There is a version of the AI conversation I do not buy.
It is the one where we talk about an “AI marketing department” as if the goal is to replace the people who make marketing matter in the first place.
I do not want a machine deciding what my brand stands for. I do not want an agent making the final call on creative that needs taste. I do not want a model owning the customer relationship, choosing where the budget goes, or pretending it understands the human truth behind a campaign.
What I do want is for every talented person on my team to stop doing work that keeps them from being great at their actual job.
I want my writer to have a research team. A subject-matter interviewer. A content librarian. A first-draft editor. An SEO and AEO translator. A fact-checker. A brand-voice sparring partner.
I want my digital strategist to have a performance analyst, an audience researcher, a testing assistant, a CRM detective, and someone constantly looking for the signal buried inside a thousand rows of campaign data.
I want creative to have a visual research assistant, a versioning team, a production coordinator, and a system that can adapt a great idea across formats without asking the creative lead to spend Friday afternoon resizing the same asset 19 times.
That is what I mean when I talk about an orchestrator model.
Not a replacement for the marketing department.
An expansion of it.
The human stays at the center. The orchestrator gives that human a team around them—one that can research, prepare, analyze, challenge, organize, and execute the repeatable work at a speed no traditional team can match.
That is a much more exciting future than “AI can write a blog post.”
Most people have experienced AI as a one-on-one interaction. You open a chat window. You ask for something. It gives you an answer. Maybe it saves you 20 minutes. Maybe it gives you a decent starting point.
That is useful. But it is not yet transformational.
The bigger opportunity is to turn AI from a tool an employee visits into a support system that works around the employee’s role.
An orchestrator sits in the middle of that system. It understands the goal, knows the context, routes work to the right specialist, brings the output back together, and makes sure nothing moves forward without the right human seeing it.
That matters because marketing is not one job. It is a hundred connected jobs: understanding the audience, finding the insight, building the message, creating the asset, adapting it by channel, setting up the campaign, measuring the response, learning from the result, and doing it better next time.
Right now, a lot of that work gets lost in handoffs. The writer does not have the full campaign context. The media person gets the creative too late. The sales team hears a different story than the market. The insights arrive after everyone has moved on.
An orchestrator model is how you give every person better support without turning the organization into a larger, slower org chart.
McKinsey’s recent marketing research describes the emerging model as a hybrid human-agent workforce: humans design and oversee networks of agents that handle more of the execution. Its estimate is that agentic AI could eventually support as much as two-thirds of current marketing activity—but the value comes from redesigning workflows, not simply installing more tools.[1]
That distinction is everything.
Good writers are rarely slowed down by the act of writing.
They are slowed down by everything around it.
They have to hunt down the latest product language. They have to find the customer proof. They have to dig through old decks to understand what has already been said. They have to translate one core idea into a blog, an email, a landing page, a social post, a sales one-pager, and a dozen versions for different audiences.
By the time they get to the part that needs actual judgment—the angle, the story, the rhythm, the emotional truth—they are already tired.
In an orchestrator model, the writer has a team that prepares the room before they walk in.
| The writer’s AI support team | What it handles | What the human writer still owns |
|---|---|---|
| Research scout | Source gathering, audience questions, category language, competitive examples | Deciding what is interesting and what is actually true |
| Context librarian | Approved claims, prior campaigns, customer stories, brand vocabulary | Choosing the story worth telling now |
| Structure partner | Outlines, content angles, channel adaptations, first-pass organization | Voice, pacing, point of view, and the final argument |
| Search translator | SEO, AEO, GEO considerations, metadata, question-based structure | Making the work useful to people before making it useful to systems |
| Editorial critic | Repetition checks, unsupported-claim flags, clarity questions, brand-voice review | Accepting, rejecting, or reshaping the feedback |
The writer is not diminished in that model. The writer is finally able to spend more of the day writing the things only a writer can write.
The system can bring ten customer questions to the table. It cannot decide which one contains the human tension worth building a piece around.
It can produce 20 headline options. It cannot feel the difference between a headline that is technically correct and one that makes someone stop because it says something real.
That is the elevation I care about.
The same thing is true on the performance side.
A great digital strategist should not spend their best hours pulling platform exports, reconciling naming conventions, checking whether a landing page UTM was broken, or manually sorting comments to find one useful audience signal.
Their job is to make decisions: where to place the next dollar, which audience deserves more attention, what the message is telling us, when a campaign needs patience, and when it needs a change.
So their team might look like this:
| The strategist’s AI support team | What it handles | What the human strategist still owns |
|---|---|---|
| Performance analyst | Pulls and normalizes channel data, identifies movement and anomalies | Deciding what the movement means and what to do next |
| Audience signal agent | Surfaces CRM patterns, search questions, sales-call themes, and engagement behavior | Understanding the market and choosing the audience strategy |
| Experiment assistant | Documents hypotheses, sets test-readiness checks, summarizes results | Determining which questions are worth testing and whether the evidence is strong enough |
| Lifecycle observer | Identifies gaps between acquisition, conversion, retention, and reactivation | Designing the customer journey and the relationship the brand wants to build |
| Budget scenario partner | Models options and tradeoffs based on agreed inputs | Making the commercial call and standing behind it |
The agent can tell you that conversion rate fell 18%. The strategist has to decide whether that is a creative problem, an audience problem, an offer problem, a landing-page problem, or a signal that the market is changing.
