I've had this conversation with at least a dozen marketing leaders over the past six months. The pattern is identical every time. They know AI search is changing things. They've read the headlines. They understand that ChatGPT and Perplexity are growing rapidly and that buyers are using these tools to research solutions. But when I ask them what they've actually done about it, the answer is almost always a variation of "not much yet." The reason is almost always the same too: they're not sure where to start, and they're not sure it's worth prioritising over the dozen other things competing for their attention.
This article makes the case that it absolutely is worth prioritising — and then gives you a clear starting point.
Why AI Search Can't Wait
The data on AI search adoption is unambiguous. ChatGPT is now used by over 100 million people, with significant and growing B2B research usage. Perplexity is growing at triple-digit year-on-year rates and is particularly popular among professional and technical users — exactly the buyer profile most B2B companies care about. Google's AI Overviews now appear on over 40% of search queries, with an AI-generated summary at the top of the results page before any traditional blue links.
The practical consequence is that a buyer researching a solution in your category may encounter AI-generated content before they ever see a single organic search result. If your brand appears in that AI-generated content, you get awareness and credibility before the buyer even starts their formal search. If you don't appear, you've missed the earliest and increasingly important stage of the research process.
For B2B SaaS companies in particular, where sales cycles are long and brand familiarity is a significant factor in purchase decisions, early-stage AI visibility is disproportionately valuable. Being the brand that appears when a buyer first explores their problem — before they've formed opinions about solutions — is a significant advantage that compounds through the rest of the buying cycle.
What AI Search Strategy Actually Means
AI search strategy is not a separate discipline from SEO. It's an extension of SEO that adds new considerations and metrics without replacing the existing ones. Your traditional SEO programme — technical foundations, content, links — remains important. AI search strategy builds on top of it with additional layers focused on AI engine visibility specifically.
The three core components of an AI search strategy are entity establishment (making sure AI engines understand who you are), content optimisation for AI citability (restructuring your content so AI engines can easily extract and cite it), and citation building (ensuring that third-party sources reference your brand in ways that AI engines can detect and use to build trust signals).
Step 1: Audit Your Current AI Visibility
Before you can improve your AI visibility, you need to understand your current position. The audit process is simple and takes about an hour. Go to ChatGPT, Perplexity, Gemini, and Claude. Ask each of them the five to ten questions your ideal buyers are most likely to ask when researching solutions in your category.
Record everything. Does your brand appear in any of the answers? If so, how are you described? Is the description accurate and positive? Are competitors appearing that you'd expect to compete with? Are brands appearing that you wouldn't consider direct competitors but are apparently seen as relevant by the AI? Are there questions where nobody in your category is being mentioned — representing an opportunity to establish presence in an uncrowded space?
This audit is your baseline. Repeat it monthly to track progress.
Step 2: Entity Establishment
AI engines build their understanding of your brand from multiple external sources. If those sources are sparse or inconsistent, the AI's representation of you will be weak or absent. The fix is systematic entity establishment across the key platforms AI engines use.
Your Crunchbase profile should be complete, accurate, and include a clear description of your category, product, and target customer. Your LinkedIn company page should describe what you do in plain, specific language without jargon. Your G2 and Capterra profiles should exist and have recent reviews. Your website's About page and homepage should clearly state who you are, what problem you solve, and who you serve in language that any AI engine can parse immediately.
Consistency matters. If your Crunchbase describes you as "a B2B SaaS platform for restaurant operators" but your website describes you as "a technology solution for the hospitality industry," the inconsistency creates ambiguity that reduces AI confidence in representing you accurately.
Step 3: Content Restructuring for Citability
Most content is structured for human readers who scan, read at varying depths, and follow their own path through a piece. AI engines extract specific passages that answer specific questions. These two use cases require different structural approaches.
For AI citability, every piece of content should lead with a direct answer to the question it addresses. The most important information should be in the first paragraph, not the conclusion. Headings should be specific questions or clear topic descriptions, not clever or abstract. Under each heading, the first sentence should directly address the heading topic before expanding into supporting detail.
FAQPage schema markup is particularly valuable. It explicitly structures your content in question-and-answer format that AI engines can directly extract. Adding FAQ schema to your key pages — particularly those targeting question-format queries — is one of the highest-ROI technical changes you can make for AI visibility.
Step 4: Original Research as Citation Bait
AI engines disproportionately cite original research. A survey, study, or analysis that generates a specific, citable statistic — "67% of B2B buyers now use AI tools in their purchase research process" — is far more likely to be cited than a generic educational article on the same topic. AI engines are looking for specific, attributable claims to include in their answers, and original research provides exactly that.
Plan to produce at least one piece of original research per quarter. Even small-scale surveys (100-200 respondents) can generate genuinely citable data. The key is that it must be a primary source — your own research, not a compilation of other people's statistics.
Step 5: External Presence Building
AI engines weight external sources heavily. Being mentioned in industry publications, on relevant podcasts, in analyst reports, and on trusted review platforms contributes to the AI's assessment of your credibility and relevance. Build a systematic programme of external presence-building alongside your owned content strategy.
Target three to five industry publications where your buyers are active. Contribute original content, research, or expert commentary. Secure two to three podcast appearances per quarter. Engage proactively with review generation on G2 and Capterra. Over a 6-12 month timeframe, this external footprint materially improves your AI citation profile and compounds with every new mention added.