AI executive, AI agency, or figure it out yourself: how mid-market companies should decide
You already know AI should be doing real work in your company. The real question is who builds it: a full-time executive you hire, an agency you put on retainer, or you and your team on nights and weekends.
Here's the short answer. Hire a full-time AI executive when AI is becoming a permanent leadership function and you can fund it at $300K+ a year. Hire an agency when you need one well-defined system built and you can live with ongoing retainers and some vendor dependency. Figure it out yourself when cash is tighter than time and a senior person will own the project like a job, not a hobby.
Full disclosure before we go any deeper: I founded Meet Caddy (Orbital Access, LLC), an AI deployment company in Dallas, Texas, so I sell an alternative to all three paths. I got here by building AI systems inside my own family of companies first: utility locating, military drones, manufacturing, real estate, and healthcare. You're getting the honest case for each path anyway. A wrong-fit engagement is bad business for everyone.
When does hiring a full-time AI executive make sense?
Hire the executive when AI is a permanent leadership function, not a project with an end date. If you run multiple business units, sit on real data infrastructure, carry compliance exposure, or ship a product with AI inside it, there's enough work to fill that seat for years. You want someone in every leadership meeting who owns the roadmap, builds the internal team, and answers for results. In regulated industries, internal accountability alone can justify the seat. And a great AI executive makes every other leader in the building sharper, with the knowledge staying inside your walls. No other option on this list gives you that.
Now the honest costs. Market compensation for someone who's actually shipped runs $300K+ a year before bonus and equity. The search commonly takes 6 to 12 months, then ramp time on top of that before anything works. The harder problem is evaluation: if nobody inside your company can judge AI competence, you're hiring blind, and this market is full of confident strategists who've never deployed anything. And one person still has to build or hire the delivery muscle underneath them.
The test: if you'd still want this person in the room three years from now, hire. If what you really want is to find out what AI can do for your business and get the first systems built, that's a project, not a position. Don't pay a permanent salary to answer a one-time question.
When is an AI agency the right call?
An agency is the right call when you know what you need built and you want professionals to build and run it for you. If the work has clear edges, say a customer support automation, a document pipeline, or a custom internal tool, a good agency brings a bench on day one: engineers, a project lead, and pattern experience from builds they've already done. No recruiting, no ramp.
The managed-service model is a genuine feature for some owners: the agency monitors, maintains, and updates the system, and you never think about it. If you have no interest in building internal AI capability, and some businesses legitimately don't, that trade can be worth the price.
The honest costs live in the structure. Retainers commonly run $50K+ a year, and the meter has no natural end. The incentives reward dependency: recurring revenue grows when you can't leave. That's a structural fact, not an accusation. Ownership is the question most buyers skip. A lot of agency-built systems run on the agency's accounts and infrastructure, which means the system can stop working when you stop paying. Timelines tend to be open-ended and tied to their roadmap, not yours. And through all of it, your team isn't learning; the capability lives outside your building and stays there.
The test: if you want output without capability, and you accept the vendor relationship as a long-term operating cost, an agency is an honest fit. Read the ownership clauses before you sign, not after.
When should you figure it out yourself?
Honestly, don't let anyone talk you out of this one too fast, including people like me. The tools have never been more accessible. An owner with real curiosity and a few protected weekends can get meaningful personal wins from off-the-shelf AI: drafting, research, analysis, meeting notes, first-pass reporting. That costs almost nothing in cash, and it builds something no vendor can sell you: literacy. Even if you later hire the executive or sign the agency, hands-on time makes you a far better buyer.
DIY is the right primary path when budget is genuinely tight, needs are simple, or a systems-minded senior person has real time carved out and the authority to change how work gets done.
The costs here are time and blind spots. Your attention is the most expensive currency in the building, and DIY spends it hard. The predictable failure mode is stalling: personal productivity use never turns into operational systems. The chatbot helps you write emails while quoting, scheduling, follow-up, and reporting all stay manual. The gap between "I use AI" and "the business runs on AI" is where most DIY efforts quietly die. There's also a discovery problem: the highest-value automations are rarely visible from the inside. Meanwhile, data security and access questions show up with nobody assigned to own them.
