How to Rebuild Your Marketing Team for the Agentic Age. Full Breakdown.
Turn a traditional marketing org into an intelligence system that gets smarter every day.
Most marketing leaders are trying to fit AI into their existing org. New AI assistants, tools, and agents, same old team boxes. It means you’re bolting AI onto a structure that was designed to do one thing: coordinate people.
That’s a mistake.
If you integrate AI into how a team works, you’ll get minimal results. I believe you have to rethink how the team works, how it’s structured, and what its core skills are. The org chart stops being a tree of functions. It becomes a system that produces output. And a system needs a different shape than a hierarchy.
How does the system work?
Most people assume this model is about replacing marketers with agents. It isn’t.
AI should remove the coordination tax, the status meetings, the handoffs, the “circling back”, and concentrate humans at the points where they actually add value. The humans don’t empty out. Their work is elevated. Fewer people coordinating other people. More marketers are directing systems, agents, and practicing their craft.
There are four layers. The important detail is the arrow on the left of the diagram: every layer reads from the bottom one, and every layer writes its learnings back into it. It’s a loop, not a ladder.
Let me take the layers one at a time and tell you how they work. This system isn’t perfect. It’s a first draft. Much of it is doable today.
Layer 1: Context — the foundation everything runs on
This is the biggest unlock available to you right now, and it’s the one almost nobody has built.
Pre-AI, your intelligence lived in your people’s heads. What the market actually wants. How a campaign really gets shipped here. Which plays work and which quietly died. When someone left, it left with them. Context is a system that captures that intelligence, market knowledge, how the team operates, what’s working, and what isn’t, and makes it readable by every human and every agent above it. It updates automatically from the work you do.
Everything else reads from this layer and writes back to it. Get it right, and every layer above gets smarter. Get it wrong, and you’ve automated your own confusion. That’s the gotcha with this model. Marketing teams aren’t used to documenting these artifacts. It’s why remote teams have a unique advantage when it comes to AI. Everything they do has an artifact.
Some notes:
This is real work. This isn’t a Notion tidy-up. It’s real data engineering, CRM, analytics, support tickets, Slack, docs, call transcripts, brand guidelines, stitched into something queryable. Most orgs see “shared knowledge base” and budget for a wiki. The honest framing is: this is infrastructure, and infrastructure has a cost. For me, it’s an investment worth making.
Someone has to own it, or it goes stale like the wiki it replaced. The answer can’t be “the team.” It has to be a named owner, most likely AI Ops or RevOps, with senior strategists curating what’s true. Layer 1 is a new discipline/investment for marketing.
It has to be deterministic, not vibes. If the same question returns a different answer on Tuesday than it did on Monday, you don’t have an intelligence layer; you have a slot machine. That means declared definitions, not just stored learnings. “Campaign” means five different things across most companies. The substrate has to fix the vocabulary before it stores the knowledge.
Don’t ingest everything. Start with your 3–5 best artifacts. A lean, trusted core beats a giant pile of noise. Someone has to decide what’s a trusted input and what’s just chatter; the system slowly poisons itself with its own bad outputs. Layer 1 needs someone with taste who understands what should make it into the layer and what should be routed away.
The full layer for larger companies includes more than just marketing.
Customer knowledge actually lives in sales and CS. Product owns the roadmap. Finance owns the unit economics. If Context is marketing-only, it’s missing most of the company’s intelligence on day one. The refined version: Context is an enterprise asset that marketing reads from and contributes to, not a thing marketing builds in a corner.
Layer 2: Execution — agents at volume, humans at the bar
Execution is where the work gets made, and it’s a mix of agents running in parallel and a number of genuine craftspeople.
Why “craftspeople” and not “marketers”? Because good enough won’t cut it when everyone has the same models. AI can draft most of a thing. The gap between forgettable and unmissable is taste, judgment, and a real understanding of the customer, and that gap is widening, not closing.
Some notes:
Agents enable humans to do elevated work. Humans don’t bolt on the final 20% like a last screw. They frame the work before a single agent runs, the brief, the angle, the bar, and they exercise discernment all the way through. The split isn’t a sequence. It’s “humans own the brief and the standard, agents do the volume in the middle.” Put the human judgment at the start and the end, not just the end.
Some of this work doesn’t compress at all. A whole category of marketing isn’t an output an agent can draft; it’s a relationship. Distribution through events, partnerships, influencers, community, and the founder’s brand. Physical touchpoints, activations, retail, and the room, that were never digitally instrumented and so never enter the feedback loop. In people-heavy marketing, human discernment is the work. The model has to leave that work alone, not pretend that an agent will absorb it.
Layer 2 doesn’t mean agents replace humans. It means that, where possible, agents augment humans, allowing them to elevate their work. However, humans are determining the prompts, setting the guardrails, and enriching the work.
Layer 3: Orchestration — the role I think matters most
Your org isn’t a tree anymore. It’s a living system producing output. Someone has to run that system: make routing decisions, monitor what the agents are producing, make sure they’re reading the right context, and feed the results back into Layer 1.
This is the newest role and, I think, the most important one to get right.
It’s probably not one role. It could be two. There’s an Orchestrator who designs the system and decides what flows where, which decisions stay human, which get agent-drafted for human approval, and which run fully autonomously. And there’s an Operator who lives in the system day-to-day, QAs the agents, and improves them. Design versus run. Most teams will need both, even if one person wears both hats early on.
Where the real decisions live. The hard part of orchestration isn’t routing tasks; it’s the delegation logic. What’s allowed to run on its own? What gets escalated? What never leaves a human’s hands? That’s a deliberate design choice, not a default, and it belongs in this layer.
