AI Set to Transform the Landscape for Martech Providers and Their Clients
Though the excitement surrounding artificial intelligence appears to be entering what Gartner designates as the “trough of disillusionment,” advancements in the technology are continuing to evolve in transformative ways, particularly within the martech space. This encapsulates the essence of the latest “Martech for 2025” report authored by Scott Brinker, the mind behind chiefmartec.com, and marketing technologist Frans Riemersma, which was unveiled today.
“AI is fundamentally changing the landscape of marketing and martech,” they explain. “While we typically avoid exaggerated claims, we anticipate that marketers and leaders in marketing operations will encounter substantial, tangible shifts due to this technology come 2025.”
These anticipated transformations extend far beyond present applications like content creation, customer personalization, and knowledge management. Speculating on all future applications is challenging since AI’s capability to generate “instant software” allows solutions to be customized for specific business requirements.
Emergence of the Martech Hypertail
The impending solutions, referred to as the “hypertail” in this report, will be developed not solely by IT and marketing professionals but also by AI agents, leading to radical modifications in the martech ecosystem.
Explore more: 7 tactics for maximizing your martech stack
“This could mark a pivotal moment when the total number of commercial applications within the tech stack reaches its peak. Future growth — which we anticipate could be exponential — will predominantly stem from bespoke software, a multitude of tailored applications, agents, and automation tools.”

Transformative changes are actively unfolding among martech providers, dramatically altering the competitive landscape for new startups versus established firms.
Major players like Adobe, HubSpot, Microsoft, Salesforce, and SAS have swiftly integrated cutting-edge AI functionalities into their offerings, utilizing both generative and classical machine learning techniques. This shift has created a competitive arena that mirrors the historical dynamic of startups vying against incumbents.
Interestingly, many AI-native startups aren’t outright competing with these well-established companies. Instead, they are rolling out innovative, standalone tools designed to automate or elevate specific marketing functions within the broader frameworks of existing platforms. These solutions, driven by generative AI from companies like OpenAI, Google, Anthropic, and Meta, serve to complement rather than directly contend.
Is a DIY Approach Feasible?
In a notable example, fintech powerhouse Klarna has opted to abandon two major service providers in favor of developing its own software solutions. They are moving away from Salesforce and Workday, instead crafting tailored CRM and HCM systems utilizing AI and composable cloud services.
While this move may seem drastic, the report highlights that “the mere possibility of such a shift underscores the enhanced feasibility of custom software development and the strategic advantage offered by more personalized digital frameworks in the realm of AI.”
This strategic shift is also facilitated by the capability of customizing AI solutions using proprietary data and unique organizational logic. This customization is primarily achieved through one of three approaches:
- Developing a proprietary model.
- Refining an existing model, and/or
- Employing retrieval augmented generation (RAG).
The most prevalent method, RAG, retrieves information from internal databases and integrates it into the prompts sent to the LLM engine. The AI’s responses are then generated with this information, enhancing the LLM’s baseline knowledge, while providing additional oversight for accuracy through RAG’s capacity to assess or adjust the LLM’s outputs.
Additional critical insights from the report include:
- The significance of data strategy: Robust data strategies are essential for successful AI integration. Cloud data repositories are becoming indispensable for aggregating and managing data. The longstanding phrase holds true: “You don’t have an AI strategy if you lack a data strategy.”
- Emphasizing composability: A versatile martech stack must consist of modular, interconnected components that allow for adaptability and responsiveness. “Utilizing a composable strategy enables us to select the finest elements and build out from there. You can create a stack specifically tailored to your business and customer requirements.”
Explore more: Composability is now a reality, according to MessageGears
The report envisions a martech environment where change is the only constant. This evolution will be propelled by shifting customer demands, with AI advancements equipping marketers with the necessary tools to adapt and even anticipate these transformations. The comprehensive report is available for download here (registration is required).
To illustrate the scope of AI-driven changes in martech, Brinker and Riemersma also created a genAI-focused version of their well-known martech landscape.

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