Welcome Back. Everything is Different.

AI has hit GTM like an earthquake. No warning. No ramp. Just a new reality.

Let me take you back to 2008, the year my son was born.

I remember being called into our COO’s office and told that I was getting a promotion. I looked down at my belly, then back at him and said, “You do know that I am about to go out on maternity leave, right?” He did. And he still handed me the promotion. At the time, it felt a little insane. In hindsight, it’s one of those moments I will never forget.

I was out for four months and came back to the office as a new mom with this shiny promotion, ready to go. My role had changed, and that part was exciting. But the way we worked—the foundation of it, the systems, the pace, the expectations—none of that had. Everything felt exactly as I had left it.

And that’s not usually the fear when you go out on maternity leave. The fear is that the organization changes without you. That there are layoffs. That priorities shift. That your role gets redefined. That you come back and have to re-prove your place in a system that moved on while you were gone.

In my case, none of that happened. The system held.

Fast forward to last Friday, when I was catching up with someone I used to work with who still comes to me for advice. She was just returning from maternity leave, after being away for the same amount of time I had been. And like most of us when we step out for a few months, she expected to come back, ramp, and catch up. Maybe a few process tweaks, maybe a few personnel changes, but nothing you can’t get your arms around in a couple of weeks.

At first, it seemed like that was true. Same company. Same team. Same job.

But as we started talking about what had actually changed in the last four months, something shifted. We got into how GTM teams are now using AI to prioritize pipeline, shape messaging, and decide who gets contacted and when. How systems are sitting on top of CRM data, not just storing it, but interpreting it and telling you what to do next.

And I could see it on her face.

Not dramatically, and probably not something she would have noticed herself, but it was there. That moment where you realize you didn’t just miss updates. You missed a shift.

She had just come back from having a baby. She was trying to get her footing again, get back into the flow, and prove to herself and everyone else that she hadn’t lost a step. And now she was staring at a version of her job that had quietly moved underneath her while she was gone, trying to figure out how to catch up before it started to feel like she was falling behind.

Photo by Shefali Lincoln on Unsplash

I was at a conference last week where we were talking about natural disasters, specifically the difference between hurricanes and earthquakes. With hurricanes, you get warning. Days, sometimes weeks. You can track the path. You can prepare.

With earthquakes, there’s no warning. No ramp. No time to get ready. Everything feels stable until it isn’t.

That’s what this felt like. Not a slow, predictable evolution of how GTM works, but an earthquake. The ground shifted while she was gone, and now she’s standing there trying to figure out if she’s still on solid footing. And the hard truth is, she’s not imagining it. The data is already catching up to what people are feeling in real time.

In the last few months alone, nearly half of companies have moved from experimenting with AI to actually paying for it and embedding it into how their teams operate. At the same time, AI usage has accelerated so quickly that in some systems, workloads are doubling in a matter of weeks.

This isn’t theoretical anymore. This is operational. It didn’t roll out slowly, and there was no clean transition period where everyone got trained and aligned and brought along at the same pace. It showed up, took root, and started changing how GTM works while people were still trying to decide if it was worth paying attention to.

She didn’t miss a phase. There wasn’t one.

This Didn’t Evolve. It Hit.

This didn’t happen slowly over time. It didn’t build in a way you could track or prepare for. It feels like it came crashing into the new year in full force.

One minute, teams were experimenting. The next, they were expected to operate differently.

If you only look at the headlines, this all still sounds manageable. AI adoption is up. Tools are improving. Companies are testing use cases. It reads like a normal curve. Gradual. Controlled. Something you can phase into over time.

But that’s not what this is.

Because the real shift isn’t that AI showed up. It’s that the expectation of what a GTM team should produce changed almost overnight.

We’re now in a world where 87% of sales leaders say they’re feeling direct pressure from executives to adopt AI. At the same time, 92% of companies are increasing their investment in AI.

Once companies start paying for something and leadership is leaning in, expectations don’t stay theoretical for long. They operationalize.

The conversation shifts, quietly but completely. It’s no longer “Should we be using AI?” It becomes a different set of questions entirely. Why isn’t this faster? Why isn’t this more personalized? Why aren’t we getting more out of what we already have?

And those questions aren’t coming from the edges of the organization. They’re coming from the top down.

The data is already reinforcing that shift. Teams using AI are reporting productivity gains north of 40% across GTM workflows, with individuals saving ten or more hours a week on work that used to be manual. At the same time, projections show that AI will reduce sales prep and prospecting time by more than half in the next few years.

So the baseline has moved. Not in a roadmap or a future-state plan, but in the day-to-day reality of how work gets done.

And this is where things start to break.

Because while adoption is accelerating, understanding is not keeping pace.

Only about a third of companies have actually scaled AI in a way that’s delivering meaningful business impact, even as adoption continues to surge, and many teams are still operating in what McKinsey describes as “experimentation mode.”

What that creates is a strange middle ground. AI is everywhere, but it’s not fully understood. It’s being used, but not deeply integrated. It’s influencing output, but not yet reshaping the system behind it.

The real divide isn’t between companies using AI and companies that aren’t. That gap is closing quickly. The real divide is between companies that are layering AI on top of their existing GTM model and those that are starting to rebuild the model itself around it.

