Your SaaS Buyer Shortlisted You in AI Before You Knew Them

Your next customer has already compared you with three competitors, read your reviews and drafted their objections. You just haven’t met them yet.

Duane Forrester walked into a truck dealership knowing more about the vehicle than the salesperson did.

He gave an AI assistant the VIN. Within minutes it had confirmed the trim, pulled the ownership and accident record, flagged known issues for that model year and traced the listing history: 280 days on the market and $5,000 already cut from the asking price.

Then it wrote his questions for him. Not “is this a good truck?” but “was the transmission replaced or just reprogrammed?”

None of those sources are new. What changed is the effort. A week of evenings became a few minutes over a coffee.

Your SaaS buyers now research the same way. They arrive at the first call with a shortlist, a set of concerns and a view of you that you had no hand in shaping.

Table of Contents

What changed in the last 12 months?
What this means for SaaS buyers
Why separate channels stall
Eight pillars, mapped to how buyers now shortlist
Your support queue is a research brief
Where to start
Sources

What changed in the last 12 months?

This shift has been building for two years, and it’s speeding up. SparkToro’s latest study, built on Similarweb data, found 68% of US Google searches ended without a click in early 2026, up from 60% in 2024. Rand Fishkin calls it the fastest acceleration in a decade. In the last six months, it has moved from habit to measurable buying behaviour.

· About half of US adults now use AI chatbots, up from a third in 2024. Searching for information is the most common use (Pew Research Center, 2026).

· 37% of consumers start a search with AI rather than Google, and 47% say AI already influences which brands they trust (Eight Oh Two, via Search Engine Land, January 2026).

· AI Overviews appeared on 43% of US Google searches by May 2026, up from 15% a year earlier (Similarweb, cited by Duane Forrester).

· When an AI Overview appears, the click-through rate for the top-ranked result drops by 58%, based on 300,000 keywords (Ahrefs, February 2026).

· In Google’s own testing, raters preferred an AI-generated answer over the top-ranked website in 73.5% of head-to-head comparisons on information-seeking prompts (Google Research, 2026).

The platforms have caught up too. Microsoft added an AI Performance report to Bing Webmaster Tools in February, showing how often your pages are used as sources in Copilot answers. Google added an AI Assistant channel to GA4 in May. Nobody builds reporting for a channel that isn’t carrying demand.

Duane is now going further and forecasting the end of the results page as we know it. In his view, the ranked list stops being Google’s default first response between 2028 and 2030, with US results replaced first and the UK and EU following a year or more behind.

The trigger, he argues, is commercial: once ads in the answer earn roughly what they earn on the results page, the default flips. The links won’t vanish. They’ll sit one step away, behind the answer.

What this means for SaaS buyers

B2B software research suits AI almost perfectly. Category comparisons, “X vs Y” questions, pricing, integrations, security and reviews used to mean a dozen tabs and a spreadsheet. Now it’s one conversation that ends with a shortlist and a list of questions for the sales call.

Three consequences follow:

· Your content is read more than it is visited. AI absorbs your pages and summarises them. Traffic can hold steady while your influence on the decision shifts elsewhere.

· The shortlist forms where you can’t see it. AI compares you with the whole market, including competitors your positioning never considered.

· Credit goes unmeasured. Influence without a click doesn’t show up in last-click reporting, so pipeline can weaken before any dashboard flags it.

Duane sums up the pace problem well. Buyers change their behaviour in weeks or months. Most businesses change on planning and budget cycles.

Why separate channels stall

We started building for this more than a year ago, when it became clear the research phase of the buying journey was moving into AI. The result is the Whippet Digital Growth Framework.

The core idea is simple: visibility, trust and measurement can’t run as separate channels any more. Each one does its job and stops at its own edge.

· SEO alone ranks well, but doesn’t guarantee a place in AI answers.

· Content alone earns visits, but misses the research happening off your site.

· Ads alone create demand, but nothing validates you when the buyer goes checking.

· Trust signals alone are proof the buyer never reaches, because nothing surfaces them.

A buyer’s validation happens in the gaps between those channels. The framework treats them as eight pillars of one system, each covering a failure mode the others can’t. Here’s how each maps to the way buyers now research and shortlist, and what you can act on whether or not you work with us.

