Let’s Map Out Your AI Search Strategy
If AI tools are recommending competitors instead of you — or if you suspect your brand is missing from AI answers that matter to your customers — this live session will give you the framework and a clear next step.
Are you doing SEO the same way you were five years ago?
If so, your competitors are quietly stealing customers you should have won — and they’re doing it inside ChatGPT, Gemini, and Google’s AI Overviews while you’re still focused on page-one rankings.
This isn’t a scare story. It’s a pattern our team, here at Exposure Ninja, sees consistently across mid-market and enterprise businesses: solid organic rankings, a well-maintained website, a competent marketing team — and yet AI tools are recommending someone else. Not because of a technical glitch, and not because your product is inferior. Simply because AI doesn’t use Google’s algorithm, and nobody has actively shaped the picture these tools have of your brand.
In this guide, based on a live strategy session with Exposure Ninja Founder Tim Cameron-Kitchen and CEO Charlie Marchant, you’ll get the exact five-component process Exposure Ninja uses when building AI Search strategies for FTSE 250 and DAX 30 brands. Work through it systematically and you’ll know exactly where you stand in AI search, what’s holding you back, and what to do about it.
Executive Summary
- AI search is now a primary buying channel. 80% of people make half their purchasing decisions within AI tools, and ChatGPT traffic converts 31% higher than non-branded organic search traffic.
- AI models don’t use Google’s ranking algorithm. Visibility in traditional search does not guarantee visibility in AI answers. You need a separate, dedicated strategy.
- The strategy has five core components: technical AI performance, a prompt library, AI sentiment and visibility analysis, AI competitor research, and targeted citation building.
- Technical foundations are the floor, not the ceiling. Getting your technical setup wrong can silently block AI crawlers from reading your site — but fixing it alone won’t make you visible.
- Prompt libraries replace keyword research. You need to identify the conversational queries your customers are actually using in AI tools, then track your visibility across them.
- Sentiment matters as much as visibility. AI tools don’t just mention brands — they editoralise. Understanding what they’re saying about you (and why) is essential before you can change it.
- Citations are the real unlock. Getting your brand featured in high-authority third-party articles is the most direct route to AI recommendation. One well-placed ZDNet feature can make you the top-recommended product in Perplexity, Gemini, and Google’s AI Mode — overnight.
- The window for first-mover advantage is open, but it won’t stay that way. Businesses that build AI search visibility now will compound it faster than those who start twelve months later.
Why Is AI Search Fundamentally Different from Google?
ChatGPT reached the fifth most visited website in the world in just over two years — rising from position 20 to position 5 in the global rankings. That kind of trajectory hasn’t been seen since the early days of Google itself.
But the scale of the shift goes beyond the headline numbers. According to SimilarWeb data, ChatGPT has 462 million monthly users, with 441 million of those overlapping with Google’s 3.2 billion. People aren’t just adding AI tools to their research process; they’re replacing steps that used to belong to search.
As Tim Cameron-Kitchen explains: “One of the patterns we see with user behaviour is that people will do their research on ChatGPT, they will find the products, services, and brands that they want to buy — and then they will head over to Google to make that purchase. To the business, this just looks like a branded conversion from Google. They don’t necessarily see the research journey that happened behind it.”
This matters enormously for how you interpret your own analytics. That spike in branded search? Some of it almost certainly originates from AI recommendations you can’t yet see. And the first-mover advantage is real: AI chatbots represent new marketing channels where the ground floor is still available if you move quickly.
The critical distinction, though, is that AI models don’t use Google’s ranking algorithm. There is some overlap — AI tools often run web searches in the background, and those searches do favour authoritative sources — but it’s far from sufficient to just maintain your traditional SEO. These tools compile answers from a much wider range of sources. They weigh trust signals differently. They have selection criteria that don’t exist in conventional search. And they engage in something closer to a conversation than a results page.
Relying on your existing SEO to carry you into AI answers is like wearing formal shoes to run a marathon. You might manage a few miles. You won’t finish the race.
What Does a Complete AI Search Strategy Look Like?
Exposure Ninja’s AI search strategy framework breaks down into three pillars and five components.
The three pillars are technical foundations, clear positioning and content, and digital PR with citations and reviews. Within those pillars, the five components are: assessing your technical AI performance, building your prompt library, running AI sentiment and visibility analysis, conducting AI competitor research, and targeting citations and topics.
