TL;DR
- The way B2B buyers research solutions has fundamentally changed. AI tools — ChatGPT, Perplexity, Gemini, Google AI Overviews — have become the front door to the buying process, used by 73% of B2B buyers as part of their purchase research.
- Buyers use AI to generate vendor shortlists, compare solutions, synthesize pricing information, and evaluate fit — all before contacting a seller. According to Forrester, 61% of the buying journey is now complete before a buyer reaches out to a vendor.
- Shortlists form early and lock in fast. Research from 6Sense shows that 95% of the time, the winning vendor is already on the buyer’s shortlist before the first sales conversation, and 80% of deals are won by the vendor the buyer preferred before any seller contact.
- AI has not created confident misunderstanding in B2B buying. It has industrialized it. Buyers could always form inaccurate views from fragmented sources. AI makes it faster, more fluent, and more convincing — producing confident misunderstanding at a scale and speed that no previous research channel could match.
- The evidence is specific. A study of AI platforms found inaccurate or misleading answers in 62% of simulated buyer queries about B2B software products. Forrester found that 20% of buyers felt less confident after using AI because they encountered unreliable information. The buyers who did not notice the inaccuracies are the larger problem — they arrived at sales conversations certain.
- The implication is a fundamental shift in what selling organizations need to do. If a buyer’s view of your solution is largely formed before they contact you, the question is not how to persuade them in conversation. It is how to ensure what they learned before the conversation was accurate.
ENaiBLD is a Buyer-Enabled Evaluation System that puts governed, accurate expertise into the AI-research phase of the buying journey, ensuring buyers form accurate views before and between sales conversations rather than discovering misalignment after the fact.
The Front Door Has Changed
Not long ago, a buyer’s first meaningful encounter with a potential solution happened on the vendor’s website, through a referral, or in a sales conversation. The vendor had significant influence over first impressions.
That is no longer the case.
A March 2026 multi-source analysis covering 680 million AI citations found that 73% of B2B buyers now use AI tools like ChatGPT and Perplexity as part of their purchase research process. G2’s 2025 Buyer Behavior Report characterized AI as “always included” in the buying journey, with buyers using it to shortlist, compare, and synthesize vendors long before outreach.
The speed of this shift is striking. In 2024, 68% of buyers reported that generative AI had no impact on their buying process. By 2025, 72% of buyers encountered Google’s AI Overviews during research, and 90% of those buyers clicked through to at least one cited source.
AI has become the starting point for evaluation. When a buyer develops a question about a category of solutions, they do not necessarily begin with a Google search, navigate to a vendor website, and read a product page. They ask an AI system. They receive a synthesized answer that compares options, summarizes capabilities, and implicitly or explicitly produces a shortlist. That answer shapes the buyer’s mental model of the market before they have had a single conversation with any vendor. The implications for selling organizations run much deeper than most current content marketing or SEO strategies account for.
How Buyers Are Actually Using AI in the Research Process
Understanding the threat and the opportunity requires being specific about what buyers are doing with AI, not just that they are using it.
Buyers are using AI tools to generate shortlists, compare vendors, evaluate pricing, and synthesize reviews — tasks that previously required visiting multiple websites. What took an afternoon of tab-switching and cross-referencing now takes minutes. A buyer who would previously have spent two hours building a comparative view of five vendors now receives a structured comparison in seconds.
Research from 6Sense found that 94% of buyers use LLMs during their buying process, and that buyers still mostly or fully define their purchase requirements 83% of the time before speaking with sales — even with AI tools accelerating the research phase.
TrustRadius research found that 40% of buyers say AI makes it easier to find information, up significantly from the prior year, and that 80% of buyers now trust AI tools at least sometimes — a 19-point increase year over year.
The pattern that emerges from this research is consistent. Buyers enter the sales process with a view of the market that was formed primarily through AI-assisted research. They feel well-prepared. They have a shortlist. They have opinions about pricing, capabilities, and fit. And they formed most of this picture without any direct input from the vendors they are evaluating. This is also why B2B sales cycles are getting longer — buyers arrive with more confident but sometimes inaccurate views that require more time to surface and correct.
The Shortlist Problem
The most strategically consequential finding in recent buyer behavior research is not how buyers are using AI. It is what AI-assisted research produces at the end of the selection phase: a shortlist that is largely locked in before the first sales conversation.
Research from 6Sense’s 2025 global study of nearly 4,000 B2B buyers found that 95% of the time, the winning vendor is already on the buyer’s shortlist before any vendor contact, and four out of five deals are won by the vendor the buyer preferred before engaging with any seller.
