Most AEO programs make the same foundational mistake: they start by tracking prompts. Teams rush to ask “does AI mention us?” before they’ve ever answered the more important question — “what do we actually want AI to say?” Without a clear picture of your brand identity, you’re measuring noise. You have no baseline, no benchmark, and no way to know whether the AI’s description of you is accurate, incomplete, or just plain wrong. The real starting point for any AEO program isn’t a dashboard. It’s a definition.
The Core Question AEO Forces You to Answer
AI systems are fundamentally comparative. When someone asks an AI assistant to recommend a project management tool, a B2B data platform, or a payroll solution, the model doesn’t describe each option in a vacuum — it ranks, filters, and recommends based on fit. That means your brand is always being evaluated relative to alternatives, not in isolation.
This forces a level of specificity that most brands have successfully avoided for years. Vague positioning — “we help teams work better” or “the platform built for growth” — collapses under the weight of comparison. AI doesn’t reward ambiguity. It rewards clarity. The core question AEO forces you to answer is: Who are you, specifically, and for whom?
If you can’t answer that with precision, AI will answer it for you — and probably get it wrong.
Why ‘Why Do We Win?’ Is the Only Question That Matters
AI answers are situational. A buyer asking “what’s the best CRM for a 10-person sales team?” will get a very different answer than one asking “what’s the best CRM for enterprise financial services?” The model is matching solutions to contexts, not handing out universal rankings.
This means being generically good isn’t enough. You need to be the right fit for specific situations — and you need to know which situations those are. That’s why the question “why do we win?” is so powerful. Not “what do we do” or “what features do we have,” but: in the moments when a customer chooses us over everyone else, what actually tipped the decision?
The answers to that question are your competitive differentiators. They’re the attributes AI needs to understand about you in order to recommend you in the right context. If you don’t know why you win, you can’t teach AI when to surface you.
Commit to What You’re NOT
One of the hardest parts of brand positioning is exclusion. It feels risky to say “we’re not for you” to any potential customer. But in the world of AI search, vague all-audience positioning is actively harmful. When you try to be everything to everyone, AI systems flatten you into a clear choice for no one.
Committing to what you’re not is just as strategically important as defining what you are. It sharpens your signal. It makes your positioning legible to AI models that are trying to match you to specific buyer contexts. And it forces internal alignment on who your real customer actually is.
Use this template to pressure-test your positioning:
We are a [product category] for [specific audience] who need [core capability]. We win because [differentiator]. We are not [what you’re not for].
Fill this in honestly. The last sentence is the hardest — and the most important. If you can’t complete it, your positioning isn’t sharp enough yet.
Mining the Data You Already Have
You don’t need to start from scratch. The raw material for your brand attribute framework almost certainly exists in data you’re already collecting — you just haven’t looked at it through this lens before.
External Data Sources
External sources give you an unfiltered view of how the market perceives you and your competitors:
- Review platforms (G2, Capterra, Trustpilot): Read the actual language customers use to describe problems, outcomes, and comparisons. This is gold.
- Reddit and community forums: Buyers talk candidly here. Search your category, your brand name, and your competitors to find unguarded opinions.
- Competitor analysis: What are competitors claiming? Where are their review gaps? Where do customers say they fall short?
- Prompt volume research: What questions are people actually asking AI about your category? This tells you which use cases and pain points are top of mind.
Internal Data Sources
Internal sources give you first-party signal that’s often higher quality — especially for B2B brands:
- Sales call recordings (Gong, Chorus, etc.): Listen for the moments deals are won or lost. What objections come up? What language do buyers use to describe their problem?
- Support tickets: Recurring issues reveal gaps between expectation and reality — and often surface the exact use cases customers care most about.
- Product usage data: Which features drive retention? Which workflows do power users rely on? Usage patterns reveal real value delivery.
- NPS responses: Open-ended NPS comments are a direct line to how customers articulate your value in their own words.
Note that B2B and B2C brands will weight these differently. B2B teams typically lean heavily on first-party data — sales calls, support tickets, and customer interviews — because public review volume is lower. Consumer brands often have richer public data from review sites and social communities, making external sources especially valuable.
Finding High-Confidence Themes
Once you’ve gathered data across multiple sources, the goal is to find patterns — specifically, patterns that emerge independently across different channels. A pain point that shows up in Gong call transcripts is interesting. The same pain point surfacing in Reddit threads and G2 reviews and your NPS comments? That’s a high-confidence signal.
Convergence across sources is your quality filter. It tells you that a theme isn’t an artifact of one channel’s bias — it’s a genuine, recurring truth about how buyers experience your category and your product.
To spot these themes:
- Tag and categorize language patterns across each source independently before comparing
- Look for the same underlying concept expressed in different words (e.g., “too complex to set up,” “steep learning curve,” “took months to onboard” — all pointing to the same friction)
- Prioritize themes that appear in at least two or three distinct source types
- Note themes that appear in competitor data but not your own — these may be positioning opportunities
The themes that survive this cross-source test become the foundation of your attribute list.
Building Your Attribute List
With your high-confidence themes identified, you can now organize them into a structured attribute framework. There are six core attribute categories every brand should define:
- Product category: How do buyers classify what you do? What category do they search when they’re looking for a solution like yours? This needs to match the language AI models use, not just your internal terminology.
- Industry / vertical: Which industries or sectors do you serve best? Be specific — “financial services” is more useful than “enterprise.”
- Pain points: What problems drive buyers to seek out a solution like yours? These should be expressed in buyer language, not product language.
- Use cases: What specific jobs does your product get hired to do? A single product can have multiple distinct use cases — each one is a potential AI recommendation context.
- Integrations: What tools do you connect with? Buyers often search for solutions that fit their existing stack, and AI models factor integrations into recommendations.
- Buyer persona: Who specifically is making the purchase decision? Title, function, company size, and context all matter. The more precisely you can define your buyer, the more accurately AI can match you to the right queries.
This list becomes your working document — the source of truth that every piece of AEO content and measurement should map back to.
Prioritizing Where to Start
With a full attribute list in hand, the next challenge is deciding where to focus first. You can’t work on everything simultaneously, and not all attributes are equally worth pursuing. The prioritization framework that works best uses three dimensions:
- Importance: How central is this attribute to your buyers’ decision-making process? Attributes that directly influence purchase decisions rank highest.
- Distance: How far is your current AI representation from where you want to be on this attribute? A large gap means more opportunity — and more urgency.
- Feasibility: How realistic is it to move the needle on this attribute given your current content, data, and resources? Some attributes require significant content investment; others can be addressed quickly.
Score each attribute across these three dimensions and multiply the scores: Importance × Distance × Feasibility. The attributes with the highest combined scores are where you start. This keeps your AEO program focused on the work that will have the greatest impact in the shortest time.
Revisit this prioritization quarterly as your AI representation improves and your business context evolves.
Before you build a single dashboard, write a single piece of content, or track a single prompt, this foundational work is what separates AEO programs that produce results from ones that produce reports. Knowing your brand attributes with precision — and backing them with real data — gives you something to measure against, something to optimize toward, and something to teach AI about who you are. The brands that win in AI search aren’t the ones with the biggest content budgets. They’re the ones that know exactly who they are, who they’re for, and why they win.