Each one informs the next. Skipping straight to citation building without understanding your current sentiment, for example, is like running a PR campaign before you know what story you want to tell. Done in sequence, these five steps give you a complete, evidence-based picture of where you stand — and a prioritised plan for improving it.
Here’s how each one works in practice.
How Do Technical Foundations Affect Your AI Visibility?
Technical AI performance is, in Tim’s own words, “the vegetables” — not the most exciting part of the meal, but important enough that skipping it creates problems downstream.
The analogy he uses is a good one: think of technical optimisation as your marathon running shoes. Mo Farah’s running shoes didn’t make him the best distance runner in the world. But wearing ordinary shoes to a marathon would have cost him the race. Technical AI performance is the same — it won’t make you visible on its own, but getting it wrong can silently undermine everything else you do.
There are four areas to address:
- Google Analytics 4 AI traffic tracking. By default, Google Analytics 4 doesn’t give you a clean view of AI-referred traffic. Create a custom session channel group that groups ChatGPT, Claude, Gemini, Perplexity, and other AI platforms together. This gives you a baseline to measure performance against as you implement everything else.
- AI crawler access. This is where businesses most commonly shoot themselves in the foot. Cloudflare, for example, has a toggle called “AI Scrapers and Crawlers” that looks like sensible protection but, when enabled, blocks AI tools from reading your site. Robots.txt settings can do the same. One Exposure Ninja client — Mazuma Mobile — had unknowingly blocked AI crawlers entirely, meaning ChatGPT and Perplexity couldn’t access or recommend their content at all.
- Technical hygiene. Broken links, 404 errors, and convoluted URL structures make it harder for AI tools to navigate your site and extract the information they need. Fix redirects, resolve crawl errors, and ensure your site architecture is logical.
- Schema implementation. Schema is a standardised format for presenting structured information — about your products, your organisation, your locations, your reviews — in the background code of your site. AI tools use it to quickly understand what a website covers. If you’re not using it, you’re making their job harder than it needs to be.
Your action here is to audit, assign, and verify. Identify all technical fixes needed, prioritise them by impact and ease of implementation (blocking AI crawlers, if that’s an issue, would be both high impact and relatively quick to resolve), and assign each to your development team with a deadline. Then check the work — verify in Search Console and test directly in AI tools to confirm your content is accessible.
How Do You Build a Prompt Library That Drives Revenue?
With traditional SEO, you tracked keywords. With AI search optimisation, you track prompts — the questions and queries people type into ChatGPT, Gemini, and similar tools when they’re researching what to buy or who to hire.
These are not the same thing. People type “plumber London” into Google. They ask ChatGPT something like “what’s the most reliable heating engineer in South London for an old Victorian terrace with a combi boiler?” AI queries are conversational, contextual, and long-tail by nature — which means the prompt library approach has to be fundamentally different from conventional keyword research.
The goal of your prompt library is to identify a representative set of prompts that your prospective customers are genuinely using — and then track your visibility in the AI answers those prompts generate. It gives you a speedometer: as you implement the rest of your strategy, you can see what’s moving and what isn’t.
A well-built prompt library covers three stages of the buying journey:
- Problem-aware prompts — the customer knows they have a problem but hasn’t identified a solution. If you sell heat pumps, this might be “how do I cut my energy bills?”
- Solution-aware prompts — the customer has identified a possible solution and is interrogating it. “How much does it cost to run a heat pump?”
- Decision-stage prompts — the customer is narrowing down their choice. “What are the most reliable heat pumps for large homes?”
Once you’ve drafted your prompt list, run it through a tracking tool — Peec AI*, Profound, and Semrush’s AI Visibility Toolkit* all allow you to see your current visibility for each prompt, the sentiment attached to your brand in those responses, how you’re positioned relative to competitors, and how frequently these prompts are being searched.
One important caution: don’t build your prompt library purely from your own instincts about how customers talk about what you do. If you’ve been in an industry for ten years, you’ve probably stopped speaking the same language as your buyers. Talk to customers directly, spend time on Reddit and industry forums, and use the natural language you find there as your raw material. The prompts that drive AI visibility are the ones that mirror how real people actually ask questions.
How Do You Know What AI Tools Are Actually Saying About Your Brand?
Being mentioned by AI tools and being recommended by them are two very different outcomes. What you want is for ChatGPT to say, “yes, there are several good options here, and you should go with this one because X, Y, and Z.” What you don’t want is a lukewarm “there are various options, including this brand” that doesn’t give the customer any real reason to choose you.
That means understanding what these tools are currently saying about you — before you can shape it.
