NIQ just published something that should make every brand manager in India stop and re-read it twice.
Their May 2026 analysis, Visibility in the Age of AI: How Product Discovery Is Being Rewritten, opens with a line that is blunt enough to quote directly: "Product discovery is entering a moment of tension." What follows is a data-backed argument that AI is not gradually changing how consumers find brands — it is doing so right now, faster than most brands have adapted to, and the gap between those who understand this and those who don't is widening every quarter.
The data NIQ leads with is striking. Nearly six in ten American consumers now rely on AI-generated summaries at least some of the time when searching online. Among Gen Z and Millennials, that figure rises to about three in four. And here is the part that should genuinely unsettle anyone in brand communications: roughly one in four Gen Z and Millennial consumers say they trust the AI summary and stop there. No click-through. No verification. No second source. The AI said it, and that was enough.
This blog is not a summary of the NIQ report. It is a deeper argument about what that shift means specifically for Indian brands — and why the PR intelligence infrastructure a brand has in place today will determine how it is described, discovered, and trusted by AI systems tomorrow. And why Nemi Insights, built specifically for India's multilingual media complexity, sits at the centre of that question.
The New Architecture of Brand Discovery
Let's start with what has actually changed, because the surface-level description — "people are using AI to search" — undersells it.
Traditional search worked like a card catalogue. You typed a query. The engine returned a ranked list of links. You clicked. You read. You formed an opinion. The brand had at least some opportunity to make its case on its own website, in its own voice.
Generative AI search works completely differently. You ask a question. The AI synthesises an answer from dozens of sources it has already indexed, weighted, and evaluated for authority. It returns a composed response — sometimes with citations, sometimes without. For a growing share of users, that response is the final answer. Not a step toward an answer. The answer itself.
What this means for brands is that visibility has been decoupled from website traffic. You can be the most relevant brand for a query and never get a click if the AI doesn't name you in its response. Conversely, a smaller brand that has built a strong presence in authoritative third-party sources — earned media, industry publications, credible reviews — can be named first in AI responses for categories where a larger competitor with better SEO has been invisible to AI systems.
The competitive dynamics have been reset. The question is not "who ranks highest on Google page one?" The question is "who does the AI trust enough to cite?"
The answer to that question is determined almost entirely by earned media — the coverage a brand has accumulated in third-party publications that AI systems treat as authoritative. According to a 2025 study cited by Edelman, 90% of AI citations driving brand visibility originate from earned and owned media, not paid placements. A Princeton study that coined the term "Generative Engine Optimization" found that AI systems strongly favour earned media — authoritative third-party sources — over brand-owned content.
This is a fundamentally new argument for why PR matters. Not just for reputation. Not just for awareness. As a direct input into how AI systems describe your brand to people who have never heard of you.
What the NIQ Data Actually Says (And What It Doesn't Say)
The NIQ report is careful, and its carefulness is worth noting.
It does not say AI has replaced traditional discovery channels. It says AI has inserted itself into the purchase journey in ways that compound as usage grows. Among all Americans, just over one in ten cited AI-generated summaries as influencing their most recent new product trial — placing it at the bottom of a ten-item list that was led by personal recommendations and online reviews. This is the headline that optimists cite: AI is still a minor influence on purchase.
But the report's more important finding is conditional, and the condition changes everything. Among consumers who use AI summaries at least sometimes — which is now the majority — AI already exerts more influence on product discovery than brand emails or text outreach. Usage and trust move together. As AI search becomes habitual, its influence on brand perception compounds.
The NIQ data also notes something that runs counter to the loudest tech narratives: traditional search engines still command notably higher trust than AI chatbots or AI-enhanced search experiences across most consumer groups. Claims that AI will render Google irrelevant appear overstated. What's actually happening is that AI is inserting a new layer of mediation between search intent and website visit — one that can either amplify or completely suppress brand visibility depending on what AI has been trained to say about you.
