I’ve spent a lot of time lately thinking about a simple question:
What does a marketing organization look like when AI is no longer just another tool in the stack?
Not when marketers use AI to write a few posts faster.
Not when someone adds a chatbot to an existing workflow.
But when you actually redesign the way the work gets done around what AI now makes possible.
A recent opportunity gave me a reason to stop thinking about that question and build something around it.
So I created an AI GTM Engine.
The original idea was straightforward. Give the system a company, an ideal customer, a business objective, and some strategic context. Then see whether AI could help one experienced marketing operator coordinate work that traditionally sits across multiple functions.
What I learned reinforced something I’ve increasingly believed:
The biggest opportunity for AI in marketing is not content generation. It is operating model redesign.
The problem with most AI marketing workflows
A lot of AI adoption still follows the same pattern.
Take an existing task. Add AI. Make the task faster.
Write the blog post faster. Create more social copy. Summarize the report. Generate an email. Produce campaign ideas.
There is nothing wrong with any of that. Efficiency matters.
But it leaves the underlying organization mostly unchanged.
Strategy still lives in one place. Content lives somewhere else. Executive communications, employee advocacy, social, lifecycle, outbound, sales enablement, and measurement often operate as separate workflows.
AI may accelerate each individual activity, but the system itself remains fragmented.
That feels like a missed opportunity.
What if strategy became the intelligence layer?
That was the idea behind the AI GTM Engine.
Instead of starting by asking AI to create deliverables, the system starts by diagnosing the business problem.
What is the company actually trying to solve?
What pressure is the customer experiencing?
Where is the positioning opportunity?
What should the company uniquely own in the market?
Only after establishing that strategic foundation does the system move into execution.
From one business objective, the engine develops an integrated approach across market strategy and positioning, core narrative, executive thought leadership, employee advocacy, brand and social, outbound and lifecycle marketing, and measurement.
The important part is not that AI can generate each of those things.
We already know it can.
The important part is that they can share the same strategic intelligence instead of being developed independently.
One narrative. Multiple voices.
This becomes especially interesting when you think about how companies communicate.
Historically, a brand message might move through a long chain.
Strategy creates the narrative. Communications interprets it for executives. Social adapts it for channels. Employee advocacy turns it into something employees can share. Sales creates its own version. Lifecycle marketing creates another.
By the time everything reaches the market, the organization may technically be talking about the same thing, but it often does not sound like it.
An AI-native operating model gives us another option.
Start with one strategic idea and allow specialized workflows to interpret it differently for different audiences.
The brand should sound like the brand.
The CEO should sound like a CEO with an actual point of view.
An employee should sound like a person with firsthand expertise.
A salesperson should sound like someone trying to help solve a customer problem.
Consistency does not have to mean sameness.
That is a much more interesting use of AI than generating five versions of the same LinkedIn post.
Employee expertise becomes even more valuable
Building the engine reinforced another idea I’ve been exploring around AI-driven discovery.
As AI systems increasingly answer questions directly, companies may need to think differently about where authority lives online.
Corporate websites and brand channels still matter.
But so do the executives, employees, subject-matter experts, and practitioners who demonstrate what the organization actually knows.
This has significant implications for AEO and GEO, or Answer Engine Optimization and Generative Engine Optimization.
As buyers increasingly ask AI systems for recommendations, explanations, comparisons, and expertise, companies need more than optimized corporate pages. They need a broader footprint of credible, firsthand knowledge associated with the people inside the organization.
When executives, employees, and subject-matter experts consistently publish useful perspectives around the problems they actually understand, they create a deeper body of authoritative content that can strengthen how both people and AI systems understand what the company knows and where its expertise belongs.
An AI-native employee advocacy system can help identify those knowledge areas, match the right experts to the right conversations, and scale their participation without turning everyone into another corporate distribution channel.
In that sense, employee advocacy can become part of a company’s AEO and GEO strategy, not by manufacturing more content, but by making its real expertise more visible, attributable, and discoverable.
That means employee advocacy has the potential to become something much larger than a distribution program.
It can become part of an organization’s authority infrastructure.
AI can help identify relevant ideas, reduce workflow friction, personalize starting points, and connect expertise to the right moments.
But the value still comes from human knowledge, judgment, experience, and perspective.
The technology creates leverage.
The people create trust.
The marketer’s role changes too
There is another implication here that I think matters for marketing leaders.
For years, scaling a marketing function often meant adding specialists.
More channels created more teams. More complexity created more coordination. More work created more headcount.
AI changes some of those assumptions.
A strong operator with the right systems can now move across disciplines in ways that were difficult even a few years ago.
That does not mean expertise disappears.
It means the leverage of expertise increases.
The marketer of the future may spend less time moving information between disconnected teams and more time designing the intelligence, workflows, standards, and systems that allow the organization to move coherently.
That is a very different job.
And personally, it is the part of AI transformation I find most interesting.
Building is becoming part of the job
There was another lesson in this project that had less to do with the application itself.
I built the AI GTM Engine using Codex and the OpenAI API, connected the strategy layer to structured outputs, created the interface, and deployed the working application online.
I am not a software engineer.
That is precisely why the experience mattered.
The barrier between having an idea and prototyping the system behind it is collapsing.
Marketers can increasingly build their own tools.
Operators can prototype workflows.
Strategists can turn operating models into functioning systems.
Leaders can test an idea before asking an entire organization to invest in it.
That changes what it means to be hands-on.
The future of marketing leadership will not just belong to people who understand AI conceptually.
It will increasingly belong to people willing to build with it.
The larger takeaway
The AI GTM Engine is still a prototype.
But the idea behind it is much bigger than the application.
AI should not simply make the old marketing organization faster.
It should make us reconsider why the organization works the way it does in the first place.
Where does strategy live? How is knowledge shared? How many handoffs are actually necessary? Which activities require human judgment? Which can be automated? How can one insight compound across the entire organization?
And how do we build systems that increase speed without sacrificing expertise, creativity, authenticity, or trust?
Those are the questions I think marketing leaders should be asking now.
Because the real opportunity is not to bolt AI onto the existing operating model.
It is to build the next one.
Try the AI GTM Engine
I built a working version of the system described above. You can explore it here: