Authority Engine
Back to blog

Buyer Research With AI: How Buyers Use It and How to Respond

Buyer reviewing AI-assisted research at a workstation

Buyer research with AI means two things at once. Your buyers use ChatGPT, Perplexity and Google’s AI experiences to learn a category, compare providers and build a shortlist. And your team can use the same tools to understand those buyers faster. Most of the risk and most of the opportunity sit in the first meaning, because the shortlist is often formed before anyone contacts your sales team. This guide covers both: how buyers research with AI, how to use AI for your own customer research, and how to check whether the answers buyers get include you and describe you correctly.

Authority Engine
See Where Buyers Find You
Get a report on your AI visibility, relevant competitors, illustrative revenue scenarios, and where implementation could begin.
Get your visibility report

TL;DR

  • Buyers use AI mainly to sort, summarize and validate options, and increasingly to compare vendors and narrow shortlists. Being known before the research starts matters.
  • AI answers can replace the multi-site comparison a buyer used to do by hand, so a vendor missing from the answer may never be considered.
  • For your own customer research, give AI real inputs (calls, tickets, reviews, interviews) and focused questions, and treat every theme as a hypothesis until several sources agree.
  • Measure visibility with a fixed set of buyer-language prompts, a named owner and a regular cadence.
  • Check accuracy as well as presence: an answer that mentions you but gets your offer wrong still costs you deals.

Table of Contents

What buyer research with AI actually looks like

In a traditional search, a buyer works through review sites, category pages, brand sites and forums, then pieces the findings together. McKinsey describes how AI-powered search can compress that work into one synthesized answer covering which factors matter, which brands excel at what, and which models have strong reviews. In the same October 2025 analysis, McKinsey reports that around 40 to 55 percent of consumers in top sectors use AI-based search to make purchasing decisions, and that 44 percent of AI search users call it their primary and preferred source of insight, ahead of traditional search at 31 percent.

The practical difference is who does the synthesis. Before, the buyer read your site next to your competitors’ sites. Now a model reads the web, forms a view of each company and hands the buyer a summary. Your website is one input into that summary, along with reviews, third-party articles, community discussions and executive profiles.

A usability study by Citation Labs shows how much weight that summary carries. Among 48 US participants completing 185 major-purchase tasks, 74 percent of AI Mode shortlists came straight from the AI output with no outside verification, and the AI’s product description drove the decision in 48 percent of AI Mode tasks versus 6 percent in standard Search sessions. It is a small study, but the direction is clear: the description the AI gives of you often is the first impression.

How buyers use AI at each stage

Buyers do not use AI in one way. The data shows it spread across the whole journey.

Consumers

Semrush surveyed 1,030 US shoppers who had tried AI in December 2025. 77 percent used AI and traditional search together, and 43 percent had discovered a new brand through AI. By task, 57 percent used AI to narrow choices, 51 percent for early discovery, 53 percent to compare products already under consideration and 50 percent for a final decision.

Shopper use of AI across four research tasks

B2B buyers

Semrush’s March and April 2026 survey of more than 600 B2B buyers found that 72 percent use AI during early research, 62 percent when comparing vendors, 48 percent to narrow shortlists and 45 percent to support a final decision. Another cut of the same survey: 66 percent use AI to explore solutions, 59 percent to understand a problem or category, 55 percent to summarize options and 53 percent to ask for recommendations.

G2’s 2026 report adds the detail on what buyers ask for. Buyers use AI to compare vendor strengths and weaknesses, learn categories, identify vendors, validate use cases, narrow shortlists, draft RFP questions and understand pricing and packaging.

Discovery versus validation

One finding deserves attention because it changes strategy. 6sense’s November 2026 research reports that 81 percent of buyers use an LLM somewhere in evaluation, but only 26 percent use one to discover vendors. Most use it to sort, summarize and validate vendors they already know. The same research says buying groups spend roughly 60 percent of an average 10.3-month cycle in selection before contacting a seller, and that the vendor favored before seller contact wins 82 percent of the time.

Read together, these numbers say two things. First, AI is rarely the only channel, so your existing reputation, reviews and content still feed what the AI says. Second, when a buyer asks AI to validate a name they heard from a peer, the answer either confirms you or raises doubt. Both discovery and validation are moments you can prepare for.

