A B2B buyer asks an AI assistant to find three platforms that meet a specific set of requirements.
The agent compares product information, checks pricing models, reads technical documentation, looks for security credentials, identifies integration requirements, summarizes customer evidence, and produces a shortlist. Nobody from the sales department knows the research happened. That scenario is already much less futuristic than it sounds.
AI has moved quickly from helping people write search queries to helping them research purchasing decisions
In 2026, 94% of business buyers reported using AI during the buying process, with generative AI and conversational search becoming particularly influential sources of information.
The next step is more consequential. AI systems are beginning to move from finding and recommending toward acting.
Visa introduced Intelligent Commerce in 2025 to support AI agents that can browse, select, and purchase within user-defined parameters. Mastercard followed with agentic payment infrastructure and an Agent Toolkit that makes its API documentation accessible to AI tools through structured, machine-readable content.
B2B procurement is not about handing over a multimillion-dollar software contract to a chatbot and heading home early. But AI can increasingly sit between the buyer and the vendor.
And that creates a new marketing question: Can an AI agent understand enough about your company to recommend you?
The buying journey is becoming machine-intermediated
For years, B2B marketing has optimized digital journeys for humans.
Make the website easy to browse, write clear landing pages, design useful comparison pages. Then, reduce friction in the form and give visitors obvious next steps.
Those things still matter. But buyers are increasingly using another interface to access the information.
Instead of opening six vendor websites, a buyer can prompt an LLM with something along the lines of:
Compare these three procurement platforms by integrations, pricing structure, implementation requirements, customer support, and enterprise security.
The AI system now has to retrieve, interpret, compare, and summarize what each vendor makes available.
So this is unlikely to become an entirely automated buying journey.
It is more likely to become a hybrid one, where machines do a growing share of the information work before humans make the higher-risk decisions.
An AI agent cannot infer the product information you forgot to publish
Marketers are used to thinking about persuasive communication. AI agents introduce another requirement: interpretability. If your pricing logic is hidden behind “Contact Sales,” product capabilities are scattered across PDFs, integration requirements only appear in developer conversations, and policies are written in vague legal language, a human buyer may eventually piece everything together.
An agent may simply have less to work with. That means some fairly unglamorous marketing assets suddenly become strategically important:
- structured product information
- clear feature and capability definitions
- technical documentation
- API documentation
- integration requirements
- pricing logic
- service limitations
- geographic availability
- security and compliance information
- customer proof
- implementation requirements
- policies and commercial conditions
Mastercard’s approach is interesting for exactly this reason. Its Agent Toolkit makes API information available through machine-readable content using Model Context Protocol (MCP), allowing AI assistants and agentic tools to interpret documentation directly.
The lesson for marketers is broader than APIs. Your digital information increasingly needs to work for both human readers and machine intermediaries.
Your product data is becoming part of your marketing
This may be uncomfortable for marketing teams accustomed to treating product data as somebody else’s problem.
But if AI becomes an interface through which buyers discover and compare vendors, the distinction between marketing content and product information starts getting blurry. An agent comparing two B2B platforms does not care much about your homepage headline saying you are “redefining the future of commerce.”
A marketplace, SaaS platform, logistics provider, data service, or industrial supplier could all find themselves being evaluated by systems that reward specificity. Marketing has spent years worrying about whether a page is persuasive enough. We now also need to ask whether the underlying information is precise enough to compare.
Generic claims become even weaker when a machine is doing the comparison
Humans can be influenced by tone, brand familiarity, visual identity, and a strong narrative. AI agents are much harder to impress with “industry-leading,” “flexible,” “innovative,” or “customer-centric.”
They need evidence. That makes the old B2B problem with generic claims even more obvious.
- “Enterprise-grade security” becomes more useful when accompanied by the relevant standards, controls, documentation, audit information, and policies.
- “Flexible integrations” becomes more useful when the buyer can find supported APIs, connectors, implementation requirements, and known limitations.
- “Trusted by global companies” becomes stronger when there are customer examples, industries, use cases, and measurable outcomes.
This does not make branding irrelevant. Human buyers still make complex decisions, and research shows that they actively seek trusted people and external validation because AI output can be incomplete or unreliable.
But brand claims increasingly need a layer of evidence underneath them that both people and systems can verify.
AI agents may start negotiating too
The transactional side is moving quickly as well. Visa’s Trusted Agent Protocol was designed to let merchants distinguish legitimate AI agents from malicious bots and communicate securely during transactions. By December 2025, Visa said partners had already completed hundreds of secure agent-initiated transactions. The B2B implications may eventually go further than checkout.
Forrester predicted that in 2026, one in five B2B sellers could be pushed into agent-led quote negotiations.
That does not mean enterprise negotiations are becoming autonomous overnight. It means parts of the process are becoming easier to delegate.
An agent could compare quotes, flag unfavorable terms, calculate total cost under different scenarios, identify missing documentation, check whether offers meet procurement rules, or propose a counteroffer within an approved range. Suddenly, pricing structure, contract logic, documentation, and policies become part of how the company presents itself to the market. That is a marketing concern as much as a technology one.
Machine-readable does not mean robot-written
There is an obvious temptation here to respond by turning every website into a database dump. That would miss the point.
Human buyers still need context. They need to understand why something matters, how it applies to their situation, what compromises exist, and whether they trust the people behind the offer.
AI agents need the underlying facts to be accessible. Good B2B digital experiences will increasingly need both layers.
For humans:
Clear positioning, useful explanations, examples, comparisons, proof, strong UX, and expert guidance.
For machines:
Consistent terminology, structured information, explicit relationships, accessible documentation, technical data, policies, APIs, schema, and dependable facts.
The next interface between you and your buyer may not be your website
The B2B buying journey has been becoming more self-directed for years. AI agents accelerate that trend because they can do something search engines could not: actively assemble the user’s buying journey. They can gather information, compare options, summarize trade-offs, evaluate documentation, and increasingly initiate actions.
Complex B2B purchases will still require people. There will be stakeholders, procurement teams, security reviews, negotiations, demos, legal conversations, and plenty of meetings that could probably have been emails.
But the work that happens before those conversations is changing. For marketers, preparing for agentic buying does not begin with building your own AI agent. It begins with a simpler question:
If a buyer asked an AI to evaluate your company today, would you give it enough information to make your case?
That answer may soon matter almost as much as what the buyer sees when they finally land on your website.
Is your digital buying journey ready for AI intermediaries?
NNC Services helps B2B companies make complex offers easier to discover, understand, compare, and evaluate across websites, AI search, content, technical information, campaigns, and digital buyer journeys.
Explore our B2B digital marketing services.
FAQs
1. What is an AI agent in B2B buying?
An AI agent is a system that can research, compare, summarize, and increasingly act on purchasing information within rules set by the buyer.
2. How could AI agents change the B2B buying journey?
They can take on more of the early research, comparison, documentation review, shortlisting, and parts of the purchasing process before sales becomes involved.
3. What information should companies make easier for AI agents to understand?
Product data, pricing logic, technical documentation, integrations, policies, security information, customer proof, and implementation requirements should all be clear and accessible.
4. Does machine-readable content replace content written for human buyers?
No, companies increasingly need both clear human-facing explanations and structured, dependable information that AI systems can interpret.
5. How can B2B marketers prepare for agentic buying?
Start by auditing whether an AI system could accurately understand, compare, and recommend your offer using the information currently available online.
