Your Brand Is No Longer Speaking Directly to the Customer

Something fundamental is changing in the customer journey.

For decades, companies competed to influence consumers directly through advertising, search, social media, reviews, retail, and content. Now another layer is appearing between the company and the customer: AI.

And here is the uncomfortable part: consumers do not need to fully trust AI for AI to influence what they buy.

That distinction matters more than it may seem. If AI becomes part of how customers research, compare, and evaluate products, companies are no longer competing only for human attention. They are also competing for how machines understand, interpret, and represent them.

And many businesses are not ready for that shift.

Influence can come before trust

McKinsey’s latest research on consumer behavior highlights an interesting contradiction. Trust in generative AI recommendations remains below 40 percent, yet consumers are already using these systems for brand discovery and purchase evaluation.

This behavior is not limited to younger consumers. McKinsey reports adoption among both Gen Z and boomers, with particularly strong usage in high-consideration purchases such as travel and growing use in beauty and fashion.

At first, that sounds contradictory. Why would someone use a technology they do not completely trust?

Because trust is not binary.

We use GPS without understanding every routing decision. We use recommendation algorithms without knowing precisely why something appeared in our feed. We use search engines while knowing that the first result is not necessarily the best result.

AI will increasingly operate in the same way. People may question its answers and still use those answers to narrow choices, compare alternatives, and decide what deserves further attention.

That leads to an important principle: influence comes before trust.

Technology does not necessarily need complete confidence from users before it begins changing their behavior. Once behavior changes at scale, businesses have to adapt whether trust has caught up or not.

The customer journey is becoming machine-mediated

Think about a traditional purchase journey. A customer wants to buy something. They search Google, visit a company website, read a few reviews, compare competitors, perhaps watch a YouTube video or visit a store, and eventually make a decision.

AI compresses parts of that journey.

Instead of opening ten websites, the customer can ask: “What are the best options for me?” “Compare these three products.” “What are the biggest complaints about this company?” “Which product offers the best value?” “Summarize the reviews.” “What would you recommend based on my priorities?”

The AI becomes a research assistant sitting between the buyer and the market.

That changes something fundamental. The company is no longer necessarily presenting itself directly to the customer. The AI may present the company instead.

That means your brand can increasingly be discovered, evaluated, and compared through an interpretation of information gathered from many other sources.

Your website is still important. Your advertising is still important. SEO is still important. But they are no longer the whole battlefield.

The 1% number executives should pay attention to

One of the most striking findings in the McKinsey research comes from an analysis of 25 brands over six months. Researchers examined approximately 2.6 million citations used by large language models.

McKinsey says only about 1 percent of LLM citations came directly from brand websites.

Think about the strategic implication.

Companies spend enormous amounts of money controlling how they describe themselves through brand teams, corporate websites, product pages, campaigns, SEO, content, and public relations.

Then a consumer asks an AI system about that company, and the answer may be built largely from information the company does not own, including reviews, forums, media coverage, comparison sites, communities, third-party articles, and other sources across the web.

The question is no longer simply: What does your company say about itself?

The question is becoming: What does the information ecosystem say about your company, and what conclusion will an AI draw from it?

That is a very different communications problem.

From SEO to AI visibility

For years, businesses learned how to become visible to search engines. We called it SEO.

Now companies are beginning to think about GEO, generative engine optimization. But reducing this transition to another marketing acronym would be a mistake.

The challenge is bigger than optimizing a page so an AI system can read it. McKinsey points to practical measures such as clearer FAQs, structured information, and detailed technical specifications that make brand content easier for LLMs to interpret.

Those things matter, but AI systems pull information from many places. So the deeper problem is information consistency.

If your website says one thing, customers say another, review platforms say something different, and independent experts describe your product differently again, AI has to reconcile those signals.

That makes reputation, product quality, customer experience, and external authority part of AI visibility. You cannot simply publish your way out of a weak reality.

And that may ultimately be healthy.

AI could make the gap between what companies claim and what markets experience increasingly difficult to hide.

Marketing is becoming an information architecture problem

This is where the conversation should move beyond the marketing department.

If AI becomes an important evaluation layer, companies need to think about the information surrounding their products as infrastructure.

Is product information accurate? Is it structured? Is it consistent across channels? Can machines understand it? Are independent sources describing the product correctly? Are customer complaints revealing recurring problems? Does the company know how major AI platforms describe its products today?

And who owns that question internally? Marketing, digital, technology, communications, or customer experience?

The answer is probably all of them.

That means AI visibility becomes another example of why AI transformation cannot be treated simply as an IT program. Technology changes the operating model around it.

The consumer is changing too

The AI shift is happening alongside broader changes in consumer behavior.

McKinsey identifies four major trends: a technology-driven path to purchase, a broader health and wellness revolution, continued growth of the experience economy, and the rise of what it calls the resourceful consumer.

There is a common thread across all four: consumers are becoming more deliberate.

They have more information, more tools, more channels, more ways to compare, and more pressure to justify where their money goes.

That makes the traditional idea of simply pushing more messages into the market increasingly ineffective. The customer does not need more information. The customer needs help making sense of too much information.

AI is perfectly positioned to fill that gap, which is why the companies that understand this transition early may gain an important advantage.

This is bigger than marketing efficiency

Much of today’s enterprise AI conversation still focuses on productivity.

How much faster can we create content? How much can we automate? How much can we reduce the cost of marketing operations?

Those are legitimate questions, but there is another side of the equation.

AI is not only changing how companies operate. It is changing how markets see companies.

That could become far more consequential.

Imagine an AI assistant consistently recommending a competitor before recommending you, describing your product using outdated information, highlighting a weakness your own marketing rarely acknowledges, or failing to mention your company entirely.

You may not see the lost customer. There may be no abandoned shopping cart, no failed advertising campaign, and no obvious attribution trail.

You simply never entered the consideration set.

That is why executives should start treating AI visibility as a strategic capability rather than a future marketing experiment.

Six questions every leadership team should ask

1. When customers ask major AI platforms about our category, do we know whether our company appears?
2. Do we know how those systems describe our strengths, weaknesses, and differentiation?
3. Is the information surrounding our products structured, current, and consistent enough for machines to interpret accurately?
4. Which external sources are shaping the AI-generated narrative about our company?
5. Who inside the organization owns AI visibility and monitors how that visibility changes?
6. Are we designing our marketing strategy for yesterday’s customer journey, or for one in which AI increasingly participates in evaluation and decision-making?

These are not questions for 2030. The behavior is already emerging.

McKinsey expects AI’s influence on the consumer decision journey to continue growing as these systems become more ubiquitous.

The companies that understand the intermediary will win

Every major technology shift creates a new intermediary.

Search engines became intermediaries between information and users. Social platforms became intermediaries between brands and audiences. Marketplaces became intermediaries between merchants and buyers.

Now AI is emerging as another layer.

But this intermediary is different. It does not simply show information. It interprets it. It compares, summarizes, filters, recommends, and increasingly, it may act.

That gives companies a new strategic challenge.

You are no longer only trying to convince the customer. You increasingly need to make sure the digital ecosystem around your company gives AI enough credible information to understand why the customer should choose you.

That requires more than a new content strategy. It requires stronger products, clearer information, better customer experiences, greater external credibility, and closer alignment between what the company promises and what the market actually experiences.

The companies that understand this early will build for it.

The companies that don’t may eventually discover something uncomfortable: their customers did not stop listening.

Someone else started answering the questions.

And that someone may increasingly be AI.

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