The internet is moving from an “attention economy” to an “interpretation economy,” where AI agents constantly evaluate, compare, and summarise the web on behalf of buyers. To survive this shift, organisations can no longer rely on traditional search and persuasion tactics alone.
Here is the overview of the core concepts
- Marketing now has two audiences: You must serve both humans (who need trust and emotion) and AI agents (which need structure and facts). If you only optimise for human attention, an AI assistant will flatten your brand in a comparison summary, and you will lose out.
- You must build a “Machine-Readable Foundation”: AI agents do not care about clever taglines or brand videos; they demand “legibility.” Businesses must operate a dense, factual “machine-readable foundation”, a system of verifiable claims, accurate pricing, product constraints, and proof points.
- The high cost of synthetic content: Using AI merely to automate and mass-produce generic content is a race to the bottom. AI agents quickly filter out vague, unopinionated fluff.
- The Reputation Risk of AI Over-Claiming: Exaggerating your company’s (or your own resume’s) AI capabilities with generic buzzwords is harmful.
- Human memory “Seeds the Prompt”: Because AI handles the tedious comparison work, the human’s primary job is setting the preference. Strong brand experiences, including offline interactions, are vital.
Ultimately, surviving this era requires a unified strategy: you must create deep memory for humans and crystal-clear structural clarity for agents.
You are losing deals you do not know you were considered for.
The internet economy is shifting under us. For 25 years, we have operated in an attention economy. You fought to get noticed. You optimised for eyeballs. You measured clicks, impressions, time on page, bounce rates. The entire digital playbook assumed that if you could capture human attention long enough, you could move someone through your funnel.
That model is not dead. But it is no longer the whole game.
I recently watched a client lose a six-figure deal to a competitor they had never heard of. The buyer never visited their website. Never attended their demo. Never spoke to sales. An AI chatbot made the introduction, ran the comparison, and built the shortlist. My client was not on it.
This is happening everywhere now. Sixty-nine percent of B2B software buyers are choosing different vendors than they originally planned because of AI chatbot guidance. Roughly a third are buying from companies they had never heard of before the assistant surfaced them.
Ask yourself: Have you changed your search behaviour now compared with 12 months ago? That should be enough to understand the weight of the shift. Many of us resisted using ChatGPT when it first came out (or Gemini, Perplexity, Claude). Now we’re all comfortable with it, and it’s part of day-to-day work.
We are moving into what is being called an interpretation economy.
AI agents are now constantly evaluating, comparing, and summarising the web on behalf of buyers. They read your website, your docs, your pricing page, your customer stories, your changelog. They read your competitors too. Then they compress all of it into a paragraph and hand the buyer a shortlist.
If your digital presence is not built for that reality, you are getting flattened in comparison memos you will never see.
Marketing now serves two audiences
Most companies still think marketing has one job: to persuade humans.
That is half the job now.
You’re also serving the AI that reads your site at 2 AM on behalf of a buyer in a different timezone, retrieves six competitors simultaneously, and compresses your entire value proposition into a three-line comparison without ever showing you the prompt. What they need is legibility.
An AI agent operates on retrieval timeouts of 1-5 seconds. If your key differentiation is buried three clicks deep, wrapped in marketing fluff, or locked in a PDF, it doesn’t exist. The agent moves on, and you lose the comparison without knowing you were in it.
Humans still need the older things. Story. Trust. Memory. Emotional resonance. Reasons to care. The marketer’s job now splits: build brand experiences strong enough that humans specifically request you by name, and build structural clarity so precise that an AI agent can explain your differentiation in 40 words without distorting it.
Those are different jobs. You have to do both.
Example: The Managed Portfolio chosen without visiting a website
Let the story make this concrete with this example of a buyer journey in the finance space.
The Broker and their Customer needed to select a managed portfolio for the customer’s investment strategy. They had a vague preference for a platform they had used before, a company in the Financial Sector. They had decent products, fair pricing, and a solid reputation. There was no strong loyalty, just familiarity.
