If your AI Share of Voice is low — or you're not sure what it is — this playbook gives you the complete process for measuring, understanding, and systematically improving it. The approach is the same whether you're starting from near-zero AI SoV or trying to move from moderate to market-leading presence.
The playbook has five phases. They're designed to be executed sequentially, but phases 4 and 5 run in parallel with the earlier phases once you're set up.
Before you start: AI Share of Voice improvement is a 3–6 month program, not a one-week sprint. The underlying mechanism — AI platforms incorporating new content into their responses — takes time. Brands that treat this as a sustained program consistently outperform those that try one-off fixes.
Phase 1: Measure Your Baseline
Know where you stand before you move
You can't improve what you haven't measured. Before any content creation or optimisation, establish your baseline AI SoV across all major platforms.
What to do:
- Build a query set of 100–500 relevant queries — spanning awareness, consideration, evaluation, and decision stages
- Submit each query to ChatGPT, Gemini, Claude, Perplexity, and Copilot
- Record which brands appear in each response (include competitors)
- Calculate your baseline AI SoV: (your citations ÷ total responses) × 100
- Note your platform distribution — are you stronger in Perplexity than ChatGPT?
Your baseline tells you three things: your absolute AI SoV, your AI SoV relative to competitors, and which platforms you're weakest on. All three shape where you focus Phase 2.
Phase 2: Identify and Prioritise Zero Coverage Gaps
Find the queries that are costing you customers
Zero Coverage gaps are the specific queries where competitors appear in AI answers but you don't. These are your highest-priority targets — they represent direct competitive losses in AI-mediated discovery.
What to do:
- From your Phase 1 data, flag every query where at least one competitor appears but your brand doesn't
- Group Zero Coverage gaps by: topic cluster, buying stage, and platform
- Prioritise by: estimated query volume, buying intent (decision > evaluation > consideration > awareness), number of competitors appearing
- Create a prioritised gap list — this becomes your content calendar
A typical mid-sized brand has 50–200 Zero Coverage gaps in a 200-query set. You won't close all of them at once — work through the prioritised list systematically.
Phase 3: Create GEO/AEO Content to Close Each Gap
Publish content that AI platforms can cite
For each priority Zero Coverage gap, you need to publish content that directly answers the query — structured in a way AI platforms can extract and attribute to your brand.
GEO/AEO content structure for each gap:
- Direct answer first: The first paragraph directly answers the query. Your brand name appears in the first sentence. Don't bury the answer after an introduction.
- Entity clarity: State explicitly who you are and what you do: "[Brand] is a [category] platform that [key differentiator]." Repeat your brand name naturally throughout.
- FAQ section: Include explicit Q&A blocks for the target query and related questions. Use FAQPage schema markup.
- Comparison elements: Where relevant, include comparison tables or structured comparisons — AI platforms extract these well.
- Factual, citable claims: Include specific, verifiable facts about your offering — numbers, features, pricing ranges, use cases. AI platforms prefer attributable specifics over vague claims.
Aim for one piece of targeted content per significant Zero Coverage cluster, not one per query. A single well-structured page can close multiple related gaps simultaneously.
How quickly does new content improve AI SoV?
Typically 4–8 weeks for initial improvement on retrieval-augmented platforms like Perplexity. ChatGPT's base model is slower — 2–4 months is more realistic for training data influence. Track per-query citation rates monthly to measure which content is working.
Automate Phases 1–3
UltraScout AI automatically measures your AI SoV, identifies Zero Coverage gaps, and generates targeted GEO/AEO content to close each one — in one platform.
Start Free →Phase 4: Build Entity Authority
Strengthen the signals AI platforms use to trust your brand
Your own content is necessary but not sufficient. AI platforms weight third-party sources heavily — particularly sources with strong authority signals that were likely present in training data. Building entity authority means ensuring your brand is clearly represented in the right external locations.
Entity authority actions (roughly priority order):
- G2 listing: Create or claim your G2 profile, complete it fully, and actively gather reviews. G2 pages rank in Google and are heavily crawled — likely in training data for most AI models.
- Capterra / GetApp: Same logic. Software and service comparison platforms are trusted sources in AI training corpora.
- Crunchbase: A clear Crunchbase entry for your company establishes entity recognition — particularly for B2B brands.
- Wikidata: Add a Wikidata entity for your brand. Wikidata is a trusted structured knowledge source that directly informs AI model knowledge graphs. Free; takes 30 minutes; high leverage.
- LinkedIn company page: Fully completed, with description matching your canonical one-liner. LinkedIn content is well-represented in AI training data.
- Schema.org Organisation markup: Implement Organisation structured data on your homepage — name, description, url, sameAs links to all your other entity profiles.
- Press and industry coverage: One article in a relevant industry publication (Search Engine Journal, Marketing Week, TechCrunch) does more for AI entity authority than dozens of self-published pieces. Pitch angles: data from your own platform, proprietary concepts you've coined, market trend commentary.
Phase 5: Monitor, Measure, and Iterate
Track progress and keep closing gaps
AI SoV improvement is ongoing — AI platforms update, competitors publish new content, and your query set should expand as your coverage grows. Monthly monitoring is the minimum cadence.
Monthly monitoring checklist:
- Re-run your core query set and recalculate AI SoV
- Compare against previous month — which queries improved? Which regressed?
- Identify new Zero Coverage gaps (competitor activity may have opened new ones)
- Measure platform-specific trends — are you improving on all 5 platforms or just some?
- Update your content priority list based on new gap data
- Expand your query set as you close existing gaps and identify new opportunity clusters
Realistic Timeline
What to expect month by month
Audit and setup
Baseline AI SoV measured. Zero Coverage gaps identified and prioritised. First GEO/AEO content pieces published. G2 listing created or claimed.
First signals
Perplexity citations begin improving for new content. Initial entity authority signals propagating. 5–10 content pieces published. Wikidata / Crunchbase entries completed.
Measurable improvement
AI SoV improving noticeably on Perplexity and Gemini. ChatGPT beginning to reflect new content. 15–25 Zero Coverage gaps closed. First press coverage ideally landed.
Compounding gains
AI SoV trending upward across all platforms. ChatGPT base model improvements visible. Competitor gap analysis updated. Program in steady state — systematic monthly gap-closing.
Common Mistakes That Slow AI SoV Improvement
- Creating content without entity clarity. If your brand name isn't clearly and repeatedly associated with your category in the new content, AI platforms may cite the content without citing the brand.
- Publishing and forgetting. New content needs to be crawled, indexed, and then incorporated into AI responses. Check that your pages are indexed and monitor whether citations appear within 4–8 weeks. If not, investigate crawl issues or content quality.
- Focusing only on your own site. Third-party entity authority (G2, press, Wikidata) is as important as on-site content. Brands that skip this typically plateau at a lower AI SoV than brands that invest in both.
- Targeting only high-level queries. Decision-stage and comparison queries ("best [category] tool for [use case]") often have Zero Coverage that's more commercially valuable than awareness-stage queries. Don't neglect them in favour of easier, lower-intent topics.
- Measuring only overall AI SoV. Platform-specific SoV can diverge significantly. A brand might be strong in Perplexity but weak in ChatGPT — requiring platform-specific optimisation strategies.