Review sites and AI search: a visibility plan for B2B software

G2 surveyed 1,076 B2B software buyers in March 2026. It is vendor research from a review platform, so read it with that in mind. It found 51% of buyers now start research with an AI chatbot more often than with Google, that AI chatbots are the top source influencing shortlists, that 69% chose a different vendor than planned because of a chatbot recommendation, and that buyers rank citations from software review sites as their top trust signal for AI answers.

TrustRadius, also a review platform, published its 2026 B2B Buying Disconnect report in July 2026 from 1,862 technology buyers. It found 63% used AI at some point in their research, 94% of those fact-check AI answers at least some of the time, and 74% use reviews to inform decisions. Use of analyst reports fell to 13%.

The pattern is consistent: buyers ask an assistant, the assistant summarizes what third parties say, and buyers check the answer against reviews. Google's own guide notes that its generative AI features can show what is being said about products and services across the web.

There is no universal list. Some categories are dominated by one platform, some by analyst peer review sites, some by marketplaces such as the Salesforce AppExchange or cloud marketplaces. Decide with data from your own prompt set.

A complete, current profile is the fastest fix you fully control, so start there.

1. Claim every profile in your category on the platforms you chose, including ones you have ignored.
2. Use the same category, one-sentence description and product names as your site and Organization schema.
3. List integrations, deployment options, supported regions and company sizes served in text.
4. Describe the pricing model, and starting prices where you publish them, consistently with your pricing page.
5. Add current screenshots and remove retired features and old product names.
6. Reply to reviews, including critical ones, with specifics rather than templates.

A five-star review that says "great tool, great team" helps your rating and little else. Assistants answer specific questions, so the reviews that shape answers are the ones with specific content: the use case, the systems involved, the size of the team and what changed.

You cannot tell customers what to write, and you should not try. You can ask open questions that invite detail, which is both allowed and more useful to readers.

Review generation is regulated. The US Federal Trade Commission's final rule, announced on August 14, 2024, prohibits fake reviews, incentives conditioned on a positive or negative sentiment, undisclosed reviews by company insiders and review suppression through threats or false accusations. Review platforms also have their own policies on incentives and disclosure. This is not legal advice; check the rules for your markets with counsel.

Within those rules, the reliable method is boring: ask every customer at the same moments, make it easy and accept what they write.

• Ask at milestones: after go-live, after the first measurable result and at renewal.
• Have customer success ask, not marketing, and give a direct link to the right profile.
• If you offer a thank-you, offer it to everyone who reviews, regardless of rating, and follow the platform's disclosure rules.
• Never gate requests by asking satisfaction first and only sending happy customers to the review site.
• Ask reviewers to mention their industry, company size and use case, which helps answers to segment-specific prompts.

Reviews are also account-based proof. A review from a company in the same industry and size band as a target account answers the question that account will ask an assistant: "Is [vendor] good for mid-size manufacturers?" Our customer proof program builds that coverage deliberately, matching review and case study requests to the segments in your target account list.

Give sales a short list of reviews by segment, and use the same quotes, with permission, in ABM campaigns. One proof asset then works in AI answers, on review platforms and in outreach.

Measure the profile, the answers and the pipeline.

• Review count and recency by platform and by segment, against two or three named competitors.
• Share of answer on category and comparison prompts, by engine.
• Accuracy of how assistants describe your product, integrations and pricing model.
• Review-site referrals in analytics and review-site mentions in self-reported attribution.
• Win rates on opportunities where the buyer cites reviews or AI research.

AI recommendation lists vary from run to run, as a SparkToro and Gumshoe.ai study of nearly 3,000 runs showed, so judge progress by how often you appear across many runs, not by one answer.

Run the prompt set, pick the two or three platforms that matter, fix the profiles and set up the milestone asks. Most teams can do that in a month.

Our generative engine optimization service covers review platforms as part of the off-site source map, alongside your own pages.

If assistants recommend competitors with fewer customers than you, talk to our team.