We built Mine My Brand specifically for this purpose. Enter your website URL and the tool will tell you how AI models across ChatGPT, Gemini, Perplexity, and Grok are currently describing your brand: the sentiment, the tone, the price positioning, the perceived strengths, and the perceived weaknesses.
The weaknesses section is particularly valuable. When we ran this analysis for Riverford, one of the consistent weaknesses flagged was premium pricing compared with supermarkets. That’s not something ChatGPT invented — it picked it up from training data and third-party websites. But knowing it exists gives you something concrete to work with.
As Tim explains: “Let’s say that we’re going into a recession. We might not want ChatGPT to tell people that we’re expensive compared to supermarkets. What we might want instead is for ChatGPT to say, yeah, Riverford is actually much cheaper than takeaways or restaurants. So we can reframe that conversation around pricing — giving it a more expensive anchor — so that Riverford is positioned as the more budget-friendly option.”
That reframing happens through content. Publish comparison articles that position your pricing against the more expensive alternative. Make sure those articles exist on your own site and on third-party sites. Over time, AI tools update their picture of your brand based on what they find.
Your sentiment analysis also gives you visibility scores for individual prompts — showing what percentage of the time your brand appears in the AI answer for each tracked query, and how positively you’re framed when you do appear. A prompt with 100% visibility but a sentiment score of 54 is telling you something important: you’re always in the answer, but the AI is hedging. That’s a content gap worth closing.
Action steps here: run Mine My Brand for your own site and your top competitors. Audit your existing content against the gaps you find. Build a content plan that directly addresses the weaknesses and neutral sentiment scores — through your own site and through third-party publications.
Who Are Your Real AI Search Competitors?
This is where the AI search audit often surprises people. Your biggest AI search competitor is not necessarily your biggest business competitor.
You might have a company you’ve been watching closely for years — the one whose job ads you monitor, whose pricing you track, whose marketing team you secretly envy. But when you look at who’s actually dominating the AI answers your customers are seeing, it might be a software company, a comparison site, or a specialist publisher that you’d never have put on your radar.
That’s why competitor identification has to be data-driven, not boardroom-driven. Tools like Peec AI and Semrush’s AI Visibility Toolkit allow you to input your prompt library and then see which brands are consistently appearing in the AI answers to those prompts — ranked by overall visibility, sentiment score, and average position.
Once you know who’s winning, you can work out why. Maybe one competitor has a comprehensive knowledge base on their website that AI tools pull from constantly. Maybe another has cultivated a strong presence on Reddit and YouTube. Maybe a third is consistently getting featured in editorial roundups on authoritative publications. These are all learnable strategies — and they’re hiding in plain sight once you know where to look.
Tim’s framing here is useful: “By looking at what your competitors are doing well and taking the good bits, you can stack them. Stack the best bits from all of your competitors into your strategy, and you can approach a stage where you build an insurmountable lead as you get first-mover advantage.”
The tool also shows you which specific web pages are being cited most often by AI tools in response to your tracked prompts. This gives you a direct view of your competitors’ content strategy — and a map of the publications you need to get featured in.
How Do You Get Featured in the Sources AI Tools Trust?
This is the most impactful component of your AI search strategy — and, as Tim will cheerfully admit, the most labour-intensive.
AI tools don’t just pull from the websites they know best. They pull from the sources their users are most likely to trust when making a specific type of decision. For consumer and B2B product categories, those sources are typically editorial roundups and “best of” lists on high-authority publications — the kind of article you’d find on ZDNet, Wired, Forbes Advisor, or industry-specific equivalents.
Here’s the mechanism, illustrated by a real Exposure Ninja campaign. Exposure Ninja secured a placement for their client Zugu Case in a ZDNet article titled “What is the Best iPad Air Case Right Now?” The next day, when users asked Perplexity the same question, Perplexity cited that ZDNet article and recommended Zugu Case as the top pick. The day after, Gemini was citing a Wired article with the same result. And Google’s AI Mode continued referencing both articles for weeks afterwards.
One article. Three AI platforms. Sustained visibility.
That 243% increase in AI traffic and 123% over-revenue-target result for Zugu Case didn’t come from on-site content alone — it came from getting the right brand mentions in the right third-party publications. The same approach drove 395% ROI for The Ordinary and $100,000+ in LLM-attributed revenue for a global financial education institution in a single month.