The test: DIY for literacy and personal productivity, starting this week, no matter what else you choose. DIY as your company-wide strategy only if someone senior owns it with hours, goals, and a deadline.
How do the three paths compare?
Side by side, including the risks nobody puts on their own website.
| Full-time AI executive | AI agency | Do it yourself | |
|---|---|---|---|
| Time to a working system | 6 to 12 months to hire, then ramp time | Open-ended, tied to the agency's roadmap | Slow and uneven, paced by your spare hours |
| Cost shape | $300K+ a year in salary, ongoing | $50K+ a year in retainers is common, ongoing | Little cash, heavy leadership time |
| Who owns the system | You, if they build before they leave | Often the vendor. Check the contract | You own everything you build |
| Your team afterward | Depends on the hire | Often dependent on the vendor | Learns by doing, if the effort sticks |
| What happens when it ends | You keep paying, or the knowledge walks out | Systems can stop when the retainer stops | It never quite ends, or it quietly stalls |
| Biggest risk | A long ramp and a hire you can't evaluate | Paying forever for a system you don't own | Stalling at personal productivity |
| Best for | AI as a permanent leadership function | A defined build you want managed for you | Tight budgets with a senior owner on point |
What gap do all three leave?
Here's the thing. None of the three gives you everything at once: a working system soon, owned by you, run by a team that stays fluent after the builder leaves. The executive gets you ownership and internal capability, eventually, at a permanent price. The agency gets you a build without ownership or capability. DIY gets you ownership without speed, and capability only if it survives contact with your calendar.
That missing middle is what a deployment engagement fills, and it's the model my company runs. Meet Caddy's flagship service, ACE, puts a fractional AI Chief Executive inside operations-heavy mid-market businesses for 90 days. I find the highest-value AI opportunities in your operation, build the automations, train your team to run them, and leave. Everything deploys at the admin level of your own Anthropic plan, so you own the system and the data outright. Your team gets hands-on training, written SOPs for every automation, and an operating manual for full independence. When the 90 days end, you take the keys. Ongoing support is there if you want it, and you won't need it to keep the system alive.
To be straight about who shouldn't do this: if AI is a permanent leadership function at your scale, hire the executive. An engagement like mine gives that hire a running start; it doesn't replace them. If you want a vendor to run everything for you forever, an agency is the honest match. If you're pre-revenue or shopping on price alone, DIY is your answer, and I'll tell you that to your face.
What questions should you ask any AI partner before you sign?
Whoever ends up across the table, me included, put these seven questions in front of them. The answers tell you more than any deck.
- Who owns the system when we part ways, and whose accounts does it run on? If it lives on their infrastructure, you're renting, not buying.
- What exactly stops working if we stop paying you? Get the answer in writing.
- Will our team be able to run this without you? Ask what training and documentation are in scope, specifically.
- What will be live by a fixed date? A timeline in the agreement beats a roadmap they control.
- How will we measure whether this was worth it? Which metrics, measured how, in math your CFO would accept.
- Who holds our data, and can our IT team control access? You want the answers to be you and yes.
- What have you built inside a real operating business, including your own? Builders have specifics. Talkers have frameworks.
A good partner answers all seven without flinching. A future problem changes the subject.
The bottom line
The way I think about it: match the path to the function. A permanent function deserves a permanent hire. A defined build you want managed forever fits an agency. Literacy and personal wins are DIY, and that part starts this week no matter what else you pick. And if what you actually want is a working system inside your business within a fixed window, owned by you and run by your own people, that's the gap the deployment model fills.
If that last sentence sounds like your situation, the discovery call is free and takes 30 to 45 minutes. I look at your operation, tell you where AI would actually pay off, and the map is yours either way. Book it at meetcaddy.com. And if one of the other three paths is your better fit, you'll hear that from me on the call.