How to Choose an AI Consultancy for a Mid-Size Company

A mid-size company buying AI consulting sits in an awkward spot. It's too big for off-the-shelf tools to fix its core systems and too small to justify a large firm's program fees and staffing. MIT's Project NANDA reported in 2025 that only 5% of custom enterprise AI tools reach production, so the main risk in choosing a partner is paying for work that never ships. This guide covers the criteria that separate a partner who ships from one who produces decks, and the questions to ask before you sign.

Decide what you're buying first

Write down the one number you want AI to change this year, such as qualified pipeline, cost per order or hours spent on a process, and who in your company owns it. If you can't name it, the first thing you need is help choosing it, which is a short discovery engagement rather than a build.

Then decide whether you want the work done for you or want your own team to learn to do it. Both are reasonable. They lead to different partners and different contracts.

Five criteria that matter

1. They start from a business number. Ask what the engagement will be judged by. The answer should be a number with a baseline, not a count of use cases or a maturity score. See revenue-focused AI consulting for what that looks like.

2. The people you meet are the people who build. Ask who will be in your weekly meetings and whether they write or review the code. At a boutique AI consultancy, they usually are the same people.

3. They ship to production, not to a demo. Ask to see live systems they've built, with tests, deploys and monitoring. A prototype proves the idea. Production proves the partner.

4. The price structure shares the risk. Hourly billing rewards slow work. Look for fixed prices per phase and, ideally, a fee component tied to the number. Our guide to how AI consulting is priced covers the trade-offs.

5. They have a plan for after they leave. If your team will own the system, ask how the handoff works week by week. Pairing, where your people work alongside theirs until they can run it alone, is the method we learned at Pivotal Labs.

Red flags

• The proposal names tools and models before it names your number.
• The first phase is a long assessment with no working software at the end.
• Nobody on the sales call will be on the delivery team.
• Success is measured by adoption, logins or use cases launched.
• There's no way to stop after the first phase without penalty.

What you should bring

A good partner can't move a number nobody owns. Bring an executive sponsor who owns the number and can make decisions quickly, access to the systems and data involved, and a few people who do the work today and can spend time with the team. You don't need your own data science team.

How Cosmic fits

Cosmic is a team of four that implements AI to move revenue, margin or hours, with AI agents doing the repetitive build work under the team's review. We start with a free 30-minute call, then a fixed-scope discovery session of two or four hours with your executive sponsor. Each sprint after that has a fixed price plus an outcome-linked fee tied to the number set in discovery. Read more about how we do AI implementation. If you're in Washington, DC, Northern Virginia or Maryland, we'll come to your office for discovery.