By 2028, Gartner expects 90% of B2B buying to run through AI agents. Writing content for AI agents is mostly the answer-engine work I already do, with the persuasion split off from the facts and the verification turned up, then sold back to you as a shiny new specialism. This piece covers what an agent extracts from your page, why a buried value proposition or hidden pricing gets you cut from its shortlist before a human sees you, and the part of the decision that still belongs to a person.
An AI agent has probably already read one of your pages, but it didn’t admire the hero image or scroll to feel the brand, and it gave your fuzzy claim no benefit of the doubt. Writing content for AI agents starts from that unglamorous reality: the reader you have to satisfy first is a machine deciding whether you belong in a shortlist, and it won’t squint past what a sympathetic human tolerates.
This is important now because the buying research step is the one moving fastest: Gartner predicts that by 2028, 90% of B2B buying will be AI agent intermediated, pushing more than 15 trillion dollars of B2B spend through AI agent exchanges (Gartner, October 2025). That’s a prediction, not a finished fact, and I treat vendor forecasts as directional. What’s already here is the research behaviour underneath it: Forrester’s 2026 State of Business Buying, drawn from nearly 18K global buyers, found 94% use AI during the purchasing process (Forrester, via AuthorityTech, April 2026).
What does “AI does the buying research” actually mean?
Agentic AI is software that does more than answer a question, it carries out a multi-step task on someone’s behalf: researching options, comparing them against stated criteria, assembling a shortlist, and in some cases completing the purchase inside its own interface.
For a B2B buyer, that looks mundane and specific: someone tells an assistant they need a scheduling tool that integrates with their existing stack, sits under a certain price, and handles a particular compliance requirement; then the agent goes out, reads a stack of vendor pages, extracts what it can, and comes back with a ranked set. The buyer never types your name into a search bar, never lands on your homepage, and may never see a single email in your nurture sequence.
The consumer data shows how fast this can reorder once trust builds: Adobe’s Q1 2026 analysis of more than a trillion US retail visits found AI-referred traffic converted 42% better than non-AI traffic in March 2026, a reversal from converting 38% worse a year earlier (Adobe Digital Insights, April 2026; TechCrunch, April 2026). That’s retail, not B2B, so I wouldn’t lift the number into a B2B deck, but the transferable part is the mechanism: someone arriving from an assistant already did the comparing and lands pre-qualified, on the strength of content you may not have written for that reader.
Why an AI agent is your least charitable reader
An agent is your existing buyer stripped of patience and goodwill, reading everything at face value. Meaning, a human prospect reads “we help teams move faster” and fills in what you probably mean, while an agent reads the same sentence, finds nothing it can extract or verify, and gives the slot to a vendor who stated what the product does.
Every soft spot you’ve been getting away with becomes a disqualifier at that level of literalness: a value proposition that gestures at an outcome without naming it, a price parked behind “contact sales” so nothing comparable can be pulled, the CTA buried three screens down that assumes the reader keeps scrolling, or the confident claim with no source, sitting next to a competitor’s identical claim that carries a citation.
None of these lose a deal with a warm human who already likes you, but all of them cost you the shortlist slot when the first reader is a machine ranking vendors on what it can lift off the page. I made a version of this argument about human readers in good writing is your competitive moat.
What makes content machine-actionable
Machine-actionable content is content an agent can read, extract a specific answer from, and act on without a human interpreting the page for it. Most of the weight sits on clear specifications, real comparison data, and pricing signals it can actually parse, and the rest is delivery.
Clear specs mean the concrete facts of what your product is and does, written as text rather than baked into an image or a diagram; comparison data means the genuine trade-offs between you and the alternatives, presented as structured evidence, because an agent assembling a shortlist can lift a table and can’t lift a vibe; pricing signals mean enough of your real pricing model on the page that an agent screening on budget doesn’t drop you for having nothing to compare. Under all of it sits a technical floor: many AI crawlers fetch raw HTML and don’t execute client-side JavaScript, so a spec or price that only appears after a script runs may as well not exist to them; Adobe’s own read on why retail growth is uneven was that a quarter of the content on retailer homepages hadn’t been made readable to language models at all (TechCrunch, April 2026).
The gap between the two readers is easiest to see side by side.
| What’s on the page | How a human reads it | How an AI agent reads it |
|---|---|---|
| “We help teams move faster” | Fills in the gaps from context and gives you the benefit of the doubt. | Finds nothing to extract; hands the slot to a vendor who states what it does. |
| Price behind “contact sales” | Mildly annoyed, might still enquire. | No comparable price signal, so you drop out of a budget-screened set. |
| A CTA three screens down | Scrolls and eventually finds it. | Doesn’t infer intent; the next step never gets surfaced. |
| A claim with no source | Trusts the brand and moves on. | Down-weights the unverifiable claim against a sourced rival. |
| A comparison written in prose | Reads it fine. | Can’t lift a structured answer; a competitor’s table wins the citation. |
| Specs rendered only by JavaScript | Sees them load in. | Fetches raw HTML, sees a blank, treats the spec as absent. |
Structuring pages so a machine can extract from them cleanly is the tactical half of this, and I wrote the step-by-step version in write for humans, structure for machines. If you want to know where your existing library already fails the test, that’s a page-by-page job I broke down in how to audit your content library for AI readiness.
