In 2026, there was a minimal growth of 0.7% in the marketing technology landscape, increasing from 15,384 to 15,505. Initially, it may seem like progress has slowed down and reached a standstill. However, beneath the surface, significant changes are taking place: around 1,500 tools were added, while more than 1,300 disappeared. This is not a sign of stagnation but rather a phase of renewal.
Over the years, the martech landscape has been a lens through which to observe subtle shifts rather than just focusing on the final number. It provides a unique perspective to witness ongoing changes.
Today, the martech landscape reveals a clear trend. The notion of Peak Martech being a myth is evident. Martech is now in its Darwinian phase, experiencing renewal and growth in value.
A significant shift is taking place, directly impacting your technology stack. The era of accumulating tools is transitioning into an era of replacing them. This transition is driven by a fundamental change in how value is generated.
SaaS platforms are no longer the primary source of differentiation but are transforming into essential infrastructure that provides stability and structure. The real value now lies in AI, which is emerging as the new value layer.
While SaaS operates based on rules and predefined logic, AI operates on language, context, and probability, enabling it to interpret, decide, and adapt dynamically.
AI is akin to adding sound to silent movies – while the foundation remains the same, the experience and value change fundamentally. This redefines the role of the stack from assembling tools to enabling the right outcomes.
The landscape is not stagnant but rather undergoing a rewiring process.
AI becomes the value layer on top of the SaaS infrastructure
If the landscape is indeed being rewired, the most noticeable impact will be seen in how companies deliver customer value. The shift towards personalization exemplifies this change.
For years, personalization relied on rules like segments, workflows, and triggers to provide predefined experiences based on customer profiles. However, with unpredictable customer journeys and uncontrollable channels, this approach is becoming obsolete.
A new era is emerging where personalization is no longer about configuring journeys in advance but about dynamically interpreting context and making real-time decisions.
This shift signifies a move from designing experiences beforehand to generating them dynamically, supported by a robust SaaS and data foundation.
This is not a minor upgrade but a transformative paradigm shift.
| OLD (SaaS Era) | NEW (AI Era) |
| Rule-based | Context-based |
| Deterministic | Probabilistic |
| Segments | Individuals in real time |
| Predefined workflows | Adaptive decisioning |
| Campaign-driven | Continuous interaction |
| Marketer-configured | AI-assisted / AI-driven |
| Static journeys | Dynamic experiences |
Renewal is the new growth
If this transformation is genuine, it should be reflected in the data – and it is.
The martech landscape is no longer solely about expansion but is now segmented into four distinct states: Growth, Renewal, Stability, and Decay. This model views inflow as opportunity and outflow as pressure, creating a market thermometer that gauges demand based on market research and customer feedback.
What’s noteworthy is not just where growth occurs but where it doesn’t.
1. Growth: Redefinition, not expansion
Categories like CMS, project and workflow, ecommerce, and iPaaS are witnessing growth not in terms of expansion but in how they are evolving. CMS is transitioning into a machine-readable structure for AI agents, while eCommerce is adapting to AI-driven discovery. iPaaS is serving as a crucial orchestration layer connecting various components. Growth is now driven by how AI transforms the operational landscape.
2. Renewal: Where the real action is
Content, collaboration, and personalization are undergoing renewal, marking the predominant trend in the current landscape. With a high influx of new ideas and simultaneous exits of first-generation solutions, the market is actively exploring the evolving needs.
Content exemplifies this trend, where the GenAI era sparked a surge in tools followed by rapid consolidation as core functionalities became standardized. A similar pattern is unfolding in personalization and collaboration.
The majority of martech now resides in the renewal phase, undergoing a rewriting process. Rather than expanding, the market is replacing initial solutions with AI-native alternatives. Renewal signifies not instability but creative destruction.
3. Stability: Mature, foundational
Foundational systems like CRM, customer service, and customer intelligence, including cloud data warehouses, are showing limited movement. While they remain crucial, their role is shifting towards foundational infrastructure rather than innovative solutions.
4. Decay: Losing standalone relevance
Categories such as chat, video, and email are experiencing a decline. Although not disappearing entirely, their significance is transforming as their functionalities integrate into broader platforms and AI-driven workflows. AI is enhancing chat and video experiences, while email is shifting from being optimized by marketers to being utilized based on AI decisions.
In the upcoming phase of martech, success will not be determined by the number of tools but by how effectively the stack enables AI to generate value. As the martech landscape undergoes rewiring, the focus should not be on adding more tools but on reevaluating how the stack delivers value. Here are two crucial steps to consider.
1. Build for value
The role of SaaS is evolving, transitioning from a differentiation platform to a foundational element that unlocks value. The objective is not to address every use case with a tool but to identify the top three to five use cases that yield the most value and prioritize them.
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This approach entails prioritizing value engineering over tool implementation. It begins by addressing three key business questions before delving into technology: identifying the most valuable customer, understanding their primary purchases, and pinpointing profit margins.
2. Build for context
In an AI-driven landscape, the biggest challenge lies in fragmentation: while 90.3% of marketing organizations utilize AI agents, only 23.3% have fully integrated them into operations.
This shift is not solely about integration but about the synergy between SaaS and AI.
While SaaS offers structure in terms of data, workflows, and consistency, AI adds value by interpreting context, making decisions, and adapting in real-time. The optimal stacks are not those with a myriad of features but those focused on a few high-impact use cases where SaaS facilitates and AI enhances.
Integration is no longer merely technical but a strategic advantage.
Context engineering is crucial: establishing an environment where the stack operates efficiently by aligning data, workflows, and decision-making around key use cases.