The implications of this are significant. If 80% of deals are won by the pre-contact favorite, and that favorite is determined during the AI-research phase, then the most important sales conversation is not the discovery call or the demo. It is the AI answer the buyer received three weeks before they reached out.
When buyers search for the best solutions for their needs, AI summarizes the market before a single blue link appears. In that instant, the vendor shortlist is set. Vendor evaluation is now compressed, algorithmically mediated, and unforgiving.
A selling organization that does not appear accurately in AI-generated answers is not just losing search traffic. It is being excluded from shortlists before a single human conversation takes place. This is the same dynamic that is reshaping what buyer intent data can and cannot tell you — AI-driven shortlisting happens before any behavioral signals appear in traditional intent platforms.
AI Has Industrialized Confident Misunderstanding
Here is the problem that makes the AI research shift more dangerous than it appears.
AI tools are fluent. They produce well-structured, grammatically sound, confidently stated answers to buyer questions. That fluency is indistinguishable from accuracy. A buyer who asks an AI system to compare two enterprise platforms receives a detailed, organized response that feels authoritative. They have no reliable way to know whether that response reflects current product capabilities, accurate pricing, or the vendor’s actual positioning.
A Kodec AI study examining AI platforms across more than 200 query cycles found that AI platforms returned inaccurate or misleading answers in 62% of simulated buyer queries about B2B software products. In some cases, AI tools quoted discontinued free tiers or legacy pricing. In others, models attributed features to the wrong products after sourcing information from competitor-written comparison articles.
This is the mechanism of confident misunderstanding at industrial scale. Confident misunderstanding is what happens when a buyer forms firm conclusions from fragmented, inaccurate sources and believes those conclusions to be accurate. It has existed in B2B buying for as long as buyers have conducted independent research. What AI has changed is the speed, fluency, and scale at which it operates.
A buyer who spent two weeks reading articles and forum posts might develop one or two confident misunderstandings. A buyer who asks five AI questions in twenty minutes can develop confident misunderstandings across pricing, feature set, integration capability, security posture, and implementation complexity — before they have visited the vendor’s website or spoken to a single human.
Forrester’s 2025 research found that 20% of buyers felt less confident in a decision because they encountered unreliable or inaccurate AI information, and 28% of procurement professionals reported the same. These are the buyers who noticed. The buyers who did not notice the inaccuracies are the more significant problem. They arrived at sales conversations carrying confident misunderstandings they had no reason to question, because the AI that produced them sounded authoritative.
Forrester has predicted that a Fortune 500 company will face legal action in 2026 for AI-generated misrepresentation, including inaccurate product information or pricing discrepancies. The commercial damage, however, is already measurable today. A single misquoted enterprise price, a wrong assumption about an integration, a security concern based on an outdated data incident — any of these, formed during AI research and hardened into confident misunderstanding, can derail a deal before the selling organization ever knows the misunderstanding exists.
Why More Content Does Not Solve This
The standard response from selling organizations to the rise of AI-driven buyer research is to produce more content — more blog posts, more comparison pages, more FAQ sections — in the hope that this content will be surfaced by AI systems and will accurately represent the solution.
This is a necessary but insufficient response.
More content helps with discoverability. It increases the likelihood that accurate information about the solution is accessible to AI systems. But it does not address what happens after the buyer forms their initial view from AI research and then enters the evaluation phase.
The buyer who formed a confident misunderstanding during AI research does not usually arrive at the sales conversation saying “I read something that might be wrong.” They arrive saying “I understand that your pricing works like X” or “I saw that you don’t support Y integration.” The confident misunderstanding has already hardened. Better content on the vendor’s website could have prevented it if the buyer had read it. But they did not read it. They asked an AI.
Forrester’s research found that buyers increasingly validate AI-generated findings through their buying network — a mix of internal stakeholders and external sources. When asked what primarily triggers engagement with providers, buyers more often cited interactions with industry experts than information from AI tools. This tells selling organizations something important. Buyers who encounter AI-generated information that they find plausible will validate it through other sources. If the selling organization is not one of those validation sources — not present in the evaluation with governed, accurate explanation — then the validation loop happens without them. This is the same gap that the missing layer in the sales stack describes, now made more acute by AI accelerating the formation of buyer views before any seller contact occurs.
What the AI Research Shift Requires
The AI-driven research shift does not require selling organizations to abandon traditional marketing and content strategy. It requires adding a layer that most current strategies are missing.
That layer is governed expertise present in the evaluation phase — not just in published content that AI systems might surface, but directly accessible to buyers who are actively evaluating. A buyer who can ask the selling organization’s governed system “how does your pricing scale for a 500-person enterprise?” and receive an accurate, specific answer is not going to form a confident misunderstanding about pricing. A buyer who gets that answer from an AI system working from a three-year-old comparison article is.
