What is agentic commerce in B2B, and why should marketers act now?
Agentic commerce in B2B is when AI agents, not people, do the researching, comparing, quoting, negotiating, and even buying. Mirakl projects agents will intermediate 90% of B2B buying by 2028, pushing an estimated $15 trillion through autonomous purchasing flows. Deloitte finds nearly 40% of B2B buyers already use agentic AI in purchasing, while only 24% of suppliers use agents in sales. That 40% versus 24% gap is the whole story: buyer-side automation is running well ahead of seller-side readiness, and the sellers who wait will lose the bids.
The vendor content that ranks for “agentic commerce” comes from commerce platforms and consultancies (Deloitte, Mirakl, commercetools, BigCommerce, Salesforce, IBM). Almost all of it treats the shift as an IT problem: clean up product data, upgrade ERP, expose APIs, pick protocols. Very little of it tells a B2B marketing team what to do with its editorial strategy, lead generation, pricing, and sales enablement. That is the gap this post fills. If your content marketing is still being built for human visitors only, start with our companion guide on marketing to AI agents as a second buying audience before you read on.
How quickly is agentic buying changing B2B?
Faster than most GTM plans assume, and on a set of measurable clock speeds. According to McKinsey, AI task complexity doubles roughly every seven months, meaning agents can now autonomously run multi-step workflows that used to take 30-plus human hours. Salesforce puts enterprise agentic-AI adoption at 33% by 2028, up from under 1% today. Deloitte reports 92% of EDI-based buyers plan to shift to API-ready channels and nearly 90% of suppliers are upgrading ERP systems to support agentic integration.
Those are not far-off projections. They are mid-cycle rollouts happening inside procurement teams now. When your buyer’s own agent is the one filtering your product data against its price floor and compliance rules, your marketing has to be built to be read by that agent. Treating agentic commerce as a 2028 problem is a 2026 planning error.
The Agent-Readiness Content Ladder: your named framework for machine-friendly content
To make content work for both a human and a buying agent, I use a four-rung maturity model called the Agent-Readiness Content Ladder. Each rung is a discrete, testable upgrade. Most B2B sites are stuck on rung one.
Most competitors talk about rung two (schema) and rung four (APIs) because that is what they sell. The rungs that fall to marketing are three and four, and specifically the content choices that move a buyer agent from “structured” to “buyable.”
Here is how to tell which rung you are on. If your pages rank in search but an AI tool gives your answer no attribution, you are at rung one. If a buyer agent can quote a specific figure from your comparison page but cannot verify a price, you are solidly at rung two. If your best white paper lives as a gated PDF that no crawler touches, you have skipped rung three. If an agent or a sales copilot can pull a quote and start a transaction from your content, you are at rung four. Most B2B teams discover they are not nearly as far up the ladder as their organic traffic suggests.
The diagnostic matters because each rung has a different owner and a different cost. Discoverable is largely SEO and PR. Structured is schema, content design, and editorial standards. Accessible is a content-operations decision about gating and format. Buyable is a commercial decision about pricing transparency, APIs, and sales enablement. When a B2B marketer asks me where to start, I tell them to fix rungs two and three first, because those are where you regain the ground that four or five years of gated, unstructured assets quietly gave away.
What do AI buying agents actually consume?
Buying agents read machine-readable content: structured HTML, schema.org markup, Q&A blocks, clear product attributes, and ungated research. They do not fill out form gates, and they mostly cannot read your locked PDFs. BigCommerce notes merchants are using tools like Feedonomics to push normalized product data directly into AI search engines such as Perplexity. Mirakl’s readiness model calls this Generative Engine Optimization: rich, scenario-specific descriptions plus explicit compatibility and Q&A blocks, which is a different discipline from old keyword-stuffed SEO. For a full treatment of how to rank in these new surfaces, see our complete guide to generative engine optimization.
The practical problem for most B2B teams is the PDF black hole. Years of strategy reports, spec sheets, and buyer guides sit behind form gates inside PDFs that AI crawlers struggle to index. A buying agent researching your market never sees your best thinking because it is unreadable where it lives. Un-gating is not optional anymore; the question is how you replace the leads.
The scale of the shift is worth making explicit, because it reframes how urgent the work is. The numbers come from the named sources already cited in this post.
Read those four cards together and the picture is clear. Buyers already moved. Sellers are behind. Friction is the biggest single cost in the deal, and it is a cost marketing can directly reduce by making content structured, ungated, and quotable.
