AI has made software easier to find and according to G2’s own 2026 research, harder to buy. That is a strange pairing on the surface.

Discovery is supposed to be the hard part but the data shows evaluation, not research, is now the longest stage of the B2B software buying journey and the reason is not that AI recommendations are unreliable – it is that a recommendation from a chatbot is not the same thing as an approval from the people who actually sign off on the purchase.

In plain terms, AI recommends software by drawing on everything it can find said about a brand across the open web – how often and how consistently it is mentioned, how it is described on review platforms and how that description holds up against competitors answering the same question. That process produces a shortlist a buyer trusts enough to act on but what it does not do is clear the separate, human-run approval process that still sits between a shortlist and a signed contract and most content about AI visibility stops at the shortlist as though the story ends there.

That gap matters more for some brands than others as being recommended by AI can get you into the conversation but it does not guarantee the buyer will choose you. If your pricing, security, proof points or product information do not hold up when they look deeper, you can still lose the deal. Getting mentioned is only half the job because what happens after the mention matters just as much.

This article looks at both halves – how the recommendation itself gets formed and the far less discussed process a recommended brand still has to survive afterward.

Where most AI visibility advice quietly stops

Most of what gets written about AI search visibility is about winning the first decision –  getting the model to name your brand, describe it accurately and place it ahead of competitors when a buyer asks a comparison question. That is real and necessary work.

But G2’s 2026 Buyer Behavior Report, based on a survey of more than 1,000 B2B software decision-makers found that evaluation has become the longest stage of the buying journey for 40% of buyers, ahead of research at 36% and the final decision at 22%.  AI may be helping buyers discover and shortlist vendors faster but it has not made the later stages of buying much easier as vendors still have to get through security reviews, budget discussions, stakeholder approvals and implementation planning before a deal moves forward.

And most of that happens outside the AI tools marketing teams are tracking. A brand can look highly visible in ChatGPT or other AI platforms and still struggle later in the buying process for reasons that AI visibility metrics alone will not show.

Two judges decide whether your brand gets chosen

There are really two judges deciding whether a brand gets chosen – the first one is the AI system, forming a view from mentions, reviews, and consensus across the web. The second is the buyer’s own organization, deciding whether to actually act on that recommendation.

Getting past the first judge is a visibility problem –  getting past the second is an evidence problem and it is run by people who never typed a prompt and do not particularly care what ChatGPT or Google AI or Perplexity  said.

The first gate – how the recommendation actually gets built

G2’s research found that 51% of B2B software buyers now start their research with an AI chatbot more often than with Google and generative AI chatbots were the single largest factor buyers cited in shaping which vendors made their shortlist, ahead of review sites, market research firms and the vendor’s own website.

Comparing vendor strengths and weaknesses was the top reason buyers turned to AI chatbots during software research in the first place and the consequence of that directly shows up in the results –  69% of buyers said an AI chatbot led them to select a different vendor than they had originally planned and one in three purchased from a vendor they had never heard of before starting their research.

This is what GeoRankers calls gate one traffic – the volume of vendor decisions now being shaped inside a single AI conversation often before a website visit happens at all. A brand that is not part of that conversation is not merely deprioritized – it is often simply not in the running by the time a human enters the picture.

Review platforms play a specific role inside this first gate. G2 found that 45% of B2B software buyers named review site citations as the single most confidence-inspiring signal inside an AI-generated answer, rising to 50% among buyers who use chatbots daily.  When an AI answer surprises a buyer, one of the first things they often do is check reviews before trusting it. So,  even if your brand shows up strongly in AI answers, a lack of credible customer reviews can quickly create doubt. Therefore, getting recommended by AI may get you onto the shortlist but reviews often help buyers decide whether you deserve to stay there.

Not every AI conversation carries the same weight

The first gate is also not one uniform gate. G2’s data also shows ChatGPT usage concentrated heavily at the discovery stage at 73%, before dropping to the low 50’s as buyers move into consideration and decision, while Claude more than doubled its share of software research usage in seven months and Copilot holds a noticeably larger share among enterprise and large-enterprise buyers likely tied to its default presence inside Microsoft 365.

Engineering and R&D roles show the most varied tool usage of any function –  meaning the person running a deep research comparison in Claude may not be the same person who typed the original discovery prompt into ChatGPT.

For a vendor, this means a strong first-gate strategy cannot assume one model speaks for the whole buying committee. The individual who opens the conversation and the individual who later validates it may be looking at different tools and both impressions carry weight by the time the brand reaches the second gate.

The second gate – what happens after the model has already said yes

This is the part of the journey that gets almost no attention in AI visibility content and it is where G2’s newer research becomes genuinely useful.

Among buyers whose evaluation stalls after a vendor has been shortlisted, IT security review is the single biggest source of delay, cited by 39% of buyers overall and by 50% among enterprise buyers specifically. Budget approval follows at 32%, and implementation planning at 25%.

None of that friction has anything to do with whether ChatGPT described the brand accurately – it has to do with whether the brand can produce a security questionnaire on request, whether its pricing model survives a CFO’s scrutiny and whether an implementation timeline holds up against a buyer’s internal deadlines.

