Customers have always researched before they buy. What is changing is where that research happens. Instead of typing short keywords into a search engine and opening several websites, many people now ask ChatGPT, Gemini, or Google’s AI Overviews to explain their options, compare providers, and suggest what to consider.
For businesses, this adds a new dimension to their online presence. Being findable is no longer enough. The information an AI tool presents about a company also needs to be accurate, relevant, and useful, because for some customers that summary is the first impression.
From searching for websites to asking questions
Traditional search rewarded short, keyword-based queries. Someone looking for accounting software might type “best accounting software” and work through the results one site at a time.
AI tools encourage a more conversational approach. The same person can describe their whole situation, such as a 20-person firm with GST filing needs and a limited budget, and ask which type of software would suit them. A homeowner can describe a kitchen renovation and ask how to choose a contractor. A parent can compare coaching institutes by teaching approach, fees, and location in a single question.
Behind the scenes, these tools often run more than one search. Google says AI Overviews and AI Mode may use a “query fan-out” technique, issuing several related searches across subtopics and sources to build one response. The customer sees a single, organised answer rather than ten separate links.
That convenience shows up in behaviour. Google says AI Overviews now reach more than 2.5 billion people each month, and AI Mode passed 1 billion monthly users about a year after launch, with its queries more than doubling every quarter. People are also asking differently: Google reports that the average AI Mode search is three times as long as a traditional search query. More of the research, in other words, now happens inside the answer, before anyone visits a website.
Why brand discovery is becoming more complex
Ranking in search results and being described well in an AI answer are related, but they are not the same thing. A company with a strong website and good rankings may still be missing from conversational answers to broader questions. Another business may be mentioned mainly because of what reviews, directories, or news coverage say about it, rather than its own pages.
Customers can also meet a brand at very different stages. Some come across it while learning about an industry; others while comparing alternatives or trying to solve a specific problem. What an AI tool says at that moment can decide which companies they look into further.
These answers are not fixed rankings. Results vary by platform, the exact wording of the question, the user’s location, and the sources available at the time. Google itself notes that AI Overviews and AI Mode may use different models and techniques, so the responses and links they show will differ. A single good appearance proves little; regular checks reveal far more.
That is why brand audits tend to repeat the same questions. White Bunnie, for example, puts 15 to 20 brand-related questions to at least four AI platforms and runs each one two or three times. An answer that changes between runs usually means the tools have thin or conflicting information about the business, which is itself a useful finding.
How businesses can respond
The work starts with the customer, not the algorithm. Businesses that handle this well tend to follow a similar pattern:
- Identify the questions customers ask at each stage of research, from “what is this?” to “who should I hire?”
- Review whether existing pages explain services, expertise, and differentiators clearly enough to answer those questions.
- Check that search engines and AI tools can actually reach and read the most important pages.
- Keep business information consistent wherever it appears.
- Build references beyond the company’s own website, such as reviews, listings and press coverage.
The aim is to make accurate, dependable information easy to find, whichever tool a customer uses. Starting from the questions potential customers are likely to ask, rather than from a list of keywords, keeps the work focused on what actually shapes their decisions.
Why independent sources matter
Customers rarely judge a business only by what it says about itself. Reviews, independent articles, industry listings and media coverage all add context, and AI tools draw on these sources too.
Recent citation data shows how far this goes. Ahrefs’ September 2026 ranking of the domains most cited in Google AI Overviews put YouTube first with 22.9% of citations among the top 50 sources and Reddit second with 18.5%, while Wikipedia held 4.0%. AI answers, in other words, lean heavily on video, community, and reference platforms, not just company websites.
There is still no universal formula for which sources an AI system will use, and each platform behaves differently. Third-party references are best treated as part of building a lasting reputation, not as a shortcut to AI citations. They need to be accurate, relevant, and backed by real business activity. Inconsistent details across platforms can confuse customers and raise the chance of an AI tool repeating the wrong information.
What this new brand discovery path means for Indian businesses
The shift matters most in categories where customers research carefully before buying: technology services, healthcare, education, professional consulting, or high-value consumer goods. People in these markets increasingly use AI tools to understand terminology, explore options, and prepare questions before they contact a provider.
The audience is already large. OpenAI chief executive Sam Altman said in February 2026 that ChatGPT had 100 million weekly active users in India, making the country one of its largest markets.
Neha Garg, a former software engineer who now advises businesses in India on search and AI-driven discovery, points to a common unnoticed gap; companies tend to describe what they do in their own terms, while customers ask AI tools about the problems they want solved. Explaining expertise in plain language that matches those questions is often the simplest place to start.
Businesses expanding into new cities or overseas markets also need to check how they appear in region-specific questions, where local competitors may be better documented. None of this replaces conventional search marketing; it builds on it.
Traditional SEO remains part of the foundation
Google’s Search Central documentation states that standard SEO best practices still apply to AI Overviews and AI Mode. It adds that there are no extra technical requirements or special optimisations for appearing in them: a page needs to be indexed and eligible to appear in Search with a snippet.
The essentials therefore stay the same: an accessible website, useful content, technical reliability and trustworthy information. AI search simply adds another setting in which those foundations matter.
The practical task is to understand how traditional search, conversational AI tools and the company’s own website work together across the customer journey, rather than treating them as separate channels.
Preparing for a changing discovery experience
AI-powered discovery is still developing, and businesses have limited control over how any single system presents recommendations. The most practical response is to improve the quality of information and keep watching how customer habits change. That includes:
- Reviewing the website pages that matter most to buying decisions.
- Fixing inconsistencies in business details across platforms.
- Checking how the business appears across relevant AI tools at regular intervals.
- Measuring enquiries and sales, not just mentions.
Expectations also need to stay realistic. Appearing in an AI-generated answer does not automatically lead to enquiries or sales. The central challenge is no longer only about ranking well. It is about making sure customers find accurate information about a business wherever their research begins.