Dual-audience strategy: writing for humans and their AI proxies
The way you win is to write for two readers at once. Your human executive wants proof, authority, and a story your brand can stand behind. Your buyer’s AI agent wants extractable facts, named sources, structured answers, and clear parameters. One piece of content can serve both if you treat them as one audience with two reading modes.
Concretely, that means every major section opens with a direct, self-contained answer an agent can quote (the “we answer the question in the first sentence” rule), paired with narrative depth and a named example a human remembers. It means every stat carries a named source and a year, because an LLM weighs cited claims more heavily. It means comparative content, “X versus Y,” because comparison blocks are the single most-cited format in AI answers. The Content Marketing Institute reports 87% of C-suite executives have made a purchase based on thought leadership content, worth up to $107 million in potential impact. Thought leadership is not dead; it just has to be machine-readable too.
As Deloitte puts it in its 2026 research on the shift, the buying decision now forms upstream, in the rules a buyer programs into its agents, rather than downstream at the moment of persuasion. That single sentence reframes where marketing effort should go. If you are still deciding whether the answer may be claimed by an AI engine rather than a ranked organic result, read our breakdown of how AEO, GEO, and SEO differ for B2B and what each is for.
Un-gate specs and white papers, keep gates on interactive tools
Un-gating kills form fills. Keeping gates hides you from AI buyers. The matrix below walks the tradeoff by asset type, so you stop guessing asset by asset.
Worked example: a manufacturing SaaS vendor with a strong “spare-part specification” white paper watched demo requests from mid-funnel buyers dry up. They un-gated the paper, converted it to a structured HTML page with Q&A blocks and schema, and moved the gate to a “request a live spec review” button. Six weeks later the page was being cited by AI research tools for the category, and the smaller flow of non-gated requests was better qualified. The trade met both audiences.
How do you defend your price premium when agents shop on raw specs?
You cannot win on slogan when an agent compares price floors, but you can give the LLM proof points that justify a premium. When two vendors share near-identical specs, the deciding signals an agent can weigh are support SLAs, compliance and safety records, uptime, migration ease, and third-party reviews. Sell those as machine-readable facts, not as adjectives.
This is the brand-equity problem in the agentic era. Viamedici warns that agents have zero tolerance for unit ambiguity or patchy descriptions, and bad data makes autonomous systems scale errors. The answer is structured proof: publish uptime guarantees, certifications, reference-ability, and named logos as data an LLM can cite. Mastercard, on the payments side, is building agent payment rails (tokenized credentials, programmable spend limits) precisely so an agent can authorize spending within bounds. Your marketing job is to give that agent a reason to authorize a higher bound for you.
Feeding the sales copilot: what marketing must build for AI-assisted reps
Sales teams are not being replaced; they are being augmented, and marketing must feed the augmentation. commercetools describes how AI takes over routine quoting, configuration, and order mapping while reps shift to consultative advisory, becoming what it calls “super sellers.” Deloitte reports suppliers lose an average of 13% of bids to manual friction and that buyers spend nearly 30% more with seamless suppliers. A sales copilot that cannot answer quickly loses that 13%.
The returns are real, and they are documented. IBM cites Coca-Cola Europacific Partners, which achieved over $40 million in cost savings and avoidance using AI-driven procurement insights, part of a broader finding that 64% of chief supply chain officers report generative AI is already transforming supply chain and procurement. That is on the buying side. On the selling side, the same economics apply in reverse: the faster your sales copilot can produce an accurate quote and a defensible position, the less friction your buyer’s agent encounters, and the more bids you keep.
To feed it, restructure your enablement assets into a machine-readable knowledge base: battle cards with clear differentiators, ROI calculators with honest assumptions, objection-handling guides with direct answers, and pricing logic that respects your guardrails. Structure them as Q&A so both an internal copilot and a buyer’s agent can pull a direct answer under pressure. Marketing owns the content; sales owns the closing. When you feed the copilot, you feed the negotiation.
A note on the protocol layer, because it keeps coming up in vendor copy and it is simpler than it sounds. Model Context Protocol (MCP) is how one agent connects to tools and data, like a socket an agent plugs into to reach your catalog. Agent2Agent (A2A), per Kibo, is how agents talk horizontally to other agents, like a phone link between a buyer’s agent and a seller’s agent. You do not need to build either to start, but you should know which one your content strategy is serving. MCP is where your structured, accessible content matters most, because that is how a buying agent pulls your specs and pricing in the first place.