Finding your target citations is the first step. Using a tool like Peec AI*, you can identify the pages most frequently cited by AI tools in response to your tracked prompts, and categorise them by type: editorial publications, user-generated content platforms like Reddit and YouTube, competitor sites, and corporate blogs. The editorial sources are the ones you want to prioritise, because they have writers and editors you can actually reach.
The outreach process itself is straightforward in principle and demanding in practice. You identify the writers and contributors behind the articles you want to be featured in. You reach out — politely, persistently, with genuine value to offer. You may not get into the 2026 version of an article, but you might get into the 2027 one by starting the relationship now. Sometimes a quick update to an existing article is all it takes.
This is a long game. It requires someone with thick skin, genuine tenacity, and the ability to handle a significant amount of rejection. But the cumulative effect of getting featured in a handful of the right places is hard to overstate.
For user-generated content, the approach is different. Reddit threads, YouTube reviews, and customer testimonials all feed AI training data. Encouraging satisfied customers to leave detailed, honest reviews on the right platforms — and fostering genuine community discussion about your product — builds the kind of distributed presence that reinforces AI recommendations over time.
Next Steps
This week:
- Create a custom AI traffic channel group in GA4 to start measuring your current AI-referred sessions
- Check your robots.txt and Cloudflare (or equivalent CDN) settings to confirm AI crawlers are not being blocked
- Run your website through Mine My Brand (minemybrand.com) to get an immediate read on how AI tools currently describe your brand
Next 30 days:
- Build a draft prompt library covering 20–40 prompts across all three stages of your buyers’ journey
- Set up a tracking tool — Peec AI, Profound, or Semrush’s AI Visibility Toolkit — and input your prompts to establish a visibility baseline
- Run an AI competitor analysis to identify who’s winning in your prompt responses and why
- Audit your existing content against the sentiment and knowledge gaps flagged in your brand analysis
Next 90 days:
- Build a prioritised list of target publications — the editorial sites AI tools consistently cite in your category
- Begin outreach to writers and contributors at those publications, with the goal of getting featured in existing or upcoming roundup articles
- Develop a pillar content strategy that covers your key topics from every angle — addressing each stage of the buying journey and closing sentiment gaps identified in your analysis
- Implement any remaining technical fixes: schema, redirect corrections, crawl error resolution
If you’d like Exposure Ninja to do this work for you, the AI Search Audit covers all five components — from technical review and prompt library to sentiment analysis, competitor research, and a prioritised citation target list — and is delivered as a full presentation to your team. The Standard audit (20 prompts, 2 competitors, online presentation) is £5,995. The Ultra audit (40 prompts, 3 competitors, in-person presentation) is £10,995. Both come with a money-back guarantee: if you implement the recommendations in full and the audit doesn’t pay for itself at least 5x in the first year, Exposure Ninja will refund you. You can book a call at exposureninja.com/review.
Resources Mentioned
- Mine My Brand — AI brand sentiment and visibility tool built by Exposure Ninja
- Peec AI* — AI search visibility tracking tool covering prompt visibility, sentiment, competitor analysis, and citation sources
- Profound — AI search visibility and analytics platform
- Semrush AI Visibility Toolkit — free trial available via exposureninja.com/semrushone*
- State of AI Search Report — source for the statistic that 80% of people make half their purchase decisions within AI tools
- Search Engine Land — source for the finding that ChatGPT traffic converts 31% higher than non-branded organic traffic
- SimilarWeb — source for ChatGPT’s rise to the 5th most visited website globally and audience overlap data
- Exposure Ninja AI Search Audit — Standard (£5,995) and Ultra (£10,995) options available
In Conclusion
The shift to AI search is not a future event. It’s happening now, in the buying journeys of your customers, in the research sessions that precede your best conversions, and in the recommendation engines that your competitors are starting to figure out how to influence.
The five-component strategy outlined here — technical foundations, prompt library, sentiment analysis, competitor research, and citation targeting — gives you a systematic way to understand where you stand and what to do about it. None of these steps is trivial, but none is beyond a marketing team that’s willing to approach AI search with the same rigour they’d apply to any other channel.
The businesses seeing the results — 243% AI traffic growth, six-figure monthly revenue from LLM sessions, 395% campaign ROI — didn’t get there by doing traditional SEO and hoping for the best. They mapped their AI search strategy deliberately, executed it consistently, and started early enough to build a lead before their competitors caught on.
The window for first-mover advantage is still open. The question is whether you’re going to take it.
*Some links within this article are affiliate links for which Exposure Ninja receives a fee for promoting (these links are not sponsored).
