How AI recommends
SaaS tools

When a B2B buyer asks an AI platform for a software recommendation, the platform goes through a multi-step process that is fundamentally different from how Google handles the same query. Google returns a list of links, typically dominated by G2, Capterra, TrustRadius and a few blog posts with titles like "10 Best CRM Tools in 2026". The user clicks through multiple pages, reads reviews and gradually builds their shortlist. AI skips all of that. It consults multiple sources simultaneously, synthesises the information and delivers a direct recommendation.

Stage 1: Understanding the requirement

AI first analyses the query to understand the buyer's specific needs. "Best CRM for a 20-person agency" tells AI the buyer needs: a CRM (category), suitable for agencies (industry), for 20 users (team size). AI uses this to filter its search. It will not recommend enterprise CRMs priced at £200 per user or solo tools without team features. The more specific the query, the more precisely AI filters its recommendations. This is why your product content needs to clearly state who your product is for, what team sizes it supports and which industries it serves. Learn more about intent classification in our guide on how AI understands search intent.

Stage 2: Gathering and weighing sources

AI consults multiple source types simultaneously. For SaaS recommendations, the most influential sources are G2 and Capterra reviews (volume, rating, review content), the vendor's own website (features, pricing, documentation), comparison articles from established tech publications, Trustpilot reviews (overall vendor reputation), community discussions on Reddit, Stack Overflow or industry forums and expert reviews from publications like TechRadar, PCMag or industry-specific blogs. Each platform weighs these sources differently. ChatGPT draws from training data plus Bing search. Claude emphasises authoritative, balanced sources. Perplexity searches the web in real time. Gemini uses Google's index. The implication: your product needs presence across all these source types, not just one or two.

Stage 3: Feature matching and filtering

AI matches the buyer's requirements against each product's documented features. If the buyer asks for "CRM with email marketing built in", AI looks for products that explicitly list email marketing as a feature. Products that mention it vaguely ("marketing capabilities") score lower than those that describe it specifically ("built-in email marketing with templates, automation workflows and A/B testing"). This is why feature documentation on your website needs to be specific and detailed, not generic marketing copy. Every feature should be described with enough detail for AI to understand what it does and how it compares to alternatives.

Stage 4: Pricing and value assessment

If the query includes a budget constraint ("under £30 per user"), AI filters by pricing. If no budget is specified, AI typically structures its response by price tier: "For smaller teams on a budget, consider [tool A] at £15 per user. For more features, [tool B] at £35 per user offers the best balance." Products with hidden pricing are excluded from budget-specific queries entirely. Transparent pricing in GBP is not optional for UK-focused SaaS AI visibility.

Stage 5: Response generation

Finally, AI generates its recommendation. For SaaS queries, this typically includes 3 to 4 products, each with a brief description of strengths, ideal use case and price point. AI aims for balanced recommendations: "HubSpot is best for inbound marketing. Pipedrive is best for pure sales pipeline management. Monday CRM is best for agencies that also need project management." Each recommendation slot is earned through strong signals across reviews, features, pricing and content. Products with weak signals in any area are unlikely to make the shortlist. For a deeper dive into the full selection process, read our guide on how AI selects its sources.