They did not visit their website. Instead, they opened an AI assistant and described exactly what was needed: the customer’s specific risk profile, a target asset allocation of 50% growth and 50% defensive assets, desired fee structures, and the customer’s long-term wealth accumulation goals. The assistant came back with three options, ranked, with the tradeoffs explained in a neat paragraph.
They picked one. It was not the platform from the company in the Financial Sector the customer had been using.
That company lost the allocation without ever knowing the customer was shopping. They could have been in the comparison. The specs for their portfolios were there, and the community chatter and mentions in financial forums were there. But they had a critical flaw in their AI Search Optimisation (AISO): when the AI attempted to trace their reputation back to the primary source on their website, it hit a wall.
Instead of providing a legible ‘machine-readable foundation’ of structured data, the company’s product page functioned as a ‘link farm’ for downloadable PDF disclosure statements. Autonomous agents operate on a strict one to five-second timeout window, meaning they cannot easily download, parse, and compare unstructured PDFs to answer queries. Sending an AI to that website was like asking a robot librarian for a quick 50-word summary of a portfolio, and instead being handed an unlabelled filing cabinet full of PDFs.
Because the AI couldn’t find 40-60 word snippable summaries, static HTML data, or the required JSON-LD schema to understand the financial products, it simply dropped the task. It went to a competitor’s website, where the answers were printed clearly on the surface in static HTML.
When they outsourced the comparison, the AI couldn’t build a consistent map of the company’s offerings, so it bypassed them entirely. They became completely invisible, losing the sale because the agent’s map of their company was worse than the agent’s map of the eventual winner.
This is what the agent layer does.
It does not replace the buyer or the financial broker. It replaces the part of the buyer that used to dig through product disclosure statements, do the tedious comparison work, and carry brand preference into it. When a company’s data is locked in PDFs rather than being machine-readable, the agent fills the vacuum with whatever competitor’s retrieved data surfaces best, leaving the unoptimized company completely out of the conversation
The data you should be watching
The shift is not theoretical anymore.
- Fifty-one percent of B2B buyers now start their software research with an AI chatbot more often than Google. That number was 29% a year ago.
- Eighty-three percent report feeling more confident in their final choice when they use AI chatbots. The chatbots are now the top source influencing which vendors make buyer shortlists.
- Just 3% of buyers say AI chatbots have not meaningfully changed their research habits. This is not fringe behaviour anymore.
- Gartner projects that by 2028, 90% of B2B buying will be AI agent intermediated. That is over $15 trillion in B2B spend flowing through AI agent exchanges.
If you think you can wait this out, you are already behind.
You must build a machine-readable foundation
Here is what most companies are missing. AI agents do not want clever taglines. They want facts. They want proof. They want structure.
You need that machine-readable foundation:
- A dense factual system of verifiable claims
- Accurate pricing
- Product constraints
- Proof points
- Customer language
- Use cases
- Competitive comparisons
For an AI crawler, your technical documentation, public APIs, and pricing tables are just as critical for lead generation as your homepage. An AI agent doesn’t care that your homepage was workshopped for six months while your API docs were written by engineers in their spare time. It reads both, weighs both, and if they contradict each other, it downgrades your credibility.
If your public surfaces are inconsistent, the agent builds an inconsistent map of your company. If the homepage says one thing, the docs say another, the pricing page hides the real packaging, and the customer stories never explain what actually changed for the customer, the agent ends up with a bad map.
When the agent has a bad map, you lose invisibly.
- You may never know that a buyer asked an assistant to compare three vendors and you were summarised poorly.
- You may never know that your product was left off a shortlist because your integration story was buried three clicks deep.
- You may never know that a procurement workflow categorised you incorrectly because your category language was vague.
Clarity becomes a structural requirement.
The high cost of synthetic content
Most of the AI-in-marketing conversation right now feels too small to me. People are focused on workflow. AI can generate ad variants, draft lifecycle emails, build landing pages, turn webinars into clips, and produce sales collateral. All of that is real. None of it is the deepest shift.
The deepest shift is that the cost of producing assets is collapsing toward zero.