Eight pillars, mapped to how buyers now shortlist

1. Strategy & Alignment. AI answers questions, so strategy has to start with questions. Define who you’re for, the roles involved in a purchase, and what each asks before they’ll shortlist you. Every other pillar works from that list. In practice: write down the 20 to 30 questions a buyer needs answered before booking a demo, grouped by role.

2. Technical SEO & Foundations. AI platforms can’t cite what they can’t crawl, parse or load, and search rankings remain a strong qualifying layer for what AI retrieves. Keep your schema too: its direct effect on AI citations looks small, but it keeps your brand consistent across platforms. In practice: check AI crawlers aren’t blocked and key pages render without heavy JavaScript.

3. AI Search Optimisation. “Prompt volume” isn’t a reliable metric, because AI inputs are long, conversational and personalised. A structured prompt set is more useful: the questions from pillar one, run regularly across ChatGPT, Perplexity, Gemini and Google AI Overviews. Being in the answer pays: on informational queries, Seer Interactive found brands cited in an AI Overview earned roughly double the organic click-through rate of brands that weren’t (2.07% against 0.94%). In practice: record whether you’re mentioned, how you’re described and who’s named instead.

4. Content System. With AI Overviews on 43% of US searches, content gets summarised far more often than it gets visited. Build depth around the questions buyers ask at each stage, with direct answers near the top of the page and structure machines can parse. In practice: one page that answers a buyer question clearly beats five thin ones that circle it.

5. EEAT & Trust Signals. When AI compares you with a competitor, it weighs evidence it can verify. Named experts, measurable case studies, original research and reviews turn claims into proof. In practice: replace “industry-leading” with a number, a named client or a cited source.

6. Multi-Platform Optimisation. AI doesn’t take your word for it. It looks for corroboration on review sites, communities, LinkedIn and video, and review platforms such as G2 and Capterra appear repeatedly among the sources AI cites for SaaS. In practice: make sure your profiles, pricing and positioning match your website everywhere they appear.

7. Demand Generation. Visibility alone doesn’t fill a pipeline. Campaigns tied to CRM stages reach buyers who have already met you in an answer and give them a reason to act. In practice: build paid and retargeting around the same buyer questions, so the message carries from answer to ad to sales call.

8. Measurement. Clicks undercount AI influence, so reporting has to be layered: pipeline contribution, branded search as a leading indicator, AI citation share and self-reported attribution. In practice: add “How did you first hear about us?” as an open field on demo forms, and keep observed data separate from modelled estimates so stakeholders trust the numbers.

Your support queue is a research brief

Most teams start Strategy & Alignment with keywords. On a recent project for a B2B SaaS platform, we started with something better.

The client used an AI chatbot as the first point of contact for service queries. That gave us 13,000 real questions, written in the language buyers and users actually use. We sorted them by user and role, stripped out the noise, and mapped the questions with commercial weight to the site’s information architecture.

It exposed missing pages, duplicated pages and journeys that dead-ended. It sharpened messaging to match how each role searches and prompts. And because content and architecture were rebuilt around real questions, retrieval in AI search improved.

Your support queue, chatbot logs, sales calls and onboarding sessions are a map of how your market thinks. Most companies only use that data to prioritise product features.

When the foundations are right, results follow. A document management SaaS that applied the first three pillars saw form submissions rise 516%, from 220 to 1,355, in one reporting period (case study).

Group of online buyers sitting around table

Where to start

Your buyer has already had a conversation about you. You weren’t in it.

Duane’s advice sets the right order: learn what is being said about you, decide what belongs in the answer, then decide what to measure. Most teams do it in reverse and buy the tools first.

Five things you can do this month:

1. Ask ChatGPT, Perplexity, Gemini and Google AI Mode the questions your buyers ask. Note who gets named and how you’re described.

2. Check the AI Assistant channel in GA4 and the AI Performance report in Bing Webmaster Tools.

3. Add a self-reported attribution field to your demo form.

4. Compare your G2 and Capterra profiles with your website, and fix anything that contradicts it.

5. Pull a month of support tickets, chatbot logs or sales call notes and list the questions that come up most.

If you’d rather start with a scored baseline across all eight pillars, that’s what our AI Visibility Audit is for.

“Mike and the team are exceptionally knowledgeable, highly communicative, and great to work with. Their insights have helped us identify opportunities and improve performance across our digital channels.”

Emma Gray, Head of Marketing, Krux Analytics