How is martech evolving in the AI era? I wanted to pose this and other pressing questions to martech pioneer Scott Brinker, who’s been keeping tabs on the industry for more than a decade.
Scott recalls that his now-famous martech landscape chart began around 2010–2011 as a simple one-page visual, designed to show marketing leaders just how dependent their strategies were becoming on technology and to convince them to invest in marketing operations and technical talent. Since then, that tidy chart has exploded into more than 15,000 solutions, mirroring the rapid proliferation of software across every business function.
Yet Scott’s advice to CMOs and CROs is surprisingly contrarian: as proud as he is of that landscape, he recommends they don’t start there. The landscape is best seen as a “thermometer” of the industry, not a shopping list. Executives should begin with strategy: who they serve, what capabilities they need to be competitive, and only then narrow down to a short list of vendors rather than being overwhelmed by thousands of logos.
Scott believes we may now be at or near “peak martech.” For the first time, the number of solutions in the landscape has essentially flattened, suggesting that it may no longer keep expanding at the same breakneck pace, because the underlying structure of the market is changing. Scott expects a bifurcation. On one side will be large, foundational platforms and data infrastructure: systems like cloud data warehouses, core marketing clouds, and key data platforms that no company wants to “vibe code” from scratch.
On the other side, Scott anticipates an expanding long tail of more specialized, often highly contextual tools, apps, agents, and workflows. Many of these won’t resemble classic SaaS companies built to $100 million in ARR; instead, they may look more like the old WordPress plugin ecosystem: small, focused solutions solving very specific problems for particular businesses or roles.
We also discuss how advanced marketing teams are navigating this evolving landscape in practice. Scott shared findings from a study of 208 top-quartile marketing operations leaders across 70 AI-related use cases. Instead of a simple “build versus buy” split, the reality is a layered approach that fits the new structure of martech. Teams are taking advantage of AI features embedded in their existing major platforms, plugging in new AI-native products where they see gaps or opportunities, and building their own custom capabilities where differentiation matters most. It’s messy and sometimes chaotic, but Scott views this as a natural consequence of operating at the intersection of a consolidating infrastructure layer and a rapidly expanding, experimental long tail.
Discussion points include:
- How advanced teams are actually building tech stacks
- The danger of waiting for the “dust to settle”
- The differential impact of AI on software engineering vs. marketing
- “Context” as the martech word of the year
- Humanity, trust, and hyper-personalization
- The resurgence of in-person events and human signals of legitimacy
- AI fatigue and sustainable adoption