Start with whether your buyers already ask AI
Before discussing budget, confirm that demand has actually moved. If your buyers still arrive mainly through keyword search, AI entry points may not affect you much yet, and funding the work would crowd out more certain investment.
The test is straightforward. Take twenty to thirty questions your buyers ask during a purchase decision — pull them from call recordings and support tickets rather than imagining them — and ask each one on the AI platforms your buyers actually use. Record whether you appear, where you appear, and whether you get replaced by an alternative.
- Draw the questions from real sales and support conversations
- Ask on the platform your buyers use, not the one you prefer
- Record both 'absent' and 'present but replaced' — the second is often the more urgent signal
Who it suits, and who it does not
GEO is not universal. It has the most leverage where decisions are long and heavily discussed, and the least where they are reflexive. Working out which side you are on matters more than arguing about whether GEO works.
- Suits: professional services, healthcare, legal, education, B2B manufacturing — categories where buyers ask for a recommendation first
- Suits: high ticket sizes and long cycles, where buyers do real research
- Suits: teams with existing content assets that can be restructured
- Does not suit: impulse purchases where nobody researches
- Does not suit: categories so commoditised that an AI answer cannot distinguish brands
- Does not suit: teams with no capacity and no intention to produce content — GEO amplifies content gaps rather than filling them
Validate cheaply before committing
The main risk is not spending money but spending it without a way to tell whether it worked. GEO has this problem in a specific way: model answers fluctuate, and a short window can show no visible change, which reads as the method failing.
Measure a baseline first. Fix a set of thirty to fifty questions, ask them across three to five platforms, and record brand mention rate, which of your pages get cited, and where competitors appear. That dataset is the reference every later change is compared against.
The measurement definitions are set out under GEO metrics. If you have no baseline yet, run a free query to establish a starting point.
Agree acceptance criteria before work starts
Acceptance criteria decided at the end rather than the beginning are how projects end in disagreement. Three groups of metric work: visibility, quality, and outcome.
- Visibility: brand mention rate and cited page count across the fixed question set
- Quality: whether cited answers are accurate, and whether sources are pages you control
- Outcome: sessions, enquiries and conversions from AI referrals, over a longer window
- Every metric needs a fixed question set and platform list, or before and after cannot be compared
Where projects go wrong
The most common mistake is treating GEO as a replacement for SEO. They target different outcomes. A page with no ranking can still be cited if its content breaks cleanly into an answer; a top-ranked page written as one block of marketing copy will not be cited.
The second is treating mention as the finish line. Being named is the start: whether the answer recommends you, cites a page you control, and brings anyone to your site is what determines whether it is worth anything commercially.
The decision is not really about keeping up with a concept. It is about whether your buyers' research already happens inside AI answers. A week of baseline measurement usually answers it on its own. Sources reviewed on 2026-10-17. This article records a method with verifiable boundaries; it does not claim that any search or AI platform will index, cite or recommend anything.