How to Decide Which Questions to Optimise for in AI SEO?

AI search rewards specificity. The brands that get cited are not the ones chasing the biggest questions, they are the ones that picked questions they could realistically win and answered them better than anyone else. Here is how to choose them.

Why choosing the right questions matters

AI search is not keyword-based. It is question-based, intent-based, and entity-based. Users rarely type keywords into chatbots. Instead, they ask fully formed questions like:

• “What’s the best platform for managing large e-commerce catalogues?”

• “What should a good AI SEO audit include?”

• “What’s the best smartwatch for triathletes?”

If your business does not appear as a recommended answer, you lose visibility long before someone returns to Google.

Businesses experiencing a decline in organic traffic need a new method: understanding which questions matter and owning the answer space strategically.

Search is shifting from blue links to AI answers. According to Pew Research, when Google shows an AI summary, people click traditional results only about half as often compared to queries without one.

Meanwhile, Google confirmed in May 2025 that AI Overviews now reach 200+ countries with 1.5 billion monthly users, and Semrush’s July 2025 study showed AI Overviews appeared in 13.14% of queries in March, which is double the rate from January.

These AI answers are most often triggered by purchase intent and comparison queries, which is the exact categories you should focus on first in generative engine optimisation.

Start with your USPs

Your unique selling points are the foundation. These are the reasons customers choose you over others. If ChatGPT gets asked a question directly tied to your USP, and you’re cited in the answer, you become the ideal recommendation.

Think:

• If you’re a restaurant → healthy + fast + affordable.

• If you’re a SaaS tool → free tier limits, integrations, or reliability.

• If you’re a jeweller → quality vs. price, certifications, or ethical sourcing.

Optimising for USP-based questions means when users ask “Which [USP category] is best in Dubai?”, so your brand is naturally positioned to appear.

The six prompt areas

Here’s how to think about each type of AI prompt, with examples and whether to prioritise now or later:

Then check whether you can actually win it

Knowing the prompt areas tells you what kinds of questions exist. It does not tell you which ones are worth your time. Run every candidate question through these five checks before you commit content to it.

Below is a clear framework that blends generative engine optimisation, AI visibility optimisation, and entity-driven AI ranking principles.

1. Start by Identifying Real User Ask Volume

Unlike traditional SEO, AI SEO isn’t limited by classic search volume tools. You need to determine how often questions are asked in AI environments.

Helpful tools and sources include:

• Perplexity’s discover feed (https://www.perplexity.ai/discover)

• AnswerThePublic (https://answerthepublic.com/)

• SEMRush Topic Research (https://www.semrush.com/topic-research/)

• Reddit and niche forums

• Customer support logs

• Sales transcripts

According to Ahrefs’ long-tail research (https://ahrefs.com/blog/long-tail-keywords/), over 92 percent of keywords have fewer than 10 searches per month, yet they convert far better.The same principle applies to AI SEO: billions of micro-questions carry powerful intent.

Your goal is not volume alone. It is viability plus relevance.

2. Evaluate Competitiveness and Brand Strength

This is where the consumer electronics example becomes especially valuable.

Large brands dominate broad AI queries due to overwhelming authority signals. According to Statista (https://www.statista.com/statistics/276623/global-market-share-held-by-smartphone-vendors/), Apple and Samsung capture more than 35 percent of global smartphone market share, and their online authority mirrors that dominance.

Trying to optimise for a broad question like “best smartphone for 2025” is a losing battle.

The trick is niching your AI SEO questions.

Ask yourself: Where can we realistically win?

• Specific workflows

• Specific integrations

• Specific user personas

• Category sub-niches

This is where AI SEO optimisation delivers the strongest results.

3. Prioritise High-Diversity, High-Specificity Questions

LLMs avoid recommending only the top brands because that would produce biased results. AI systems deliberately inject diversity into recommendations to avoid monotony. This means smaller brands have better opportunities in:

• niche use cases

• specialised workflows

• unique integrations

• underserved verticals

This is even stronger in B2B, where diversity scores are high due to user expectations of choice.

Example opportunities include:

• “best CRM for healthcare scheduling teams”

• “best project management tool for compliance-heavy industries”

• “tools for automating multilingual customer support”

These are highly winnable and often very lucrative.

4. Map Questions to Your Expertise Ownership Zone

Your brand is not meant to win every query. Your brand is meant to win the right ones.

This is where your Expertise Ownership Zone becomes essential. It aligns with the core AI SEO pillars:

• Entity clarity

• Topic authority

• Citation-worthiness

• Trust signals

• Structured, high-quality answers

According to Google’s documentation for AI Overviews (https://developers.google.com/search/docs/appearance/ai-overviews), structured and authoritative content increases your chance of appearing in generative summaries.