Beware of the most commoditised layer
If your entire AI strategy as a marketer is “we can make more stuff,” you are building your career on the most commoditised layer of the job. Every serious marketing organisation will have content production pipelines within two years. That will be table stakes.
The harder question is what all that stuff is supposed to do.
Using AI to automate and mass-produce generic content is a race to the bottom. AI agents filter out vague, unopinionated fluff fast. If you are not opinionated about what makes you different, provable about why that matters, and specific enough to survive compression, you get averaged out.
Every surface becomes source material
Agents do not respect the org chart. They read what is publicly available and form a model of your company. Your website, product pages, docs, pricing, comparisons, integrations, customer stories, changelog, blog, help center, third-party listings. All of it.
I’ve spent years watching businesses treat docs as a support issue and pricing as a sales problem. That organisational divide just became a commercial liability.
Here’s the problem: your organisation chart says docs belong to product, pricing belongs to sales, and the changelog belongs to engineering. The AI agent doesn’t care. It reads all of it, builds one unified picture of your company, and when those surfaces contradict each other, it marks you as inconsistent and moves on.
When an AI can’t find clear pricing in five seconds, it moves to the competitor whose structure is clearer, regardless of whose ‘job’ pricing clarity was supposed to be. Marketing has to get serious about product truth, not as a nice-to-have but as the foundation of how the company gets understood.
The reputation risk of AI over-claiming
If you work at a company that feels behind, the pressure to sound AI-native will be intense. Competitors will say they are AI-native. The board will ask what your AI story is. Sales will want sharper AI language because prospects are asking. Someone will point to a competitor’s homepage and ask why you do not sound like that.
The temptation will be to stretch. Do not.
AI-washing buys short-term air cover and creates long-term trust debt. It makes the company harder for agents to understand and harder for humans to believe. It forces sales to explain around the website. It forces customer success to manage disappointment they did not create.
Both human buyers and AI comparison systems actively filter out unprovable, synthetic-sounding claims. If the product is not AI-native, do not call it AI-native.
Describe what it is, plainly.
If the company has one narrow AI feature, explain the narrow feature in plain terms. If the AI work is mostly internal, better tooling for support or faster ops, say that. Internal AI is not embarrassing. Pretending it is external is.
The same trust-debt logic applies to you as an individual. If you are positioning yourself for a role in 2026, the pressure to AI-wash your own profile runs just as strong. A LinkedIn bio polished into AI-native language by a rewrite tool signals nothing distinctive to a recruiter whose research workflow has already absorbed ten thousand identical bios.
What gets through is the opposite of polish: a point of view someone could quote back to you, a project described in enough operational detail that the reader can picture the work being done, a tradeoff you made that another candidate could not credibly claim.
Human memory seeds the prompt
Agents do not make brand less important. They make brand different. People are still going to remember companies, trust certain names more than others, and carry preferences into agent-mediated decisions. They will say, “Find me something like this.” They will say, “I saw a demo from that company and it looked real.” They will name the brands their friends mentioned.
Human memory becomes more precious as more of the transaction is mediated. The agent may do the comparison, retrieve the options, summarise the trade-offs, and recommend a shortlist. But the human still supplies the preference. The human names the brands they remember and sets the constraints.
So you have to ask a sharper brand question. Beyond “how do we get attention,” the real question is: what memory do we want a human to carry into an agent-mediated decision?
That memory has to be specific.
- It cannot be “innovation”
- It cannot be “productivity” or “AI-powered growth.”
- It cannot be a generic claim that any competitor could produce after one decent prompt.
A useful brand memory is a specific category belief.
A repeated phrase. A product behavior people associate with you. A demo that sticks. A customer transformation. A founder’s point of view that no competitor could fake, paired with visual language that someone recognises across surfaces. The human should remember something clear enough that it can become an instruction to an agent later.
- “Find me the one that does X.”
- “Compare this to that company I saw.”
- “Show me tools like this, but for my industry.”
- “Find the product with the best workflow for a sales-led team of fewer than fifty.”
That is the test. Did your brand work hard enough that a buyer can describe you in a single, specific sentence to a piece of software they trust?
