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How AI chooses

How AI chooses software products

Software evaluation compressed into a prompt: a founder describes the team, the workflow, the budget and the tool being replaced, and the assistant answers with a shortlist. For SaaS companies, that shortlist is the new first-page ranking. Here is how products get onto it.

Daniel Arons, Co-founder and CEO of Aethon AI
Daniel Arons · Co-founder & CEO, Aethon AI
Eight years building digital marketing programs across SaaS, financial services, and consumer brands · Updated July 2026

The moments that trigger the question

B2B software moments arrive in roles: the practitioner hitting a wall in the current tool, the founder consolidating spend, the ops lead with a migration deadline, security clearing a vendor, procurement asking who else uses it. Each role phrases differently and pulls different evidence, and products routinely win the practitioner conversation while losing the economic-buyer one about the same purchase.

The sources the answers lean on

For software recommendations, assistants lean on review platforms and their structured ratings, comparison articles and category listicles, community threads where users speak plainly, documentation and changelogs as proof of substance, and vendor pages that state pricing, limits and fit honestly. Refusing to publish pricing has a specific cost here: the assistant fills the gap from third parties, accurately or not.

Why the same software products keep winning

The products that keep getting named supply comparable facts, real pricing, honest fit statements, named alternatives, and their review and community record corroborates the claims. Category incumbents dominate generic asks through sheer citation depth, but focused tools win the specific moments, this team size, this stack, this compliance need, whenever their evidence matches tighter than the incumbent's.

Run the test yourself, and what software products can do about it

The test costs nothing: ask ChatGPT, Gemini, Claude and Perplexity the questions above, phrased the way a real person would say them, and write down who gets named and what sources appear. Run each question twice on different days, since answers vary and patterns matter more than single runs.

For SaaS teams, the fixes: publish pricing and honest comparison pages, keep review profiles active, and show up in the community threads your buyers read, as named people with disclosure. For software products that want the systematic version, the free Aethon audit runs your real buyer questions across all four assistants with screenshots, and the SaaS playbook turns the gaps into a work plan.

Frequently asked questions

How does AI choose which software to recommend?

By matching the described need to comparable evidence: review-platform ratings, comparison content, community sentiment, documentation depth, and vendor pages with real pricing and fit statements. Products supplying verifiable specifics beat products supplying adjectives.

Can a SaaS company pay for ChatGPT recommendations?

No. The shortlist inside the answer is synthesized from trusted sources and cannot be bought. Labeled ads exist separately and do not alter the recommendation text.

Why does a small tool beat the category leader in some AI answers?

Because the question carried constraints, team size, stack, budget, compliance, and the small tool's citable evidence matched them precisely. Assistants score fit against evidence, which is winnable by focus.

What should a SaaS company fix first for AI visibility?

Publish real pricing and honest comparisons, activate review platforms, and correct entity facts everywhere. Then measure the four assistants monthly on your buying moments. The free Aethon audit shows your current shortlist standing.

See where your brand stands in AI.

Book a 30-minute call and we run your top prompts through ChatGPT, Gemini, Claude, and Perplexity, live.