TL;DR
- G2’s March 2026 survey of 1,076 B2B software buyers found that 50% now start their vendor research with AI chatbots, and 71% use AI tools as part of their software evaluation process.
- Buyers arrive at vendor conversations more researched than ever. Only 10% of buyers do minimal research before contacting a vendor. 37% conduct detailed comparisons and 18% perform extensive due diligence before first contact.
- When evaluating AI-powered vendors specifically, buyers ask a different set of questions. IDC’s 2025 Tech Buyer Survey found that 81% of buyers and 88% of C-suite executives now prioritize ethical AI use when selecting technology partners.
- The new evaluation questions focus on accuracy, governance, and accountability: how does the AI know what it knows, what happens when it does not know, and how is its output verified? These questions did not exist in software evaluation two years ago.
- Buyers do not trust AI-generated information alone. Forrester found that buyers validate AI research through their buying network: peers, industry experts, and providers directly. More than 60% require a trial before committing to a purchase.
- For selling organizations, this shift means the evaluation conversation has moved earlier and become more demanding. Buyers who arrive having used AI to research your solution may carry confident misunderstandings that need to be addressed before any sales conversation can progress.
ENaiBLD is a Buyer-Enabled Evaluation System built to meet buyers at the new standard: governed, accurate, role-specific explanation that gives buyers the verified information they now actively seek before and during vendor evaluation.
The Evaluation Has Changed
The way B2B buyers evaluate software has shifted in ways that marketing and sales teams are still catching up to. The shift is not primarily about which tools buyers use, though that has changed too. It is about when evaluation happens, how deep it goes before any vendor conversation takes place, and what questions buyers now bring to those conversations.
G2’s March 2026 survey of 1,076 B2B software buyers found that half of buyers now start their vendor research with AI chatbots. The same survey found that 71% use AI tools as part of their software evaluation process, and that 83% report feeling more confident in their final vendor choice when AI chatbots are their top research source.
G2’s Chief Innovation Officer Tim Sanders described the shift precisely: the Yellow Pages compressed the market into the big book, Google compressed it into the first page of results, and now AI chatbots are compressing it into a single answer. Every layer of that compression has changed the leverage point at which vendor selection happens. Today that leverage point is before the first sales conversation, inside the AI answer the buyer received when they asked which solutions to consider.
But the compression is only part of what has changed. The depth of evaluation before vendor contact has also shifted. Research from Responsive found that only 10% of buyers do minimal research before reaching out to a vendor. Thirty-seven percent conduct detailed comparisons across pricing and capabilities, and 18% perform extensive due diligence including financial reviews and case studies, all before making contact. By the time a buyer reaches a selling organization, they have often formed a sophisticated picture of the market. Whether that picture is accurate depends on the quality of the sources they used.
Why AI-Powered Vendors Face Different Questions
Buyers have always evaluated software vendors on capability, fit, price, and implementation. Those questions have not disappeared. But for AI-powered vendors specifically, a new category of question has emerged that did not exist at meaningful scale two years ago.
IDC’s 2025 Tech Buyer Survey found that 81% of buyers and 88% of C-suite executives now prioritize ethical AI use when selecting technology partners. That is not a philosophical preference. It is a procurement criterion. Buyers have learned, through their own experience and through the growing volume of AI accuracy incidents in the market, that not all AI is equally reliable, and that the difference matters commercially.
The questions this shift generates are specific. How does the AI know what it knows? Is it constrained to a defined, approved knowledge source, or is it drawing on general training data and the open internet? What happens when the AI does not know the answer? Does it acknowledge uncertainty or does it fabricate a confident-sounding response? How are its outputs reviewed and what accountability exists for claims it makes?
These are the questions that separate a buyer who understands the governed AI versus ungoverned AI distinction from one who does not. And increasingly, buyers do understand it, because they have experienced the consequences of AI tools that produced inaccurate information during their own research.
Forrester’s 2025 Buyers Journey Survey found that 20% of buyers were less confident in a decision because they encountered unreliable AI information. Among procurement professionals, that figure rose to 28%. These buyers have encountered AI inaccuracy directly. They arrive at vendor conversations with a specific wariness about AI claims that did not exist in the pre-generative-AI buying environment.
The Validation Behavior That Has Changed Evaluation
One of the most important findings in recent buyer behavior research is not that buyers are using AI more. It is that buyers are systematically validating what AI tells them.
TrustRadius’s 2025 research found that 62% of frequent AI users always or very often fact-check AI-generated information. Forrester’s 2026 State of Business Buying research documents this behavior in detail: buyers use AI for speed and efficiency in research, but they validate findings through what Forrester calls the buying network, a combination of internal stakeholders and external sources including peers, industry experts, and vendors directly.
This validation behavior has a direct implication for AI-powered vendors. Buyers who have researched a solution using AI tools will look for the vendor to either confirm or contradict what the AI told them. If the vendor’s governed, accurate explanation matches the AI’s output, confidence builds. If there is a discrepancy, the buyer faces a choice between the AI source they used and the vendor’s direct explanation. Their response depends on how much they trust each source.
