Table: How to Evaluate Which AI Questions to Optimise For

Why Choosing the Right Questions Matters in AI SEO

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.

The Core Framework for Choosing the Right AI SEO Questions

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.

Example: Consumer Electronics Decision Flow

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.

Key Opportunities Created by This Strategy

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.

Signs You’ve Chosen Strong AI SEO 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