That is not administrative work. That is the work.
And it is exactly the kind of work we should be creating more room for.
Creative teams have probably felt the contradiction of AI more than anyone else.
On one hand, the tools can generate visual directions, variations, formats, mockups, and production-ready pieces faster than we have ever seen. On the other hand, it is easy to look at a feed full of AI-generated content and realize that more output is not the same thing as more originality.
That is why I do not think the goal is to let an agent “be creative.”
The goal is to take the mechanical weight off the creative process so people can protect the part that makes creative work valuable: the point of view.
A creative lead’s AI support team can handle visual reconnaissance, production variations, aspect-ratio adaptation, asset tagging, accessibility checks, and the unglamorous task of getting every approved concept to every channel in the format it needs.
The creative lead still decides the idea. The visual world. The tension. The thing worth saying. The level of craft. The moment when a direction is surprising enough, specific enough, or human enough to make it into the world.
I do not want AI to make creative people less necessary. I want it to give them enough capacity to make their best work more often.
That is a very different ambition from using AI to make a larger pile of assets.
Here is the piece that makes this work: none of those individual support teams should operate in a vacuum.
Your writer’s research should inform the strategist’s audience assumptions. Your strategist’s performance signal should influence what creative tests next. Your creative team’s approved visual language should carry through to the landing page, the email, the paid ad, and the sales follow-up.
The orchestrator holds that shared context.
It knows the business goal, the campaign brief, the audience, the approved claims, the channel plan, the brand standards, and the feedback from the last cycle. It makes sure the right specialist gets the right slice of the work—and it brings the learning back to the people who need it.
You can think of it as a very good chief of staff for every marketer on the team.
It does not replace the team leader. It makes the team leader more effective.
This is also why I would not start with one giant “marketing agent.” Marketing is too broad, too contextual, and too consequential for that. I would start with small, clear support roles connected by a shared orchestrator.
Anthropic’s guidance on agentic systems makes the same point in technical language: use predictable workflows for defined work, use agents when a problem genuinely requires flexibility, and add complexity only when it improves the outcome.[2]
That is good advice for marketers too.
The better this system gets, the more obvious it becomes which work should belong to people.
| What the human owns | Why it matters more in an AI-enabled department |
|---|---|
| The point of view | A brand cannot outsource conviction. Someone has to decide what it believes, what it will not say, and what it wants to be known for. |
| The strategic choice | Agents can surface options; people have to choose the tradeoff and accept the consequence. |
| Taste | The difference between acceptable and exceptional is often not a rule. It is a human standard. |
| Empathy and relationships | Customers, sales partners, executives, and collaborators need to feel understood—not processed. |
| The exception | Real marketing is full of moments that do not fit the playbook. That is where experienced judgment earns its value. |
| Accountability | Someone still has to say, “This is the decision. I own it.” |
This is not just a philosophical point. It is an operating one.
McKinsey’s 2025 global survey found that organizations seeing the most value from AI are much more likely to redesign workflows and establish clear processes for when model outputs need human validation.[3] MIT Sloan similarly notes that in agentic implementations, the hard work is frequently data engineering, stakeholder alignment, governance, and workflow integration—not simply prompting a model.[4]
That tells me the job is not to make people get out of the way.
The job is to design a system that lets the right people show up at the right moments with better information, more options, and more energy for the decisions that matter.
I would start with one person and one workflow.
Not because the ambition is small. Because trust is built through useful work.
Start with the writer. Build their support team around a recurring content workflow. Give it approved brand context, real customer evidence, a clear brief format, and a human editorial checkpoint. Watch where it creates time. Watch where it creates friction. Improve the handoffs.
Then do the same for the digital strategist. Then creative. Then lifecycle. Let each person help design the team around their actual role instead of handing them a generic AI tool and calling it transformation.
The sequence matters:
When I built my own Claude-based marketing system, the real win was not simply creating more. It was cutting production time by about 60% and moving work that used to take weeks into days because the context, workflows, and review logic were already in place.
That did not make the people involved less valuable.
It made their judgment travel farther.
I do not think the best marketing departments of the next few years will be the smallest ones.
I think they will be the teams where each person can operate at a level that previously required a much larger support structure around them.
The writer with a research desk, editorial partner, and distribution brain.
The strategist with a full-time analyst, audience researcher, testing assistant, and lifecycle observer.
The creative lead with production capacity that does not steal time from the idea.
The marketing leader with a clear view of what is happening across the system—and more time to coach people, improve the strategy, and build the relationships that actually move the business.
That is not a department without humans.
It is a department where humans finally get to do more of the work that makes them irreplaceable.
Mike Bloomstine is a B2B marketing strategist specializing in IT services and AI-powered marketing systems. He writes about marketing strategy, brand building, and the intersection of technology and go-to-market execution at mikebloomstine.com.
I help B2B and B2C teams build marketing engines that compound — strategy first, systems underneath.