This is what we call the second gate – the internal approval process a recommended vendor still has to clear, run by a group of people often in security, finance, and operations who were never part of the AI conversation that put the brand on the shortlist in the first place.  A brand can perform well in AI search and still get stuck later in the buying process if it treats visibility as the end goal.

The AI discount – when being recommended by a chatbot becomes a liability, not an asset

There is a third wrinkle here that most vendors are not prepared for.

Forrester’s State of Business Buying, 2026 report, drawn from its 2025 Buyers’ Journey Survey, found that 36% of B2B buyers felt more confident in their decision because they used generative AI during research  but 20% said they felt less confident after running into unreliable or inaccurate AI output.  Among procurement professionals specifically, that number rose to 28% and 22% said they had wasted time chasing down poor AI-generated information, both meaningfully higher than the buyer population overall.

That is not a contradiction so much as two different comfort levels living inside the same organization. A buyer might trust an AI chatbot enough to use it for the first pass of research, while procurement, which Forrester found is now getting involved earlier in the buying cycle than in prior years, treats the same AI-generated shortlist as something to verify rather than something to accept. Forrester’s data backs this up in a concrete way – when a purchase specifically involves generative AI features, the buying group evaluating it doubles in size compared with purchases that do not, because those decisions draw in more cross-functional scrutiny.

This is what we refer to as the AI discount – the credibility tax a vendor pays when part of a buying organization treats an AI-generated recommendation as a hypothesis to test rather than a conclusion to accept regardless of how well the vendor performed inside the original shortlist. A brand that only optimizes for the AI’s answer is not accounting for the fact that the answer itself can become the thing a sceptical, larger buying group spends its evaluation cycle picking apart.

What clearing both gates actually requires

Pulling this together, a SaaS brand trying to win AI-influenced deals needs to prepare for two different kinds of scrutiny – not one.

Clearing the first gate is about consistent, accurate presence across the web – enough independent mentions and reviews that a model has real material to draw on and enough consistency across ChatGPT, Gemini and Perplexity that the shortlist a buyer sees does not fall apart the moment they cross-check it.

Clearing the second gate is about having the unglamorous proof ready before it is asked for – a current security questionnaire, transparent pricing logic that survives budget scrutiny, a realistic implementation timeline and documentation written for people who are evaluating the brand precisely because they do not fully trust the AI’s word for it. None of that is content built to be cited by a model- it is the content built to be handed to a procurement team.

What this means going forward

Treating AI visibility as a top-of-funnel problem made sense when the only question was whether a brand got named at all. That question is largely settled now for any brand doing the basic work of being mentioned and reviewed consistently. The harder, less examined question is what a brand has ready for the people who see the AI’s answer and decide to check it themselves before they act on it.

Worth sitting with –  if your product were named in tomorrow’s AI answer for your category, does your team already have the security documentation, implementation proof and pricing clarity the buyer’s own organization is going to demand, or, does the deal stall exactly where G2’s data says most deals are stalling now?

GeoRankers tracks the first gate, how your brand actually appears across ChatGPT, Gemini, Perplexity, and Google’s AI systems, model by model, alongside where competitors are winning the answer instead. Getting that part right is still the precondition for everything downstream. If you want to see where your brand currently stands, you can sign up and run your own category through it.

Read – The GEO Guide for a fuller walkthrough of how generative engine optimization works end to end.

Frequently Asked Questions

How does AI recommends software to buyers?

It draws on everything it can find said about a brand across the web, including mention frequency, review platform data, and how consistently a brand is described relative to competitors answering the same question, rather than ranking pages the way a search engine does.

Does getting recommended by AI guarantee a sale?

No. G2’s 2026 Buyer Behavior Report found evaluation, not research, is now the longest stage of the B2B buying journey. A brand can be named in the AI’s answer and still stall at internal security review, budget approval, or implementation planning.

What is the “second gate” in AI-influenced software buying?

It’s the internal approval process a recommended vendor still has to clear after the AI names it, run by people in security, finance, and operations who weren’t part of the original AI conversation. IT security review is the single biggest source of delay, cited by 39% of buyers and 50% of enterprise buyers.

Why do buyers check review sites after an AI recommends a vendor?

Because 45% of B2B software buyers say review site citations are the most confidence-inspiring signal inside an AI-generated answer. Buyers use peer reviews to verify what the AI told them before acting on it.

Does every AI model recommend the same vendors?

Not necessarily, and usage is shifting fast. Similarweb data shows ChatGPT’s share of AI web traffic fell from roughly 77% to about 54% in a year, while Gemini and Claude both gained meaningfully, so a brand’s answer needs to hold up across more than one model.

Is internal resistance to AI-driven recommendations common?

Yes. Forrester’s State of Business Buying, 2026 found 20% of B2B buyers felt less confident in a decision after encountering inaccurate AI output, rising to 28% among procurement professionals specifically, and buying groups evaluating AI-related purchases are twice the size of those that don’t.