Forrester’s finding that buyers more often cite interactions with industry experts than AI information as the primary trigger for engaging with providers points to where trust currently sits in the evaluation hierarchy. AI compresses and shortlists. Experts validate. The vendor who can serve both functions, providing AI-accessible content that earns placement in early research and governed, accurate explanation that survives validation scrutiny, is the vendor who moves through evaluation most reliably.
The trial data from Forrester reinforces this. More than 60% of buyers engaged in some form of trial before committing to a purchase, with that figure rising for larger and higher-cost purchases. The trial is the ultimate validation mechanism. It is buyers demanding proof that the confident claims made during evaluation, by AI tools and by sellers alike, actually reflect what the solution delivers in practice.
The New Evaluation Questions by Role
The shift in how buyers evaluate AI-powered vendors is not uniform across the buying committee. Different roles bring different versions of the new evaluation criteria.
Executive and economic buyers
C-suite buyers are asking the governance question at a strategic level. IDC’s finding that 88% of C-suite executives prioritize ethical AI use reflects an executive concern about organizational risk, reputation, and the regulatory trajectory for AI in commercial applications. The executive question is not primarily about feature accuracy. It is about whether the vendor’s AI is the kind of AI the organization can stand behind publicly.
Procurement and finance
Procurement professionals represent the most skeptical segment of the buying committee on AI accuracy. Forrester found that procurement professionals are more likely than other buyer types to report negative experiences with AI-generated information. They are more demanding of direct evidence, more likely to require trials, and more likely to push back on AI-generated claims that cannot be independently verified. For AI-powered vendors, procurement represents the hardest validation gate in the buying process.
Technical and IT evaluators
Technical buyers ask the architectural questions: how is the AI constrained, what retrieval mechanism is used, how is hallucination prevented, and what auditability exists. These are the questions addressed in detail in the governed AI article in this series and in the CISO evaluation article in this series. Technical evaluators increasingly distinguish between AI products that are architected for accuracy and those that rely on prompt engineering as their primary governance mechanism.
Operational and end-user stakeholders
Operational stakeholders ask the reliability questions: will this AI produce consistent answers across different users and contexts, what happens when it encounters an unusual question, and how does the organization manage updates to the AI’s knowledge base over time. These buyers have the most direct experience of what AI inaccuracy costs in practice, because they are the ones who deal with its consequences in their day-to-day workflows.
Across all of these roles, the common thread is a shift from trusting AI claims toward requiring AI evidence. The evaluation question that has replaced ‘what can your AI do’ is ‘how can we verify that it does what you claim’.
What Confident Misunderstanding Looks Like in This Context
The new evaluation environment has a specific confident misunderstanding risk that differs from the general pattern documented elsewhere in this series.
In the general pattern, buyers form confident misunderstandings from fragmented, ungoverned sources during independent research. In the AI vendor evaluation context, buyers face two distinct confident misunderstanding risks simultaneously.
The first is the standard risk: buyers using AI tools to research an AI-powered vendor receive inaccurate information about that vendor’s capabilities, governance approach, or pricing. The Kodec AI finding that 62% of AI-generated answers about B2B software are inaccurate applies equally to AI vendors as to any other software category. A buyer who researches an AI-powered sales tool using an AI chatbot may receive an answer that reflects the vendor’s capabilities from eighteen months ago, or that confuses the vendor’s offering with a competitor’s.
The second risk is specific to AI vendor evaluation: buyers may form a confident misunderstanding about what a specific AI governance claim means. A vendor who claims their AI is governed may mean that their AI is constrained to a defined knowledge base with architectural enforcement. Or they may mean that their AI has been prompted not to deviate from certain content. Those are meaningfully different things, and a buyer who does not understand the distinction may conclude that both vendors are equivalently safe when they are not.
For sellers of AI-powered solutions, both confident misunderstanding risks need to be addressed. The first requires being accurately represented in the AI-generated research buyers conduct. The second requires ensuring that buyers can access specific, concrete, verifiable explanation of what the vendor’s AI governance actually means in practice, not just in marketing language.
What This Requires of Selling Organizations
The shift in how buyers evaluate AI-powered vendors requires selling organizations to address the evaluation differently than they have for traditional software.
The preparation question is different. Traditional software evaluation preparation focuses on capability demonstration and competitive differentiation. AI vendor evaluation preparation requires being able to answer the specific governance, accuracy, and accountability questions that buyers now bring to the conversation. Sales teams who cannot answer questions about their AI’s knowledge architecture, hallucination prevention mechanisms, and auditability are not prepared for the evaluations buyers are now conducting.
The evidence requirement is different. Buyers who have experienced AI inaccuracy in their own research do not take AI capability claims at face value. They require verifiable evidence. That means third-party certifications where available, architecture documentation that can be reviewed by technical evaluators, and the ability to demonstrate governance in practice rather than asserting it in marketing materials.
