None of the four steps below require a developer, a redesign, or a budget. Each one is something a business owner can finish this week, and each is backed by a specific, cited measurement rather than a general best-practice guess — so the order below isn't arbitrary, it follows the size of the effect each fix has shown in the research behind it.
1. Claim and fix your Yelp, Bing Places, and Facebook listings
Yext's citation-sourcing analysis, built from 6.8 million citations, found ChatGPT draws roughly 49% of its local business citations from third-party directories — Yelp chief among them.1 No other single action on this list touches as large a share of what ChatGPT actually cites from. If only one item gets done this week, this is the one with the largest measured upside.
Go to each platform, search the business, and check three things: is the listing claimed, is the category correct, and does the profile have enough content — photos, a real description, a handful of reviews — for a crawler to have something worth citing. An unclaimed listing with a generic category is close to useless even if the business itself is excellent. This can be finished in a single sitting.
2. Get fresh reviews this week if you're under the rating floor
The SOCi 2026 Local Visibility Index (350,000+ locations, 2,751 brands) found AI platforms apply different rating floors before recommending a business: ChatGPT around 4.3 stars, Perplexity around 4.1 stars, Gemini around 3.9 stars.2 Check the current rating on Google, Yelp, and Facebook separately — they can differ by several tenths of a point — against the floor for each platform.
If below any of these floors, the fastest lever is a short, direct push for fresh reviews — asking recent satisfied customers directly rather than a passive "leave us a review" link — combined with responding to any negative reviews from the last month. Recency compounds the effect: BrightLocal's 2026 Local Consumer Review Survey (n=1,002) found 74% of consumers trust only reviews from the last three months, and 32% trust only the last two weeks.3 Three to five fresh reviews this week is a realistic target, not a full rebuild of the rating — this is the start of an ongoing habit, not a one-time task.
3. Check and fix robots.txt for the six required AI crawlers
Visit yourdomain.com/robots.txt directly and check for disallow rules naming any of the six crawlers that matter for AI visibility: GPTBot, ClaudeBot, PerplexityBot, Google-Extended, CCBot, and Bytespider. Unlike the review or directory gaps above, this is a binary check — either a crawler is blocked or it isn't, and if it is blocked, no amount of good content or reviews compensates, because that bot cannot read any of it.
A blanket disallow is more common than a targeted one
Most accidental blocks aren't a deliberate decision to exclude AI crawlers — they're a broad User-agent: * / Disallow: / rule added years ago by a site builder or security plugin, before AI crawlers were something anyone was thinking about. If found, removing the rule or adding explicit allow rules for the six crawlers above is typically a five-minute edit.
4. Rewrite your homepage's top paragraph
Read the first paragraph of the homepage as a stranger would. Count adjectives ("best," "trusted," "leading," "premier") against concrete, checkable facts (years in business, a specific service, a specific location, a specific number). The Princeton GEO study found content built around cited, specific claims earns roughly 40% more AI visibility than adjective-led content.4 "Trusted by the community" gives an AI model nothing to cite. "Family-owned since 2003, specializing in same-day emergency repairs" gives it a fact it can quote.
This doesn't require a copywriter — it requires cutting every adjective that isn't attached to a fact and replacing it with a fact that's already true about the business. An afternoon is enough for the homepage and the two or three most important service pages.
What to skip this week
Schema markup and an llms.txt file both get recommended constantly in AEO advice, and both sound like they should matter. The measured evidence this week says otherwise, at least as a priority against the four items above.
Schema and llms.txt: measured, and measured small
Ahrefs tested schema markup's effect on AI citation across 1,885 pages and found the impact ranged from -4.6% to +2.2% — not a statistically meaningful effect in either direction.5 A separate Ahrefs study of 137,210 domains found 97% of llms.txt files are never read by AI bots at all.6 Both are real, specific, measured findings — not a guess that these don't matter, but a measurement that they currently don't move the needle the way directory presence, reviews, crawler access, and content specificity do.
None of this means schema and llms.txt are permanently pointless — the AI ecosystem changes fast, and a null result today isn't a null result forever. It means that in a week with limited hours, the four items above have a measured, larger effect, and schema/llms.txt can wait for a week with more time.
"Directories, reviews, crawler access, content — in that order — move AI visibility by measured, meaningful amounts. Schema and llms.txt, measured the same way, currently don't. Spend the week on the four that do."
Notes and sources
1 Yext local citation-sourcing analysis, 6.8 million citations examined. Finding cited: ChatGPT draws ~49% of local citations from third-party directories (Yelp chief among them). yext.com
2 SOCi 2026 Local Visibility Index. Dataset: 350,000+ business locations, 2,751 brands. Figures cited: AI recommendation rating floors (ChatGPT ~4.3★, Perplexity ~4.1★, Gemini ~3.9★). uberall.com/soci
3 BrightLocal Local Consumer Review Survey 2026. Sample: n=1,002 US consumers. Findings cited: 74% of consumers trust only reviews from the last 3 months; 32% trust only the last 2 weeks. brightlocal.com
4 Princeton GEO (Generative Engine Optimization) study. Finding cited: content built around cited, specific claims earns roughly 40% more AI visibility than generic, adjective-led content.
5 Ahrefs schema markup study. Dataset: 1,885 pages. Finding cited: schema markup's measured effect on AI citation ranged from -4.6% to +2.2%, not statistically significant. ahrefs.com
6 Ahrefs llms.txt study. Dataset: 137,210 domains. Finding cited: 97% of llms.txt files are never read by AI bots. ahrefs.com