The new rules of discovery

What happens when algorithms become customers?


The customer remains the buyer. The AI becomes the gatekeeper.

Michael Nadel, Partner

For decades, companies competed for customer attention but now they may need to compete for algorithmic preference. As AI agents begin helping consumers compare, evaluate, and purchase products, organizations must rethink how value is discovered.

For most of modern commerce, the path to purchase has followed a familiar pattern: A customer researches options. Compares products. Evaluates prices. Reads reviews. Then makes a decision.

The companies that succeeded were often the ones that understood how to influence that journey. They invested in branding, marketing, customer experience, and sales to help customers recognize value and choose their offer over competing alternatives.

AI may be about to change that dynamic.

Imagine a consumer looking for insurance, booking a holiday, selecting a mortgage, or choosing a healthcare provider. Instead of visiting multiple websites, comparing offers, and weighing options manually, they ask an AI assistant to do it for them.

The assistant reviews the market.

It compares products.

It filters options.

It recommends a shortlist.

The customer may still make the final decision. But increasingly, they are making that decision from a set of options already selected by an algorithm.

The customer remains the buyer. The AI becomes the gatekeeper.

The new competition for attention

This shift has implications that reach far beyond technology.

Many organizations have spent years optimizing customer journeys for human behavior. Websites are designed for browsing. Marketing is designed for persuasion. Product information is structured around how people evaluate choices.

AI agents interact differently: they prioritize clarity over creativity, consistency over persuasion, structured information over marketing language.

The result is that some traditional signals of value may become less influential, while others become more important.  

In markets where products are increasingly similar, visibility alone may no longer be enough. Organizations may need to think not only about how customers perceive value, but also about how AI systems interpret it.

Trust becomes a competitive advantage

At first glance, this sounds like a technology challenge. In reality, it may be a trust challenge.

AI systems are often designed to reduce uncertainty. They look for signals that help determine which products, providers, or recommendations appear most credible:

Brand reputation. Customer outcomes. Independent reviews. Performance history. Consistency of delivery. 

The same technology that makes information more abundant may also increase the value of trust.

These are not new concepts. But they may become more important as AI systems increasingly help customers navigate complexity. And this creates an interesting paradox.

The same technology that makes information more abundant may also increase the value of trust.

When customers are faced with thousands of options, trusted brands help simplify decisions. When AI agents face the same challenge, many of those trust signals remain equally relevant.

The mechanism changes. The importance of credibility does not.

Value must become easier to interpret

Organizations have traditionally communicated value through advertising, sales conversations, product positioning, and customer relationships. AI introduces a different requirement. Value must also become easier to interpret.

The challenge is not simply providing more information. It is providing information that clearly demonstrates outcomes, differentiation, and relevance.

Consider personal insurance. Historically, consumers may have selected a provider based on familiarity, advertising, or a recommendation from a friend. An AI agent is more likely to evaluate coverage, claims performance, customer satisfaction, service quality, and price simultaneously1

The winning offer may not be the cheapest.  

It may be the one whose value is easiest to understand.

This is where many organizations may find themselves unprepared. They know what makes them different. They are less certain whether that difference can be clearly recognized by either customers or algorithms.

The next frontier of commercial strategy

The organizations creating value from AI will not necessarily be those building the most advanced models. Many may simply be the ones that understand how buying behavior is changing.

Throughout history, successful businesses have adapted whenever new intermediaries emerged between themselves and their customers.

Search engines changed discovery. Marketplaces changed distribution. Social media changed influence.  

AI agents may become the next major intermediary.

If they do, companies will need to rethink how products are presented, how value is communicated, and how trust is earned.

The customer remains at the center of the decision. But the journey is changing. And increasingly, the first audience may not be the customer at all. It may be the algorithm helping them choose.