What a fully AI-first GTM motion actually looks like, and how you can start building yours now.
For most of my career, growing a Go To Market (GTM) function meant growing a capable team. More channels meant more tools and specialists. More markets meant more headcount. Managing the org to keep it aligned and moving was a big part of what the job required.
I was good at it. Building teams, developing people, understanding multiple GTM platforms, stitching together the function, orchestrating hand offs and execution. That was the craft I'd built and the identity that came with it. When I decided to leave and build AI-First go-to-market from scratch, at a highly intelligent startup building the platform for exactly that, I wasn't aware of how much an AI-First mindset would change the GTM operational framework.
What this model makes possible goes well beyond what I thought I was signing up for.
How My Day Changed
My days used to start with reviews. Each team or individual. Each tool. Each set of metrics and outcomes. Pulling everything together into a picture I could actually act on took most of the morning. Adjustments, alignments, and team communication were major activities. Strategy and innovation got discussed as an add on to operational review.
Now, my Super Agent updates me right away on all go to market activities. I open a conversation with my Super Agent to follow up on any insights or recommendations that intrigue me. It's connected to everything that matters like GA4, our ad platforms, the product's user data, the competitive landscape, the community. In minutes I can see what's happening across the full customer journey, not just the slice of it any one tool or team used to own.
I can immediately engage in optimization or strategic thinking. I can immediately debate new ideas, simulate outcomes, approve new copy and execute with my agents. It's not just that work scales. The breadth of what we do scales too. The Super Agent is my business partner. My agents become my execution momentum. This is still a very busy day, with important stakes and thoughtful review. But for someone who is always anxious to see new ideas executed, I feel a high sense of agency and satisfaction that I can improve outcomes.
What My Work Is
I set objectives. I evaluate whether the system is producing coherent outcomes against them. I make the judgment calls the agents can't make, like what the objective should be, how to resolve tensions between competing priorities, how to handle the situation we haven't seen before.
I translate my experience like what I know about buyers, about channels, about what the data tends to understate, into how I shape the agents and what I ask them to optimize toward. The instincts I spent years developing didn't disappear in this model. They found a different application: not in managing people through execution, but in designing the system that executes.
I spend a big part of my day with the Super Agent now. Together we analyze, design, and execute. From that activity I spawn a growing workspace of agents that execute real work, 24/7. I scale like I have never scaled before.
Some of what I gave up like the team, the clear ownership, the professional identity that came with running a function, was compensated with more range, more speed, and the ability to try things I'd never have had bandwidth for before. The strengths I wasn't sure would transfer did. And new skills that prepare me for the emerging AI-Native ecosystem are being honed daily.
My Agent Team
When a conversation with the Super Agent produces something solid, like a competitive analysis, a campaign brief, a content approach that performs, we don't use it once and move on. We turn it into an agent: structured, repeatable, ready to run on a schedule or triggered when we need it. Chat, create, run. That is our simplified journey. The Super Agent output is sometimes the end. More often it's the beginning of something that keeps delivering.
What's accumulated around that rhythm is what I think of as my actual team: a growing host of agents covering different parts of the GTM motion, a shared library of skills they draw from such as our brand voice, our competitive framework, our content standards, our data methods, and a system memory that gets richer with every cycle. The agents don't start from scratch each time. They carry the context of what we've learned. That's what makes the system compound rather than just repeat.
Monitoring agents running on a schedule and watching competitors, campaign performance, site behavior, product usage, community signals. Every morning something has already surfaced that I didn't have to go looking for. My competitive intelligence updates. My SWOT evolves. The picture stays current without me maintaining it.
Suggestion agents keep nothing static. They generate new headline variants, image concepts, content angles, and campaign ideas from what the monitoring agents are actually seeing. The question isn't "what should we try?" It's "which of these is worth running now?"
Execution agents I trigger when I'm ready to act. They create and execute landing page copy, keyword updates, email builds, images, videos, social posts. I review, I publish. I am the director of a comprehensive set of motions, from decision to deployment.
From Agents to an Agent System
The goal is not simply a giant library of agents. What I'm building toward now is the layer above: agents that coordinate across functions together, run a full campaign as a coherent motion, and carry the context of the complete customer journey in every decision.
