How B2B Buyers Actually Use AI to Research Software

Two-thirds of B2B buyers now use AI tools to research and shortlist software vendors before they ever speak to a sales rep. If your company isn’t showing up in those AI-generated answers, you’re not on the shortlist. That’s the short version. The longer version involves understanding exactly what buyers type into ChatGPT, Perplexity, and Gemini, how buying committees divide up AI research tasks, and what you can do to make sure your product appears in the results. The shift is already well underway, and most SaaS companies haven’t adapted their marketing to account for it. What follows is a practical breakdown of how B2B buyers actually use AI to research software, drawn from recent data and our experience working with over 160 B2B SaaS and tech companies at Gripped. If you sell software with a complex buyer journey, this matters to you right now.

The shift in how B2B buyers research software

For years, the B2B software buying process followed a predictable pattern: Google search, click through a few vendor pages, download a whitepaper, get added to a nurture sequence, eventually talk to sales. That pattern is breaking apart.

B2B buyers now spend roughly five hours in AI-powered search for every one hour they spend with a sales rep. That ratio tells you where the real evaluation happens. Buyers are forming opinions, building shortlists, and ruling out vendors long before any human conversation takes place.

The reason is straightforward. AI tools give buyers a synthesised answer instead of ten blue links. A marketing leader evaluating project management software doesn’t want to visit eight vendor websites and compare feature pages manually. They want a summary: “Which project management tools are best for B2B SaaS teams with under 50 people?” AI tools deliver that summary in seconds.

This isn’t a niche behaviour. Two-thirds of UK buyers now use AI to choose suppliers, and adoption is accelerating among software buyers specifically. Forrester’s 2026 predictions flag that B2B organisations failing to adapt their content strategies for AI discovery will see measurable drops in pipeline. The buyers haven’t disappeared. They’ve just moved to a channel most vendors aren’t optimising for.

Where buyers start their research now

The starting point for software research has fragmented. Google still plays a role, but it’s no longer the default first step for many buyers. Instead, buyers open ChatGPT, Perplexity, or Gemini and ask a question in natural language.

Perplexity has gained particular traction among B2B researchers because it cites sources inline, giving buyers a way to verify claims and click through to original material. ChatGPT is used more for broad category exploration and comparison. Gemini, with its integration into Google Workspace, catches buyers who are already working inside that ecosystem. Each tool has a slightly different use case, but the behaviour is the same: buyers want a consolidated, conversational answer rather than a list of links to sort through.

Review sites like G2, Capterra, and TrustRadius remain important, but their role has shifted. Buyers still check them, but often after AI tools have already pointed them in a direction. The reviews serve as validation rather than discovery. Similarly, analyst reports from firms like Forrester and Gartner still carry weight, but buyers increasingly expect AI tools to summarise those reports for them rather than reading 40-page PDFs.

One pattern we see repeatedly at Gripped is that B2B ordering and research behaviour is shifting toward AI-first discovery, with traditional search becoming a secondary verification step. If your SEO strategy assumes Google is the top of the funnel, you’re building on an assumption that’s already outdated for a significant portion of your buyers.

What buyers ask AI tools

The questions buyers ask AI tools are different from what they type into Google. Search queries tend to be short and keyword-driven: “best CRM for SaaS.” AI queries are longer, more specific, and often include context about the buyer’s situation.

Here are real examples of the kinds of prompts B2B software buyers use:

• “What are the best customer success platforms for B2B SaaS companies with 200 to 500 customers and a mid-market sales motion?”
• “Compare Hubspot and Salesforce for a 40-person SaaS company that needs strong integration with Slack and Jira.”
• “Which cybersecurity vendors specialise in compliance for financial services companies operating in the UK and EU?”
• “What’s the total cost of ownership for Snowflake versus Databricks for a data team of 10?”

Notice the specificity. Buyers aren’t searching for a category. They’re describing their exact situation and asking for a tailored recommendation. This means AI tools need to understand your product’s positioning at a granular level: who it’s for, what it integrates with, how it’s priced, and where it fits relative to competitors.

The other pattern worth flagging is that buyers ask follow-up questions. A single AI session might start with a broad category query, narrow to a comparison of three vendors, then drill into specific features or pricing. This conversational research journey means your product needs to show up consistently across multiple related queries, not just one.

How the buying group uses AI

B2B software purchases rarely involve a single decision-maker. A typical buying group for a SaaS product includes three to eight people: a champion, an economic buyer, a technical evaluator, and often end users who’ll need to live with the tool daily.

Each of these people uses AI differently during the research process.

The champion, usually a marketing leader or department head, uses AI to build the initial shortlist and create an internal business case. They’ll ask broad questions about category leaders and then generate comparison summaries they can share with the wider group.

Technical evaluators ask AI about integrations, API documentation, security certifications, and data residency. Their queries are precise and often reference specific standards: “Does [vendor] support SSO via SAML 2.0 and SCIM provisioning?” If AI tools can’t find clear answers about your technical capabilities, your product gets flagged as a risk.

Economic buyers, typically a CFO or VP of finance, use AI to benchmark pricing and understand total cost of ownership. They’ll ask about hidden costs, implementation timelines, and contract flexibility. Queries like “What’s the average implementation cost for [category] tools at companies with 100 to 300 employees?” are common.

End users tend to check AI for usability opinions and real-world feedback. They’ll ask about learning curves, common complaints, and how the product compares to what they’ve used before.

