For years, business-to-business (B2B) marketing has relied on buyer personas. These are composite sketches of ideal customers meant to guide messaging and content strategy. We give each persona a name, attributes and funnel stages, hoping to reach the right audience with the right message. Each shapes how we structure campaigns, craft content and align teams.
This approach made sense when buyers followed predictable paths and marketing controlled the flow of information. But that’s no longer the case. Buyers now navigate independently, using generative AI to explore vendors, compare solutions and shape their own journey. They’re not waiting for messaging, but rather they’re gathering it on-demand.
According to Forrester, nearly 9 in 10 B2B buyers now use GenAI at some point in their decision-making. That’s more than a trend. For B2B marketers, it’s a signal on changing buyer behavior. However, some haven’t grasped what this means—both for how companies organize their growing number of internal AI initiatives and how they think about better understanding and engaging external stakeholders with help from AI.
Offering a More Fluid Buyer Journey
As the path to purchase becomes more exploratory, the old assumptions about who a buyer is and how they engage are less reliable. AI enables a more flexible approach that can help marketers respond to behaviors in real-time rather than relying on static classifications.
The goal isn’t to eliminate segmentation, but to augment it. AI gives marketers the ability to adjust content and conversation in response to individual preferences, timing and context. What emerges is something more dynamic: personalization based on interaction, not just inference.
This evolution reflects a broader truth that precision is no longer about hitting the right persona, but about adapting to the right moment.
What Happen When Our Target Moves?
The shift is especially clear in how we think about engagement. The traditional marketing model of building personas was aimed at fixed targets. We grouped buyers by industry, role or funnel stage, and tailored content accordingly. Success depended on how close we could get to the mark.
AI introduces a different possibility. Rather than assuming who someone is, systems can respond to what they say and do. For example, a voice AI interface could be trained on a company’s internal documentation (such as whitepapers, FAQs and case studies) and interact directly with website visitors. Users wouldn’t need to declare a role or fill out a form. They simply could ask questions, and the system would respond with content tailored to their query.
That experience can be adjusted in real-time by region, format and topic depth without requiring a marketer to predefine the path. It’s a small but important step toward what personalization was always meant to be.
Real-time engagement like this only works when AI is applied with intent. That starts with solving real business problems instead of adopting tools for their own sake.
Applying AI Where It Matters
The most effective teams start with use cases, not technology. They ask: What slows us down? What could improve conversion? Where do we waste the most time?
In another example, marketing teams might structure cross-functional “squads” around core functions like content development, demand generation and product marketing. Each squad can test AI applications aligned to their specific goals. ChatGPT might support blog creation while its Deep Research modules accelerate competitive insight and Google NotebookLM helps with sales training or meeting prep.
Every use of AI should be grounded in a business outcome—faster execution, more relevant content, better-informed outreach. That clarity keeps the focus on building an AI “toolbox” to support key goals of any’s team’s strategic investments in GenAI.
Build, Test, Repeat
Applying AI at scale requires more than strategy. It needs the space and time to experiment. That’s why some teams create dedicated studios or labs where they can safely explore AI before rolling it out.
This creates a controlled environment to test, refine and fail quickly. Once a tool or technique shows promise, agile squads take it forward. The goal is not perfection, but progress to learn fast and scale what works. It also keeps experimentation tied to marketing metrics that matter, including acquisition costs, sales cycle time and buyer engagement.
Personalization Without Assumptions
Applying AI successfully must also consider how we can deliver personalized results. As marketers, we shouldn’t only be pushing content to a general audience segment, but also letting the buyer set the terms and then responding accordingly.
This shift shows up in many ways. A product marketer might use AI to surface the most relevant features based on live input. A content strategist might see navigation patterns and adjust recommendations mid-session. A demand gen lead might explore how AI can optimize engagement as the conversation evolves.
These are emerging practices that, when implemented with care, bring marketers closer to how buyers already operate.
A Different Kind of Readiness
B2B marketers have always talked about “meeting buyers where they are.” That phrase has new weight in a world shaped by AI.
Buyers are ready to lead the interaction. They’re using tools that help them ask smarter questions and move faster toward a decision. Marketers who build with that in mind—focusing on adaptability, value and context—are better positioned to earn attention and trust.
Precision-driven personalization is becoming a more scalable and responsive complement to traditional persona strategies. It may not be a total reinvention, but it is a realignment. One that rewards those willing to listen more, assume less and move at the speed of the buyer.