Focus on questions that let you give the clearest, most authoritative answer.

5. Consider Underserved Long-Tail Questions

Long-tail questions are where most brands are invisible and where AI search models have the largest gaps.

Examples include:

• “How to run an AI visibility audit for a niche e-commerce store?”

• “What’s the best workflow for consolidating siloed customer data?”

• “How to choose the right AI SEO consultant?”

These are not always high volume, but they are:

• intent-heavy

• low-competition

• extremely aligned with ICP needs

• highly optimisable

According to Moz’s study on long-tail optimisation (https://moz.com/blog/long-tail-seo), long-tail queries convert significantly higher than head terms. The effect is even stronger in conversational AI environments.

Evaluating question types at a glance

Worked example: consumer electronics

Let’s return to the electronics example because it illustrates the framework perfectly.

Broad Query:

“best smartwatch 2025”

• Ask Volume: Very High

• Competitiveness: Extremely High

• Likelihood of Citation: Very Low

• Dominated by: Apple, Samsung

Specific Category Query:

“best smartwatch for marathon runners”

• Ask Volume: Medium

• Competitiveness: Medium

• Likelihood of Citation: High

• Winner: Garmin, Polar

This is exactly how you determine the questions to optimise for in AI SEO:by stepping away from questions you can’t win and stepping into questions you can dominate.

Real example: Flash Facial, Dubai

Flash Facial, a beauty clinic in Dubai, came to Indexed Lab invisible in AI search results. They weren’t showing up when users asked ChatGPT purchase intent questions like “Where can I get a quick facial in Dubai Hills Mall?” or “What’s the fastest skin treatment before an event?”

By focusing on their USPs (speed, affordability, convenience) and optimising content for purchase intent + competitive prompts, we helped Flash Facial move from invisible to a top AI recommendation.

The result? They started appearing in Gemini and ChatGPT answers and became a trusted choice for customers in Dubai looking for quick facials, which is driving new bookings directly from AI discovery. (Read the case study here)

How to pick your starting questions

When working with clients, we recommend this step-by-step:

1. Map your USPs: what 3 things really set you apart?

2. List customer questions: what do people ask before buying (sales calls, reviews, DMs)?

3. Sort them into prompt areas (purchase, competitive, sentiment, brand-specific, discovery, experimental).

4. Choose 2 areas to start: usually purchase intent + competitive.

5. Use tools to expand: try our free AI SEO Target Prompt Generator by inserting your domain.

   1. We ran our domain (indexedlab.com) on the tool and this was the output

6. Optimise content: create direct, structured answers (FAQ schema, short paragraphs, lists).

Note: For businesses with a decent volume of google data on what your users searched before visiting your site, you can also use those datapoints to derive questions to optimise for!

Common mistakes to avoid

• Trying to cover all areas at once: it spreads your resources too thin.

• Forgetting USPs: generic answers won’t win; AI needs strong signals of what makes you different.

• Not validating with real user queries: if your sales team or reviews show people care about one comparison, start there.

• Blocking crawlers: if AI can’t reach your site, none of this matters (see our 3 pillars guide).

Key opportunities this creates

AI search models prefer diversity and niche authority. This opens three strategic opportunities:

1. Claim expertise in specific use cases

2. Target long-tail questions where big players don’t participate

3. Build authority in workflows and integrations

These areas dramatically increase your chances of being cited by ChatGPT, Gemini, Perplexity and Google AI Overviews.

Counterarguments and limitations

No strategy is perfect, and it's important to acknowledge the nuances.

Counterargument 1: Low volume means low impactHowever, AI shifts search to high-intent conversations where a single user can represent major revenue.

Counterargument 2: We want to rank for broad category questionsAspirational, but unrealistic. The competitive moat is too large.

Counterargument 3: LLMs hallucinate, so what’s the point?True at times, but hallucinations drop significantly when models have structured, niche-specific ground truth.

Counterargument 4: Big brands may later expand into your nichePossible, but you gain defensibility and early entity authority by being first.

Some marketers argue you should optimise for brand-specific questions first, since those directly mention you. While useful, this only works if people already know your brand. If your visibility is dropping, the bigger opportunity is to win new discovery via purchase intent and competitive prompts.

Others ask: won’t AI models change what questions they prioritise? Yes, but according to Semrush’s July 2025 study, purchase intent and comparison queries remain the most common triggers for AI answers. Start where the volume and value already are.

Signs you have chosen strong questions

• They reflect real user pain points

• They are specific enough to avoid mega-brand dominance

• They capture high-intent buyers

• They focus on workflows and outcomes

• They align with your AI visibility strategy

• You can provide the best possible answer

• They support recovering traffic lost to AI