If yes, the agent layer becomes an amplifier.
If no, the agent layer flattens you into the category mean.
Weaving humans and agents together
These two layers should reinforce each other. If your human brand says one thing and your agent-readable reality says another, the company gets weaker in both directions.
- The human-facing work creates memory, preference, trust, and language.
- The agent-facing work creates clarity, structure, evidence, and retrievability.
If your story is memorable but your product truth is incoherent, the agent will flatten you badly. If your public materials are well-structured but the brand has no memory, you may show up in comparisons but fail to be chosen.
The best marketers refuse to choose between the two.
Marketing teams need to become more technical
You do not need to become an engineer. You do need to become more technical than marketers used to be.
However, you and the team around you should understand what the website and docs expose to retrieval, and which parts are invisible because they live behind login walls or in PDFs no one indexes. You should understand how agents compare products: what fields they look for, what they treat as authoritative, how they weigh third-party reviews against vendor claims.
You should understand how internal content pipelines are evaluated and where they fail. You should understand why structured claims matter as a way of giving the agent something specific to cite.
As a function in the business, Marketing teams need to stay relevant and understand that it is a shift to a function that has become more technical. The marketers who refuse to learn that will spend the rest of the decade complaining that the website team (or Agents!) will not take their meetings.
Operators of meaning
The best marketers will feel more like operators of meaning than producers of campaigns.
You build the agent clarity audit. You walk every public surface an agent might use to understand the company: homepage, product pages, docs, pricing, comparisons, integrations, customer stories, changelog, blog, help centre, and third-party listings. You ask what picture emerges from each, and what picture emerges in aggregate.
Your marketing role becomes an investigator role
You identify contradictions. Missing proof. Vague claims. Stale docs. Buried integrations. Pricing that hides the real shape of the business. Comparisons unsupported by evidence. Places where customers describe the product better than you do.
You build the human memory map. You ask what one or two things a person should remember after encountering the company once. Then you test whether the homepage, demo, founder narrative, customer stories, sales motion, and product experience actually create that memory.
You build the production pipeline. You automate what can be automated, but you do it with standards. The pipeline generates, yes. More important: it rejects. It rejects vague copy, unsupported claims, off-brand visuals, generic positioning, and content that is technically correct but strategically useless.
The discipline of saying no inside the pipeline is what keeps quality from collapsing as volume scales.
And here you find yourself right back at the beginning, using agents and search to scan your own content, how it shows up, how your competitors show up, what’s accurate and what needs adjustments… all through the lens of AI-based search (AISO, GEO, AI Search, Agentic Search… you get the picture).
AI-enabled vs. AI-native marketing and teams
The real difference between an AI-enabled marketing team and an AI-native one comes down to this.
- The AI-enabled team uses tools to do the old work faster.
- The AI-native team redesigns what marketing is responsible for.
Marketing becomes the steward of how the company is understood across human attention and machine interpretation. The marketer knows what the company can honestly claim, what customers actually say, what proof exists, what agents will retrieve and how, and which memories matter.
The marketer also knows where the company is being vague because the strategy is vague, and has the standing to say so. That is a much better job than focusing solely on producing more assets. It is also a much harder one.
The split is here, and it’s already shaping how you show up
The marketers who can hold both layers at once are the ones worth betting on. The ones who think they can pick one side and ignore the other will spend the next few years wondering why their work stopped connecting. Marketing is splitting in two.
- Memory for humans.
- Clarity for agents.
You cannot wait for perfect clarity on how this plays out. The buyers are already using AI to make decisions. The agents are already reading your website. The comparisons are already happening.
The question is whether you are being remembered and understood, or averaged out and forgotten. I have spent 27 years helping businesses solve complex digital problems. I have seen platforms fail because the strategy was unclear, projects collapse because the machine-readable foundation was missing, and companies lose deals they never knew they were considered for.
This shift is different
It is structural. It changes what marketing is responsible for at the root. The companies that adapt will not be the ones celebrating content velocity. Content velocity will be table stakes. The winners will be the companies that become clearer, more trusted, more memorable, and easier to choose.
That work starts now.
