How to Influence What AI Says About You Before Prospects Contact You

Your sales team is noticing something. Prospects arrive on calls already knowing your category, already having a short list, sometimes already leaning toward a competitor. They did their research. They just didn't do it on Google.

G2's March 2026 survey of 1,076 B2B software buyers found that 51% now start their software research with an AI chatbot more often than Google. According to 6sense's 2025 B2B Buyer Experience Report, buyers make first contact with a seller about 61% of the way through their buying process, down from 69% in earlier studies. By the time they reach you, most of the journey is already behind them.

The practical implication: by the time a prospect sends your sales team an email, an AI system has probably already formed and delivered an opinion about your company. That opinion shaped the shortlist. It may have determined whether you were on it.

The question your team is asking, how do we influence what those systems say, is the right question. The answer is not simple, but it is knowable. It starts with understanding how AI systems form their opinions in the first place.

How AI Systems Actually Form Opinions About Your Brand

Most companies approach this problem as a content problem. Publish more. Optimize better. Update the website. That framing misses the mechanism.

Large language models form their understanding of your brand through two distinct processes, and your website plays a smaller role in both than most marketers assume.

Parametric memory: what the model learned before you showed up

The first process is training. During pretraining, a model ingests billions of pages of web content, including news articles, industry publications, Reddit discussions, review platforms, forum threads, and Wikipedia. It learns statistical associations: which brands appear alongside which categories, which attributes get paired with which company names, which vendors show up when buyers describe a specific type of problem.

That knowledge is frozen at the model's training cutoff. It persists for months or years, regardless of what you publish afterward. Research points the same way on where the signal comes from. A University of Toronto study of how AI search engines choose sources found a systematic bias toward earned media over brand-owned content. AirOps, in its own research on more than 21,000 brand mentions, reports that 85% of brand mentions originate from third-party pages rather than the brand's own domain. That is one vendor's study, so read the number as a signal, not a benchmark.

The implication is direct: your owned content is not the primary input. The internet's collective record of your brand is.

Retrieval-augmented generation: what the model finds right now

The second process is retrieval. Most production AI systems, including ChatGPT with browsing, Perplexity, and Google's AI Overviews, supplement their parametric memory by querying the live web at the moment a user asks a question. They pull current content, synthesize it, and attribute sources.

This is where your recent web presence matters. Fresh coverage in authoritative publications, updated third-party reviews, and well-structured owned content can all influence retrieval. The key word is "authoritative": the retrieval layer is not neutral. It weights sources by perceived credibility, and credibility is earned through the same signals that have always mattered in communications, consistent presence in publications your buyers already trust.

The two layers work together. In my view, if your brand is not already represented in the sources models learned from, with accurate and favorable associations, fresh citations alone will not rescue you. You need both.

Five Steps to Shape What AI Says About Your Company

Understanding the mechanism makes the intervention obvious. You cannot edit a model's weights. You cannot pay for placement. What you can do is systematically improve the quality and consistency of the signals that models draw from. Here is how to do that.

Step 1: Audit what AI systems currently say

Before you can improve your AI representation, you need to know what it is. Run structured queries across ChatGPT, Perplexity, Claude, and Google's AI Overviews. Use prompts that mirror how your buyers actually research, not queries about your brand name specifically, but category-level questions:

• "What are the best [your category] tools for [your buyer's use case]?"

• "Which [your category] vendors do enterprises typically consider?"

• "What should I look for when evaluating [your category] providers?"

Document which brands appear, in what context, with what attributes, and which sources get cited. This is your baseline. If your company is absent, you know the problem. If your company appears but with inaccurate or unfavorable framing, you know what to correct.

Doing this manually is useful for getting a feel for the landscape, but it does not scale. Tools such as Searchable and Peec AI are built for this problem. They track how your brand appears across the major answer engines on a consistent prompt set, including mention rate, cited sources, sentiment, and share of voice against competitors. Either gives you a structured baseline that manual spot-checking cannot.

Note: different models will give different answers because they have different training data, different retrieval capabilities, and different knowledge cutoffs. Audit all of them.

Step 2: Build a consistent, authoritative entity record

AI models learn about your brand from every mention they encounter across the web. Contradictory information weakens the signal. If your company name, description, founding date, category, and key differentiators vary across your website, LinkedIn, G2, Crunchbase, industry directories, and press coverage, the model's understanding of you becomes blurry.

