Three Actions You Need to Take to Thrive in the Marketing Age | MarTech

AI is changing the way marketers work, how stacks are built, and how customers make decisions. To understand where these shifts are happening, Scott Brinker and I conducted the AI ​​Context Engineering in Marketing Survey (n=103, October 2025). We’ve identified three key actions that every marketing and martech leader must master now.

• Embrace three agent domains: Marketers are already working with a growing mix of Agents for Marketers, Agents for Customers and Agents of Customers, each reshaping the customer journey in different ways.
• To assemble hybrid stacks: Stacks are evolving into hybrid systems that combine deterministic SaaS with probabilistic AI. Integrating data, context and language-based intelligence becomes essential for a complete customer view.
• Be the change agent: Technology alone does not create value. Marketers must shift from a mindset of efficiency to a mindset of effectiveness, using AI to enhance creativity, decision-making, and new capabilities.

Together, these themes outline how AI is transforming modern marketing and what leaders must do next to stay ahead.

Three AI agents serve different purposes

90.3% of participants say they use AI agents somewhere in their stack. It’s safe to say: AI agents in martech are no longer experimental.

They pop up across the ecosystem and you can categorize them in many ways, from the underlying technology to their speed and depth of reasoning. But ultimately, AI agents are tools designed to serve humans. The most helpful way to understand them is to ask a simple question: Who do they work for? Who benefits from it?

Dig Deeper: How Agentic AI is Changing the Future of Marketing

When you look through this lens, the interaction between marketers and customers reveals three distinct domains.

1. Agents for marketers are designed for marketers and used by them to improve daily workflow, increase team efficiency, and accelerate content and campaign execution.
2. Agents for customers iinteract directly with customers, but marketers still determine the instructions, rules, and guardrails that shape their behavior.
3. Customer agents, a new category, are completely beyond the marketer’s control. They are managed by customers and guide their journey on their own terms, from research to evaluation to decision making.

Based on our research data, Agents for Marketers are used most often, with organizations using an average of 3.5. Companies are more careful with Agents for Customers and use an average of 1.71. And while Agents of Customers operate outside the marketer’s control, organizations still have an average of 1.46 tools or tactics to serve or influence these buyer-side agents.

Taking a step back, we can see how agents rank in order of adoption across all the agent types and use cases we studied.

1. Content production agents lead by a wide margin. 68.9% of respondents use this Agent for Marketers. Brainstorming, producing, and personalizing content represented the first big wave of generative AI use cases in marketing, and they remain easiest to execute with a human reviewing the output. It’s no surprise that these use cases are the most common.
2. Customer service chatbot agents (Agent for customers) come next. 54.4% of survey participants use chatbots on their websites or mobile apps. In addition to customer service, the acceptance of other Agents for Customers is declining sharply.
3. Agents of Customers are the true source of disruption in this new era. Buyers are increasingly turning to AI assistants such as ChatGPT, Claude, Gemini and Perplexity to research and evaluate products. A new discipline is emerging: AEO – AI Engine Optimization or Answer Engine Optimization, also called GEO or Genative Engine Optimization. The practices are still early. In our survey, the only widely adopted tactic was publishing AI-optimized content at 63.1%. Yet only 13.6% reported measuring AI inclusion rate or agent referral conversions!

Dig deeper: Martech stacks evolve through capabilities, not tools

Build hybrid stacks

With 90.3% of participants using AI agents somewhere in their stack, the next question is how to integrate them effectively. A stack is a collection of tools, and those tools are the habitat for customer data. Their job is to keep that data flowing, connecting, and forming a coherent understanding of the customer.

AI agents depend on that data. To deliver real value, they must work together with all the tools in the stack so they can access the context around each data point. This shifts the stack design. Managing tomorrow’s stack means balancing deterministic, rules-based SaaS with probabilistic, language-based AI. Integration is no longer optional. The real challenge is how AI connects to the different data types and sources in the organization.

Already, 37.9% of companies integrate AI agents directly with their cloud data warehouse or lakehouse. But a warehouse is just the starting point. Stacks contain numerous internal resources that provide the context agents need to form a comprehensive view of the customer. For example, a slight decrease in product use in itself means very little. When linked to support tickets, call transcripts, campaign engagement, or knowledge base interactions, the picture becomes clearer. Is the customer stuck? Frustrated? Evaluate alternatives? AI can only answer those questions if it has access to the surrounding signals.

This is why the most common internal data sources connected to AI agents today contain the richest context: customer profiles from a CRM or CDP (61.2%) and brand or marketing assets from a DAM or CMS (also 61.2%). These two resources anchor the two most important tasks of any agent workflow: understanding the customer and speaking back to the customer with the right message.

AI also enables the extraction of context from sources outside the stack. Companies are increasingly supplementing internal sources with external, unstructured data. The most common examples include third-party customer or prospect data (50.5%), customer and prospect websites (49.5%), and activity on social platforms such as LinkedIn, YouTube, Instagram and X (43.7%).

These signals are especially valuable in B2B, where the buying journey is long, distributed and often opaque. However, we also see spillovers to B2C, supported by the increasing use of external intent signals (35.9%).

Become the change agent!

Although the word agent suggests autonomyIn all the cases we examined, there are people involved in deciding, approving, reversing, or revising. That brings us to the fourth type of agent, the change agent. You.

As change agents, we must shift our mindset from saving time to creating value. Simply put: change from efficiency (alone) to effectiveness.

• Efficiency: Do more with less. Optimize what already exists.
• Effectiveness: Do better with more. Rethink what is possible.

Many companies are pursuing efficiency, essentially digital labor arbitrage, and are using AI to cut costs and increase short-term profits. However, the long-term winners will focus on effectiveness or digital workforce augmentation, using AI to augment human creativity, context and decision-making. This is where sustainable value is created.

Think about it this way.

• Efficiency gives you speed.
• Effectiveness gives you direction.

You can automate yourself and end up in a dead end if you never ask yourself if you are still solving the right problem. The promise of AI is not just about productivity. It’s a possibility.

The shift from efficiency to effectiveness transforms AI from a cost-saving tool into a growth engine, not only improving operations but also reinventing offerings, customers and markets. The real return on AI starts when you stop counting minutes saved and start counting new opportunities. Your imagination is the only limit.

Dig deeper: ABM is not the B2B savior we were promised

When the Wright brothers built the first airplanes, they had no idea they were paving the way for airports, catering services, baggage handling, cabin crew jobs and air cargo carriers. Yet these industries emerged because the core breakthrough created space for new value to be formed around them.

Imagine what AI can do for your customers and step into the role of change agent within your company.