't staff properly.

Seniority

The person who sells the work does the work.

Twenty-five years building technology practices inside PwC and Guidehouse, and more than $100 million in professional services delivered. You get that person in the room — not a partner at the pitch and an analyst afterward.

Scope

We stay through operations.

Strategy decks are easy to produce and easy to ignore. Our engagement model is built to end in a running system with someone accountable for it, which is why the last stage never really ends.

Fit

Mid-market economics, enterprise discipline.

We work with companies between $20M and $500M — energy services, professional services, education, and PE-backed operators. Large enough that the returns are real, small enough that the national firms send their most junior people.

Principle

Technology that respects the people using it.

We build AI that removes the worst hours from a job, not the person doing it. That's a commitment we'll put in writing, and it shapes what we'll take on.

C. Get in touch

Start with a conversation, not a proposal.

Thirty minutes on your operation and where the hours go. If there's nothing here worth doing, we'll tell you that — it's a faster answer than a discovery engagement.

Principal Chris McConn
Based in Houston, Texas