Establish a canonical description of your company and enforce it everywhere:

• Your website's About and Services pages

• LinkedIn company profile

• G2, Capterra, and relevant review platforms

• Press release boilerplate

• Wikipedia (if your company has a page)

• Industry association directories

This is not glamorous work. It is also not optional. Consistency is how models learn that your brand belongs in a specific category.

Step 3: Earn third-party coverage in publications your buyers trust

This is the highest-leverage step, and the one most companies underinvest in relative to owned content production.

Muck Rack's Generative Pulse research found that earned media accounted for 84% of AI citations in its May 2026 edition, and that paid and advertorial content was a marginal 0.3%. Muck Rack's definition of earned is broad: it covers journalism, but also academic research, government sites, and encyclopedic sources. The finding is not that journalism alone drives citations. It is that AI systems lean overwhelmingly on third-party sources rather than on what a brand says about itself.

The practical target is consistent coverage in:

• Industry-specific publications your buyers already read

• Analyst reports and briefings (Gartner, Forrester, IDC)

• Credible technology and business press

• Peer review platforms with verified user accounts

In my experience, one profile in a major publication matters less than consistent mentions across ten relevant mid-tier publications over twelve months. Models look for patterns and consensus. A single high-profile placement encodes one data point. Sustained coverage encodes a category association.

This is also where executive thought leadership for AI search becomes strategically important. When your executives are cited in third-party coverage as subject matter authorities, the model learns to associate your brand with expertise in that domain, not just with a product category.

Step 4: Structure your owned content for retrieval

Your website and blog will not dominate AI citations. But they are not irrelevant either, particularly for the retrieval layer of AI systems that query the live web.

Content that performs well in retrieval tends to share a few characteristics:

The broader principle from understanding agentic awareness applies here: AI systems reward clarity and authority, not volume. A single well-constructed page that directly answers a buyer's question will outperform a hundred pages of loosely relevant content.

Step 5: Monitor and measure AI visibility over time

Most marketing teams do not have a systematic way to track what AI systems say about their brand. This is a gap worth closing, because the signals you are building take months to compound, and you need to know whether they are working.

A practical measurement approach:

• Prompt tracking: Run a consistent set of category-level queries across major AI systems monthly. Track which brands appear, in what position, with what framing. Tools like Searchable and Peec AI automate this and make the results comparable over time.

• Citation monitoring: Track which of your owned and earned content gets cited in AI responses. This tells you which content the retrieval layer is treating as authoritative, and whether the evidence supporting you comes from your own pages, editorial coverage, or third-party sources.

• Sentiment analysis: Note the language models use when they describe your company. Are the associations accurate? Favorable? Are there persistent inaccuracies you need to correct through new coverage?

G2's research on the answer economy found that 85% of buyers view a vendor more favorably when an AI chatbot names it. The inverse is also true: being absent from the shortlist that forms inside a chatbot session is a competitive disadvantage your sales team will never see coming.

What This Requires You to Rethink

There is a temptation to treat AI visibility as an extension of SEO, a set of technical optimizations you can hand to a specialist and check off a list. That framing will produce mediocre results.

The reason is structural. SEO is primarily about your own content. AI representation is primarily about what others say about you. The inputs are different, the timelines are longer, and the discipline required is closer to communications strategy than content production.

A few things this means in practice:

Earned media is not a PR vanity metric anymore. When AI systems lean this heavily on third-party sources, coverage in credible publications is a direct input into buyer research. The communications function needs to be measured accordingly.

Consistency compounds. A brand that earns ten relevant mentions per month across authoritative sources will outperform a brand that earns one hundred mentions in a single quarter and then goes quiet. Models learn from patterns, not peaks.

The sales team's observation is a measurement instrument. If prospects are arriving more informed, better qualified, or already leaning toward a competitor, those patterns tell you something about your AI representation. Treat them as signals, not anecdotes.

The buying journey has reorganized around a phase most companies cannot see. 6sense's 2025 Buyer Experience Report found that the vendor a buyer prefers before first contact with any seller still wins about 80% of deals. That preference forms during the anonymous, AI-assisted research phase your sales team never observes.

You cannot attend that meeting. But you can shape what gets said about you before it starts.

For organizations that want to build this capability systematically, the Actual Agency solutions page outlines how we approach AI representation, earned authority, and measurement for B2B companies.