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What happens to marketing when the person buying from you is no longer necessarily the first audience for your marketing?
AI in B2B marketing is changing who, and what, receives your marketing messages. What happens when the person buying from you is no longer necessarily the first audience for your marketing?
I have been working through this for a while. And the more I look at it, the more I think the answer sits not in a new set of tactics, but in understanding something that has changed about who, and what, is on the receiving end of your marketing.
The Human-AI Marketing Matrix: A New Approach to AI in B2B Marketing
When I look at how marketing works now, I no longer see one straightforward relationship between a brand and a buyer. I see four different interaction types happening at the same time. That is what sits behind what I’m calling the Human-AI Marketing Matrix.
The idea is simple. We have always segmented audiences by things like sector, role, need, behaviour or buying stage. Now I think we also need to think about how our marketing is being received, interpreted and passed on.
Because sometimes a human is reading it directly, sometimes a human is asking AI to find and interpret it, sometimes AI is summarising it back to a human, and increasingly, information may pass between AI systems before a person ever sees it.
So the matrix looks at four interactions:
- Human to Human: a person communicates directly with another person, emotion, trust, experience, relationships, events, community, direct mail, conversations.
- Human to AI: a person asks AI to research, interpret, compare or recommend, so your brand needs enough credible information available for AI to work with.
- AI to Human: AI finds, filters and presents information back to the buyer, so content needs clarity, evidence, authority and enough distinctiveness to survive summarisation.
- AI to AI: one machine creates, structures, retrieves, evaluates or passes information to another, so marketing needs to become increasingly machine-readable, attributable and verifiable.
The point isn’t that marketers now need four completely separate strategies. The point is that one marketing strategy increasingly needs to work across all four.
The Four Cs
To make the matrix practical, each interaction has a different priority:
What happens when AI gets a seat on the buying committee?
B2B buying was already complicated. A purchase rarely involves one person making one rational decision after reading one piece of content. There are stakeholders, budgets, risk concerns, internal politics, comparisons, procurement processes and competing priorities.
Now there is another participant in the room: the AI assistant.
It may not sign the contract, but it can influence the shortlist, the language used to define the problem, the alternatives considered and the information that reaches the buyer first.
That is why “being discoverable” now means more than ranking for a keyword. Your company needs to be understandable when an AI is asked a detailed, conversational question.
This is already becoming a meaningful part of B2B buying and selling: McKinsey reported in 2025 that 19% of B2B decision-makers were implementing generative-AI use cases, with another 23% in the process of doing so.
For businesses thinking about AI in B2B marketing, this creates a practical challenge. How do you make sure your brand is understood and represented accurately when buyers increasingly rely on AI to research their options?
The answer isn’t AI-generated noise
There is a lot of anxiety about content that “sounds AI-generated”. Fair enough. Generic, over-produced, interchangeable content is tiring for humans.
The answer isn’t to reject content that AI can understand, it is to create content that is useful to both humans and machines.
AI doesn’t need more vague thought leadership. It needs clear signals:
- What do you actually do?
- Who is it for?
- What problem do you solve?
- What makes your approach different?
- What proof do you have?
- What outcomes have you created?
- What are the limitations, trade-offs and use cases?
And humans need exactly the same thing, with more context, personality and confidence.
The issue isn’t whether content has been created with AI assistance. The issue is whether it contains original thinking, credible evidence and a point of view that could not have come from anybody else.
Google’s guidance for appearing in AI-powered search experiences points site owners back to helpful, people-first content, crawlability and structured data that matches what is visibly on the page.
You don’t need to learn a secret machine language. You need to become clearer, more credible and more consistent.
Human to AI: create content that can be understood
The first new discipline is human to AI.
This is the content we create so that AI systems can accurately understand our business, not to manipulate them or flood them with filler, but to give them something solid to work with.
That means:
- Clear service and product pages, written in plain language
- Specific industry, customer and use-case pages
- Case studies with real context and outcomes
- Consistent company descriptions across your website, social channels and third-party profiles
- Useful FAQs that answer the questions buyers actually ask
- Named experts with visible expertise
- Structured information that makes your content easier to retrieve and interpret
The point isn’t to write for a robot. The point is to make sure the truth about your business is available in a form that a robot cannot easily misunderstand.
AI to human: win the recommendation, not just the click
The second discipline is AI to human.
This is what happens when a buyer delegates early-stage research to an AI assistant. They may never type your brand name into Google. They may ask for a recommendation, a comparison or a summary.
Your goal isn’t simply traffic. It is to be accurately represented when AI answers the question.
That shifts the measurement conversation. We should still care about rankings, sessions, conversions and pipeline. But we should also ask:
- Does AI know what category we belong in?
- Does it associate us with the right customer problems?
- Does it understand our differentiation?
- Does it surface credible proof?
- Does it recommend us for the use cases we want to win?
This is a visibility problem, but it is also a positioning problem. If your own website, sales materials, case studies and external mentions describe you inconsistently, an AI system has no reason to construct a clear story on your behalf.
AI to AI: prepare for the machine-mediated journey
The most interesting, and slightly unsettling, category is AI to AI.
We are moving towards journeys where one AI creates a brief, another summarises vendor options, another reviews a document, and a human receives the distilled output. In that world, your carefully crafted marketing message may be reduced to a few sentences before anyone on the buying committee sees it.
That isn’t a reason to panic. It is a reason to ensure the few sentences are accurate.
Your marketing must leave behind strong, repeatable signals:
- A simple explanation of what you do
- A clear ideal customer profile
- Distinct language around your value
- Verifiable proof
- Public expertise
- Consistency across channels
- Enough substance for an AI to cite, compare and recommend
The brands that succeed will not be those producing the most content. They will be the ones producing the clearest body of evidence.
So where do humans fit?
The answer is: everywhere.
The more automated discovery becomes, the more valuable genuinely human interaction may be.
Events, live webinars, workshops, direct mail, thoughtful follow-up, strong communities, useful conversations, a sales call where someone understands the problem, a customer story told by a real customer.
These aren’t old-fashioned tactics to abandon in favour of AI. They are increasingly how brands prove there are real people, real expertise and real conviction behind the automated layer.
If AI helps buyers narrow the field, human experience is often what helps them choose.
The new job of marketing
Marketing has not stopped being human-to-human.
It has become a broader discipline: human to human, human to AI, AI to human and AI to AI.
We still need to make people feel something, build a brand they remember and understand messy buying committees and complex decision-making.
But we also need to make our businesses legible to the systems now helping those people make decisions.
As AI in B2B marketing becomes more influential, businesses need to think carefully about how their expertise, positioning and evidence are communicated across every stage of the buyer journey.
It is marketing that is clear enough for machines, useful enough for buyers and human enough to be worth choosing.
Where does this leave marketing?
Good positioning, a clear strategy, a real understanding of your buyer, something useful to say and the ability to build trust still sit at the heart of effective marketing.
What has changed is the environment those things now have to work in.
For now, I would start with four questions:
Are we creating connection?
Are we giving AI enough clarity?
Do we have the credibility to survive the summary?
And is our story consistent wherever it is being found?
If the answer to one of those is no, that is probably where I would start.
I would genuinely like to know what other marketers and commercial leaders make of this framework too. Does it reflect what you are seeing? Is there another interaction I have missed? Or are we in danger of overcomplicating something that is still fundamentally about getting the right message to the right person?
Tell me. I am still working through this one.
With love,
Sidi
Your on-demand marketer 💖💙