The goal is to be the most accurate source a buyer can access during their research phase. Not the most visible. The most accurate. When a buyer encounters a discrepancy between what an AI told them and what a governed evaluation system tells them, the discrepancy itself becomes a trust signal. The vendor who can demonstrate accuracy in direct evaluation is the vendor who earns the shortlist position — and keeps it.
6Sense’s research confirms that the preliminary vendor choice formed during the selection phase predicts the final outcome 80% of the time. That choice is now being formed during AI-assisted research. The selling organizations that will win in this environment are the ones that ensure their solution is represented accurately in that research phase — not just by publishing content, but by making governed, accurate expertise directly available to buyers who are actively forming the views that will determine shortlists. This is the core argument for digital-first GTM strategy in the AI era: being digitally present is not enough if what buyers encounter is ungoverned and inaccurate.
The Bottom Line
AI has become the dominant research channel for B2B buyers. It shapes shortlists, forms first impressions, and determines vendor preferences before most selling organizations have had a single conversation with the buyer. That is the new reality of B2B buying, and it is accelerating.
The opportunity is significant for selling organizations that invest in being accurately represented during the AI-research phase. The risk is equally significant for those that do not — because AI-generated confident misunderstanding, formed before the first meeting and hardened through the validation process, is one of the primary drivers of stalled deals, late-stage objections, and post-purchase dissatisfaction.
Buyers are not going to stop using AI to research solutions. The question is whether the understanding they develop through that research is accurate enough to support a confident, well-grounded decision — or whether it is confident misunderstanding waiting to surface at the worst possible moment.
The selling organizations that answer that question by investing in governed evaluation infrastructure alongside their content strategy will find that the AI research revolution is an advantage rather than a threat.
Frequently Asked Questions
How are B2B buyers using AI in their research process?
Buyers are using AI tools to generate vendor shortlists, compare solutions, synthesize pricing information, evaluate capabilities, and review competitive landscape — tasks that previously required visiting multiple websites over hours or days. Research from 6Sense found that 94% of buyers use LLMs during their buying process, and Forrester found that 61% of the buying journey completes before any vendor contact.
When in the buying process do buyers typically use AI tools?
AI tools are most heavily used in the early selection phase, before any vendor contact. Buyers use them to define the problem space, identify potential solutions, build a comparative view of the market, and arrive at a preliminary shortlist. By the time a buyer contacts a seller, AI has already significantly shaped their view of the market and their preferences within it.
What is the shortlist problem and why does it matter?
Research from 6Sense found that 95% of the time, the winning vendor is already on the buyer’s shortlist before the first seller contact, and 80% of deals are won by the buyer’s pre-contact favorite. If that shortlist is formed primarily during AI-assisted research, then the most important factor in winning a deal is not the sales conversation — it is how accurately and favorably the solution is represented in the AI-generated answers the buyer received weeks before reaching out.
How does AI research create confident misunderstanding?
AI tools are fluent and produce confidently stated answers regardless of their accuracy. A buyer who receives an AI answer about pricing, features, or integrations has no reliable way to distinguish accurate from inaccurate information. When that answer is wrong, the buyer forms confident misunderstanding — a firm conclusion built on inaccurate information that they believe to be accurate. A Kodec AI study found that AI platforms returned inaccurate answers in 62% of simulated buyer queries about B2B software products.
Is confident misunderstanding from AI research worse than from traditional self-directed research?
In degree, yes. Traditional self-directed research was slow enough that buyers might encounter corrective information or consult enough sources to surface contradictions. AI research is fast, fluent, and synthetic — it produces confident-sounding answers in seconds. A buyer can develop confident misunderstandings across multiple dimensions of evaluation in a single session, before visiting the vendor’s website or speaking to anyone.
Why doesn’t publishing more content solve the AI research problem?
More content improves the accuracy of information AI systems can draw on, which is genuinely helpful. But it does not address what happens after a buyer has already formed confident misunderstanding from AI research and entered the evaluation phase. At that point, the misunderstanding has hardened into a belief the buyer carries into every subsequent interaction. Preventing confident misunderstanding requires governed expertise directly accessible in the evaluation phase, not just content published on a website.
What should selling organizations do differently in response to AI-driven buyer research?
The response has two components. First, ensure that accurate, structured, machine-readable information about the solution is publicly accessible and optimized for AI citation. Second, make governed evaluation infrastructure directly available to buyers during the research and evaluation phase — a system that can answer the specific questions buyers bring from AI research accurately, and that can correct confident misunderstandings before they harden into late-stage objections.