In 2026, the state of marketing technology grew by 0.7%, rising from 15,384 to 15,505. At first glance, it seems that it has stopped and reached its limit. But that headline number hides what’s really going on underneath: nearly 1,500 tools have been added, while more than 1,300 have disappeared. It’s not a growth. It’s a remake.
For many years, we do not use the martech landscape not for the final number (although that is what excites many people), but to see the deep and subtle changes happening before our eyes. It gives a unique point.
What it shows today is clear. Peak Martech is a myth. Martech is entering its Darwin phase. The state of martech is being updated. The number is increasing.
That’s the shift. And that change has direct consequences for your stack. The season of accumulating tools opens the season of changing them. At the heart of this change is a structural change in the way 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. The actual price goes on that basis. AI is becoming a value layer.
Where SaaS works with pre-defined rules and logic, AI works with language, context, and probability. It’s not just about making apps. It interprets, determines, and adapts.
It’s as if AI added sound to silent movies. 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 getting the right results.
The world is not flat. It is being reinstalled.
AI becomes a value layer on top of SaaS infrastructure
If the environment is repositioned, the most visible impact will be on how companies create customer value. Nowhere is that shift more apparent than in personalization.
For years, personalization has been defined by rules. Stages, workflow, risks. If the customer fits the profile, they get a pre-defined experience. This worked in a world where customer journeys were relatively predictable, and channels were manageable.
That world is disappearing.
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Retrieving structured data, such as a customer’s age or city, probably doesn’t make sense. This is where SaaS remains as important as infrastructure. But as AI becomes the value layer, personalization is no longer about stopping the journey. It’s about continuously interpreting context and deciding how to respond in real time.
The shift is subtle but profound: from pre-engineered experience design to dynamic production, powered by a robust SaaS and data base.
This is not a sustainable development. A paradigm shift.
| OLD (SaaS Era) | NEW (AI Era) |
| It is based on law | Based on the content |
| To decide | Probabilistic |
| Sections | People in real time |
| Predefined workflow | Determining variables |
| A campaign is underway | Continuous collaboration |
| Prepared for sale | AI-assisted / AI-driven |
| A constant journey | A powerful experience |
Stimulating new growth
If this change is real, it should be reflected in the data. And it does.
The martech landscape is no longer dominated by pure growth. Instead, it is distributed in four distinct states: Growth, Regeneration, Stability, and Decay. In this model, entry indicates opportunity, while exit indicates pressure. Together, they create a market thermometer that shows how martech marketers interpret demand through market research and customer feedback.
The highlight is not where growth happens, but where it doesn’t.
1. Growth: Redefining, not expanding
CMS, project and workflow, ecommerce, and iPaaS are growing. These are not new categories. They are reshaped. CMS is evolving into a machine-readable infrastructure for AI agents. eCommerce is adapting to AI-driven acquisitions. iPaaS becomes the orchestration layer that connects everything. Growth happens when AI changes the work that needs to be done.
2. Renewal: Where real action exists
Content, interaction, and personalization are being updated. This is a prominent pattern in today’s environment. High input meets high output. New ideas come in quickly, while first-generation solutions go out just as quickly. The market is fully discovering what the new demand really is.
Content is a very clear example. The GenAI boom has resulted in an explosion of tools, followed by rapid consolidation as basic skills become commodities. The same dynamic now applies to personalization and collaboration.
Most of the martech is now reinvented. It is being rewritten. The market is not growing. It replaces first-generation solutions with AI-native ones. Regeneration is not instability. It is creative destruction.
3. Stability: Maturity, foundation
Key systems such as CRM, customer service, and customer intelligence (including cloud data warehouses) show limited movement. They remain important, but their role is shifting to basic infrastructure rather than innovation.
4. Decay: Loss of independent coherence
Chat, video, and email are on the decline. These categories are not disappearing, but their roles are changing. Functionality is embedded in comprehensive platforms and AI-driven workflows. AI enhances chat and video. Email is moving from a system you develop to a channel that AI decides to use.
The winners in this next phase of martech will not be the companies with the most tools. They will be the ones with the stack that allows AI to create the most value. When martech is retooled, the answer is not to add more tools. It’s time to rethink how the stack creates value. Here are two steps you should take.
1. Build on value
The role of SaaS is changing. There is no place for separation. It is the foundation that unlocks value. The goal is not to cover all use cases with the tool. It is to identify the three to five use cases that deliver the most value and focus on them first.
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This means learning the importance of developers first, rather than tools. Value engineering begins by answering three key business questions before addressing technology. It starts with three questions.
- Who is your most important customer?
- What do they buy the most?
- Where is the margin?
Only when these are clear does automation start to make sense. The goal is not to use tools, but to create an environment where AI can work effectively within a clear value model.
2. Build for context
In the world of AI-driven execution, fragmentation becomes the biggest obstacle: 90.3% of marketing organizations now use AI agents in some way, yet only 23.3% have implemented them in full production.
Change is not just integration. It’s about how SaaS and AI work together.
SaaS provides structure: data, workflow, consistency. AI creates value at the top: interpreting context, making decisions, and adapting in real time. The value comes from the intersection of these two layers.
The best stacks are not very feature rich. They are highly aligned, focused on a small number of high-impact use cases where SaaS enables, and AI augments.
Integration is no longer just about technology. 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, workflow, and decision-making are compatible with a common set of use cases.