The pre-contact stage is different. Because 90% of buyers now do substantial research before making contact, and because that research increasingly happens through AI tools, the representation of a vendor in AI-generated answers is now a pre-contact evaluation criterion. Buyers who receive inaccurate information from an AI about a vendor’s governance approach, pricing, or capabilities may disqualify the vendor before any human conversation takes place.
And the post-contact validation stage is different. More than 60% of buyers now require a trial before committing to a purchase. For AI-powered vendors, the trial is not just a feature demonstration. It is a governance demonstration. Buyers are watching to see whether the AI behaves the way the vendor described. Whether it defers appropriately when it does not know. Whether its outputs are consistent and accurate across different users and contexts. The trial is where AI governance claims are tested against AI governance reality.
The Bottom Line
B2B buyers are evaluating AI-powered vendors through a different lens than they apply to traditional software, and that lens sharpens with every passing quarter as AI accuracy incidents accumulate in the market and buyer sophistication increases.
The buyers who have experienced AI research inaccuracy, who have encountered confident wrong answers from AI tools during their own evaluation processes, are the buyers who now bring governance questions to vendor conversations. That population is growing rapidly. G2’s finding that 83% of buyers feel more confident when AI chatbots are their top research source reflects the adoption curve. The Forrester and TrustRadius findings on validation behavior and skepticism reflect what happens after the adoption curve matures.
For selling organizations, the window for addressing this shift proactively rather than reactively is available now. The buyers who are asking the new evaluation questions can be served well by vendors who have done the work to ensure their AI is genuinely governed, accurately represented, and demonstrably reliable. The buyers who are not yet asking these questions will be, because the market is moving in one direction on AI accountability.
The selling organizations that understand this shift, and equip their teams to meet the new evaluation standard, will find that the buyers arriving at their conversations are not obstacles to be managed. They are partners in a process that both parties now have a stake in getting right.
Frequently Asked Questions
How has the B2B software evaluation process changed with AI?
G2’s March 2026 survey of 1,076 buyers found that 50% now start vendor research with AI chatbots and 71% use AI tools as part of their evaluation process. Buyers arrive at vendor conversations more deeply researched than ever before. Only 10% do minimal research before making contact. The majority conduct detailed comparisons, and nearly one in five performs extensive due diligence before any vendor conversation. AI has not shortened evaluation. It has moved more of it upstream, before vendor contact.
What new questions do buyers ask AI-powered vendors that they do not ask traditional software vendors?
The new questions focus on accuracy, governance, and accountability: how is the AI constrained to produce accurate outputs, what happens when it does not know the answer, how are outputs verified, and what auditability exists for claims the AI makes. IDC found that 81% of buyers now prioritize ethical AI use in vendor selection. These questions reflect buyers’ direct experience with AI inaccuracy in their own research and their awareness that AI governance is a differentiator, not a given.
Why do buyers validate AI-generated information rather than trusting it directly?
Because they have encountered inaccuracies. TrustRadius found that 62% of frequent AI users always or very often fact-check AI-generated information. Forrester documented that buyers validate AI research through their buying network, which includes peers, industry experts, and vendors. The validation behavior is not skepticism about AI generally. It is a learned response to specific experiences of AI inaccuracy that have produced poor research outcomes.
What is the role of product trials in AI vendor evaluation?
Trials have become the primary validation mechanism for AI claims. Forrester found that more than 60% of buyers engage in some form of trial before committing to a purchase, with that figure rising for larger and higher-cost purchases. For AI-powered vendors, the trial serves a specific purpose: it tests whether the AI’s governance claims are accurate in practice. Buyers are watching whether the AI defers appropriately when it does not know, whether its outputs are consistent, and whether the accuracy the vendor claimed during evaluation holds in real conditions.
What is the confident misunderstanding risk specific to AI vendor evaluation?
There are two. First, buyers using AI tools to research an AI-powered vendor may receive inaccurate information about that vendor from the research AI itself, since AI accuracy problems apply to every software category including AI vendors. Second, buyers may form confident misunderstandings about what specific AI governance claims mean in practice, for example, concluding that all vendors claiming governed AI are equivalent when the architectural differences between them are significant. Both risks require vendors to provide specific, verifiable explanation rather than general governance assertions.
How should sales teams prepare for buyers who have already researched using AI tools?
Sales teams need to be prepared to address the specific governance and accuracy questions buyers now bring, not just capability questions. They need verifiable evidence for AI claims, not just marketing assertions. They need to understand that buyers may have formed confident misunderstandings from AI research conducted before any vendor contact, and those misunderstandings need to be identified and corrected before the evaluation can progress productively. The conversation that meets buyers at this level earns trust. The conversation that simply repeats marketing claims does not.
How does this shift affect the pre-contact stage of the sales process?
The pre-contact stage has become an evaluation stage in its own right. Buyers who research a vendor using AI tools before making contact are already forming views about that vendor’s governance approach, capabilities, and credibility. If the AI-generated information they encounter is inaccurate or if the vendor is simply absent from AI-generated answers about the category, disqualification can happen before any human conversation. Selling organizations that ensure they are accurately represented in AI research tools address the pre-contact evaluation stage proactively.