Generative AI Crossed the Chasm. Agentic AI Hasn’t. That’s Where It Gets Interesting.

I’ve spent 20 years in marketing and communications at high-tech and cybersecurity companies, mostly on the B2B side. In that time, I’ve watched plenty of technologies navigate the long road from novelty to necessity: cloud computing, SaaS, zero trust, you name it. Each one followed a recognizable adoption pattern, and each one eventually hit the same critical inflection point that Geoffrey Moore described in his 1991 classic, Crossing the Chasm.

Before I go any further down this path, I want to be clear that I’m not one to read all of the sales, marketing and general business books. I’m really not. But I always come back to Crossing the Chasm because to me this book isn’t just about business strategy, it borders more on the field of anthropology and at its best it actually very closely approaches philosophy.

For what I hope are obvious reasons, Crossing the Chasm is very top of mind for me and many of my marketing cohort as well right now, because we all agree that AI is following that pattern too. I won’t cover every angle on how AI fits into the Crossing the Chasm lifecycle nearly as exhaustively as Jakob Nielsen did in this August 2025 post of his Substack. I would recommend that everyone read his analysis.

However, I would like to suggest a twist that I think deserves a lot more attention than it’s getting: we have to stop thinking that AI is a single adoption curve. It’s several, running in parallel, at very different speeds. 

A Quick Refresher on the Chasm

For those who haven’t read Moore’s book (or haven’t revisited it in a while), the technology adoption lifecycle breaks down into five segments: 

• Innovators, the roughly 2.5% of technology enthusiasts who embrace new products for their own sake
• Early Adopters, the 13.5% of visionaries who see strategic opportunity and are willing to take calculated risks
• The Early Majority, a pragmatic 34% who want proven solutions that integrate well with what they already have
• The Late Majority, another 34% who adopt only when something becomes an established standard
• And Laggards, the remaining 16% who resist change until it becomes invisible

Moore’s crucial insight was that the gap between Early Adopters and the Early Majority isn’t just a gap, it’s a chasm. The visionaries want revolutionary change. The pragmatists want evolutionary improvement. The marketing strategies, product positioning, and even the features that excite one group can actively repel the other. Countless promising technologies have died in that chasm.

AI Contains Multitudes

Here’s where things get interesting in the year 2026. The data, the analysts and countless self-appointed thought leaders tell us that “AI has crossed the chasm.” Consumer adoption is well into the early majority. Enterprise adoption of generative AI tools is moving solidly in that direction too. According to multiple recent studies, we’re in what many are calling the “dawn of AI’s early majority era.”

But I believe that framing, while accurate at the macro level, is misleading. “AI” as a category contains multitudes- or at least radically different types and applications of technology. What’s more, I think it’s clear to see that they’re each on their own adoption timeline. From my perspective, it’s helpful to distinguish between at least three distinct curves:

Generative AI - Across the Chasm

Tools like ChatGPT, Claude, Gemini, and Midjourney have achieved mainstream adoption. People are using them to draft emails, brainstorm ideas, create images, summarize documents, and much more. This is early majority territory. The conversations here are less about “should we use this?” and more about “how do we use this well?” The focus has shifted to usability, reliability, governance, and ROI—exactly what you’d expect when pragmatists take the wheel.

Copilot AI - Approaching the Chasm

AI assistants embedded in existing workflows—think GitHub Copilot, Microsoft 365 Copilot, Salesforce Einstein—are gaining real traction but are still in the late early adopter phase for most enterprises. Organizations are piloting them, measuring impact, and trying to figure out where they deliver genuine productivity gains versus where they’re just expensive autocomplete. The chasm is right in front of them, and whether they cross it will depend on whether vendors can demonstrate clear, measurable value to pragmatic buyers.

Agentic AI - Still on the Innovator Side

And then there’s agentic AI—autonomous systems that can plan, execute multi-step workflows, make decisions, and take action with minimal human oversight. This is where most enterprises are firmly in the innovator or early adopter phase. Most enterprise trust frameworks aren’t there yet. Many governance models are embryonic. The majority of organizations are experimenting in sandboxes, not deploying in production. The chasm for agentic AI is still well in the future.

Are Marketers Ahead of the Curve?

Here’s something else I’ve been chewing on: I have a strong intuition that marketers, as a functional group, tend to sit further toward the early adopter end of the adoption curve than many of their peers in other departments; finance, legal, operations, even IT in some cases.

Of course, I am biased as a marketer who has evaluated, justified, procured and deployed a ton of different technologies in the last 20 years. In the past few decades marketing has been an early testbed for new and emerging enterprise technology. We adopted marketing automation before most of the business understood what it was. We were early movers on social media, content platforms, data analytics, and personalization engines. Our work is inherently experimental. We’re comfortable with testing and iterating.

But this can also be a liability. If marketing is two adoption stages ahead of the rest of the organization, we risk building strategies and buying tools that the broader business isn’t ready to support. We can become the department that’s always excited about the next shiny thing which, fairly or not, can erode credibility when we need the rest of the C-suite to invest. In my opinion, this is part of why marketing’s adoption of new technologies outpaced the business in the last 20 years and part of why marketing started to be viewed as a cost center. 

That said, I believe the movement toward agentic AI can be different for marketers because although agentic AI can have specific marketing use cases, the adoption of agentic AI can be justified and can follow a similar deployment strategy as it does in the rest of the business. 

A Note from the B2B Side of the Fence

I’ll be transparent: my career has been overwhelmingly B2B, and I’m still building my understanding of the B2C landscape. But one thing that’s become increasingly clear to me is that on the B2C side, consumer adoption of these technologies has a much larger and earlier impact on marketing strategy. When millions of consumers start using generative AI to research products, compare options, and even make purchasing decisions, the entire demand generation playbook has to shift fast.

In B2B, we have a bit more buffer. Our buyers tend to be pragmatists by profession. Procurement cycles are longer. But the wave is coming, and the organizations that start thinking about how agentic AI will reshape B2B buying behavior will be the ones best positioned when it does.

So Where Do We Go from Here?

Moore’s framework is over 30 years old, but it’s never been more relevant. The organizations that succeed in this moment won’t be the ones that treat AI as a monolith. They’ll be the ones that recognize which adoption curve they’re on for each type of AI, tailor their strategies accordingly, and invest in the training, workflow redesign, and governance that will carry them from early adoption to genuine mainstream value.

For marketers specifically, I think the opportunity is to lead that conversation not just within our own teams, but across the business. We’re well positioned to do it. But only if we’re honest about where we actually are on each of these curves, not where we wish we were.

Jessica is a marketing and communications leader with 20 years of experience at high-tech and cybersecurity companies.