I ended my last blog making the case that content strategy is the first thing Agent-Based Marketing (the new ABM) breaks, and the first thing it rewrites. At Cannes Lions just over a month ago, that argument came to life in nearly every conversation I had with customers. But what struck me most wasn't the content question - everyone was on the same page there. It was a harder one: if buyers are now doing most, if not all, of their research through AI tools or interfaces, how do we as marketers know how to respond to their audience? We don't know what they want, where they are in their decision, or when they're ready to talk to us. This new intent data is richer than ever, but it's happening somewhere we can't see. And figuring out how to find it, read it, and act on it might be the most important marketing challenge of this moment.
That's what this post is about. Part 1 was about where buyers arrive. Part 2 was about what they find when they get there. This post is about what we can't see and what marketers can do about it.
The most important signal we’ve ever had is one we can’t see.
For 20 years, the B2B world has tracked buyer intent from all types of web engagement: clicks, page views, form fills, searches, and time spent on page. We’ve built our entire measurement, lead, and journey management strategy around scoring models, attribution frameworks, journey stages, and sales handoffs. This has evolved a lot, and it’s become quite sophisticated because all of this occurred with our audience interacting on surfaces we have visibility into.
Prompts change everything. At Forrester’s B2B Summit, researchers shared that the average prompt is 15 to 23 words long. That’s way longer (and way more context) compared to the 3 to 4 words in a traditional search on Google. A buyer typing “What’s the best marketing platform for an enterprise team in the B2B / tech industry that needs to optimize visibility and engagement with humans and AI agents?” is sharing significantly more intent in one prompt than they might in a simple Google search of “web visibility product” or website visits.
That’s the paradox of this moment. The signal just got dramatically richer. But it is essentially invisible.
The traditional customer journey has never been linear — we know this. But at least it was observable. We could track our audience moving from the Google search to a blog post to the product page to a demo. We could understand, by their online journey, where they were in their buying journey. We could enrich our signals with third-party intent data. We can still do all of that today, but now the most influential moments are happening outside of our trackable spots and inside conversations with ChatGPT, Gemini, Claude, etc. As expected, these conversations generate rich intent data that is invaluable, but on platforms that do not necessarily share it back.
While Forrester projects organic search traffic will fall by 50 to 75 percent, conversions are expected to grow as visitors who do arrive on owned property are three times more likely to convert. Now marketers face an attribution and measurement problem. Understanding the middle of the journey, where buyers are prioritizing and vetting vendors, is going dark. Not only that, but it’s not the human doing the work — it’s a machine (I will talk more about this a bit later in this blog).
From buying stages to buyer mindsets.
Here’s where our industry needs a reframe. We are moving from trying to determine buying stages to understanding the mindset of a buyer.
We’ve mapped buyers to stages for decades: discover, explore, evaluate, buy, use, renew. Even though we know this journey isn’t linear, we have continued to score and qualify leads or accounts against this model. The whole purpose of developing a nurture program or an engagement model is to move buyers from one stage to the next.
But when today’s buyers interact with LLMs, they are not progressing through stages. Instead, they are showing their mindset, meaning a specific problem they're trying to solve or a decision they're trying to make. A stage tells you where a buyer sits on your map. A mindset tells you what they're actually trying to accomplish.
This is intent, but not intent in the traditional sense. This intent is robust, conversational, and natural language. And if observed and analyzed effectively, it can reveal not only buying intent or journey stage, but the buyer's role, their business problem, their industry, and how deeply they understand the solution they're looking for. This is what rich intent looks like in the agentic era, and it can reveal the buyer's mindset.
The difference matters. The "validate my shortlist" mindset vs. the "prove ROI" mindset would both be considered in the “evaluate" (mid/late funnel) stage. But each buyer in reality needs two different things — one needs a review, the other needs proof. If you treat them the same way, you're solving the wrong problem for at least one of them.
What this means for scoring, qualification, and handoffs.
Scoring models need to adapt: Our current models that capture the buyer’s journeys are still valid. And buying groups we’ve built won’t disappear. But what we are currently missing is that these models are scoring only a fraction of the activity on the surfaces we own. And our buying group is only valid for human participants, a fraction of what truly represents the buying group of today.
Intent data needs to expand: Again, our intent data isn’t useless now, either. We should continue to rely on it as we are understanding of top of the funnel intent. But the providers many of us depend on face their own version of this disruption: their models rely on web traffic datasets which track less of the journey than before. We need to push ourselves and the vendors we work with to redefine “intent data” in the agentic era.
The handoffs: In a B2B purchase cycle, before a buyer speaks to a sales rep, they’ve already completed 90% of their journey and decision-making. We are trending even closer to the zero-click purchasing model in B2B, closer to the consumer model than ever before. As the discovery and evaluate stages are happening inside AI tools, the handoff to sales is completely different now. It’s no longer a trigger to send to sales, but needs to be thought of as a gradual, orchestrated transition.
And finally, attribution: Don't over-rotate on channel attribution in its traditional sense. Every channel now plays a dual role: it's both an acquisition and engagement vehicle and a contributor to your brand visibility. If you're optimizing channels for a predefined journey or single outcomes, you could be optimizing your way out of your own brand visibility. That's a thread I'll pull on more in Part 4.
When the buyer is a machine.
Today’s buying groups often see the first vendor interaction happening machine-to-machine. Agents are evaluating compliance requirements, benchmarking performance, prioritizing technology by use cases, modeling cost vs. value, etc. This is where we are today.
But tomorrow (and even today) will get even more complex. The machine won’t just prioritize and vet products, but it will be the actual buyer — even in the B2B world.
We’re not fully there yet, but we are not that far from this reality either. So teams that are changing their mindsets to begin understanding machine-to-machine signals will have a leg up against everyone else.
How we’re approaching this at Adobe.
Our engagement model is changing, too. We’re rethinking how we capture signal, score intent, and orchestrate the transition from marketing to sales — and two announcements we made at Cannes Lions are central to how we’re doing it.
Adobe Brand Visibility, announced at Cannes, is a single solution to understand and optimize your brand presence across both human and AI-driven experiences. It brings together Semrush's data, including the world's largest database of search and prompt behavior, with execution capabilities, giving marketers visibility into how their brand appears across AI-driven and traditional surfaces. The signals that matter most aren't the ones we're buying. They're the ones we're earning.
Adobe CX Enterprise Coworker, which went generally available at Cannes, is our answer to the handoff problem. You define the outcome. Coworker builds the workflow, pulls in the right data, activates the right specialized agents, and monitors performance against the goal in real time.
What comes next?
The new agentic signal is hard to capture. The customer journey’s most impactful moments are invisible. Scoring models & qualification thresholds drive only a portion of what’s happening. And the buying group now includes humans and agents. Visibility for B2B marketers is very different in today’s world. And that’s where your brand comes in.
In Part 4, I’ll make the case that brand might be the most underrated asset in the agentic era.
This is Part 3 of a 5-part series on Agent-Based Marketing:
Part 1 — The New ABM: Why the most important acronym in B2B marketing just got rewritten
Part 2 — The New ABM: Why content strategy needs a ground-up rethink, not an SEO upgrade
Part 3 — The New ABM: Prompts, Not Clicks: Rethinking signal, intent, and the customer journey (you are here)
Part 4 — The Great Equalizer? Why brand matters more in an agentic world
Part 5 — Building the Agentic Marketing Org: Operating model, mindset, and the work ahead
