AI Is Making Your First Impression. Your Buyers Are Checking Its Work.

The first explanation a buyer receives about your company may no longer come from your website, sales team or marketing content.

It may come from AI.

More specifically, it may be assembled from your website, analyst commentary, customer evidence, press coverage, conference material, competitor content and anything else the system considers relevant.

According to Forrester’s State of Business Buying 2026, 94% of business buyers now use AI during the purchasing process. They are using it to understand unfamiliar markets, compare suppliers, summarise technologies and work out which questions to ask.

That does not mean AI is making the decision. It means AI is increasingly shaping the buyer’s first understanding of the market.

Buyers then test that understanding with people they trust.

Forrester found that AI-generated information is often validated through peers, product experts, analysts and other credible sources. The average buying decision now involves 13 internal stakeholders and nine external participants, with the number rising as purchases become more complex.

AI has not replaced the buying group. It has given it a starting point.

Your first impression may be created elsewhere

Before a buyer visits your website, an AI system may already have explained what your company does, placed you in a category and compared you with other suppliers.

It may also have formed a view of your strengths, weaknesses and relevance to the buyer’s problem.

That is a meaningful change.

Traditional search gave buyers a set of links. They still had to open those sources, compare the information and reach a conclusion.

AI increasingly produces the conclusion first.

The commercial question is therefore no longer only whether your business can be found. It is whether the version being assembled is accurate, credible and useful.

A company can be visible and still be misunderstood. It can appear in the answer while being positioned in the wrong way. It can be included in a supplier comparison without its real differentiation coming through.

For technical B2B companies, that matters.

The difference between two scientific platforms, specialist ingredients or industrial technologies is often difficult to explain in a few sentences. The value may depend on the application, regulatory environment, operating conditions or risk the buyer is trying to reduce.

When that context is missing, AI will still try to provide an answer.

It may simply provide the wrong one.

Vague positioning creates room for interpretation

AI is very good at filling in gaps.

That is useful when the available information is clear and consistent. It is less useful when the company’s proposition is spread across several webpages, described differently by each business unit and supported by evidence hidden in old PDFs.

Many technical companies still rely on broad language such as “innovative solutions,” “customer-focused expertise” or “sustainable value.”

These statements may be technically true, but they do not give buyers or AI systems much to work with.

When the proposition is unclear, the system must infer what matters. When the differentiation is vague, it may default to comparing suppliers on obvious criteria such as geography, price, size or basic functionality.

That can leave a genuinely differentiated business looking much the same as everyone else.

The issue is not only whether AI mentions the company. It is whether it understands why the company deserves to be considered.

Buyers use AI for speed and people for confidence

Forrester’s findings show an important distinction.

Buyers use AI because it helps them process information quickly. They use people because complex purchases still carry risk.

AI can explain a category, identify suppliers and summarise technical claims. It can help a buyer move from limited knowledge to a useful starting point in a short period of time.

What it cannot always provide is confidence.

That still comes from credible people, relevant evidence and direct experience.

A peer recommendation matters because the peer has dealt with the consequences of the decision. A technical expert matters because they can explain the reasoning behind a claim. An analyst can add market context. A customer can show whether the solution worked in practice.

For technical businesses, this makes expert visibility more important.

Scientists, engineers, clinicians, application specialists and commercial leaders should not sit entirely behind the corporate brand. Their knowledge helps buyers test whether the AI-generated version of the proposition stands up.

This does not mean turning every technical expert into a content creator. It means making credible knowledge easier to find, understand and verify.

The website now has two jobs

B2B websites have always supported different stages of the buying journey. Some content creates awareness, some helps buyers compare options, and some provides proof.

That has not changed.

What has changed is that the website is now serving two audiences at the same time.

The first is the human buyer who visits the site.

The second is the AI system using the site as source material before that visit takes place.

Both need similar things: clear definitions, consistent terminology, specific claims, relevant evidence and enough context to understand where the proposition fits.

The content also needs to show who stands behind the expertise and where the limitations or trade-offs sit.

This sounds straightforward, but many corporate websites are built through compromise.

Product wants technical precision. Sales wants flexibility. Legal wants caution. Leadership wants ambition. Brand wants consistency.

The result is often polished and professionally written, but difficult to distinguish from competitors.

That has always weakened marketing.

AI now makes the weakness easier to reproduce.

More content will not fix unclear positioning

A common response to changes in AI search is to produce more content.

That may improve visibility, but volume alone will not improve understanding.

Ten articles repeating the same vague proposition do not create stronger positioning. They create more material from which the same vague summary can be produced.

The better starting point is to improve the evidence behind the proposition.

Can the company explain clearly what it does? Can it show where the offer is most relevant? Can it connect technical claims to customer outcomes? Can it support those claims with data, examples and credible expertise?

Can the same core story be found across the website, sales material, expert commentary, customer evidence and external coverage?

This is what makes a proposition easier to understand and harder to misrepresent.

The objective is not to control every AI-generated answer. That is unrealistic.

The objective is to provide enough clear, consistent and credible material that the right interpretation becomes the easiest one to reach.

This is a positioning issue before it is a search issue

There is already a growing industry around answer-engine optimisation and AI search visibility.

Some of that work will be useful.

But it starts too late if the business has not first decided what it wants the market to understand.

Before asking how to appear in AI-generated answers, companies need to ask more basic questions.

What category do we want to be associated with? Which customer problems are we best placed to solve? What makes our approach meaningfully different? What evidence supports that difference?

Most importantly, does the proposition still make sense when reduced to three sentences?

When the answer is unclear, the problem is not the search engine.

It is the positioning.

AI does not create a strong commercial story. It works with the material available.

When that material is clear, evidence-led and consistent, AI can help buyers understand the business more quickly.

When it is vague, fragmented or overly corporate, AI can summarise the confusion just as efficiently.

Marketing has less control over the journey

Marketing has never had complete control over what buyers think, although websites and digital analytics sometimes gave us the impression that we did.

We wrote the content, created the campaign, drove the traffic and measured the visit.

The buying journey was always more complicated than the dashboard suggested.

AI makes that more visible.

A buyer may begin with an AI-generated summary, test it with colleagues, speak to an industry contact, review external evidence and only then visit the company website.

Much of that activity will remain outside the supplier’s view.

Marketing may have less visibility into the route, but it still has influence over the information buyers encounter along the way.

That influence comes from clear positioning, stronger proof, credible expert voices and consistent market presence.

The companies that benefit will not necessarily be those producing the most content or chasing every new search tactic.

They will be the ones that make their value easier to understand, trust and act on.

AI may now help create the first impression.

Marketing still has a responsibility to give it better material.

Originally published on LinkedIn ↗