The standard pitch for AI search goes like this: buyers ask a chatbot for a list, the chatbot names three vendors, and you either make that list or you vanish. It's a discovery story. The data released this month says it's mostly an evaluation story.
In a Bain & Company snap chart published October 23, 2026, from a July 2026 survey of 207 B2B IT decision makers, 54% of software buyers use generative AI almost always or most of the time to research vendor capabilities and customer feedback. 51% do the same when comparing shortlisted vendors. Only 46% lean on it that heavily to build the initial vendor list.
Meanwhile, the marketers on the other side of that research are mostly flying blind. Demandbase research released October 20, 2026, at its GO conference in New York found only 35% of B2B marketers systematically track how their brand appears in AI search.
This B2Bcentr report shows which sample produced each number, where AI actually sits in the software buying journey, how big the tracking gap is, and what to fix before 2027 budgets lock.
Key Takeaways
- 54% of software buyers mostly research vendors with AI.
- AI use peaks at research and comparison, not list building.
- Only 35% of B2B marketers track their AI search presence.
- Review sites (38%) now edge chatbots (37%) as the top shortlist source.
- Negotiation is the least AI-heavy stage, at 32%.
Which sample produced the 54%
The lead number comes from the Bain B2B IT Decision Maker Survey, fielded in July 2026 with 207 respondents and published as a snap chart by Bain partner Natasha Sommerfeld on October 23, 2026. Bain asked: how often do you and your team use generative AI tools at each step of evaluating and purchasing software?
The definitions matter. In Bain's chart notes, "almost always" means more than 75% of the time and "most of the time" means 50% to 75%. Bain excluded "some of the time," "rarely," and "never," so every figure below is heavy use, not occasional use.
| Buying stage | Almost always | Most of the time | Heavy AI use (combined) |
|---|---|---|---|
| Researching vendor capabilities and customer feedback | 21% | 33% | 54% |
| Comparing shortlisted vendors | 22% | 29% | 51% |
| Building initial list of vendors to consider | 23% | 23% | 46% |
| Negotiating pricing and contract terms | 11% | 20% | 32% |
Source: Bain B2B IT Decision Maker Survey, July 2026, n=207, published October 23, 2026. Combined shares are computed from Bain's unrounded chart data, so they can differ by one point from the sum of the rounded parts.
Bain's headline reads: about half of B2B software buyers use generative AI most of the time to research, compare, and shortlist vendors. That's accurate. The ranking inside it is the more useful part.
The folklore: AI search is a top-of-funnel discovery channel
Most AI search advice treats the chatbot as a new front door. Get named in the first answer, and the rest of the funnel looks after itself.
Bain's ranking cuts against that. Heavy AI use is highest when buyers dig into capabilities and customer feedback (54%) and when they compare finalists (51%). Building the first list comes third at 46%. So AI does its heaviest work after a vendor is already in view.
G2's July 2026 data points the same way. In the G2 2026 Buyer Behavior Report, based on more than 1,000 B2B software buyers, 82% sourced software recommendations from an AI chatbot in the last 24 months. Half of those buyers said AI had its greatest impact when narrowing and comparing options.
G2 also found evaluation is now the longest stage of the buying journey, surpassing research for the first time. And review sites (38%) overtook AI chatbots (37%) as the top source shaping which vendors make a shortlist.
| Signal | Number | Sample |
|---|---|---|
| Heavy AI use when researching vendors | 54% | Bain, n=207, July 2026 |
| Heavy AI use when comparing finalists | 51% | Bain, n=207, July 2026 |
| Heavy AI use when building the first list | 46% | Bain, n=207, July 2026 |
| Sourced recommendations from a chatbot (24 months) | 82% | G2, 1,000+ buyers, July 2026 |
| Top shortlist source: review sites vs chatbots | 38% vs 37% | G2, 1,000+ buyers, July 2026 |
| Top AI use case: understanding total cost of ownership | 51% | G2, 1,000+ buyers, July 2026 |
Sources: Bain B2B IT Decision Maker Survey (October 23, 2026) and the G2 2026 Buyer Behavior Report (July 22, 2026). Different samples: read them side by side, never add or average them.
That's the shift. A chatbot that names a vendor once is a mention. A chatbot that buyers use to check reviews, compare features, and model costs is an evaluator, and evaluators need evidence.
The seller-side gap: only 35% track it
The Demandbase study, run by G2 for Demandbase in September 2026, interviewed 124 B2B marketers in demand generation, growth, digital, or field marketing roles at U.S. companies with 250 or more employees. Results went public October 20, 2026.
Only 35% systematically track how their brand appears in AI search. Yet 62% have already changed their search strategy for AI, and 64% have tried to influence how AI describes their brand.
