Most AI visibility content online is written generically, as if the advice applies identically to a consumer app, a crypto exchange and a cybersecurity vendor. The structural work does transfer across industries almost unchanged. The earned-media strategy, the part that actually builds AI systems' trust in a claim, has to be built differently for each one, because the outlets and buyers differ.
The structural layer is nearly universal
Clear, consistent facts across every public profile; a genuine FAQ; an llms.txt file; structured data. This work looks close to identical whether the brand is a fintech platform or a legaltech vendor. It's necessary groundwork, not a differentiator, because every competitor doing AI visibility work correctly is also doing this.
The earned-media layer has to be built per industry
This is where genuine differentiation happens, and where a generic GEO playbook falls short:
- Fintech and crypto buyers, and the AI systems modelling that space, weight coverage in specific financial and crypto trade press, CoinDesk and Cointelegraph in crypto's case, differently from generic business press.
- Cybersecurity is a market where AI systems (like human buyers) are unusually sceptical of vendor self-description, which makes third-party validation, security trade press, analyst commentary, disproportionately valuable for citation purposes.
- Legaltech sells into a risk-averse audience, and the sources that actually move a risk-averse buyer (and by extension, the sources an AI system treats as credible for that audience) are legal trade press and named law-firm relationships, not general tech coverage.
- AI and deep tech companies face the opposite problem from most: too much generic, hype-driven content already exists in the category, which means the outlets that still carry credibility are the ones that demand real technical substance.
Why this matters for a B2B buyer's actual behaviour
B2B buyers increasingly start vendor research inside an AI system rather than a search engine, asking it to compare options or summarise a category. A vendor invisible to that first pass loses consideration before a human salesperson is ever involved. This makes AI visibility a pipeline concern, not just a marketing one, for B2B categories with long, research-heavy buying cycles.
What a properly built AI visibility programme looks like for B2B
Audit how the brand currently appears (or doesn't) across ChatGPT, Perplexity, Gemini and Google AI Overviews for the specific questions its actual buyers ask, not generic category terms. Fix the structural gaps. Then run the media relations programme that earns the industry-specific corroboration an AI system needs to cite the brand with confidence. See our fintech, cybersecurity and legaltech pages for how the industry-specific version of this works in practice.
The uncomfortable part for most B2B marketing teams
This isn't a one-off project. AI systems retrain and re-crawl, and a brand that earns citation once and then goes quiet loses ground to competitors who keep the corroborating coverage coming. Treating AI visibility as a campaign rather than an ongoing discipline is the most common reason results plateau after an initial push.