A head of operations at a 60-person professional services firm needs new software. Two years ago she would probably have typed a category term into Google, opened several vendor sites, checked a review platform and asked a peer for a recommendation.
Today she may type something closer to this: “We need a platform that handles X, integrates with Y and suits a company of our size with a small IT team. What should we consider?”
The answer she gets can shape her first shortlist before she visits a vendor website or speaks to a salesperson. That doesn’t mean it decides anything. It does mean the first impression of a software company may now form somewhere the company can’t see.
This article is about the buyer’s side of that change: what B2B software buyers now do, where AI enters their process, and where it doesn’t.
The research on AI in B2B buying is growing quickly, and the numbers don’t line up neatly.
These figures measure different things. Forrester’s is broad “any use” across business purchases of every kind. Gartner’s asks about a specific recent purchase. G2’s covers software buyers only, about seven months later, and asks where they start. G2 also runs a review platform and sells AI-visibility products, so it has a commercial interest in this trend. Its figures are best read as a vendor-produced signal, not neutral measurement. 6sense, cited below, is also a vendor.
Taken together, they support a cautious conclusion. AI is now a common part of B2B research, and among software buyers it is increasingly where research begins. None of the surveys shows that AI has replaced conventional buying, and a single survey shouldn’t be read as proof of a universal shift.
AI adds a layer at the front, and sometimes in the middle:
The path is rarely linear. A buyer might ask an AI tool to explain an unfamiliar category, check two of its suggestions on a review site, go back to the AI with a follow-up about integrations, ask a colleague, then open a vendor website. G2’s 2025 data shows how mixed the sources are. GenAI chatbots ranked first among sources influencing vendor shortlists at 17.1%, with software review sites close behind at 15.1%. Those are two sources with similar weight, not one displacing the other.
Nor is the pattern uniform. In the same 2025 report, SMB buyers still leaned on Google as their top research source (47%), while enterprise buyers relied more on review sites and AI search. How far AI has changed discovery depends on who is buying.
Traditional search rewards a keyword. A buyer types “CRM software” and receives a category-level results page. The buyer does the interpretive work of narrowing it down.
Conversational AI lets a buyer start with a situation instead:
What CRM would work for a 50-person B2B company with a long sales cycle, Salesforce integration requirements and a small operations team?
That question contains company size, sales model, technical constraints and internal capacity. Discovery becomes contextual. The buyer is no longer only looking up vendors. They’re describing a problem and asking for a first interpretation of the market.
It also changes the buyer’s starting position. They may arrive at a vendor’s website already holding a partial view of the category, a short list of names and a set of follow-up questions.
The limitations matter as much as the potential. The quality of the answer depends on the sources the system draws on, how current they are and how well the question was framed. A specific question can produce a confident, well-organised answer that is still incomplete.
Find me software” is a retrieval task. “What should I consider for this situation?” is an interpretive one. The second question asks the system to weigh features, use cases, company size, integrations, pricing information, positioning, public reviews and third-party commentary against the buyer’s stated context.
Buyers appear to know this. Forrester reports that only 36% of respondents felt more confident they had made a better-informed decision because they used generative AI, and buyers expressed skepticism about accuracy. G2’s software-buyer sample is more positive: 83% reported feeling more confident in their final choice. The gap probably reflects different populations and different wording. It’s a reminder that “AI helps buyers” and “buyers trust AI” are separate claims.
Comparison is where AI may compress the most work. A buyer can ask an AI tool to compare three platforms for a mid-sized financial services firm, say which is easiest to implement, or list the weaknesses of each. G2’s 2026 report suggests this is a leading use. Comparing vendor strengths and weaknesses was the top use of AI chatbots in software research at 41%, ahead of basic product research, vendor identification and use-case validation.
The consequence for software companies is that they may increasingly be evaluated inside an answer that also describes their competitors. Comparative context matters more than it used to. How a category is framed, which alternatives appear alongside a product and what its limitations are said to be can all shape a buyer’s view before any direct contact.
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This doesn’t mean AI systems rank vendors objectively. They synthesise whatever public information exists, with all its gaps. Buyers should treat AI comparisons as a first pass, not a verdict.
The evidence supports this separation. Forrester describes buyers using generative AI as a starting point, then turning to peers, experts and providers to confirm what those tools produce. Gartner’s survey of 645 buyers found that 69% prefer to validate AI-generated insights with sales reps. That sits alongside Gartner’s separate finding that a majority of buyers, 67%, prefer a rep-free experience overall. Buyers want to self-serve, and they also want a human check when the stakes rise.
G2’s data points the same way on the limits of AI’s role. Nearly two-thirds of buyers cap AI agents at research or recommendations, and the same report notes that evaluation is now the longest stage for 40% of buyers, ahead of research and decision. If AI shortens discovery, the harder work moves downstream, into verification.
So the sensible reading is that AI may accelerate discovery and influence shortlisting, while validation, evaluation and the purchase decision stay human.
If shortlists form early, and AI increasingly shapes the early stage, then the question of who gets considered takes on more weight. G2 reports that buyers who took software recommendations from AI chatbots were more likely to buy from their initial shortlist than those who didn’t (80% against 65% in at least three of their last five purchases). That is a correlation from one vendor’s survey, and it shouldn’t be read as proof that AI causes shortlist loyalty.
The old question was how to rank for a product keyword. Buyers using AI tools raise a wider set of questions:
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A useful test is whether a buyer, or an AI tool answering for one, could find and understand each of the following:
Limitations deserve attention. A vendor that says plainly where it isn’t a fit gives buyers something a comparison can rely on, and gives AI systems accurate material to summarise.
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which problems does it solve?
who is it designed for, in terms of size, sector and maturity?
what supports the company's claims?
what does adoption involve in time, effort and resources?
when is it not the right choice?
who uses it, and what independent evidence exists?
The role of B2B marketing is widening from generating attention to making the company understandable during research. SEO, product marketing, content, PR, review presence, thought leadership, case studies, executive visibility and third-party authority all feed the same thing: the body of information a buyer meets when they start asking questions.
The strategic point is that a buyer can encounter a company long before the company knows who the buyer is. Marketing has to prepare the public information environment around the brand, because much of the earliest research is invisible to the vendor.
Be discoverable. Can buyers encounter the company at all?
Be understandable. Can they tell what it does and who it’s for?
Be comparable. Can they evaluate it against alternatives fairly?
Be verifiable. Can they find independent evidence for what it claims?
Be relevant. Does it fit the buyer’s actual situation?
Discoverability alone is not enough. A company can be found and still be misunderstood, unverifiable or wrong for the buyer’s context.
B2B software discovery is moving toward a model in which buyers ask increasingly specific questions and receive increasingly contextual answers. Websites, search engines, review platforms, sales teams and human judgement all remain part of the process. What changes is the order in which buyers meet them.
A software company may now have to earn consideration before it earns a website visit, and then earn trust through the verification that follows. AI can put a vendor in front of a buyer, but it cannot give the buyer a reason to believe the vendor. That still comes from evidence, from independent voices and from the company being clear about what it does.
