Beyond the pilot: How to scale AI adoption across global revenue teams

From pilot programs to enterprise-wide change

For many companies, their AI journey starts small. Whether it's experimenting with GPT for content audits or trialling Copilot to support meeting preparation, these early-stage projects often begin with a focused scope and a few enthusiastic early adopters.

And in many cases, the initial results are promising; reps save time, content becomes more personalized, and there's genuine excitement about what's possible.

But these pilots often reveal a harsher reality; success in one team, region, or use case doesn't automatically translate across an enterprise. As AI pilots show promise, organizations are suddenly faced with a bigger challenge of rolling out these tools at scale without creating chaos.

We've seen this scenario repeatedly through Go-to-Market Mornings: what begins as a well-contained experiment can quickly become fragmented when scaled too quickly, or without the right structure behind it.

Why enablement is the missing link in AI adoption

AI tools don't exist in a vacuum, and introducing them without a clear enablement strategy leads to confusion more than impact. When sellers are handed AI tools with little context or support, questions naturally arise:

• What's in it for me?
• When am I supposed to use this?
• How do I know the content is accurate?
• Will this actually help me close more business?

When you can't answer these questions, you can't drive adoption. And worse, when AI tools are introduced without content governance, outdated or off-brand information can quickly find its way to customers.

AI adoption isn't just about technology, it's about behavior change. That requires thoughtful programing, consistent communication, and reinforcement. In short, it requires enablement to lead.

Through our own internal programs and customer conversations, we've seen the power of embedding AI into structured initiatives. AI works best not as a standalone tool, but as part of a coordinated enablement motion tied to onboarding, sales plays, new messaging rollouts, and more.

Local wins don't equal global scale

One of the clearest insights we've heard from GTM Mornings is that success in one market doesn't guarantee global traction. Cultural norms, tech readiness, and leadership priorities vary across regions, and so do sellers' attitudes towards AI.

While one team may embrace a new AI-powered tool enthusiastically, another might resist it entirely. Often, tenured sellers who've built their careers on relationship selling are slower to adopt new workflows. In some cases, they view AI tools as a burden — just "one more thing" to figure out.

That's where consistency in enablement becomes a differentiator. The organizations that scale most effectively are the ones that take the time to tier their enablement programs, adapt rollouts to local needs, and identify peer champions who can lead from within.

Rather than assuming adoption will cascade organically, these teams create structured, repeatable experiences, combining pre-work, live training, manager coaching, and in-the-flow reinforcement.

Turning experimentation into repeatable programs

The most successful organizations don't treat AI as a side project. They treat it as a core part of their enablement strategy. What that means is:

• Aligning AI tools with existing GTM priorities
• Connect AI-generated content to approved, compliant, and current materials
• Design programs with clear learning paths, targeted use cases, and structured reinforcement
• Plan for feedback early — not every rollout will stick the first time

Several Seismic customers have approached this by running targeted pilot programs with cross-functional teams. This way, they can collect feedback, refine messaging, and then expand into broader releases once value is proven.

Other customers have layered AI into existing enablement initiatives, rather than creating entirely new ones. For example, integrating AI-powered objection handling into an existing discovery skills program, or using generative content tools to support a new industry vertical launch. This approach creates continuity and drives adoption by showing AI's value in context.

What does success actually look like?

As AI tools become more embedded, leadership naturally wants to know: Is this working? And the answer depends entirely on how success is defined. Usage metrics like logins, queries, and completion rates can provide a baseline. But the more mature organizations are looking deeper, with questions like:

• Are reps saving measurable time in preparation?
• Are meetings more tailored and buyer-centric?
• Is content usage aligning to win rates?
• Are sellers more confident and consistent?

At Seismic, we apply a framework that moves from engagement to enablement to performance. Looking not just at usage, but at behavior change and business outcomes. When AI tools are embedded into well-planned programs with these metrics in mind, it's easier to tell the story of impact.

Where pilots end, performance begin

The next wave of AI adoption won't be defined by innovation alone, but by execution. It's not enough to run a pilot. To drive real change, GTM leaders must embed AI into the rhythms of how sellers learn, operate, and engage buyers — with enablement guiding the way.

We're helping organizations do just that — through both our internal enablement work and conversations at GTM Mornings, where customers have shared how they're navigating this shift. From experimentation to transformation, we're partnering with teams to scale what's working, one program at a time.

Ready to turn your pilot into performance? See how Seismic helps teams adopt, scale, and succeed with AI-powered enablement — book a demo today.

Or, join us in person at our next Go-to-Market Mornings event to hear how enablement leaders are turning AI strategy into revenue outcomes.