The practical takeaway: your product needs to be represented accurately across all these dimensions. If AI tools can only find your marketing copy but not your technical documentation, you’ll pass the champion’s filter but fail the technical evaluator’s. Buying groups use AI to stress-test vendors from multiple angles simultaneously.

What this means for your AI visibility

If you’re not showing up in AI-generated answers, you’re invisible to a growing share of your buyers. This isn’t speculation. Sixty-six percent of B2B buyers now use AI for supplier research, and that number is climbing.

AI tools don’t generate answers from nothing. They synthesise information from publicly available sources: your website, review sites, industry publications, analyst reports, documentation, forums, and third-party content. The quality and consistency of those sources determine whether your product appears in AI answers and how favourably it’s described.

Three factors drive AI visibility for software vendors:

1. Source diversity. AI tools cross-reference multiple sources. If your product is only described on your own website, AI models treat that as less authoritative than a product mentioned across review sites, industry blogs, and comparison articles.
2. Positioning consistency. If your homepage says you’re an “enterprise data platform” but G2 categorises you as a “business intelligence tool” and your CEO’s LinkedIn says you’re an “analytics solution,” AI tools struggle to place you in the right context. Consistent entity descriptions across the web matter.
3. Question-answer alignment. AI tools look for content that directly answers the questions buyers ask. If nobody has published clear answers to “How does [your product] compare to [competitor]?” or “What’s the pricing for [your product]?”, AI tools either skip you or guess, and guesses are often wrong.

This is the core of what’s now called Generative Engine Optimisation, or GEO. It sits alongside SEO, not as a replacement but as an additional discipline. Strong SEO foundations are an input to GEO. Without them, AI tools have less quality material to draw from.

How to show up in AI research

Getting your product into AI-generated answers requires deliberate work. Here’s what actually moves the needle.

Start with a GEO audit. Check how ChatGPT, Perplexity, Gemini, and Claude currently describe your company and category. Ask the same questions your buyers would ask. Note where competitors are cited instead of you, which sources the tools pull from, and where the information is inaccurate. This gives you a gap analysis to work from.

Build content around buyer questions, not keywords. Traditional SEO targets search terms. GEO targets the natural-language questions buying groups ask AI tools. Create content that directly answers comparison queries, pricing questions, integration specifics, and use-case fit. Structure it clearly so AI tools can extract and synthesise the answers.

Invest in third-party coverage. AI tools weight independent sources more heavily than vendor-owned content. B2B strategies that prioritise earned media and third-party validation tend to perform better in AI-generated results. Pursue reviews on G2 and Capterra, contribute guest articles to industry publications, and ensure your product is mentioned in relevant comparison pieces.

Fix your structured data and entity descriptions. Make sure your company name, category, and core positioning are described consistently across your website, LinkedIn, Crunchbase, review profiles, and any directories where you appear. AI models build entity graphs from these sources, and inconsistencies confuse them.

At Gripped, we run GEO as a distinct workstream alongside SEO for our SaaS clients. The process includes auditing current AI visibility, building topic clusters around buyer questions, and systematically improving third-party citations and entity consistency. It’s not a one-off project. It requires ongoing measurement and iteration, much like SEO did a decade ago.

Keep your technical documentation public and well-structured. Buyers’ technical evaluators ask AI about integrations, security, and compliance. If that information is locked behind a login or buried in PDFs, AI tools can’t access it, and your product won’t appear in technical queries.

Common questions

Which AI tools do B2B buyers use most?

ChatGPT remains the most widely used AI tool for general software research, followed by Perplexity and Gemini. Perplexity has carved out a strong position among B2B researchers specifically because it provides source citations, which matters to buyers who need to justify their recommendations internally. Gemini sees heavy use among organisations already embedded in Google Workspace. Claude is growing in popularity for longer, more analytical research tasks. UK-based AI adoption data for B2B shows consistent growth across all major platforms, with no single tool dominating completely. The practical implication is that you can’t optimise for just one AI tool. Your content and entity data need to be accessible and consistent enough for all of them to pull from.

Does AI research replace Google search?

Not entirely, but it’s taking over specific parts of the buyer journey. AI tools are strongest in the early research and comparison phases, where buyers want synthesised answers rather than a list of links. Google still plays a role in verification, finding specific pages, and navigating to known resources. Forrester’s 2026 predictions indicate that traditional search will remain relevant but will increasingly serve as a secondary step after AI-based discovery. For SaaS marketers, this means maintaining strong SEO while adding GEO as a parallel discipline. Treating them as either/or is a mistake. The buyers who find you through AI will still Google your company name to verify what they’ve learned.

The way B2B buyers research software has changed faster than most marketing teams have adapted. AI tools are now a primary research channel, and if your product isn’t represented accurately and consistently across the sources those tools draw from, you’re losing pipeline before you even know a buyer was looking. The companies that treat GEO as seriously as they treated SEO five years ago will have a measurable advantage in how often they appear on shortlists.

If you’re a SaaS or tech company trying to figure out where your pipeline is leaking and whether AI visibility is part of the problem, Gripped can help. We work exclusively with B2B SaaS and tech businesses, and our focus is on the metrics that matter: CAC, LTV, and qualified pipeline, not vanity numbers. Get your free growth audit and we’ll show you where buyers are finding you, and where they’re not.