How b2b buyers research vendors now

How b2b buyers research vendors now is simple to answer: they use AI to narrow the field, ask people they trust what they missed, then check vendor sites and sales teams for proof.

By the time a prospect books a call, the shortlist may already be set.

A 250-person cybersecurity company gets a SOC 2 audit finding about access reviews. The VP of IT doesn't start by filling out five demo forms. They ask a former colleague which vendors have survived a similar mess. Someone searches the vendors' founders and security leaders on LinkedIn. Another person asks ChatGPT to compare implementation effort, SSO support, data residency, and contract terms.

Sales sees the meeting request at the end of that process. It looks like discovery. Usually, it's validation.

How b2b buyers research vendors now

Forrester's 2026 Buyers' Journey Survey found that 94% of nearly 18,000 global business buyers used AI during their most recent purchase process, up from 89% in 2025. More than half used it to compare vendors, and 47% used it to build an internal business case before contacting a vendor. MR Research's breakdown of the Forrester data covers the specific use cases.

The important part isn't the exact percentage. It's where AI sits in the process.

A finance leader might ask an AI tool to compare payment platforms on reconciliation, audit readiness, and implementation risk. An IT director may check whether a vendor supports SCIM and data residency. A revenue leader may ask for agencies that have worked with a Series B SaaS company selling into regulated industries.

The answer becomes a first filter. It can pull from your website, customer stories, review sites, LinkedIn posts, news coverage, or sources you don't know about. It can also get things wrong.

That makes the website less of a starting point and more of a place buyers visit to confirm what they've already heard. They look for security documents, product limitations, pricing clues, implementation details, and material they can forward to the rest of the buying group.

The buying group researches in parallel

B2B research isn't one person moving through a tidy funnel. It’s several people investigating different risks at the same time.

The operator wants to know whether the product fits the workflow. Finance wants a credible business case. Security wants documentation. Legal wants contract language. An executive sponsor wants to know whether the change is worth the internal trouble.

Forrester puts the typical B2B decision at 13 internal stakeholders and nine external influencers. Whatever the exact number in your market, the practical result is the same: the person who downloads your report may not be the person who asks for a security review. The person who finds you may not be the person who gets the budget approved.

Your ideal customer profile should reflect that. Firmographics and job titles aren't enough. Add the event that starts research, the people likely to join later, the proof each person needs, and where they look for it.

A 75-person fintech with a new compliance lead won't research vendors like a 2,000-person manufacturer replacing an aging ERP. The trigger is different. The risks are different. So is the evidence required to move forward.

AI is fast. Trust is still human.

AI helps buyers get oriented quickly. It doesn't remove the need for judgment.

Buyers still ask peers, read reviews, check analysts, and look at the people behind a company. They want to know whether the vendor understands the operational problem, not just whether it can repeat a feature list.

This is where many teams get LinkedIn wrong. A company page full of funding announcements and product slogans doesn't help much. A founder explaining why a customer implementation failed, or why a common buying criterion is misleading, gives buyers something useful to assess: judgment.

Research cited in LinkedIn's 2026 analysis of vendor research suggests buyers use the platform to assess credibility, executive thinking, and social proof. That matches what shows up in late-stage calls. Prospects quote a founder's post or mention a customer example that sales didn't know they had seen.

The question isn't, "Did this company publish this month?"

It's, "Would I trust this team with the problem that gets me blamed if it goes wrong?"

Generic content gets ignored

Most companies respond to AI-driven research by publishing more category pages, comparison posts, and broad explainers. That is usually the wrong response.

Buyers and AI systems both need material with enough detail to use. "We help businesses grow efficiently" says almost nothing. A clear explanation of how a processor change affects reconciliation during a healthcare company's year-end close is much more useful.

A data platform selling to fintech teams could explain what a SOC 2 report does not prove about production access. A managed outbound agency could show what changes when a newly hired VP of Sales needs qualified opportunities without doubling the SDR team. Those examples give a buyer something to compare against their own situation.

Specific content is also easier to forward internally. Someone can send it to finance and say, "This is the issue we're dealing with." That's a much stronger job for content than filling a publishing calendar.

And owned content isn't the whole authority picture. Research from Muck Rack and Generative Pulse found that more than 85% of non-paid AI citations came from earned media sources. A polished website helps, but it won't carry the full burden. Customer references, independent reviews, analyst mentions, and credible commentary matter because they give buyers evidence that doesn't come directly from the vendor.

My view is blunt: teams spend too much time trying to sound authoritative and not enough time saying something checkable. A narrow claim backed by a real customer situation beats a grand category essay almost every time.

Outbound has to meet the research already underway

Outbound still works. It just doesn't create awareness from nothing as often as teams assume.

A prospect may receive a cold email after researching your company for two weeks. If the message ignores what they've been trying to solve, it feels irrelevant. If it names the trigger, the prospect has a reason to reply.

A message to a recently funded SaaS company shouldn't lead with "We help companies build pipeline." It could mention the new VP of Sales, the move upmarket, and the pressure to produce qualified opportunities without hiring a large SDR team. That gives the buyer a way to test whether your experience matches the problem.

The same applies to account-based marketing. The account list isn't the strategy. The research environment around each account is.

For each target account, identify what caused the investigation to start and which roles will challenge the shortlist. Then give each role the evidence it needs. The operator needs implementation detail. Finance needs measurable impact. Security needs documentation. The executive sponsor needs confidence that the project won't create a new political or operational problem.

What to measure now

Don't throw away website sessions or form fills. They still tell you something. They just don't tell you enough.

Ask new opportunities what they used before contacting you, and record the answer in the CRM. Don't force every influence into "organic" or "outbound." Track whether target accounts arrive with a defined problem, a shortlist, and evaluation criteria.

Useful signals include:

• branded searches and direct traffic from priority accounts
• engagement from non-lead stakeholders at named accounts
• first calls that begin with security, comparison, or business-case questions
• buyers who can already describe your position in the category

Forrester has reported that some B2B companies are seeing website traffic declines of 10% to 40% as research moves into AI answer engines. A lower session count can hide more research activity, especially when the site visit happens after the shortlist is formed.

The real question is whether your company was present, accurately described, and credible before the buyer decided who deserved a meeting.