Read that twice. Most teams are steering a channel they don't measure.
| Seller-side metric | Share of B2B marketers |
|---|---|
| Changed search strategy for AI search | 62% |
| Tried to influence how AI describes their brand | 64% |
| Systematically track brand presence in AI search | 35% |
| Site traffic rose over the past year | 44% |
| Site traffic fell over the past year | 15% |
| Team is the same size or smaller than two years ago | 72% |
Source: Demandbase research (fielded by G2, September 2026, 124 U.S. B2B marketers at companies with 250+ employees; published October 20, 2026).
Two details stand out. Company websites held up: 44% of marketers saw traffic rise over the past year against 15% who saw a decline, per Demandbase. That fits Bain's finding, since buyers who research with AI still check the vendor's own pages.
The second is capacity. In the same Demandbase study, 72% of teams are the same size or smaller than two years ago, and 70% face shorter timelines. A tracking program that needs a new hire won't happen. It has to fit inside the existing team.
What the 54% means for pipeline
If buyers do their heaviest AI work during research and comparison, sellers meet them later and better informed. That lines up with B2Bcentr's B2B sales cycle length report, which shows sellers only see the last stretch of a much longer buyer journey.
It also explains why win rates are hard to move with seller-side AI alone. B2Bcentr's AI in sales win rates report found AI adoption on sales teams rarely shows up as better win rates. If the comparison happened in a chatbot before the first call, the deal was partly decided before the rep's AI tools ever ran.
For baseline conversion math, B2Bcentr's B2B win rate report is the companion read. Win rate is where a weak AI comparison shows up first.
What to change this quarter
None of this needs a new team. It needs the existing team to aim at the stages where Bain shows AI is used most.
1. Track a fixed prompt set every month
Pick 20 to 30 prompts that mirror real evaluation questions, not just "best X software." Include comparisons against named rivals, pricing questions, and integration questions. Log which vendors get named and what the answer says about you. That alone would move a team from the 65% that don't track systematically into the 35% that do.
2. Publish comparison and proof pages
Comparison is the second-heaviest AI stage at 51%. Give the models something to quote: honest comparison pages, customer results with numbers, security documentation, and implementation timelines on public URLs.
3. Treat review sites as AI inputs
Customer feedback is part of Bain's top stage, and G2 reports review sites now edge chatbots as the top shortlist source. Recent, specific reviews feed both the human and the machine read.
4. Put pricing logic in public
Negotiation is the least AI-heavy stage at 32%, but understanding total cost of ownership is the top AI use case in the G2 report, cited by 51% of buyers. A public pricing explainer gets quoted during research instead of debated in procurement. B2Bcentr's AI agent pricing report covers which models buyers now call fair.
Where this data breaks
Name the limits before you put these numbers in a board deck.
- Bain's sample is small: 207 IT decision makers. Treat the stage ranking as directional, not as a precise market share.
- All three studies are self-reported. They show what people say they do, not logged behavior.
- Demandbase sampled U.S. marketers at companies with 250+ employees. A 30-person startup may track more or less.
- G2 runs a review site, so its finding that review sites lead the shortlist deserves a second source before you rebuild a budget on it.
- The samples differ. Never add or average Bain, G2, and Demandbase figures into one number.
Who shouldn't over-react: sellers in categories where buyers rarely research online, such as deals sourced almost entirely through partners or referrals. For them, heavy AI research is a slower-moving trend.
FAQ
What percentage of B2B software buyers use AI to research vendors?
About half use it heavily. 54% of 207 B2B IT decision makers in the Bain B2B IT Decision Maker Survey use generative AI almost always or most of the time to research vendor capabilities and customer feedback.
At which buying stage do software buyers use AI the most?
Research and comparison. Heavy use is 54% for researching vendors and 51% for comparing finalists, versus 46% for building the first list and 32% for negotiation (Bain).
Do B2B buyers still trust review sites over AI chatbots?
Slightly, for shortlists. The G2 2026 Buyer Behavior Report found review sites (38%) overtook AI chatbots (37%) as the top source shaping which vendors make a shortlist.
How many B2B marketers track their brand in AI search?
About one in three. Only 35% of 124 B2B marketers in the Demandbase research systematically track how their brand appears in AI search, even though 62% changed strategy for it.
Is AI search killing B2B website traffic?
Not for most teams yet. 44% of marketers in the Demandbase research saw site traffic rise over the past year, and 15% saw it fall.
How should a small marketing team start tracking AI search visibility?
Start with a fixed monthly prompt set. Run 20 to 30 real evaluation prompts, log which vendors get named, and fix the proof gaps first, since comparison is a 51% heavy-use stage (Bain).
Next read: buyers compare vendors with AI before the first call. See how long the part of the cycle sellers actually see lasts in the B2Bcentr sales cycle report.
The next data point to watch is whether AI-tracking adoption moves past one in three by the 2027 survey wave. Until then, the teams that measure their AI answers will be the ones that can explain a lost comparison. Start with B2Bcentr's GEO vs AEO vs SEO breakdown to decide which layer to fix first.