Who actually does this? From what I’m seeing, it’s one of three people: a RevOps lead, a GTM product manager who came up through MarketingOps, or an AI generalist.
Layer 4: Leadership — direction, not coordination
The new leadership job is to set direction for a system, refine it, and get output from it. You stop coordinating people and start steering an engine. You decide what the system should optimize toward, make the judgment calls it can’t, and feed strategy back into Context so the whole thing inherits your point of view.
Some notes:
Is the current CMO even equipped to lead this? One stat speaks to this: only around 15% of CEOs believe their CMO is AI-savvy. Most marketing leaders were selected for their ability to coordinate people and manage the org chart effectively. This model asks them to direct systems instead. That’s a genuinely different skill, and I won’t pretend it’s an easy switch. For some, it’s learnable. For some, it’s a hiring reset. If the leader can’t even see that the rebuild is needed, the rebuild happens to the team, not with it.
Why is leadership the human-only layer? Not because leaders shouldn’t use agents, they absolutely should, including for strategy and brand work. It’s human-only because someone has to own the taste the system optimizes toward and carry the judgment calls when the model is confidently wrong. You can automate the work. You can’t automate accountability for it.
Push back against this model
I heard the following two, which I felt were fair, and wanted to answer:
“Is this actually new — or is it just a good marketing process with better tools?”
Fair, but. A lot of this is what great marketing has always demanded: clear thinking, real craft, tight feedback loops. What’s genuinely new is the substrate underneath it. A shared intelligence layer that survives turnover was simply not possible before. The roles change as the unit of work shifts from “a person does a task” to “a person directs a system that does the tasks,” and, in turn, those roles make the system smarter. Today, the most intelligent marketer doesn’t make the entire team smarter just by working; in this system, they do.
“Where do entry-level marketers learn the craft if the middle concentrates?”
The fear is, if you remove the rungs people used to climb, the *entry* work where judgment is quietly built, you risk a generation that can operate agents but can’t tell good from great. My working view: the entry path shifts from “do the task” to “operate the system and build the context,” and IC fluency with AI has to come before you redesign the org, not after. But anyone who tells you this is solved is guessing. It’s one of the hard AI problems to solve.
Until Next Time,
Happy AI’fying
Kieran
AI won’t transform teams unless you redesign how the work flows.
Kieran — this landed hard because I'm living it, not theorizing about it.
I'm the VP of Marketing at a B2B technology company (managed IT, telecom, security) going through this exact rebuild right now. Your four-layer model gave me the vocabulary for what I've been building over the past six months without having a clean framework to describe it.
Here's where we are:
Layer 1 (Context): We built a persistent knowledge layer that captures institutional intelligence across AI sessions — market context, customer knowledge, operational decisions, what we've tested and what failed. Every AI agent we build reads from it. Every session writes back to it. When I saw you write "pre-AI, your intelligence lived in your people's heads — when someone left, it left with them," I felt that. We lost a team member in Q1 and because the context layer existed, the knowledge didn't walk out the door. That would not have been true 12 months ago.
Layer 2 (Execution): We just posted the role you're describing — but we call it a Marketing Manager externally and operate it internally as a Marketing Operator. Execution-first, AI-enabled, individual contributor. Not a strategist, not a team builder. We built an AVA behavioral assessment benchmark specifically calibrated for this profile: high assertiveness (the engine), low sociability (solo operator), situational conformity (follows direction). The hiring toolkit includes an 8-question interview rubric designed to filter out strategists and attract operators. Your line about "the gap between forgettable and unmissable is taste, judgment, and a real understanding of the customer" is exactly why we're hiring a human here instead of going fully agentic. Agents do the volume. The human sets the brief, holds the standard, and serves as the quality bar.
Layer 3 (Orchestration): This is where I sit. I'm transitioning from VP of Marketing to what we're calling Chief AI Architect — designing the system, deciding what flows where, which decisions stay human, which get agent-drafted, and which run autonomously. We've built AI agents for content (SEO + AEO with schema code), with social media and an agentic SDR in development. All automation runs on n8n, applications built with Claude Code. The Marketing Manager becomes the day-to-day Operator in your framework — QA'ing agents, improving workflows, running the system I design.
Layer 4 (Leadership): Our president pushed back on the initial restructuring plan because marketing wasn't generating ROI. Fair. But the conversation shifted when I brought data: Q1 lead attribution, channel test results ($33K across four programs, all eliminated or inconclusive), and two restructuring options with honest tradeoffs. He approved the direction. His role now is exactly what you describe — setting what the system optimizes toward and making the judgment calls it can't.
The part of your article that hit hardest: "Only around 15% of CEOs believe their CMO is AI-savvy." I've spent the last year proving to my president that I'm in that 15% — not by talking about AI, but by building agents that work, pulling data that tells the truth, and proposing structures that save six figures annually while increasing output. The rebuild earned trust through results, not presentations.
One challenge you didn't fully address: Layer 1 ownership during leadership transition. When the person who built the context layer moves into an orchestration role across the whole company, who owns the marketing-specific context? The new operator doesn't have the architectural thinking to maintain it. The orchestrator is now spread across five departments. We're solving this by capturing everything during a defined transition window, but it's a real gap in the model.
Appreciate the framework. It's rare to see someone describe what's actually happening in the trenches instead of what might happen theoretically.
— Keven Ellison, VP of Marketing → Chief AI Architect, AIS (Las Vegas)