Step-by-step: the 90-day agentic-readiness content audit
You do not need a replatform to start. Here is a low-code plan any B2B team can run inside its current CMS in a quarter.
- Month one, audit discoverability. Prompt Perplexity, ChatGPT, and Claude with your top five category questions and note which brands get cited and why. Pull your Search Console queries to see where you already earn impressions. Build a spine: the 20 pages that should be your agent-visibility workhorses.
- Month two, structure and un-gate. Convert your highest-value PDFs into structured HTML pages with schema markup and Q&A blocks. Add an executive-summary lead on every white paper. Move gates from “download the document” to “book a review” so agents keep access and you keep a signal.
- Month three, feed enablement. Partner with sales operations to turn battle cards, ROI data, and technical FAQs into a Q&A knowledge base. Add machine-readable pricing tiers that publish base rates and keep negotiated floors behind authentication.
Worked example: a five-person software team running HubSpot completed the audit with one content strategist, one schema-savvy editor, and one part-time developer. They un-gated three PDFs, added schema to their comparison page, and shipped an “agent-ready” spec library. Their AI-cited share-of-voice on category queries rose measurably over the quarter without a single IT ticket.
What most teams get wrong about agentic commerce
The most common mistake is treating agentic commerce as an IT project. Product, engineering, and procurement teams will happily own the protocols and the ERP migration, but the buying decision is formed by content long before an agent places an order, and that content is yours.
A second mistake is keeping everything gated. Every locked PDF is an asset your buyer’s agent cannot read, and the team still reports form fills as if nothing changed. A third is chasing vague “AI visibility” without an answerable question or a named evidence chain. Agents cite claims; they do not reliably cite adjectives. If your stat does not name its source and year, an LLM has no reason to trust it over a competitor who names one.
What to do next
Start today with one un-gated, structured asset and one category question. Run an AI-search audit against your top five technical queries, convert your best PDF into a schema-marked HTML page, and restructure one comparison article with direct answers. Measure your share of citations in your top three AI tools for that query, not just your organic traffic. That single asset becomes a repeatable pattern, and a pattern becomes your ladder.
If you need the vendor-grade numbers to take to your team, the sources in this post (Deloitte, Mirakl, McKinsey, commercetools, Salesforce, IBM, BigCommerce) are current and cite the same forecasts. Lead with the 40% versus 24% gap and the 90% by 2028 projection, then propose the 90-day audit as your first deliverable. For the broader question of how to earn the citations that make you visible to these agents in the first place, our breakdown of trust architecture in the AI era is the companion piece.
Frequently asked questions
What is agentic commerce in B2B?
Agentic commerce in B2B is buying in which autonomous AI agents research products, compare suppliers, generate quotes, negotiate within set rules, and place orders on behalf of a company. It moves the buying decision upstream into the buyer’s rules and policies rather than downstream browsing and persuasion.
How much of B2B buying will be intermediated by AI agents?
Mirakl projects AI agents will intermediate about 90% of B2B buying by 2028, representing roughly $15 trillion in transaction flow. Deloitte already reports almost 40% of B2B buyers use agentic AI in purchasing, a figure that outpaces the 24% of suppliers using agents in sales.
Will agents replace B2B salespeople?
No. Agents take over routine quoting, configuration, and order mapping while reps shift to consultative advisory, what commercetools calls “super sellers.” Marketing feeds this by structuring enablement content into machine-readable knowledge bases that sales copilots and buyer agents can both pull from.
Should I un-gate all of my content?
Not everything. Un-gate technical spec sheets and research white papers that agents need for evaluation, but keep a gate on interactive tools and workshops where human interaction still qualifies. Publish transparent base pricing, and keep only negotiated floor prices behind authentication.
How is content marketing different for AI buyers?
Content for AI buyers must be direct, structured, and source-backed. Every major section should open with a standalone answer, every stat needs a named source and year, and comparison and Q&A blocks should use real markup rather than images. This makes it readable by an agent while still serving a human reader.
What is the biggest content risk in the agentic era?
The PDF black hole. High-value assets trapped in gated PDFs are invisible to AI research tools, so your best thinking never reaches the agent doing the buying. The fix is converting that content to structured, ungated HTML with schema and direct answers.
