In 2026, the marketing technology landscape they grew by just 0.7%, going from 15,384 to 15,505. At first glance, it appears to have reached a plateau and reached its limits. But that headline number hides what’s really happening beneath the surface: Nearly 1,500 instruments have been added, while more than 1,300 have disappeared. This is not stagnation. It is renewal.
For years we have used the martech landscape not for the final number (although that is what excites most people), but to observe the profound and subtle changes happening right before our eyes. It offers a unique observation point.
What emerges today is clear. Peak Martech is a myth. Martech is entering the Darwin phase. The martech landscape is renewing itself. The value is growing.
This is the change. And this change has direct consequences on your stack. The era of accumulation of tools is giving way to the era of their replacement. At the heart of this transition is a structural shift in how value is created.
SaaS platforms are no longer the primary source of differentiation. They are becoming infrastructures: systems of record, workflow engines, and integration layers that provide stability and structure. The real value is moving above that foundation. Artificial intelligence is becoming the level of value.
Where SaaS operates on rules and predefined logic, AI operates on language, context and probability. It doesn’t just run workflows. Interprets, decides and adapts.
It’s as if artificial intelligence added audio to silent films. The basics remain the same, but the experience and value change dramatically. This changes the role of the stack. It’s no longer about assembling the right tools. It’s about getting the right results.
The landscape is not flat. It is being rewired.
AI becomes the layer of value on top of SaaS infrastructure
If the landscape is rewired, the most visible impact will be in how companies create customer value. Nowhere is this change more pronounced than in personalization.
For years, personalization has been defined by rules. Segments, workflows, triggers. If a customer matches a profile, they receive a default experience. This worked in a world where customer journeys were relatively predictable and channels were controllable.
That world is disappearing.
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Retrieving structured data, such as a customer’s age or city, probabilistically makes no sense. This is where SaaS remains essential as infrastructure. But as AI becomes the value layer, personalization is no longer about trip configuration. It’s about continuously interpreting the context and deciding how to respond in real time.
The change is subtle but profound: from the upfront design of experiences to their dynamic generation, powered by a solid SaaS and data foundation.
This is not an incremental improvement. It’s a paradigm shift.
| OLD (SaaS era) | NEW (AI Era) |
| Rules-based | Context-based |
| deterministic | Probabilistic |
| Segments | Individuals in real time |
| Predefined workflows | Adaptive decision |
| Campaign-driven | Continuous interaction |
| Configured by the marketer | AI-assisted/AI-driven |
| Static travel | Dynamic experiences |
Renewal is the new growth
If this change is real, it should emerge from the data. And it does.
The martech landscape is no longer dominated by pure growth. Instead, it is distributed across four distinct states: growth, renewal, stability, and decay. In this model, inflow signals opportunity, while outflow signals pressure. Together, they form a market thermometer that reflects how martech providers interpret demand through market research and customer feedback.
What stands out is not where growth happens, but where it doesn’t.
1. Growth: redefining, not expanding
CMS, projects and workflows, e-commerce and iPaaS are growing. These are not new categories. They are in the process of remodeling. CMS is evolving into a machine-readable infrastructure for AI agents. eCommerce is adapting to AI-driven discovery. iPaaS is becoming the orchestration layer that connects everything. Growth happens where AI changes the work that needs to be done.
2. Renewal: Where the real action is
Content, collaboration and personalization are getting a makeover. This is the dominant model in today’s landscape. High inflow meets high outflow. New ideas are coming in quickly, while first-generation solutions are coming out just as quickly. The market is actively discovering what the new need really is.
Content is the clearest example. The GenAI boom triggered an explosion of tools, followed by rapid consolidation as core capabilities became commoditized. The same dynamic is now occurring in personalization and collaboration.
Most martech is now being revamped. It’s being rewritten. The market is not expanding. It is replacing first-generation solutions with native AI ones. Renewal is not instability. It’s creative destruction.
3. Stability: mature, fundamental
Core systems such as CRM, customer service and customer intelligence (including cloud data warehouses) show limited movement. They remain essential, but their role is shifting towards core infrastructure rather than innovation.
4. Decay: loss of relevance in its own right
Chat, video and email are reducing. These categories are not disappearing, but their role is changing. Features are absorbed into broader platforms and AI-driven workflows. Artificial intelligence is updating chats and videos. Email is transforming from an optimized system to a channel that AI decides to use.
The winners of this next phase of martech won’t be the companies with the most tools. They will be the ones with a stack that allows AI to create the most value. If martech is rewired, the answer is not to add more tools. It’s time to rethink how the stack creates value. Here are two steps to take.
1. Build value
The role of SaaS is changing. This is no longer where differentiation lives. It’s the foundation that unlocks value. The goal is not to cover every use case with one tool. It’s about identifying three to five use cases that offer the most value and focusing on them first.
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This means learning to design value first, rather than tools. Value engineering starts by answering three key business questions before tackling the technology. We start with three questions.
- Who is your most valuable customer?
- What do they buy most?
- Where is the margin?
Only once these principles are clarified does automation begin to make sense. The goal is not to implement tools, but to create an environment where AI can operate effectively within a clear value model.
2. Build for context
In a world of AI-driven execution, fragmentation becomes the biggest limitation: 90.3% of marketing organizations they now use AI agents in some way, but only 23.3% have deployed them in full production.
Change is not just about integration. It’s about how SaaS and AI work together.
SaaS provides structure: data, workflows, consistency. Artificial intelligence creates value first and foremost: it interprets context, makes decisions and adapts in real time. Value emerges from the intersection of these two layers.
The best stacks are not the most feature-rich. They are the most aligned, focused on a limited number of high-impact use cases where SaaS enables and AI amplifies.
Integration is no longer just technical. It is a strategic asset.
It’s about context engineering: creating the conditions for the stack to work effectively, not by adding more tools, but by ensuring that data, workflows, and decision-making are aligned around a common set of use cases.