For Indian brands, there is one more dimension of this finding that deserves specific attention. NIQ's data is U.S.-centric. India's AI search adoption curve is, if anything, steeper and faster. India and Southeast Asia are the fastest-growing markets for ChatGPT and Perplexity referral traffic, with year-over-year growth of 190 to 210%. Perplexity recently saw India surpass the United States as its largest market by traffic, driven partly by the Airtel Pro partnership that reportedly drove 640% user growth in Q2 2025. Google AI Mode reached 100 million users in the U.S. and India within months of its wider rollout. The behavioural shift NIQ is documenting in America is arriving in India simultaneously — in some cases faster, because Indian consumers, already accustomed to voice-first and mobile-first information seeking in their native languages, are adapting to conversational AI naturally.
The Earned Media Connection: Why PR Is Now Infrastructure
Here is where the argument lands, and it is worth being precise about it.
The content that feeds AI systems comes primarily from three places: the open web they crawl, the authoritative databases they index, and the training data they absorbed before their knowledge cutoff. What all three have in common is a heavy bias toward credible, frequently-cited, third-party sources. Not your website's about page. Not your press releases. The articles about you. The industry analyses that reference you. The regional news coverage that describes what your brand stands for in local markets. The expert quotes attributed to your leadership in publications that AI systems trust.
Research from Superlines, analyzing 34,234 AI responses across 10 platforms over 30 days in early 2026, found that brands in the top 25% for web mentions get 10 times more AI visibility than others. The top 50 brands receive about 28.9% of all mentions in AI Overviews. The concentration of AI visibility, in other words, mirrors the concentration of earned media coverage — not advertising spend, not website authority, not social follower counts.
This creates a very specific strategic implication. Your PR program is no longer just building reputation. It is building the training signal that AI systems use to describe your brand to future customers who ask about your category. Every article in a credible publication, every quote in a regional newspaper, every analysis in an industry report — these are not just ephemeral coverage moments. They are persistent signals in the AI systems that will answer questions about your sector for years.
And here is the India-specific complication that makes this ten times more complex for Indian brands than for their Western equivalents.
India has 155,000+ registered publications in 22+ official languages. The authoritative publications that an AI system crawls and trusts in Karnataka are not the same ones it trusts in West Bengal, and neither of those overlaps with the publications it trusts in Rajasthan or Tamil Nadu. The media ecosystem is not one ecosystem. It is hundreds of overlapping ecosystems, each with its own credibility hierarchy, its own dominant languages, its own most-trusted voices.
When a consumer in Tamil Nadu asks ChatGPT or Perplexity "which is the best FMCG brand for X product," the AI's answer is shaped by the coverage it has indexed across Tamil-language publications, English publications with regional credibility in the South, regional digital portals, and social discussions happening in Tamil. A brand that has built strong earned media in Hindi-language national press but has zero presence in South Indian vernacular media is not just missing those readers — it is missing the training signal that shapes how AI describes it to those readers.
A brand that does not know it has this gap cannot close it.
The Monitoring Problem That Compounds the Visibility Problem
This is where Nemi Insights enters the picture — not as a vendor pitch, but as a structural solution to a structural problem.
If earned media in regional languages across India is now one of the primary inputs into how AI systems describe your brand to Indian consumers, then knowing what that earned media looks like — its volume, its sentiment, its narrative patterns, its geographic distribution, its competitive context — is not optional. It is prerequisite intelligence for any serious brand strategy in 2026.
The gap between what most Indian brands know about their own earned media landscape and what that landscape actually looks like is, for most brands, enormous. Here is why.
Most monitoring tools available to Indian brands were built for English-language media in Western markets. Their multilingual capabilities for India are typically translation-dependent — meaning they detect content in Hindi, Tamil, or Telugu, run it through a machine translation API, and apply English-language sentiment analysis to the translated output. This process destroys nuance at every step. The emotional weight, the cultural register, the rhetorical conventions of regional language coverage get flattened into generic sentiment classifications that often bear no relationship to how the coverage actually lands with its audience.
The result: brands are making strategic decisions about their PR programs based on sentiment data that is, for significant portions of their actual media footprint, unreliable.
