Kea Insight / AI Search

AI Has Made Your Reputation Executable

By Bram Weerts

B2B buyers can ask an AI system to compare vendors before visiting a single supplier website. They describe a problem, add their industry or technical requirements and ask which companies deserve consideration. The answer may introduce your business, leave it out or repeat a version of it that is years out of date.

That is what I mean by reputation becoming executable. Information scattered across customer evidence, research, reviews, partner pages and public commentary can be assembled into an answer when a buyer asks. Your company’s market reputation can now shape an initial evaluation without anyone inside the business knowing it has happened.

AI is taking on part of the research

In January 2026, Forrester reported that 94% of business buyers in its research used AI during the buying process. Its findings also emphasised the need for validation: buyers sought reassurance from peers, product experts, analysts and other trusted sources when AI outputs appeared incomplete or unreliable.

A separate Gartner survey of 645 B2B buyers, conducted in August and September 2025, found that 45% used generative AI during a recent purchase. Gartner also reported that 69% preferred to validate AI-generated insights with a sales representative. The studies measure different populations and questions; their percentages are not directly comparable.

The implication is not that AI replaces human judgement. Enterprise buying still involves technical risk, procurement, internal consensus and commercial accountability. AI can, however, influence which questions buyers ask and which suppliers they investigate before a salesperson joins the conversation.

Recognition is different from discovery

Ask an AI what your company does and it may produce an accurate description. That is useful, but you have already supplied the name. The harder test is whether the company appears when a buyer describes a problem without mentioning any vendor.

A January 2026 preprint on 112 Product Hunt start-ups explored this distinction through 2,240 queries using GPT-4o mini and Perplexity Sonar with web search. It reported much higher recognition when products were named than inclusion in open discovery questions. This was a limited study of particular products, models and prompts, not a universal benchmark for enterprise technology.

The distinction is still a useful management test. Knowing that an AI can describe the business does not establish that it will introduce the business to a buyer who has never heard of it.

Your website cannot carry the whole reputation

Clear public content helps buyers and search systems understand what the company does. Its capabilities, use cases, customer evidence and market category should be easy to find and interpret. Technical accessibility matters because evidence that cannot be retrieved cannot help that particular answer.

Publishing more pages does not make an unsupported claim credible. A company can call itself a leader repeatedly, but buyers still need reasons to believe it. Relevant customer results, informed independent perspectives and accurate partner descriptions give the market something more substantial to assess.

Different AI systems use different sources, and some answers rely on information supplied by the buyer. Their outputs also vary with the question and over time. No amount of publishing guarantees a place in an AI-generated shortlist.

Outdated market memory can travel faster

A business may have grown from a point solution into a broader platform while the market still remembers its first product. Analysts may use the old category. Partner pages may describe a narrow use case. Customer stories may reflect deployments from several years ago. Internally, the transformation feels obvious; externally, the evidence has not caught up.

A new homepage does not automatically update those references. An AI answer can retrieve the older descriptions and present them as a coherent account of the company today. The result may be misleading without being a deliberate invention: the available evidence itself is behind the business.

CMOs should treat repeated outdated descriptions as a reputation issue to investigate. Identify the sources that can be corrected, publish current evidence and update the people whose understanding influences the market. The work extends beyond the website.

Analyst relations contributes to market understanding

Industry analysts help buyers interpret technology markets, categories, capabilities and trade-offs. Their questions can reveal weak positioning or missing evidence, while informed engagement helps them understand what has changed in a company and why it matters to their research.

That does not mean a private briefing feeds a public AI model. An analyst mention does not guarantee an AI recommendation, and AR cannot control an independent conclusion. Its contribution is to a more accurate, better-supported understanding of the business across the market.

The same preparation can sharpen executive explanations, improve sales materials and expose a gap between the company’s claim and its proof. Those benefits matter whether a buyer arrives through research, a colleague, a partner, Google or an AI assistant.

Test the questions buyers ask before they know you

Start with a small set of realistic buying situations. Use the industry, scale, requirements and constraints that matter to your actual customers. Keep a record of the tool, date, question, answer and any cited sources so the team can distinguish a repeated pattern from a single result.

Which providers should a buyer consider for this specific problem?
Which alternatives fit the buyer’s architecture, industry or operational requirements?
How is our company described compared with the alternatives?
What concerns are raised, and which current evidence addresses them?

Repeat the exercise across more than one system and a few natural variations of the question. Investigate inaccurate descriptions and missing evidence. Do not turn one response into a fixed ranking or claim that a content edit caused a change without supporting evidence.

The practical objective is a market position that buyers can understand and verify. Clear language, current customer proof and informed independent perspectives make that work stronger. AI has increased the speed at which reputation can affect a decision. It has not removed the need to earn it.

For the complementary publishing perspective, read AI Search Will Not Save a Weak Market Story. The Kea AR Knowledge Map connects the wider guidance.

Put this into practice

Give your market story stronger evidence.

Bring leadership, product and marketing together to test the story, identify proof gaps and prepare for informed analyst questions.

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