Winning the AI Race
Competitive Analysis
Mapping your AI landscape
Before you can win, you need to know where you stand. Map your AI landscape by running your top 20 category prompts across all four major AI engines — ChatGPT, Claude, Gemini, and Perplexity.
For each prompt, record: who gets cited first, who gets cited at all, what language the AI uses to describe each brand, and which brands appear across all four engines vs. only one. This map tells you where the opportunity is.
Share of voice basics
AI share of voice measures the percentage of relevant AI responses that mention your brand vs. competitors. It's not about being mentioned once — it's about how consistently you appear across the prompts that matter in your category.
Brands that dominate AI share of voice don't necessarily have the best product. They have the clearest positioning, the densest external mention footprint, and the most comprehensive content coverage of their category.
Where you're losing — and why
Once you have your map, look for patterns in where you're losing. Are you absent on comparison queries? Missing from "best tool for X" prompts? Only appearing on some engines but not others?
Each pattern points to a specific fix: absent on comparisons → create comparison content. Missing from "best for X" → rewrite your positioning. Inconsistent across engines → build external citation coverage from sources each engine trusts.
The 30-Day Sprint
Week 1: Fix your positioning
Start by rewriting your core positioning. If your brand isn't showing up, the model probably doesn't know exactly what problem you solve or for whom.
Rewrite your homepage, product page, and every profile where your brand is described. Make the category, buyer, and core problem unmissable. The AI needs clear language to learn from.
Week 2: Publish two pieces of category content
Create two assets that make the AI's job easier: one comprehensive category guide and one direct comparison piece against the strongest alternative in your market.
These pieces should be specific, useful, and honest. The point is not to publish more content. The point is to create the exact sources an AI engine can use when a buyer asks a high-intent question.
Week 3: Earn five external mentions
Reach out to five external sources with real authority in your category — review sites, newsletters, analyst blogs, or community admins — and earn mentions in the context you want AI engines to learn.
Each mention should reinforce the same positioning your owned content is using. External authority compounds only when it tells the same story as your site.
Week 4: Measure and double down
Re-run your baseline prompts and compare them to week 0. Which queries improved? Which engines still leave you out? Which pages or sources seem to have made the difference?
Use that data to decide the next sprint. AI visibility is not a one-month project. It is a compounding process where every measurement loop sharpens the next move.
Staying Ahead
Building a monitoring practice
One-time visibility sprints decay. Models update. Competitors publish. The brands that hold their AI visibility lead are the ones that monitor it continuously and act quickly when things shift.
Set up a weekly cadence: run your top prompts, record the results, flag anything that changed. It takes discipline, but it is the difference between leading the market and reacting after the narrative has already changed.
Long-term moats
Short-term gains come from better positioning and stronger content. Long-term moats come from becoming a trusted source in the category overall.
Brands that build this moat are cited not just because of one page, but because years of content, reviews, mentions, and proof have taught the model that they belong in the answer set by default.
Ready to apply what you learned?
Start tracking your AI visibility →