Guide · 6 min read
How to Build an AI Operating Model for Marketing (Not Just Add AI Tools)
By Fredrika Frenkiel, Head of Studio & AI Creative Operations at Lunar, founder of Code of Alfred. About my work
Short answer
An AI operating model for marketing is a defined system for how work moves from brief to delivery with AI built into specific stages, not a collection of individual AI tools used inconsistently across a team. Building one means designing the workflow, the decision rights, and the governance layer together, rather than layering tools onto an unchanged process. The model is designed around the needs of the business, so it makes what already works more efficient instead of reinventing the wheel.
1. What an AI operating model is, in one paragraph
An AI operating model is the documented system that defines how marketing work moves from brief to delivery, which stages AI executes, which decisions stay human, and what governance keeps quality and compliance intact as volume grows. It is not a tool stack, a policy PDF or a pilot project. It is the operating layer a marketing operations AI specialist designs so that AI produces compounding throughput for the whole organisation instead of scattered personal productivity.
2. Understand the difference between using AI tools and having an AI operating model
Most marketing teams are in the first category: individuals using AI tools inconsistently, with no shared workflow, no shared standards, and no compounding benefit across the team. An operating model means the AI usage is structured, the same workflow runs the same way regardless of who's executing it, with clear stages, clear ownership, and shared infrastructure.
3. Design for the business you have, not the one the tooling assumes
The point of an operating model is not novelty. It is throughput. Start from what already works: the planning cadence people trust, the review step that catches real problems, the naming convention everyone already follows. Those are assets, and an AI layer that respects them gets adopted in weeks instead of quarters. Then look for the friction the business actually feels, briefs arriving as loose messages, the same asset rebuilt for five markets, approvals waiting on one calendar, and put AI exactly there. Every candidate change should answer one question: which existing, working step does this make faster, cheaper or more consistent? If the answer is that it replaces a process nobody complained about, it is reinventing the wheel and it will cost you the team's trust before it delivers anything.
4. Map the full brief-to-delivery workflow first
Before designing anything AI-related, document how work actually flows today: where briefs originate, how they're structured (or aren't), how projects get planned, produced, reviewed, and shipped, and across how many markets or formats. This map is the actual object you're redesigning, AI is a layer on top of it, not a replacement for having one.
5. Define what AI owns at each stage
For each stage of the workflow, assign ownership explicitly: AI generates first drafts and variations, structures and tags incoming briefs, scales one approved asset into multiple formats or markets, and analyses performance data for patterns. Humans retain strategic prioritisation, creative judgment, stakeholder relationships, and final approval. Write this as an explicit reference document the team can point to, not an implicit assumption.
6. Build the automation layer to move work between stages, not just generate content
A real operating model connects stages automatically: a brief submitted in one place structures itself, creates the relevant project and folders, and notifies the right people, without a person manually shepherding every handoff. This is often where the largest time savings live, more than in content generation itself.
7. Encode governance into the system, not into a static document
Brand tone, quality standards, and compliance requirements should be built into the tools people use daily, reviewed automatically, not manually checked against a PDF under deadline pressure. This is what allows the model to scale without quality eroding as volume increases.
8. Treat the model as something that evolves, not a one-time build
An AI operating model should improve as you learn what works, new bottlenecks emerge, new regulations appear, new tools become available. Build in a regular review cadence rather than treating the initial build as finished.
How to do it, step by step
- 01
Understand tools vs. an operating model
Recognise the difference between individuals using AI tools inconsistently and a shared, structured workflow the whole team runs.
- 02
Map the full brief-to-delivery workflow
Document how work actually flows today, across every stage and market, before designing anything AI-related.
- 03
Define what AI owns at each stage
Assign AI ownership of generation, structuring, scaling, and analysis; keep prioritisation, judgment, relationships, and sign-off human.
- 04
Build the automation layer between stages
Connect stages automatically so work moves without a person manually shepherding every handoff.
- 05
Encode governance into the system
Build brand tone, quality, and compliance standards into daily tools rather than a static document.
- 06
Treat the model as evolving
Review and adjust the operating model on a regular cadence as new bottlenecks, tools, and regulations emerge.
This is exactly the kind of system I've designed and built for a multi-market marketing organisation, brief to delivery, with AI built into the operating model itself, not bolted on top of it. See how I approach this in practice.
Common questions
- What is an AI operating model?
- An AI operating model is the defined system for how marketing work moves from brief to delivery with AI built into specific stages, including decision rights, automation between stages and governance encoded in the tools people use daily.
- How is an AI operating model different from an AI strategy?
- A strategy states intent. An operating model states mechanics: who briefs, what AI generates, who reviews, where files live, what is measured. Only the second one changes throughput.
- How long does it take to build an AI operating model?
- A first working version for one workflow typically takes weeks, not quarters. It is then extended workflow by workflow as each one is proven.
- Who owns the AI operating model in a marketing organisation?
- Marketing operations owns it, usually led by a marketing operations AI specialist working with creative, legal and technology stakeholders, because it spans workflow, data, governance and tooling at once.
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