GEO for people who have to hit revenue targets
Most GEO and AI search budget recommendations optimize for the wrong KPI. Increased sales and profitability are the objectives. AI search visibility to the right searchers at the right time is simply another way to drive revenue.
A citation is visibility. A booked opportunity, incremental sales, and a new customer. That’s performance. Those outcomes are correlated, but they’re not the same. The gap between them is where a lot of GEO budget goes to die.
That’s why the first job isn’t getting cited more. It’s getting cited in the recommendation prompts that actually precede a purchase in your category.
Approaching GEO differently
I’ve been optimizing for organic search visibility since before there was a Google to optimize for.
In the late 1990s, I helped build some of the earliest paid search bid management technology, back when “search marketing” meant buying keywords on platforms that powered search engines most people have since forgotten.
I mention this for calibration, not nostalgia. I’ve sat through a lot of “everything you know is dead” moments, and that experience taught me to separate the shifts that change the work from the ones that only change the vocabulary.
Generative engine optimization, or AI search as many people prefer to call it, is one of those shifts. How people research and decide has changed, and it’s not changing back because even Google and Bing are integrating AI into the SERP.
But most GEO coverage is written by people who don’t carry a quota. The result is coverage that’s either a dictionary entry or a sales deck.
I want to offer the perspective of someone accountable for revenue, not just showing up in a screenshot.
See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
The content changes that matter
Write for prompts
Stop writing for the keyword and start writing for the prompt.
The backlog of “what is” explainer articles that read like every competitor’s adds little to AI answers, since engines can assemble that information from anywhere.
Content designed for buyer selection is different. Build it around the real buyer question: Which provider fits this specific situation?
Include named criteria, honest tradeoffs, and the situations where you’re the wrong choice. Engines reward that kind of candor because it reads like a recommendation.
Publish data only you own
Lean on data only you own. Original, proprietary data is one of the strongest citation magnets because it’s the one thing a model can’t assemble from a hundred other pages.
A single defensible number that nobody else can publish will often outperform a quarter’s worth of generic posts because, once it’s pulled into an answer, it tends to be attributed by name.
If you’re sitting on first-party data, turn one piece of it into a number worth quoting.
Show who’s behind your content
Put real people on the page. Use named authors with real credentials and bios, not a faceless “admin” byline.
The models are working to resolve who you are and whether you’re credible before repeating you, so make that easy.
Remove content that weakens your brand
Prune aggressively. Interchangeable content doesn’t just fail to help. It dilutes your brand’s signal.
The test is simple: If a competitor could replace your logo with theirs and publish the article unchanged, it’s not working for you and may be working against you.
Cutting that kind of content is uncomfortable, and it’s usually the right call.
Dig deeper: 4 types of content decay and how to fix each one
The technical changes that matter, and the ones that don’t
Make your content accessible to AI crawlers
Confirm AI crawlers aren’t blocked and that your pages are indexed in Bing because ChatGPT’s web search relies on Bing’s index. You can’t be quoted from a page the retrieval layer never sees.
Pull key content out of client-side JavaScript so it lives in the HTML.
Make pages easier for AI to cite
Make publish and update dates visible, and keep genuinely refreshed pages current. Freshness is a real factor in AI answers, and materially updated pages often start getting cited again.
Structure pages so the claim comes first and the supporting evidence follows. Use comparison tables where buyers are weighing options because that’s the format engines can lift cleanly.
Ignore the technical hype
The llms.txt file is being promoted as a must-have, but Google has said it doesn’t use one, and there’s no strong evidence that it changes anything.
Schema markup is good technical hygiene, but it’s not the lever many vendors imply.
No amount of technical cleanup rescues thin content. The plumbing helps good content get found. It doesn’t make weak content worth citing.
Dig deeper: Technical SEO for generative search: Optimizing for AI agents
Where the effort gets wasted
The biggest GEO mistakes come down to priorities:
- Making citations on broad informational queries the primary objective. They have limited value, but they’re a worthwhile byproduct of the work you should be doing anyway.
- Over-indexing on a single platform. AI engines draw from overlapping but different sources, so you have to earn visibility across all of them.
- Letting a monitoring tool become the scoreboard. Watching mention counts climb means little if none of those citations reached a buyer. Rising citation counts on the wrong queries aren’t progress. They’re a more expensive vanity metric.
One more correction is worth internalizing: Ranking first in Google no longer guarantees you’ll appear in the AI answer for the same query. The overlap between top organic results and AI-cited sources is much smaller than many people assume.
Treat AI visibility as its own surface and measure it on its own terms. But don’t make AI visibility the KPI. Focus on the marketing and sales initiatives that drive it.
Measure against pipeline, not applause
This is the part most definitional articles skip. Here’s the framework worth holding yourself to.
- Start by defining a money-query set — the specific recommendation prompts real buyers use when they’re close to making a decision. Usually, that’s a few dozen prompts, not a few hundred.
- Track citation share only for that set. Being named in three out of 10 recommendation answers in your category tells you something. Total brand mentions across the web tell you very little.
- Connect those citations to revenue you can defend. Tag AI-referred sessions in analytics, pass them into your CRM, and follow them through to qualified opportunities and closed business the same way you would any other channel.
- Set expectations on timing, too. There’s usually a lag of several weeks between publication and its appearance in AI answers. Don’t panic at week two, and don’t declare victory at week three. Judge performance by the quarter.
The honest test for any citation is simple: Did it create a qualified conversation that wouldn’t have happened otherwise?
If you can’t trace a line from the answer to a real buyer, it’s brand awareness. Fund it and measure it as awareness, not performance.
Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
The part that hasn’t changed
The acronyms have changed more times than I can count. The discipline hasn’t. The brands that win are the ones willing to do work competitors can’t copy and to measure success against revenue rather than applause.
That was true when we were identifying the most valuable PPC keywords in 1998 alongside the best SEO keywords in 1996, and it’s true now. If you have a revenue target to hit, optimize for the answer that puts a buyer in front of you, and ignore the rest of the noise.
