A new study has found that when users ask ChatGPT which brand is best in a category, it rarely names a single clear winner — the answer varies significantly depending on how the question is phrased, what context is provided and which model version is used. For Indian brands hoping to dominate AI search the way they might dominate a Google first position, this changes the strategy significantly. Here is what building real AI share of voice actually requires.
What the Study Found
The study ran thousands of product and service category queries through ChatGPT across multiple phrasings and found that only a small minority of topics consistently produced the same dominant brand recommendation. For most categories, the brand named as ‘best’ changed significantly depending on whether the question was phrased as ‘which is the best,’ ‘what do experts recommend,’ ‘what is most popular,’ ‘what is best value’ or ‘what is best for a beginner.’ This means a brand that dominates one question phrasing may be completely absent from another — and buyers use all of these phrasings depending on their context and intent.
Why Question Phrasing Changes the Answer
AI models generate answers by pattern-matching across the content they have been trained on and, in retrieval-augmented systems, the content they retrieve at query time. Different phrasings retrieve different content and trigger different patterns. A brand that appears consistently in expert-review content will dominate ‘what do experts recommend’ queries but may be absent from ‘best value’ queries if pricing content about the brand is thin. Building genuine AI share of voice requires appearing reliably across all question types, not just the most flattering one.
Mapping the Question Types Your Category Uses
The first step is mapping the full range of question phrasings buyers in your category use when querying AI. This includes best-overall queries, best-for-specific-use-case queries, comparison queries against specific competitors, price and value queries, beginner and expert queries, and regional or location-specific queries. Each phrasing type requires a different type of content to answer well. A brand that maps these and builds content for each phrasing type systematically builds a more defensible AI presence than a brand optimising for a single query pattern. AI visibility tracking tools are what turn this mapping from guesswork into a measurable gap list.
Building Content That Covers Every Question Type
In practice, this means ensuring your content library explicitly addresses: category expert recommendations, comparison outcomes against your top three competitors, value-for-money analysis relative to alternatives, beginner-specific and advanced-user-specific use cases, and location-specific queries where relevant. This is not about producing thin keyword-targeted pages for each phrasing — it is about building substantive content that genuinely addresses each angle, so AI models have reliable source material to draw on regardless of how the question is asked. A structured content marketing programme built around this mapping consistently outperforms ad hoc content production, and the structure of the content hub itself determines whether AI systems can retrieve any of it.
The Role of Digital PR in AI Share of Voice
The study found that brands appearing across the most diverse range of AI question types were also the brands with the broadest editorial mention footprint — they had been written about across the most publication categories and topic angles. This validates the investment in broad-based digital PR rather than narrow category-specific press coverage. A brand that has been discussed in a technology publication, an industry trade, a consumer review outlet and a regional business media outlet is more likely to appear across diverse question types than a brand with deep coverage in a single outlet type — the same reason source diversity now outweighs link volume.
Working With DigiVeritaz on AI Share of Voice
DigiVeritaz builds multi-question-type AI visibility programmes for Indian brands, combining content mapping, structured page production, digital PR and measurement through tools like Scrunch and Ahrefs Brand Radar into a single managed programme. Book a free AI share of voice audit to see how consistently your brand appears across the different question types in your category — and what it would take to build the broadest possible AI presence.
Frequently Asked Questions
Why does ChatGPT name different best brands depending on how you ask?
Different question phrasings trigger different retrieval patterns and generate different statistical associations based on the content the model has processed. The same brand may be strongly associated with ‘expert recommendation’ queries but weakly associated with ‘best value’ queries if the content supporting each angle differs.
How many question types should I optimise for?
Map the five to seven most common question phrasings buyers in your category use — best overall, best for use case, best value, comparison with specific rivals, regional or beginner queries — and ensure substantive content addresses each one.
How long does it take to build meaningful AI share of voice?
In competitive categories, consistent content and digital PR investment typically produces measurable AI mention improvement within 90 to 180 days. The timeline is faster for less competitive niche categories where current AI citation is thin.
Can paid advertising help with AI share of voice?
Not directly — AI answer engines do not currently include paid placements in their citations. AI share of voice is built through organic content quality, editorial PR and schema implementation rather than paid media.
Explore our generative engine optimisation services or contact us at +91 99566 55662.