AI-native marketing operations, actually adopted.
Most marketing teams are slow for boring reasons - a brief that takes four rounds, a report someone rebuilds by hand every month, a launch that needs six people in a thread. I find those processes, rebuild them as AI-native workflows, and make sure the team actually uses them. Speed, consistency, and adoption - measured, not assumed.
- Marketing teams where the work is fine but the process is the bottleneck
- Leaders who bought AI tools that nobody on the team opened twice
- Lean teams carrying the workload of a much larger one
- Organisations that want AI in the workflow, not just in the pitch deck
From manual to AI-native.
Seven things I build, in roughly the order they pay off.
Process audit & opportunity map
I map how work actually moves through your team - intake to delivery - and find the slow, manual, repeated steps. Then I rank them by hours returned and risk removed, so we start where the payback is obvious rather than where the technology is interesting.
AI-native workflow rebuild
The top processes get rebuilt rather than patched. Full cycle: identify the problem, design the workflow, build it, ship it, then measure what it actually changed after implementation - not before.
Reporting & planning, rebuilt for speed
The monthly report nobody enjoys building becomes something that assembles itself and reads consistently every time. Same for planning inputs. This is usually the fastest visible win, which makes it a good place to start.
Campaign launch automation
Launches involve too many people confirming things to each other. I automate the coordination - briefs, approvals, asset routing, scheduling - so the overhead drops and the handoffs stop dropping things.
Brief quality control
A quality check on briefs before they go out. Ambiguity caught at the brief stage is cheap; caught at the review stage it costs a round of work. This is the single highest-leverage check in most marketing teams.
Intake & prioritization framework
A clear way for work to enter the marketing organization and get ranked, so the team stops being reactive to whoever asked most recently.
Adoption & change management
The part most AI projects skip and then die from. Tools nobody adopts are shelfware. I train, document, and stay close enough through the transition that the new way becomes the default way.
Built by someone who runs on this stack.
I am not a consultant who read about this. My own operation runs on it - briefing, content scoring, reporting and reconciliation all handled by workflows I built and maintain, because I have a lean team and no appetite for busywork. I wrote up how I audit them in the AI operating system audit.
Questions, answered
What is AI marketing operations?
The work of rebuilding how a marketing team operates - intake, briefs, launches, reporting, prioritization - so that AI does the repetitive parts and people do the judgment parts. It is distinct from using AI to make marketing content. This is about the process, not the output.
How is this different from your AI discovery work?
AI discovery and owned growth is outward-facing: getting your brand recommended, then converting and keeping the customer. This page is inward-facing: how your team works. Some clients need one, some need both, and they are scoped separately.
We already bought AI tools and nobody uses them. Is that fixable?
Usually, and it is the most common version of this problem. Low adoption is rarely a tool problem - it is that the tool was dropped into an unchanged process, with no owner and no training. The fix is to rebuild the workflow around it and manage the change deliberately.
How do you measure whether it worked?
We agree the metrics before anything is built - typically cycle time on a named process, revision rounds per brief, hours returned per month, and active use of the new workflow across the team. Then we baseline, ship, and re-measure. If a workflow did not move a number, we change it or drop it.
Do you replace people with AI?
No, and I would be cautious about anyone who leads with that. Every team I have worked with was already under-resourced. The point is to stop spending senior time on coordination and formatting so it goes into work that actually needs a person.
Can you do this as an employee rather than a consultant?
Yes. This maps closely to in-house roles building AI-enabled marketing operations, and for the right company I would consider going in-house. Here is the résumé and the fit.