By the time your company declared an AI transformation, your reps had already started one.
Without you.
They found ChatGPT. They built their own prompts. They're using it to write emails, prep for calls, summarize meetings. Nobody assigned this. Nobody trained them. Nobody measured it.
And here's the thing nobody wants to say in the transformation steering committee:
The transformation didn't wait for the initiative.
The Declaration Problem
Every large company right now has some version of the same document. An AI transformation strategy. A steering committee. A set of use cases to pilot. A budget. A timeline.
These things are real and they matter. But they operate on a fundamental misunderstanding of how AI transformation actually works — which is that it is not a project you initiate. It is a force you are either steering or ignoring.
Rory Casbon, a revenue leader who has sold through multiple technology transitions, put it plainly in a conversation I had recently: AI transformation is a naturally occurring event. Not a cloud migration you plan for. Not an ERP rollout you project manage. It is happening in the background of every company simultaneously — in the tools vendors are shipping, in the workflows reps are building individually, in the expectations buyers are arriving with.
You are not choosing whether to transform. You are choosing whether to steer.
The Fragmentation Tax
Here is what unsteered AI transformation produces: fragmentation.
Every rep has their own prompts. Nobody shares them. The manager doesn't know what's working. The company can't replicate the results. The institutional knowledge lives in one person's ChatGPT history and disappears when they leave.
This is not a hypothetical. It is the current state of most mid-market sales teams.
Someone on the team discovered that a specific prompt sequence dramatically improves discovery call prep. They're using it every day. Their numbers are up. Nobody else knows why. The manager attributes it to talent. The company attributes it to luck. The rep attributes it to their own intelligence.
All of them are wrong. The system produced the result. But because the system is invisible and unshared, the result is unrepeatable.
Individual AI usage produces individual results. Systematic AI deployment produces compound results.
The gap between those two things is not a tool problem. It is a leadership problem.
The Visceral Gap
There is a specific kind of leader who shows up to every AI conversation well-prepared. They have read the McKinsey reports. They know the statistics. They have opinions on MCP architecture and the difference between agents and skills. They can hold a sophisticated conversation about frontier model capabilities.
They have not built anything.
I have read far more about meditation than I have actually meditated. I know the neuroscience. I know the research on cortisol and stress response and long-term neuroplasticity. I still do not know what it feels like from the inside.
AI leadership right now is exactly that problem.
The leaders who are actually moving their companies forward are not the ones who read the most. They are the ones who built something embarrassingly small and discovered what was already possible. A workflow. A prompt sequence. An automation that saves four hours a week. The scale doesn't matter. The visceral experience does.
You cannot lead a transformation you have not experienced. Reading about it is not the same as knowing it from the inside.
What Steering Actually Looks Like
Steering AI transformation is not the same as managing an AI project.
Managing a project produces a deliverable. Steering a transformation changes the operating model.
The difference is visible in what gets measured. A project gets measured by milestones — use cases piloted, tools deployed, training sessions completed. A transformation gets measured by whether the new behaviors are compounding.
Are the prompts being shared? Is the best workflow from one rep becoming the baseline for everyone? Is the company's AI capability improving week over week, or is each person starting from scratch every Monday morning?
Compounding is the signal. Fragmentation is the failure mode.
The companies that will look back in three years and wonder how they fell so far behind are not the ones who failed to declare an AI transformation. They are the ones who declared one, ran a few pilots, measured completion rates, and called it done — while the transformation continued without them.
The Question Worth Sitting With
Not: does your company have an AI transformation initiative?
But: is your company's AI capability compounding?
Are the individual discoveries becoming shared practices? Are the shared practices becoming infrastructure? Is the infrastructure learning?
Individual usage is a start. Systematic deployment is the goal. Compounding capability is the moat.
The transformation is already happening. The only question is whether you are building a system around it — or watching it happen to you.
Karthiga Ratnam is Category Designer and CMO at GTM Buddy, a Revenue Activation company. GTM Buddy builds the agentic infrastructure that turns individual AI usage into compounding revenue capacity. gtmbuddy.ai
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