Discovery versus validation

Trust still has limits

Buyers do not accept AI blindly. A February 2026 Local Impact survey of 1,068 US consumers found that 6 in 10 use AI often or almost always when researching purchases, yet 41 percent would trust reviews over AI when the two conflict. For a business, that means your review profile and your AI presence should tell the same story.

How to use AI for your own customer research

The other half of this topic is using AI to understand the people you sell to. Columbia Business School’s guide lists four opportunities for generative AI in market research: accelerating existing practices, replacing some practices with synthetic data, filling gaps in market understanding, and creating new types of data and insight. It also notes AI can synthesize lengthy interview transcripts and other documents, which is where most teams get quick value.

A workable process looks like this.

  1. Gather real inputs first. Pull sales call notes, support tickets, customer interviews, survey responses, CRM data, website analytics and reviews. Guidance from Every recommends combining these sources before asking AI for themes.
  2. Ask focused questions. Instead of “analyze my customers,” ask why prospects hesitate, which objections recur, what language signals urgency, and where onboarding confuses people.
  3. Look for repetition across sources. A theme that appears in calls, tickets and reviews is worth acting on. A theme from one source is a lead to check.
  4. Turn themes into buyer questions. Write the questions in the buyer’s own words: what they ask before they know your name, what they ask when comparing, and what they ask before signing. Our guide to buyer-intent questions goes deeper on this step.
  5. Validate with people. Review the findings with sales, support, product and leadership before they shape strategy.

Teams that want to automate parts of this workflow can look at AI market research workflows that connect AI agents to analytics and competitive data. Whatever tooling you use, the quality of the inputs decides the quality of the findings.

G2’s State of AEO 2026 study shows why the buyer-question step matters: 50 percent of respondents already lead prompt selection with buyer research, drawing on sales conversations, prospect questions and customer interviews. Only 29 percent keep a structured prompt set with a named owner and a refresh cadence. The research you do on buyers becomes the list of prompts you monitor.

Keep the limits of AI research in view

AI speeds up analysis, but a few cautions keep the output reliable.

  • Synthetic personas are not real customers. Ipsos warns that AI-generated personas can look realistic without representing actual consumer groups, and should be grounded in current, representative research and used to complement human-led work.
  • Findings can be generic and hard to trace. The Market Research Society cautions that language models can amplify bias and hallucinations and produce flattened results that are hard to trace or qualify. Ask the model to cite the passage behind each theme, and check it.
  • Handle customer data carefully. The FTC’s issue spotlight on surveillance pricing notes that large-scale consumer data collection can expose sensitive information and enable harmful inferences. Before you paste transcripts or CRM exports into any AI tool, remove personal identifiers and confirm with your legal or privacy lead that your use matches your customer commitments.

How to check what AI says about your company

Presence is the first test: when a buyer asks about your category, are you named? Accuracy is the second: when you are named, is the description right? Both can be checked with a simple audit.

Build the prompt set

Write 20 to 30 prompts in buyer language, grouped by stage:

  • Problem and category: “What should a mid-sized manufacturer look for in a [your category]?”
  • Discovery: “Which providers help with [problem] for [your type of customer]?”
  • Comparison: “How does [your company] compare with [alternative type]?”
  • Validation: “Is [your company] a good fit for [use case]? What are its strengths and weaknesses?”
  • Pricing and packaging: “How is [service] usually priced, and what does [your company] include?”

G2 recommends establishing a baseline across the answer engines buyers use, setting a monitoring cadence, agreeing on a small set of indicators, anchoring prompts in buyer language and giving the prompt set an owner and a refresh schedule. Follow that pattern: one person owns the list, and you rerun it on a schedule, because answers shift between runs and between platforms.

Score what comes back

For each answer, record whether you were named, where you appeared, how you were described, which sources the AI cited and which competitors appeared beside you. Data Axle suggests tracking recommendation share, representation accuracy, digital-footprint completeness and AI-influenced pipeline, and listening for how customers say they found you. A simple table keeps it consistent:

Measure What to record What a gap suggests
Recommendation share How often you are named across your prompt set Buyers meet others first
Representation accuracy Whether services, audience, pricing terms and location are described correctly Your public information is inconsistent or thin
Source quality Which pages the AI cites about you Third-party pages may be outdated or missing
Footprint completeness Whether your profiles, listings and expert bios agree Data conflicts confuse the model
Pipeline influence Prospects who mention AI in sales conversations Add a “how did you hear about us” question

For a deeper look at tracking methods, this overview of measuring AI search visibility covers metrics and sample prompts.

