In 2026, the marketing technology landscape only increased by 0.7%, from 15,384 to 15,505. At first glance, it seems to have stalled and reached its limits. But that number hides what’s really going on beneath the surface: nearly 1,500 tools have been added, while more than 1,300 have disappeared. It’s not stagnation. It’s renewal.
For years, we’ve used the martech landscape not for the bottom line (although that’s what most people are excited about), but to observe the deep, subtle changes happening before our eyes. It offers a unique point of view.
What he shows today is clear. Peak Martech is a myth. Martech enters its Darwin phase. The martech landscape is being renewed. The value increases.
It’s change. And this change has direct consequences on your stack. The era of tool accumulation is giving way to that of their replacement. At the heart of this transition is a structural change in how value is created.
SaaS platforms are no longer the primary source of differentiation. They become infrastructure: systems of record, workflow engines, and integration layers that provide stability and structure. True value lies above this foundation. AI becomes the value layer.
Where SaaS works on rules and predefined logic, AI works on language, context and probability. It doesn’t just run workflows. He interprets, decides and adapts.
It’s like AI adding sound to silent films. The foundation remains the same, but the experience and value fundamentally change. This changes the role of the stack. It’s no longer about assembling the right tools. It’s about enabling the right results to be achieved.
The landscape is not flat. It is being redone.
AI becomes the value layer on top of SaaS infrastructure
If the landscape is reimagined, the most visible impact will be on how businesses create value for their customers. Nowhere is this shift more pronounced than in the area of personalization.
For years, personalization has been defined by rules. Segments, workflows, triggers. If a customer matches a profile, they receive a predefined experience. This worked in a world where customer journeys were relatively predictable and channels were controllable.
This world is disappearing.
Your customers are searching everywhere. Make sure your brand introduces himself.
The SEO toolkit you know, plus the AI visibility data you need.
Start free trial
Start with
Retrieving structured data, such as a customer’s age or city, makes no sense, probabilistically. This is where SaaS remains essential as an infrastructure. But as AI becomes the value layer, personalization is no longer about configuring journeys. It’s about constantly interpreting the context and deciding how to respond in real time.
The change is subtle but profound: from designing experiences in advance to dynamically generating them, 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 |
| Determinist | Probabilistic |
| Segments | People in real time |
| Predefined workflows | Adaptive decision |
| Campaign focused | Continuous interaction |
| Configured by the marketer | AI-assisted/driven |
| Static trips | Dynamic experiences |
Renewal is the new growth
If this change is real, it should show up in the data. And it is.
The martech landscape is no longer dominated by pure growth. Instead, it spans four distinct states: growth, renewal, stability, and decline. 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 is happening, but where it is not happening.
1. Growth: redefinition, not expansion
CMS, projects and workflows, e-commerce and iPaaS are growing. These are not new categories. They are being remodeled. The CMS is evolving into a machine-readable infrastructure for AI agents. E-commerce is adapting to AI-driven discovery. iPaaS becomes the orchestration layer that connects everything. Growth happens where AI changes the work that needs to be done.
2. Renewal: where the real action lies
Content, collaboration and personalization are renewed. This is the dominant model in the current landscape. High inflow meets high outflow. New ideas arrive quickly, while first-generation solutions disappear just as quickly. The market is actively discovering what the new need really is.
Content is the clearest example. The GenAI boom sparked an explosion of tools, followed by rapid consolidation as core capabilities became commoditized. The same dynamic is now playing out in personalization and collaboration.
Most martechs are now in the renewal phase. It is currently being rewritten. The market is not expanding. It replaces first generation solutions with native AI solutions. Renewal is not instability. It is 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 fundamental infrastructure rather than innovation.
4. Degradation: loss of autonomous relevance
Chat, video and email are decreasing. These categories are not disappearing, but their role is evolving. Features are absorbed into broader platforms and AI-driven workflows. AI improves chat and video. Email goes from being a system you optimize to a channel the AI decides to use.
The winners in this next phase of martech will not be the companies with the most tools. These will be the ones with a stack that allows AI to create the most value. If martech is being rewired, the answer is not to add more tools. It’s time to rethink how the stack creates value. Here are two steps to follow.
1. Create value
The role of SaaS is evolving. This is no longer where the differentiation lies. It is the foundation that unlocks value. The objective is not to cover all use cases with a tool. It’s about identifying the three to five use cases that provide the most value and focusing on them first.
See the full picture visibility of your search.
Track, optimize and win in Google Search and AI from a single platform.
Start free trial
Start with
This means learning to create value first, rather than tools. Value engineering begins by answering three key business questions before tackling technology. It starts with three questions.
- Who is your most valuable customer?
- What do they buy the most?
- Where is the margin?
Only once these elements are clear does automation start to make sense. The goal is not to implement tools, but to create an environment in which AI can operate effectively within a clear value model.
2. Build according to context
In a world of AI-driven execution, fragmentation becomes the biggest constraint: 90.3% of marketing organizations are now using AI agents to some extent, 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. AI creates additional value: interpreting context, making decisions and adapting in real time. Value emerges at the intersection of these two layers.
The best batteries are not the most feature-rich. They are most aligned and focused on a small number of high-impact use cases where SaaS enables and AI amplifies.
Integration is no longer just technical. It is a strategic asset.
This is 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.