Agents that coordinate across other agents carrying the shared objective. Skills and agents that verify and protect brand voice and visual design. Agents that judge creation, execution, and evaluate results.
This is where AI-First moves from point execution to a full GTM system. That's where the model gets genuinely different from anything I ran before. I'm not there yet. I'll need help getting there.
What Does Hiring Look Like for AI-First?
I haven't hired the next practitioner yet. As I watch the scope of the machine grow, I know we will. Expectations demand it. Working out what that looks like is something we're actively in the middle of. But when you don't hire based on a functional role, the search looks different.
What I know so far: it's not an org chart. It's closer to a group of strategists working on a shared problem, with agents as the tools we use to act on it. No lanes to protect. No handoffs to manage. My colleague and I work through things together, not in a reporting structure, but in a collaboration. We challenge each other's reasoning. We review each other's agent architectures the way we used to review campaign briefs.
The skill profile that matters in this environment is different from what GTM hiring has traditionally looked for. Systems thinking and the ability to see how the parts interact, where the leverage points are, how a decision in acquisition ripples into retention. Critical thinking and the ability to evaluate what the agents are producing and know when the system has drifted from its goal. Strategic instinct, because when execution is fast and iteration is cheap, the quality of the objective matters more than ever. The function specialist, excellent within a lane, is less relevant here. What accelerates this model is the practitioner who can design a system, evaluate its outputs, and keep asking whether the whole thing is pointed in the right direction.
But what matters more than any specific skill is the AI-First mindset. The genuine belief that agents can do more than you currently expect them to, and the discipline to keep designing toward that rather than settling for what's comfortable. The willingness to let go of functional identity and think in outcomes. The curiosity to keep learning as the capabilities keep moving.
That mindset is harder to hire for than any technical skill. And it may be the thing that determines how far any of this goes.
If You're Still Managing a Team
You probably should be. This isn't a case for abandoning what's working. It's a case for starting to build alongside it.
The practical entry point: pick one motion that currently requires coordination across multiple people or tools, and build an agent for it. Something that monitors, surfaces, and recommends — so that a decision that currently waits for a meeting or a report starts arriving on its own. See what that changes about how you spend your time.
Then ask yourself: what else is in that category?
The organizations building toward this now are accumulating something that doesn't transfer — the compounding record of decisions and outcomes that makes the system smarter over time. That context is specific to your customer base, your market, your judgment. The earlier you start, the more of it you have.
This is the direction GTM is heading. Everything I described in this piece such as the Super Agent, the agent team, the system memory, the compounding context, is what I built on CREAO.
Frequently asked questions
What is the core shift in an AI-First GTM model?
The core shift is moving from managing people through execution to designing the system with AI, that executes through AI agents. The manager's role is now to set objectives, resolve competing priorities, and evaluate whether the system is producing coherent outcomes.
What is the Super Agent and how does it function?
The Super Agent acts as a business partner. It contains the required memory and skills for reliable execution. It updates the manager on all go-to-market activities. It is connected to everything that matters (GA4, ad platforms, product data, competitive landscape) to provide a complete picture of the full customer journey, allowing for immediate engagement in optimization or strategic thinking. It executes tasks, and creates reusable agents that can be scheduled or triggered for reliable ongoing automation.
How are AI Agents and Agent Systems defined in this new GTM model?
In this GTM model, the Agent Team is a host of agents (including Monitoring, Suggestion, and Execution agents) that draw from a shared library of skills (like brand voice and content standards) and system memory. The goal is to build a full GTM System where agents coordinate across functions, run entire campaigns as a coherent motion, and carry the context of the complete customer journey in every decision.
What is the most important skill for hiring in an AI-First environment?
The most important factor is the AI-First mindset, which includes the genuine belief that agents can achieve more, the willingness to let go of functional identity, and the discipline to keep designing the system toward better outcomes. Other key skills include systems thinking, critical thinking, and strategic instinct.
What is the practical entry point for a manager looking to adopt this model?
The practical entry point is to pick one motion that currently requires coordination across multiple people or tools and build an agent for it. This allows a decision that usually waits for a meeting or report to start arriving on its own, allowing the manager to see what changes about how they spend their time.
