A practical field guide to AI visibility.

An AI recommendation can look simple: a person asks for an accountant, and the engine returns a short list. The work behind that answer is less simple. Answer engines tend to combine information from their training data, live web search when available, business profiles, directories, reviews, articles, and the firm’s own website. The exact mix varies by engine and by question.

Clear entity information often helps. A firm name, location, services, leadership, and client focus should agree across the firm’s website and third-party profiles. A focused page about tax planning for dental practices in Austin gives an engine more context than a general services page. It also gives other sites something specific to cite.

Third-party evidence tends to matter because a firm describing itself is only one signal. Detailed reviews, professional directories, association listings, local coverage, and useful contributed commentary may reinforce what the firm says. In our checks, cited sources often explain why one firm appears while another does not.

This is not a fixed checklist. Engines change and different prompts can produce different shortlists. The practical first step is measurement: ask the questions a buyer would ask, save the answers, and identify repeated patterns. Then improve the weakest inputs and check again.

Traditional search gives marketers a familiar position: first, fifth, or somewhere beyond the first page. AI answers usually do not behave that way. They often present a compact recommendation, mention several firms in prose, or return no firm names at all. A single ranking number cannot describe that output well.

Share of answer is a practical alternative. Start with a stable set of buying-intent questions. Ask each question across the answer engines you care about. Then divide the number of answers that name your firm by the number of usable answers returned. If three of 32 answers name the firm, its share of answer for that prompt set is 9.4 percent.

The prompt set matters. A firm serving startup founders in Austin should not measure itself against vague national questions. City, niche, and service make the questions closer to a real buying decision. Keeping those questions stable also makes later checks more useful.

Share of answer does not prove that an AI mention created a client. It does show whether the firm enters the consideration set for defined questions. Pair the number with named competitors and cited sources. Those details point to the profiles, pages, reviews, and third-party mentions that may deserve attention.

First, check whether the firm is described consistently. The same name, location, website, phone number, and service language should appear across important profiles. Conflicting information may make the entity harder to interpret.

Second, review service pages. Broad claims like “full-service accounting” give little context. Pages that explain who a service is for, where it is offered, and how the work is handled tend to provide clearer evidence.

Third, read the reviews. Detailed reviews that describe a client type and engagement can be more informative than short praise. Never script reviews, but make it easy for clients to describe the work in their own words.

Fourth, inspect third-party citations. Answer engines often draw on directories, association profiles, publications, and local sites. Compare the sources cited in answers with the places where your firm has a complete, accurate presence.

Fifth, check technical clarity. Organization and service schema, crawlable pages, sensible titles, and accurate contact details can help machines interpret the site. None of these steps promises a mention. Together, they tend to improve the quality and consistency of the evidence an answer engine can find. Measure again with the same questions after meaningful changes.