Seven Ways Generative AI Has Impacted Martech

Seven highlights of the Martech-for-2025 report

We released a 108-page report on the impact of Generative AI on Martech. That impact is not only huge. It is also versatile. The disruption caused by Generative AI often feels like drinking water from a firehose. 

Therefore, in this article, we’ll provide an overview of how Generative AI has made its way into the Martech market and the Martech stacks of companies. For all the details, download the full report.

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The Impact of GenerativeAI on the Martech Market

There are five ways Generative AI has influenced commercial Martech. 

1. Indie Tools. Small, standalone AI-powered solutions, often built with minimal funding, focus on automating or augmenting specific tasks.
2. Incumbent Platforms. Prevalent, well-established Martech platforms from the past decade that continue to lead by integrating new technologies, acquiring startups, or embedding AI-driven tools into their ecosystems.
3. Challenger Platforms. Emerging platforms seeking to disrupt the market by offering innovative, AI-driven solutions. These platforms aim to redefine workflows or expand Martech’s capabilities, positioning themselves as alternatives to established platforms.
4. Instant Software. When an AI agent creates a custom application on the fly to fulfill a prompt or request—operating seamlessly in the background and invisible to the user—it can be considered custom or “instant software”.
5. Service-As-A-Software. While Software-as-a-Service (SaaS) is software offered as a service, Service-as-a-Software is the service itself performed by the software, field by the rise of agentic AI capabilities.

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1. Indie tools

The advent of Generative AI has spurred the creation of numerous startups. In the Martech space alone, we’ve identified over 2,324 new GenAI-specific tools. Download the PDF with clickable logos here.

We call these indie tools because most have little or no institutional VC funding. It is remarkable how much can be built so quickly with minimal investment today. These Indie Tools predominantly form the Long Tail of Martech.

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2. Incumbent tools

At the other end are the Head (and Torso) tools—incumbent platforms that have embraced Generative AI by embedding it into their existing ecosystems. Our research among 283 survey respondents shows that The use cases for Generative AI in these platforms are as frequent as those seen in indie tools.

3. Challenger tools

Challenger platforms disrupt traditional Martech categories. For example, ChatFactory.ai offers a one-page website that functions as a dialog box, allowing users to ask questions instead of browsing the site, challenging the status quo of CMS platforms.

Challengers emerge from both indie and incumbent tools. While indie tools often lack sufficient cash flow to disrupt, incumbents have the financial resources but lack the urgency. This may create an opportunity for Torso tools to bridge the gap.

4. Instant Software

True disruption comes from unexpected angles. While incumbent and challenger platforms compete, one of AI’s most significant impacts is the rise of "instant software." For example, prompting ChatGPT creates software in the background to respond to a query.

Instant software, enabled by Generative AI and low-code/no-code platforms, allows businesses of all sizes to build tailored solutions rapidly. These tools democratize software development, extending innovation beyond traditional enterprise environments and expanding the Long Tail into a Hypertail.

5. Service-as-a-Software

While Software-as-a-Service (SaaS) is software offered as a service, Service-as-a-Software involves the software itself performing the service. For example, Jasper AI autonomously writes marketing copy based on a brief. The software performs content creation, a task traditionally handled by humans.

This concept represents a shift in how software operates. Traditionally, software-assisted human labor, enhances efficiency, creativity, and the ability to manage digital assets. Today, agentic AI—capable of performing tasks autonomously—transforms software from an assistant into a direct provider of services, redefining technology’s role in the workforce.

The Impact of GenerativeAI on Martech Stacks

Leveraging Generative AI in your Martech stack involves fueling both customer data and content creation. Customer data helps understand customer needs, while content creation enables the personalization of messages that drive purchases.

In stacks, customer data is managed in the Data Layer, while content personalization is processed in the Content Layer. These layers have evolved differently and at different speeds.

1. The Evolving Data Layer + APIs

Cloud data warehouses and lakehouses, such as Snowflake and Databricks, now form the backbone of the modern data stack. These systems aggregate structured and unstructured data across organizations, enabling marketing teams to access richer customer insights from departments like sales and customer service.

The shift from linear, one-way data flows to circular, two-way ecosystems has unlocked immense value. Generative AI thrives in this environment, leveraging unstructured data to deliver innovative solutions embedded within apps, as standalone tools, or as custom-built software tailored to business needs.

2. The Underdeveloped Content Layer + Brand LLMs

Despite advancements in the data stack, less progress has been made in managing content as "data." Visual and branded content has been harder to organize and leverage compared to structured, API-accessible data.

This is beginning to change with Generative AI and multi-modal models. A "brand LLM" or "brand master file" could serve as a universal content layer, representing a brand's identity and personality. This model can generate campaign-specific variations, fine-tuned for specific contexts, enabling hyper-personalization. Significant advancements in universal content layers are expected by 2025, unlocking new opportunities for content innovation.

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