Reimagining the Marketing Operating Model for an AI-Enabled World

Most marketers I speak to are experimenting with AI. Some have already seen early wins. But few feel confident their marketing team is ready to scale it.

The reason? Most teams are trying to plug AI into an operating model that wasn’t designed for it.

That comes at a cost — not just in efficiency, but in impact.

Without the right operating model, AI trials remain experiments.

With it, you get faster speed-to-market, more consistent execution, and smarter allocation of spend — all of which directly impact growth and margins.

It’s like bolting a new engine onto an old car. It might run. But it won’t win the race.

If marketing is going to keep delivering commercial impact in an AI-enabled world, we need to evolve how it’s done.

What is a Marketing Operating Model?

A marketing operating model is the blueprint for how marketing delivers impact. It defines how work gets done, how decisions are made, and how people, partners, and platforms come together to drive growth.

Done right, the marketing operating model becomes a strategic asset — enabling brand distinctiveness, accelerating experimentation, and linking marketing investment more tightly to commercial outcomes like revenue, share, and customer experience.

There are six core components:

1. Culture and Ways of Working: How marketing gets done

Culture is king. The most successful organisations in the world — from special forces units to Formula 1 teams, elite kitchens, and yes, even faith-based communities — operate on the strength of their culture. It’s what shapes how people behave under pressure, how they respond to change, and how they make decisions when the playbook doesn’t cover it.

In marketing, this means:

• Rapid learning and iteration

• Testing as a default, not an exception

• Cross-functional teaming and shared language

• Communities of practice (e.g. brand, content, media, analytics) that foster peer-to-peer growth

In an AI-enabled world, ways of working must evolve — from hand-offs to loops, from briefing to prompting, and from perfectionism to test-and-learn.

Here are a few behaviours that become more critical for AI-enabled marketing teams:

• Use AI by default: Treat AI tools as the first step in exploration, not a last resort

• Work in the open: Share prompts, results, and learnings with peers

• Close the loop: Give feedback into what worked, what didn’t, and why

• Model the mindset: Leaders ask curious questions, encourage small bets, and celebrate fast learnings — not just perfect outcomes

These behaviours don’t just “happen.” They’re supported by systems, incentives, and rituals — from hackathons to prompt libraries to AI learning KPIs. The goal is to make the right behaviours easier and more desirable.

2. Critical Decisions and Decision Architecture: Who decides what, how, and when

I've learnt through my work at at Bain & Co and seen it time and again since, that strong operating models are anchored in clarity about decisions. What are the highest-impact decisions in marketing? Who makes them? Based on what inputs? How often?

This includes big calls (like go-to-market strategy or brand repositioning), but also high-frequency, day-to-day decisions — like creative iteration, media spend shifts, or personalisation logic.

AI supercharges the latter. By reducing the cost of input generation (e.g. insights), recommendation (e.g. pattern recognition), and execution (e.g. automation), it unlocks faster, more consistent decision cycles.

3. Capabilities and Talent: What the team is great at

To take advantage of AI, marketing teams need more than tools — they need range.

• Human-centred designers who can translate strategy into creative

• Data-fluent marketers who can interrogate insights

• Strategic prompters who can get the best out of AI models

• Leaders who can integrate human and machine inputs into business decisions

These capabilities don’t need to exist in every person — but they must exist in the system.

I explore this in more detail in “Is My Marketing Team Ready for AI?”

4. Structure and Roles: How talent is organised

Once the capabilities are in place, structure determines how they’re deployed. Key questions include:

• Which roles are embedded in squads vs centralised?

• How are new AI-enabled roles integrated (e.g. prompt engineers, data translators, automation leads)?

• What span of control and coordination is needed to ensure consistency without killing speed?

This is where we define the marketing “org chart” — but also how it flexes across teams, regions, and partners.

5. Partners and Ecosystem: Who you work with and how

No marketing function is an island. From agencies to SaaS providers to AI startups, external partners are essential. A good operating model defines:

• Which partners own which outcomes

• How governance and incentives align

• How in-house teams build the muscle to lead, not follow

As AI shifts the make vs buy line, the partner ecosystem must adapt.

6. Data and Technology: The enablers of performance

Data and tech aren’t just enablers — they’re part of the operating model. That includes:

• Which tools are used, and by whom

• How data flows across the stack

• What gets activated where — and how reliably

In an AI context, this also includes data stewardship: ensuring that training data, performance metrics, and feedback loops are robust, inclusive, and strategic.

What’s Changing: 5 Shifts we see AI-Enabled Marketing Teams

Building on the model above, here are five common shifts we see in AI-enabled teams (including a visual summary to bring it to life).

How many of these shifts are already underway in your marketing team?

1. Culture: From passive alignment to proactive experimentation

In the best teams, AI isn’t just tolerated — it’s explored, tested, questioned, and shared.

• Prompt libraries become part of onboarding

• Teams run weekly “prompt jams” or “hack hours”

• Leaders ask, “What did we try?” not just “What did we deliver?”

The goal is a culture where AI isn’t feared or siloed — it’s fluid, visible, and normalised.

2. Decisions: From slow consensus to fast cycles

AI enables more decisions to move to the edge — closer to the work. That means:

• Clear decision rights

• Transparent criteria

• Guardrails, not gatekeepers

Where old models required lots of meetings, new models favour empowered teams with AI-powered prompts, benchmarks, and real-time feedback loops.

3. Skills: From fragmented learning to full-stack fluency

Not every marketer needs to prompt like a pro. But every team needs a mix of strategic thinkers, great editors, and technical partners who can:

• Brief AI tools effectively

• Validate AI outputs

• Spot when to automate vs escalate

That means shifting from role-based upskilling to team-level fluency.

4. Structure: From fixed roles to fluid squads

As capabilities evolve, roles become more dynamic. We see more:

• Loosely held job descriptions

• Agile pools of creative, data, and ops talent

• Co-located humans + machines solving problems together

The org chart matters less than the choreography.

5. Partnerships: From execution vendors to capability builders

Traditional partner models often relied on scale and specialism.

But in an AI-enabled world, great partners:

• Co-create and co-train

• Bring playbooks and infrastructure

• Help your team get better, not just get things done

This post builds on a broader conversation I started in “AI & Marketing: The Questions Leaders Can’t Afford to Ignore”

Let’s Talk. If you’re transforming how your marketing team works — or even just curious how others are doing it — I’d welcome the chance to connect. Let’s swap ideas, share experiences, or explore what an AI-enabled marketing operating model could look like for your team.