How to Improve AI Share of Voice: 5 Levers and a 90-Day Plan

In Muck Rack's May 2026 analysis of more than 25 million links cited by ChatGPT, Claude and Gemini, earned media accounted for 84% of AI citations and paid content for 0.3%. That ratio explains why most attempts to improve AI share of voice stall: teams rewrite their own site while AI engines build answers mostly from what other people publish about them. Improving the number means working on five levers in a specific order, starting with a diagnosis of where you are losing.

Start from the prompts you are losing, not from a content calendar

Every improvement plan starts with a measurement you trust: a fixed prompt set, run several times per engine, with mentions, competitors and cited sources recorded. Our guide on how to measure AI share of voice covers that method. What you need from it here is a short list: the prompts where competitors appear and you do not, the engines where your score is weakest, and the pages those engines cite instead of you.

That list is your citation gap, and it turns a vague goal into specific work. A brand missing from "best payroll software for small businesses" because every answer cites the same two comparison articles has a different problem from a brand missing because its own pricing page cannot be read by AI crawlers.

Lever 1: make sure AI engines can read you

Technical access comes first because nothing else works without it. Engines that search the web at answer time, such as Perplexity, ChatGPT search, Google AI Overviews and Google AI Mode, can only cite pages their crawlers are allowed to fetch. A robots.txt rule written years ago, or a security setting that blocks unknown bots, can quietly remove a site from those answers.

Check which AI crawlers your robots.txt allows, as explained in our guide to what each AI bot obeys in robots.txt, and whether your CDN blocks them by default, a common case with Cloudflare's default settings. This lever is usually the fastest to fix and the first to show results on engines with live retrieval.

Lever 2: earn mentions where engines already look

The Muck Rack data puts journalism alone at roughly 27% of AI citations, with academic, government, encyclopedic and third-party business sources making up most of the rest of the earned share. Ahrefs' study of 75,000 brands points the same way from the search side: branded web mentions were the factor most correlated with visibility in AI Overviews (0.664), well ahead of link-based signals.

In practice, take the sources your citation gap revealed and work them directly: trade publications that cover your category, comparison and "best of" articles, review platforms, and community threads where buyers compare options. Our study on review profiles and AI citation rates shows how much weight review platforms carry.

AI engines mostly repeat what independent sources say about a brand. Improving AI share of voice is first a matter of being described, often and consistently, in the places those engines read.

Lever 3: publish content that answers the exact prompts

Owned content still matters for "how to" and definition questions, where Muck Rack found engines lean more on reference and brand-owned material than on news. The format makes a measurable difference. In the GEO study by Princeton and IIT Delhi researchers, presented at KDD 2024, adding cited sources, quotations and statistics to content raised its visibility in generative engine answers by up to 40%.

Write one page per important prompt, open each section with a direct answer, and back claims with named, dated data. Our guide to answer-first content details the structure.

Lever 4: describe your brand the same way everywhere

AI engines assemble a picture of your brand from every source they read. If your homepage, LinkedIn page, review profiles and directory listings each describe what you do differently, that picture stays blurry and recommendations go to competitors with a clearer one. Align the one-sentence description of your product, your category and your main use cases across all of them.

Structured signals help engines connect those sources to the same entity: Organization schema with sameAs links, as covered in our guide to schema markup for AI citations, and a consistent entry in Wikipedia and Wikidata where your brand qualifies for one.

Lever 5: correct how you are described, not only whether you appear

A higher share of voice is worth little if answers describe you with outdated pricing, a discontinued product or a lukewarm qualifier. Review the wording engines use for your brand in the answers you already appear in, trace inaccurate claims back to the pages they come from, and get those pages corrected or outweighed by accurate ones. Our guide to fixing wrong AI answers about your brand walks through that process.

A 90-day plan to put the levers in order

Days 1 to 30 are for the baseline and the foundations. Run your prompt set, record your share of voice per engine and the competing sources, fix crawler access, and align your brand description on your own site and main profiles. These changes are cheap and remove the reasons engines cannot cite you at all.

Days 31 to 60 go to third-party presence and the pages behind your biggest gaps. Pitch the publications and comparison articles that keep appearing in answers for your weak prompts, update review profiles, and publish or rewrite the owned pages that should answer those prompts directly.

Days 61 to 90 are for measuring and doubling down. Rerun the same prompt set without changing it, compare engine by engine, and put the next month's effort into what moved. The AI share of voice benchmarks help put the new score in context against your category.

Why some engines move faster than others

Engines that retrieve sources at answer time react to new pages within days or weeks: a new comparison article that cites you can appear in Perplexity answers soon after it is indexed, as explained in our analysis of how Perplexity decides which sources to cite. Answers that lean on what a model learned during training change only when the model is updated, which can take months. That is why a 90-day plan usually shows its first gains on retrieval-heavy engines while the rest catch up over the following quarters.