How do you get into the consideration set an agent assembles?
The shortlist is decided upstream, before your sales team hears anything, which is the part that should change how you plan. In human buying it was already true that most vendors were chosen before a rep was contacted: 6sense’s 2025 Buyer Experience Report found that 95% of the time, the vendor that wins the deal was already on the buyer’s Day-One shortlist (6sense, via Omnibound, May 2026); an agent hardens that logic because it builds the ranked set from what it can read and verify across the open web, and there’s no charming your way in after the fact if it never pulled you in the first place.
Getting into that set is a citation problem before it’s a persuasion problem; the agent is reaching for sources it can extract a clean answer from and attribute, and the research on what earns them is consistent: the Princeton GEO study across 10K queries found that adding attributed statistics lifted a page’s visibility in generative engines by around 41%, and citing external sources by up to 115% for lower-ranked content (Aggarwal et al., KDD 2024). Treat the exact magnitudes as directional, since the engines have moved on since 2024, but the direction has held everywhere I’ve applied it.
What still needs a human in the decision
Now the limit, because the breathless version of this story oversells it: the agent screens the field, but a human still makes the actual decision. Gartner found that 69% of buyers prefer to validate AI-generated insights with a sales rep at key decision points, and predicts that by 2030, 75% of B2B buyers will prefer sales experiences that prioritise human interaction over AI (Gartner, August 2025). The machine is very good at narrowing a field on stated facts, but it doesn’t build confidence in a six-figure commitment or read the room in a buying committee, and it certainly doesn’t carry the risk when a call turns out wrong.
“Buyers still turn to sales reps to validate AI-generated insights and support decision-making at critical moments in the journey,” Robert Blaisdell, VP Analyst at Gartner, told Demand Gen Report (May 2026).
So the play is to make the facts extractable enough that you survive the machine screen, then keep a human-facing layer where persuasion, proof, and trust-building happen for the person weighing the decision.
A practical checklist for content for AI agents
Run this against your highest-intent pages first: product pages, comparison pages, pricing, and the content a buyer’s agent would reach for when screening your category.
- Lead every key page with the answer in the first 40 to 75 words: what the product is, who it’s for, and the pricing model, before any brand throat-clearing.
- State the value proposition as a plain claim a machine can quote, with the specifics attached. “Move faster” is a mood, but “cuts approval time from days to hours” is a fact an agent can compare.
- Put specs, integrations, and pricing signals in readable text and tables. Anything trapped in an image or a JavaScript-rendered widget is invisible to a crawler that reads raw HTML.
- Attribute every proof point to a named source or your own data.
- Build the comparison a buyer’s agent is going to assemble anyway, as a table on your own page, before a competitor frames it for you.
- Make each page answer one question and stand on its own, so a fragment lifted out of context still holds up as a true claim.
- Keep a human layer for the persuasion the agent doesn’t do, because the person signing off still needs a reason to trust you.
None of this is a new content function, it’s the standard you should have been holding anyway, applied to a reader who has stopped being polite about the gaps. Teams already writing plainly and sourcing every claim, with pages built for extraction, have far less to fix than the ones leaning on a warm human’s willingness to fill in the blanks. If you’ve been meaning to tighten the substance, a literal-minded buyer is a decent reason to stop meaning to.
Frequently asked questions
Content for AI agents is web content structured so an autonomous AI system can read it, extract a specific answer, and act on it without a human interpreting the page first. In a B2B buying context, that means an agent doing research on a buyer’s behalf can pull your specs, pricing signals, and claims, compare you against rivals, and decide whether you make the shortlist. It’s the same substance-first content discipline good marketers already use, held to a reader with no patience for vagueness.
No. The agent typically screens and shortlists; the human still makes the final decision and still reads the page. Gartner found 69% of buyers prefer to validate AI-generated insights with a sales rep at key moments, so persuasion and trust-building for the human buyer stay essential. The move is to make the facts machine-extractable so you survive the agent’s screen, while keeping a human-facing layer for the person who signs.
SEO optimises to rank a page so a human clicks it, and answer engine optimisation (AEO) structures content so AI systems cite it in an answer. Writing for AI agents extends that to the buying task itself: the agent goes further than citing you, evaluating you against stated criteria and deciding whether to put you in a shortlist it hands to a buyer. The underlying work overlaps heavily, since all three reward clear, extractable, well-sourced content, but the stakes shift from visibility to consideration.
Partly, and the forecasts run ahead of today’s reality. Gartner’s 90%-by-2028 figure is a projection, not a current state. What is already measurable is that the large majority of B2B buyers now use AI somewhere in the purchasing process, and consumer data shows AI-referred visitors arriving pre-qualified after researching inside an assistant. The research and shortlisting step is moving now, so making your content machine-actionable is a present-tense job, not a 2028 one.
