The Operator Function: The Role That Makes AI Marketing Work

The Operator Function is the role that designs how AI capabilities fit together. It sits between the tools a company buys and the outcomes it wants, and what it produces is architecture rather than campaigns. An operator decides which atomic capabilities connect to which, in what order, with what data passing between them, and what the resulting system does without a person present. This is a strategic role, though doing it well takes technical fluency. The distinction that matters is scope. Everyone else on a marketing team owns a piece of the work. The operator owns how the pieces behave as one system.

Think of Scott Brinker’s 2025 martech landscape with its 15,000+ solutions as components. Each tool does something useful in isolation. But without a unifying architecture:

• Data doesn’t flow between systems
• Insights don’t inform decisions
• Optimizations don’t compound
• Strategy remains disconnected from execution

This is why the Pile of Parts Problem is the defining failure mode of AI marketing. The Operator Function is the solution.

The core insight: Having AI tools is like having engine parts. Parts don’t make an engine. You need architecture and someone to design and run it. That’s the Operator.

Most teams do not have an Operator because the role was never created. Marketing org charts fill with people who administer tools and people who ship campaigns. Neither job description includes asking how the pieces should fit together. So the question goes unasked, and the consequence shows up in the adoption data. McKinsey's 2025 State of AI report puts AI adoption at 88% of marketing organizations. Only 6% qualify as AI High Performers seeing attributable business impact. Nearly every team has bought in. Almost none can show a business result. That distance is what an unstaffed function looks like from the outside. Closing it starts with naming what existing titles quietly leave out.

Most marketing teams have tool administrators but no one asking the architecture questions. According to McKinsey’s 2025 State of AI report, 88% of marketing organizations have adopted AI, but only 6% are “AI High Performers” seeing attributable business impact.

The gap isn’t tools. It’s not talent. It’s the missing Operator layer.

Gartner’s 2025 Marketing Technology Survey found martech utilization at 49%. Half of what companies pay for goes unused. The Operator Function exists to fix this.

These are two different jobs and neither replaces the other. Marketing Operations owns the connections that already exist: making tools talk, keeping data moving, repairing what breaks. The Operator Function owns what should exist at all. That is a design question rather than a maintenance question, and it gets answered before the integrations are built rather than after. A company can have excellent marketing operations and no operator. It looks like a well-maintained stack that never compounds, where everything works and nothing improves on its own. The failure in the other direction is faster and more obvious, since a system nobody maintains stops running whatever its architecture.

Marketing Ops is necessary but not sufficient. You need both the plumbing and the architecture. The Operator designs the architecture; Marketing Ops maintains the plumbing.

The job is definable, which is what separates it from general talk about strategic leadership. An operator is accountable for four things: how capabilities connect, what runs unattended, how data moves without manual handoffs, and how much of the work the system takes over next. Each exists to prevent a specific failure that marketing teams hit repeatedly. That framing is deliberate. A responsibility defined by the failure it prevents can be checked, because the failure either appears or it does not, and a role nobody can check becomes a title. Three of the four are design work. The fourth is a direction of travel.

The Operator Function has four core responsibilities. Each maps to a specific failure mode it prevents. According to Accenture’s AI maturity research, architecture separates leaders from laggards.

The Operator’s job is to move the organization up the autonomy ladder. According to SAE International’s autonomy framework (adapted from autonomous vehicles), there are distinct levels of human involvement. University of Washington researchers recently adapted this thinking for AI agents, proposing roles from operator to observer.

I built on both frameworks to create the L1 to L5 Autonomy Model for marketing. The Operator’s job is to move workflows from L1 (humans do everything) toward L3 (AI executes, humans approve) and beyond.

The bottleneck is people, not technology. This role needs depth in two areas that rarely meet in one person: knowing which marketing outcomes are worth pursuing, and knowing how to build systems that pursue them. Broad familiarity with both is not enough, because the decisions an operator makes sit exactly where the two intersect and neither side can be delegated. The scarcity is structural. Those skills are learned in different places and rewarded on different career tracks, so the market produces very few people carrying both. Context engineering has since joined them as a third requirement, and it is the one most teams have never staffed.

The Operator needs to be a Pi-Shaped Marketer: someone with two deep vertical skills connected by broad knowledge. The emergence of context engineering as a discipline has added a third critical competency.

Context engineering is the discipline of curating all information an AI system sees before generating output. For the Operator, this means structuring brand guidelines, ICP definitions, campaign history, and validation rules as context that shapes every AI interaction. Most AI tools fail not because the model is weak, but because the context is missing.

The T-shaped generalist (broad knowledge, one deep specialty) is no longer sufficient. As Harvard Business Review noted, “AI won’t replace humans, but humans with AI will replace humans without AI.” The Operator is the human with AI.

The talent gap: According to Forrester’s 2025 B2B research, 94% of B2B buyers use genAI to inform decisions, but only 19% of organizations have AI live in production. The gap is Pi-Shaped talent who can bridge strategy and technology.

There are three ways to acquire this capability and only one wrong answer, which is assuming it will emerge on its own. You can build it from people you already have, hire it, or buy the design and keep the running of it in-house. Cost, speed and durability trade off differently across the three, and the choice depends less on budget than on whether anyone internally is already close to the role. Whichever route you take, the capability has to end up inside the company. An architecture nobody internally understands is a dependency rather than an asset, and it decays the moment the arrangement that produced it ends.

As Braze’s martech research shows, the organizations winning with AI aren’t those with the most tools. They’re the ones with the architecture to connect them. BCG’s 2025 AI research confirms only 5% of companies are “future-built” and generating substantial AI value at scale. The difference is architecture. The Operator designs that architecture.

Pro tip: Start by auditing your current stack. For each tool, ask: “What does this connect to? Who designed that connection? Who optimizes it?” If the answer is “no one,” you’ve found where the Operator Function needs to focus first.

For the complete framework, see the AI Marketing Framework. For the problem the Operator solves, see The Pile of Parts Problem.