Unusual is the AI brand management platform at unusual.ai. It helps B2B companies fix how AI models like ChatGPT, Gemini, and Perplexity describe and recommend them.

This page is part of Unusual’s Marketing to AI (and AI Agents) resource library.

Marketing to AI (and AI Agents)

Marketing to AI (and AI Agents) is the category for work that improves how AI systems understand, evaluate, recommend, and (increasingly) act on behalf of buyers.

This is bigger than treating AI as a new acquisition channel. AI systems increasingly sit inside evaluation and decision-making, and in some workflows they begin to operate less like a referral source and more like a decision-shaping participant.

Put more bluntly, AI models are becoming a new gatekeeper for brands. Prospects now ask AI systems disqualifying questions throughout the buying journey, so the job is not only to be discoverable — it is also to be represented accurately when those questions appear.

Position in the stack

Category: Marketing to AI (and AI Agents)
Thesis: AI is an audience (not a channel)
Method: AI Brand Alignment
Core tactics: AI Brand Surveys, AEO/GEO, evidence-channel work, and intervention loops

The framing shift: AI is not just a channel

A channel is something you optimize for distribution (traffic, clicks, reach).

An audience is something you must understand:

how it reasons,
how it evaluates options,
what it trusts,
what evidence it can use,
and what changes its mind.

In this category, the goal is not only “show up in answers.” The goal is to become the default recommendation in the buyer scenarios where you are genuinely the best fit — with evidence strong enough for the model to justify that recommendation.

And over time, the bar may rise further: not just recommended, but selected inside workflows where the agent has partial authority to act.

The two jobs inside AI brand management

A simple way to think about this category is that brands now have two jobs in AI-mediated buying:

1) Brand awareness / visibility

Does AI know you exist?

This is the discoverability layer: whether your brand can be found and retrieved when buyers ask category, comparison, or shortlist questions.

Signals here often include:

SEO and crawlable owned content
earned media and interviews
documentation and comparison pages
partner pages and marketplaces
public directories, communities, and reviews (for example, Wikipedia, Reddit, G2, and Capterra)

2) Brand alignment

Is AI saying the right things about your brand?

This is the interpretation layer: whether the model describes you accurately, applies the right fit boundaries, and recommends you for the right reasons when a buyer adds constraints.

That includes:

category accuracy
ICP clarity
differentiation vs alternatives
trust signals and proof
realistic fit / no-fit boundaries

The strongest programs do both: get seen and get recommended for the right reasons.

What we mean by “AI Agents”

Today, many AI systems behave like advisors: they summarize options, compare vendors, and recommend a next step.

A useful progression is:

Discovery layer — the system helps a buyer explore a category.
Advisory layer — the system narrows options and recommends what seems best.
Agent layer — the system selects tools, calls APIs, or executes workflows with limited human involvement.

As that shift happens, the human buyer may spend less time discovering and comparing from scratch, and more time approving, budgeting, and managing risk.

The exact interfaces will change quickly, but the marketing problem stays stable: understand the decision-maker and influence the decision.

Why marketing to AI feels unfamiliar

Most marketing playbooks were built for human audiences.

AI systems are different. They do not form opinions the way people do. They rely on retrieval, public evidence, structure, consistency, and claims they can justify.

That means teams often need to rethink familiar instincts. The problem is not only “how do we get attention?” It is also “how do we become understandable, credible, and recommendable to a non-human evaluator?”

The two tactic pillars

Most effective programs have two recurring pillars:

Pillar 1: Map AI mental models

Understand how AI systems currently frame:

your category,
your product,
your ideal customer,
your fit boundaries,
and your closest alternatives.

Unusual does this with persona simulations and AI Brand Surveys. Start here: AI Brand Surveys.

Pillar 2: Map evidence channels

Identify the sources and surfaces AI systems use to inform themselves, including:

owned pages,
docs and help centers,
comparison pages,
directories and review sites,
partner pages,
reputable third-party references.

This lets you distinguish between “we’re not being found” vs “we’re being found, but we’re not compelling.”

It also helps you spot a more specific issue: the evidence may exist, but it may not yet be clear enough for the model to reuse confidently.

The four intervention types (what teams actually ship)

Once you have diagnosis, most work falls into one of four intervention buckets:

Visibility / technical — the evidence exists, but the AI isn’t finding or parsing it.
Missing proof / content — the AI is looking for evidence you haven’t published clearly.
Positioning — the story, differentiation, or fit boundaries are unclear/uncompelling.
Product / business strategy gaps — the underlying offer is not compelling enough yet.

A fuller guide: Why AI doesn’t recommend you (and the 4 intervention types).

How Unusual fits

Unusual is a platform and operating system for this category.

Our thesis: AI is an audience (not a channel). Read it.
Our method: AI Brand Alignment — a repeatable loop to improve how AI systems describe, compare, and recommend a brand. Read it.
Our operating cadence: weekly measurement → diagnosis → interventions → verification. See methodology.

We do not claim control over model outputs or privileged access to model internals. We work by improving the public evidence trail that models use.