Because once AI enters the system, even in small ways, expectations don’t stay contained. They expand. What used to take days is now expected in hours. What used to require a team is now expected from an individual. The definition of “good” quietly shifts, and it rarely shifts back.

That’s why everything feels faster. Not just because the technology is moving quickly, but because the expectations attached to it are scaling just as fast.

This Is What It Looks Like When the Model Changes

A few weeks ago, I needed to pull together a list of conference attendees we should be talking to.

Not just a generic list, but something we could actually use. The right people, aligned to our priorities, and clear on who we should be spending time with and why. The kind of work that usually turns into hours of digging, cross-referencing, validating, and then trying to stitch it all together into something coherent. This is the kind of work GTM teams have always done. It’s foundational. And historically, it’s been time-intensive by design.

But this time, the process looked very different.

I started with an AI model, but I didn’t treat it like a search engine or a content generator. I treated it like a thinking partner. I had it build an initial view, then refine it, then pressure test it. Then I pushed it further. I asked it to prioritize attendees based on real, contextual signals—recent role changes, outstanding RFPs, and where companies were likely sitting in their budget cycles. Signals that typically live across multiple systems and take a lot of manual effort to piece together.

And suddenly, this wasn’t just a list. It was a point of view.

But the real shift happened when I connected this to our CRM system. Now it wasn’t operating in a vacuum. It had context. It could map those prioritized attendees against our existing relationships, our pipeline, and our historical engagement. It could highlight where we already had traction, where we had gaps, and where there was real opportunity to lean in.

What started as external research became internal intelligence, which is where most teams still struggle. Sales reps today spend over 60% of their time not actually selling, but piecing together information across systems. This was something we could act on immediately.

This is where I think the conversation around AI in GTM often falls short. Most teams are still using it at the surface level. Drafting emails faster. Summarizing notes. Generating content. All useful. All incremental. But that’s not where the real leverage is.

The leverage comes when AI is connected to your system of record. When it understands your business well enough to interpret signals, not just surface them. When it can take fragmented inputs and turn them into something that actually informs decisions. That’s also why companies leaning into AI are seeing outsized gains, with top-performing teams reporting significantly higher revenue growth tied to AI-driven insights, not just automation.

Inside our own team, this is starting to show up in how we operate day to day. We’re generating morning reports that surface what actually matters, signals across pipeline, prospect activity, and where attention should go, without someone having to manually piece it together. We’re automating first drafts of outbound emails so teams can start from something informed and relevant, not a blank page. And we’re enriching our CRM with augmented data, layering in context that helps us understand not just who a prospect is, but what’s happening around them.

At the same time, we’re using AI to identify patterns in what prospects are actually asking for, not just what we assume they care about, and refining messaging in near real time based on those signals instead of waiting for a quarterly reset. What used to be fragmented across research, planning, and execution is starting to come together into a more continuous loop.

And once you experience that, even in a small way, it becomes very hard to go back. Because the question stops being “how do we do this faster?” It becomes “why are we still doing this the old way at all?”

This is where many teams are underestimating what’s happening. They’re adopting AI, but they’re not yet operating with it. They’re accelerating outputs, but not rethinking the system that produces them. And without that shift, the gains stay incremental.

The real opportunity is to embed AI into the operating model itself. To use it not just to produce more, but to decide better. To connect dots that were previously too time-consuming to connect. To surface insights that change how you prioritize, not just how you execute.

That’s the difference. And that’s where the gap is starting to widen.

The Ground Isn’t Going to Settle

I keep thinking about that conversation. What it feels like to step away for a few months, do one of the most important things in your life, and come back expecting to catch up, only to realize the ground moved while you were gone. Not in the ways we’re used to fearing. Something deeper. The system itself changed.

That’s what makes this different. It’s the kind of shift where 3-4 months is enough to change how work gets done. Where the gap isn’t just knowledge, it’s muscle. Where the people who have been operating inside these systems are building instincts that are hard to replicate from the outside. And where catching up isn’t just about learning new tools, it’s about relearning how to think.

That’s why this feels like an earthquake, not a hurricane. There was no warning, no timeline, no clean rollout. Just a sudden shift, followed by a scramble to understand what just changed and what it means for how you operate going forward. And unlike a hurricane, there’s no recovery phase where things go back to normal.

And it’s not slowing down. Gartner projects that AI will reduce time spent on prospecting and meeting preparation by more than 50%, which means the baseline for how fast GTM teams are expected to operate is already changing.

So this isn’t going to settle. The question isn’t whether things will go back to how they were. They won’t. The question is how quickly you adapt to what they’ve become.

The teams that are leaning in now aren’t waiting for clarity or perfect systems. They’re building the muscle in real time, learning how to operate with AI, not just around it, and integrating it into decisions that actually matter.

And that compounds. The teams that start earlier aren’t just ahead, they’re building instincts, workflows, and advantages that get harder to catch over time.

Meanwhile, the teams that are still treating this like a phase, something to test, something to wait on, are falling further behind than they realize. Not because they aren’t capable, but because the environment they’re operating in has already moved on.

That’s the part that’s easy to miss.

This isn’t about whether AI is useful. It’s about whether your operating model has already been reshaped by it, with or without you.

Four months used to be a blip.

Now it’s enough for the ground to shift beneath you.

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To learn more about my GrowUp framework and how it can help grow your leadership style visit: Michelledenogean.com