Your Next User Isn’t Human

This document is aimed at brands that want to remain visible in a world where consumers increasingly delegate purchasing decisions to AI assistants. A study by ARD Media (Heßler, 2026) documents that autonomous software agents are already a reality as intermediaries between brands and end consumers. Please read the following sections in full before you consider your current communication strategy sufficient.

Section 1: Understand the operating principle before you begin.

Algorithms work differently from people. That sounds trivial, but it isn’t when you see how few brands have drawn the consequences so far.

A person decides emotionally, contextually, sometimes irrationally. They remember a campaign that moved them. They follow a recommendation, they buy because a package appealed to them. An AI assistant does none of this. It evaluates sources by consistency, structure and authority. It cites what is machine-readable. It recommends what is marked as reliable in its training data. The most emotional campaign in the world leaves no impression on it if the brand architecture behind it isn’t right.

This is not a creativity problem. It is an infrastructure problem.

Section 2: Visibility to algorithms is not a question of budget. It’s a question of structure.

Anyone who believes that more media budget also means more visibility to AI systems hasn’t understood the operating principle.

Generative Engine Optimization (GEO for short) is the discipline that describes how brands become discoverable and recommendable for AI systems. Aggarwal and colleagues defined the concept in an ACM study in 2024: digital presences must be structured as machine-readable sources of authority so that AI systems classify them as relevant and cite them. GEO thus follows a different logic from classic search engine optimization. It’s not about keywords and click rates, but about whether a brand is recognizable as an authority. An algorithm asked which running shoe it recommends doesn’t fall back on the most beautiful ad. It falls back on what is described most consistently and credibly in its sources.

Warning: Creative excellence alone is no protection.

Many brands are creatively strong and structurally invisible. Campaigns that move people leave no traces in machine-readable systems unless they are backed by consistent, structured information. Both are necessary. Just one of them is no longer enough.

Section 3: Address two target groups without neglecting either.

The most common wrong reaction to this insight is the assumption that you have to choose: either emotional communication for people or structured communication for algorithms. This assumption is wrong.

The brands that will win in the long term have understood that both are expressions of the same strategic clarity. A brand that really knows what it stands for (what it solves, why it’s better, who confirms it…) can translate that into an emotional campaign and at the same time into a machine-readable architecture. The starting point is the same. The translation is different. Whoever doesn’t have this starting point fails with both target groups at once.

Warning: Inconsistency is punished.

AI systems are sensitive to contradictions. A brand that claims one thing on its website and another in trade articles loses algorithmic credibility. People forgive inconsistency. Algorithms ignore what they can’t classify.

Section 4: Frequently asked questions

Isn’t this just SEO under a new name? No. Classic search engine optimization aims at ranking in results lists. GEO aims at being cited and recommended by generative AI systems as a reliable source. The mechanisms are related, but not identical. Anyone treating GEO as SEO 2.0 will be turning the wrong dials.

Do we have to rewrite our entire communication strategy? No, but add to it. The point is to add a layer that has been missing so far: the machine-readable translation of what a brand stands for. GEO doesn’t appear in any classic agency value chain. It is a new field of competence that agencies have to develop.

How long does it take for results to become visible? Longer than with classic campaigns. Algorithmic authority doesn’t come from one-off measures, but from consistent signals over time. That is uncomfortable in an industry used to quick results. It is the truth all the same.

Section 5: Common user errors

Error 1: The campaign is finished. So is the strategy. A campaign is a moment. Algorithmic visibility is a state that has to be maintained continuously. Whoever stops after the launch loses what they have built.

Error 2: We’re well known. So we’ll be found. Awareness among people and visibility to algorithms are two different things. Many of the world’s best-known brands are algorithmically weak because they never learned to make their strength machine-readable.

Error 3: This is an IT problem. GEO is not a technical discipline. It’s a strategic one. The question doesn’t begin in the IT department. It begins with a brand knowing what it stands for.

Error 4: We’ll wait until the market has stabilized. The market isn’t stabilizing. It keeps evolving. Whoever waits until the rules are fixed will find that others have already been playing by them.

Section 6: Getting started

Don’t begin with the technology. Begin with the question of what your brand is. Clearly enough to explain it to an algorithm. If that answer is hard to give, it isn’t a GEO problem. It’s a strategy problem that becomes a GEO problem. And that’s the good news: it can be solved. But it has to be solved before a machine decides whether your brand exists.