Watch for the AI traffic signal

Traffic data supports the same story. Adobe reported that AI-driven traffic to retail sites grew 12x between July 2024 and February 2025, and by February 2025 AI-referred visits had a 23 percent lower bounce rate and lasted 41 percent longer than other traffic. The conversion gap with non-AI traffic narrowed from 43 percent to 9 percent over that period. Separate AI referrals in your analytics so you can see the trend on your own site, while remembering that many AI-influenced buyers never click and simply arrive later by name.

How to earn a place in the answer

The stakes are not trivial. McKinsey projects that by 2028, $750 billion in US revenue will funnel through AI-powered search, and that brands unprepared for it may see traditional search traffic fall by 20 to 50 percent.

The work breaks into a few areas.

Say the same thing everywhere. Data Axle advises standardizing product descriptions, features and pricing terminology and keeping company, product and people data consistent. If your homepage, LinkedIn page, directory listings and press mentions describe you differently, the model has to guess.

Publish what buyers ask. The same guidance recommends explainers, comparisons, benchmarks and decision guides in buyer language. Answer the comparison and validation questions on your own site, plainly, so the model has accurate material to draw from. Our post on generative engine optimization explains how this layers on top of SEO rather than replacing it.

Remove technical barriers. If AI crawlers cannot read your pages, nothing else matters. Our ChatGPT SEO checks and our guide to structured data for AI search cover the basics.

Build credibility beyond your site. Because buyers also trust reviews and peers, third-party mentions, community participation and executive expertise all shape what the AI says. See how this fits with how AI search platforms cite sources.

Show real customers. Proof works at both ends: it persuades the buyer and gives AI factual material. Our digital marketing agency case study shows what that looks like in practice.

Our perspective

Most companies treat AI search as a new channel to check. We treat it as the point where buyer consideration now forms. The question is not only whether a model mentions you. It is whether a buyer who is researching a problem, comparing providers and deciding whom to contact meets an accurate, credible picture of your company before they ever reach sales.

That is why we pair commercial strategy with doctoral-level research. Buyer questions and semantic research tell us what buyers ask. Strategic content answers those questions. Contextual backlinks, community participation and executive LinkedIn content build the external credibility models and people both weigh. Monitoring shows where your position is strong or thin, and monthly site-health recommendations give your team clear fixes to implement.

We do not see this as a software subscription or a one-time audit. Visibility changes as models, competitors and buyer questions change, so we manage the strategy and execution continuously. For established businesses, where the right provider can still be missed if buyers meet competitors first, that managed approach is how we help you compete for consideration.

Dr. Patrick McAvoy

See where you stand today

If you want to know how AI answers present your company against the alternatives buyers consider, start with our AI Visibility & Revenue Opportunity Report. It gives you a personalized view of your current position in AI-assisted research.

Authority Engine

If you are ready for ongoing strategy and execution, our Managed AI Visibility service covers buyer-question research, content, authority-building, monitoring and refinement for established businesses whose customers research and compare before they buy.

FAQ

Do buyers really use AI to find new vendors?

Yes, though more often to evaluate than to discover. Semrush’s consumer survey found 43 percent had discovered a new brand through AI, while 6sense found only 26 percent of B2B buyers use an LLM to discover vendors. Plan for both: be known beforehand, and be described well when buyers validate you.

What is the best way to use AI for customer research?

Feed it real material such as call notes, tickets, interviews and reviews, then ask narrow questions about objections, hesitation and language. Treat a theme as strategic only when it shows up in several sources, and have sales, support and leadership confirm it.

Can AI-generated personas replace talking to customers?

No. Ipsos says personas should be grounded in current, representative research and should complement human-led work. Use them to organize and communicate what you learned from real customers, not as a stand-in for them.

How do I know if AI describes my company correctly?

Run a fixed set of buyer-language prompts across the platforms your buyers use, and record whether you appear, how you are described and which sources are cited. Compare each description with your actual services, audience and terms, then repeat on a schedule with a named owner.

How is this different from SEO?

SEO aims to rank pages. AI visibility aims to have your company named, cited and described accurately inside a synthesized answer. The two share foundations such as crawlable pages, strong content and credibility, so you build on SEO rather than replace it.

Sources