StrategyApril 28, 2026· 7 min read
Your next buyer will ask an AI, not a search bar. Be the answer.
A growing share of B2B software discovery now runs through AI assistants — buyers describe a problem and receive a synthesized shortlist instead of ten blue links. To be in that synthesis, content must be answer-first (direct, quotable responses to real queries), structurally clear (FAQs, schema, llms.txt), and consistent across the site, because LLMs reward sources they can cite cleanly and verify repeatedly.
Watch a founder evaluate software in 2026: they don't search 'best ABM tools' and open ten tabs. They tell an assistant their situation — team size, motion, budget — and get three candidates with reasoning. If your category page can't be quoted into that reasoning, you weren't shortlisted; you were never seen.
How LLMs choose what to cite
- Extractability: a crisp two-sentence answer near the top of the page beats the same answer diluted across 800 words.
- Verifiability: claims that agree with the rest of your site (and the wider web) get repeated; contradictions get dropped.
- Structure: headings that mirror real questions, FAQ schema, tables — machines parse what's organized for parsing.
- Specificity: 'plans start at $99 for 600 pages' is quotable; 'flexible pricing for teams of all sizes' is noise.
The answer-first writing pattern
Every page targets one question a buyer would actually type, and answers it completely in the first paragraph — definition, mechanism, qualifier. The rest of the page earns depth: evidence, edge cases, comparisons. Humans skim it happily; machines quote the top. (You're reading the pattern right now — this post opens with its own extractable answer.)
The site-level layer
- llms.txt: a plain-text map of what your product is, costs, and does — the README your site offers to machines.
- Schema everywhere it's true: FAQPage, Article with real dates, SoftwareApplication with real pricing.
- Internal consistency: one canonical one-liner and price set, repeated verbatim across pages — repetition is how models gain confidence.
- Honest comparisons: 'when a competitor fits better' content gets cited precisely because it's rare and useful.
The compounding part: assistants remember category associations. Show up reliably for 'personalized landing pages at scale' and adjacent queries inherit you. The content moat of the LLM era is being the cleanest source in a category you can credibly claim — which is a much fairer fight than out-backlinking incumbents.
Questions people ask
What is AEO?
Answer Engine Optimization: structuring content so AI assistants (ChatGPT, Claude, Perplexity, Google's AI overviews) can extract, trust, and cite it when answering user questions — the LLM-era complement to SEO.
Does traditional SEO still matter?
Yes — LLMs retrieve from the same web search indexes crawl. AEO isn't a replacement; it's writing the content you were already ranking in a form machines can quote verbatim.