AEO Strategy for Enterprise SaaS: What The Research Says About AI and Your Brand

You've heard the sermon by now: "Optimise for AI, optimise for AI." Usually followed by a list of tips you've already read four times this quarter.

When founders ask me what an AEO strategy for enterprise SaaS actually involves (AEO being Answer Engine Optimization, or GEO, or LLMO, depending on which acronym you've committed to), I give them three levers: breadth (lists, comparisons, broad explainers, because that's how models start researching), structure (tables, labeled sections, content a machine can parse), and external verification (proof that lives outside your own domain). I've written about how LLMs change discovery for B2B SaaS before, and those three levers still hold.

A recent study explains why they work better than I've managed to, and adds a few findings worth acting on. In How to Get AI to Surface Your Brand (HBR, June 2026), John Gale, Luca Cian, and Luc Wathieu asked ChatGPT, Claude, and Gemini for product recommendations across multiple categories. Brooks, a mid-sized running-shoe brand, appeared reliably. Nike appeared inconsistently.

Their explanation: AI systems recommend brands based on how well they match a specific user need, not on popularity. The authors call it a shift from symbolic positioning to evidentiary positioning. Awareness and storytelling matter less. Clearly defined features, validated performance claims, and external endorsements matter more.

If you market a consumer brand built on storytelling, that's a big adjustment. In enterprise SaaS we already sell on benefits and proof, so parts of this are old news. Three findings aren't.

Finding 1: Only 8.4% of brands show up consistently across AI platforms

Of the 716 unique brands surfaced in the study, only 8.4% appeared consistently across ChatGPT, Claude, and Gemini. Most appeared on one platform only.

Other datasets show the same fragmentation: roughly 11% of cited domains overlap between ChatGPT and Perplexity for the same query, and 71% of cited sources appear on one platform only. The engines pull from different indexes. ChatGPT retrieves through Bing and over-indexes on Wikipedia and consensus sources. Gemini builds a query-specific corpus from Google's index and Knowledge Graph. Perplexity leans on Reddit and recent content. Claude prefers deep, structured sources.

So, four platform playbooks?

No. The brands in the consistent 8.4% didn't run four playbooks. Per the study, hitting the three boxes (clearly defined features, validated performance claims, strong external endorsements) is what earns mentions everywhere. Cross-platform visibility is the reward for evidentiary depth, not for platform hacks.

Prioritisation is still sensible. If your buyers research on Reddit, or shortlist on G2 and Capterra, start there and tick the three boxes on that surface first. Just don't build your strategy around one platform's current retrieval habits. Reddit's citation share dropped 23% in a single month in late 2025, and Perplexity's Reddit citations fell 86% after Reddit sued them over scraping. Retrieval quirks change faster than your content calendar.

Finding 2: "Just specific enough" messaging is now an AEO liability

From the research: what matters is whether a model can arrive at your brand as a credible answer to a specific problem.

Enterprise SaaS marketers are trained to do the opposite. We build messaging that's "just specific enough": three or four value points, tight enough to survive a buying committee's attention span. Correct for humans. Wrong for retrieval. SaaS companies that include specific metrics in their content see a 27% increase in LLM citations: the actual percentage improvement, the timeframe, the customer's industry.

My first instinct was to split by channel. Keep first-touch ads and cold-account ABM campaigns simple, make blogs and community contributions dense. That split doesn't hold, because the same asset serves both readers. Your prospect skims the blog post; the model retrieves from it. And LLMs cite passages, not pages. One paragraph gets pulled into an answer, the rest gets ignored.

Layer within the asset instead

• Top layer, for humans: the three-to-four-point value narrative, skimmable.
• Deep layer, for machines: named features, quantified outcomes with timeframes, comparison tables, integration specifics, the problem language your ICP actually uses.

Some formats only carry one layer (an ad can't hold a spec table, and shouldn't). Anything on your website or in a community should carry both.

Finding 3: Vocabulary shaping is category creation with a different success condition

The Brooks example is a vocabulary story. Brooks wins goal-oriented queries ("best shoe for overpronation," "stability shoe for marathon training") because its content is built on the functional problem language runners use, and that language appears wherever runners talk.

This is the muscle enterprise marketers flexed ten years ago when we were "defining the category": coin the frame, and buyers search in your terms. It matters most for startups that want to win goal-oriented queries without outspending established competitors. Volume doesn't help much here. On review platforms, a few hundred detailed, recent reviews routinely outperform tens of thousands of shallow ones.

The success condition moved from repetition to adoption

The old category playbook worked through repetition: events, ads, analyst briefings, until the term stuck in buyers' memory. The new one only pays off when third parties use your language. If your category term exists only on your own domain, the fan-out queries AI runs behind the scenes won't connect buyer problems to it, because buyers describe problems in their own words and models search accordingly. Note that Brooks didn't invent "overpronation". It attached itself to terms runners, reviewers, and communities already used.

Practically, this makes vocabulary shaping and external verification the same play. In your G2 or Capterra review campaigns, prompt customers with the specific metric ("we cut onboarding from 2 weeks to 3 days") and the specific term you're trying to establish, instead of a generic review ask. Reviews that mention specific features, use cases, and outcomes are what AI extracts and cites. Same logic for case study quotes, community answers, and podcast talking points.

Your case studies and product pages are evidence. They become validation when the same language and numbers show up on domains you don't own.

The bottom line

The three levers haven't changed: breadth, structure, external verification. The research supplies the frame that connects them, plus one useful implication for early-stage teams in Enterprise SaaS.

AEO is the first discovery channel where the challenger isn't structurally disadvantaged. You can't outspend the category leader on awareness. You can out-evidence them: sharper problem definition, more specific claims, denser third-party proof for a narrower need. That's what Brooks did to Nike, and Nike has a bigger media budget than your entire category.

Sources: Gale, Cian & Wathieu, HBR (2026) · ZipTie citation analysis · Profound AI platform citation patterns · Search Engine Land, 30M-source citation study · Search Engine Land, query fan-out guide · Discovered Labs, third-party validation signals · CMSWire on Reddit citation volatility

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