AI Marketing Operations
Somebody on your team is already using AI, quietly, in a way nobody has written down. That's not adoption — that's a shadow process with no standards attached. I map how work actually gets produced here, build the AI system that removes the real constraint, and train your team on it. The last time I built infrastructure before demand, marketing-sourced qualified leads grew 17x in eighteen months. Your team runs it.
Free AI Visibility snapshotEvery marketing team I've run had more good ideas than it could ship. Never a shortage of things worth doing. Always a shortage of throughput.
The old fix was headcount, which is slow, expensive, and gets cut first. The new fix is supposed to be AI, so the tools got bought and the licenses got assigned and everyone was told to experiment.
What actually happened is that three people found workflows that work, nobody wrote them down, output got faster in some places and worse in others, and there's no way to tell which is which.
Here's the uncomfortable part: ungoverned speed isn't a win. It produces more work than anyone can review, in a voice nobody approved, with facts nobody checked — and the review queue becomes the new bottleneck. You didn't remove the constraint. You moved it downstream onto whoever has to catch things.
And they behave nothing alike.
Lives with the person who built it. Undocumented, unversioned, unreviewed. Dies when they take a vacation or take another job. Produces demos.
Written down, governed, owned by a role instead of a person. Survives turnover. Produces throughput you can plan against.
Pilots are where every team starts. Almost none of them make the crossing.
Campaigns are episodes. Systems are the series.
Your current production process, mapped, with the constraint named and quantified.
Installed and working in your environment, not a slide deck describing one.
The workflows your team invokes daily.
Approvals, escalations, and the never-list.
So the next hire onboards without you.
After handover, because the first month of real use always finds something.
You could run this yourself. That's the design goal, not a limitation. Everything is documented well enough that you could take it in-house the day I leave. The option is the point.
The receipt
At a global B2B manufacturer I inherited a marketing function with almost no pipeline contribution. I built the infrastructure first — CRM from zero across three brands, lead scoring, lifecycle stages, attribution — then scaled demand.
17x
Eighteen months later, marketing-sourced qualified leads had grown 17x.
Most AI rollouts underperform because the demand for output arrived before the system that governs it. Same sequence here. Infrastructure first, demand second.
It isn't a tool recommendation. You almost certainly have the tools already. The problem is that nobody defined how work moves through them, so every person invented their own version.
It isn't headcount replacement. I've never built one of these to cut a team, and I'd tell you on the call if that's what you're after. The constraint being removed is production capacity, not payroll.
It isn't a dependency. Documentation, training and source files hand over at the end. If you want ongoing support it's a separate retainer you can start or stop — not something buried in the build.
Flat project fee, agreed before we start. No hourly billing, no scope creep — if the scope changes we agree a new number before anything moves.
Exact pricing comes with your proposal, scoped to team size and how many workflows are in the build. Teams with a current style guide cost less than teams starting from a blank page.
AI marketing operations is the system layer between your marketing team and the AI tools they use — the documented workflows, brand governance, approval rules and measurement that turn scattered individual use into repeatable team throughput. Without it you have a set of licenses and a set of habits, not a capability.
Six to eight weeks for a full build, from production audit through documented handover, plus a 30-day tuning window afterward. Work is shown weekly rather than delivered in one lump at the end.
No. The constraint being removed is production capacity, not headcount. Teams that use this ship more of the work they already wanted to ship, with the review burden going down rather than up because standards are enforced at the draft stage.
Claude by default, because its skill and project structure handles governance rules best. The approach works in any major model, and if your team is already standardized on something else, the build happens there.
You do, outright. Source files, prompts, documentation and training recordings hand over at the end of the engagement.
Yes, and that's the design goal. Training is included and recorded, and the documentation is written for someone who wasn't in the room. The goal is that your team is making its own changes inside the first month.
That's the usual starting point. Adoption fails when there's no owner, no standard, and no measurement — so it stays a personal habit for two people and never becomes a process. The audit finds where it broke down before anything gets built.
Before we talk about your production system, let's see what the AI engines currently say about you. It's free, it takes 48 hours, and it usually reframes the conversation.
Company, website, how a buyer would describe you, two competitors. Takes about a minute.
I make sure the request is a real business — a quick confirmation by email or phone. It's verification, not a sales call.
One page, inside 48 hours of verification: what the engines said, who they named instead of you, and whether the full report is worth running.
I run a limited number each month, so there may be a short wait. The snapshot carries no pitch — the report sells itself or it doesn't.
Takes about a minute. The more precise the last two answers, the sharper the report.
I'll only use this to run and send your snapshot. No list, no sequence you didn't ask for, and you can tell me to delete it at any time (see the privacy policy). I run a limited number each month, so there may be a short wait.