Nemi Insights was built to solve exactly this problem.
Founded in 2016 in Delhi-NCR, ISO 9001:2015 certified, with a team of 60+ analysts and 200+ clients served across India — Nemi is the platform that monitors your earned media footprint across India's full linguistic complexity, not just the English-language slice of it.
Its coverage spans 2,400+ sources across 14+ languages. That includes print newspapers in Hindi, Tamil, Telugu, Kannada, Malayalam, Bengali, Marathi, Gujarati, Odia, and Punjabi. Regional broadcast. Digital portals in every major state. Social media in vernacular languages. The publications that Dainik Jagran's 55+ million readers see every morning. The news that shapes opinion in Andhra Pradesh and Tamil Nadu and Rajasthan simultaneously. The regional coverage that feeds the AI systems that will answer questions about your brand for Indian consumers for years to come.
What the AI Visibility Gap Looks Like in Practice
Let's get specific, because abstractions are easy to dismiss.
An FMCG brand launches in five states. English coverage is strong — economic press, consumer tech media, startup press. The brand's communications team runs their monitoring dashboard and sees healthy sentiment, growing share of voice. What they don't see: two of the five states have regional-language coverage that is negative, driven by a competitor narrative that started in a Marathi-language digital portal and spread across Maharashtra's local WhatsApp channels. That negative narrative is now being indexed by AI systems. When consumers in Pune ask ChatGPT about products in this category, the AI is citing those Marathi-language sources. The brand is losing ground in Maharashtra's AI-mediated discovery without knowing it exists.
A healthcare brand manages a product controversy. The English press coverage is balanced — the brand's response was covered fairly, the issue appears resolved. The communications team closes the crisis file. What they don't see: the controversy is still running in Tamil and Telugu health media, framed significantly more negatively. Those articles are being read by the regional audiences that matter most to the brand's southern market growth. They are also being indexed. A year from now, when consumers in Chennai or Hyderabad ask an AI system about this brand, the AI will have indexed the Tamil and Telugu coverage alongside the English coverage, and its assessment will be shaped by all of it.
A startup raises funding and generates national press. The coverage is overwhelmingly positive in English media. The founding team feels the moment has gone well. What they don't know: in the state where they're based, regional media has been covering a labor dispute involving a vendor — coverage that the startup didn't know existed because it was in Kannada-language digital portals that their monitoring tool doesn't track. That coverage is creating a narrative thread that AI systems in Karnataka will draw on. When a potential enterprise client in Bangalore asks their AI assistant about this startup before a contract decision, the AI's response will include signals the startup never managed because they never saw them.
These are not hypothetical edge cases. These are recurring patterns in how Indian brand narratives actually develop — starting in regional language media, building through vernacular digital channels, and eventually either crossing to national visibility or quietly shaping AI-mediated brand perception in specific markets without ever becoming a "national story."
Nemi's platform is designed to surface exactly these patterns before they compound. Its pan-India print and digital monitoring across 25+ languages means the Marathi portal story, the Telugu health coverage, and the Kannada labor narrative are visible to the brand's communications team in real time — not six months later when the damage has been done and AI systems have already indexed it.
Nemi's Intelligence Layer — Built for This Moment
The 2024 platform upgrade Nemi announced was, in retrospect, precisely the right product evolution for the moment the NIQ report is describing.
Nemi AI-powered sentiment analysis does not run translation-first pipelines. The sentiment engine is trained for Indian language content — it understands the rhetorical conventions, emotional registers, and cultural idioms that make regional language coverage mean what it means to its actual readers. The sentiment classifications you see in the Nemi dashboard reflect how the coverage lands in its original language, not what a translation of it looks like after processing by an English-language NLP model.
For brands managing their earned media as an input to AI visibility, this is not a minor feature difference. It is the difference between knowing what your regional media footprint looks like and having a systematically distorted picture of it.
The Media Score — Nemi's composite metric measuring coverage volume, share of voice, sentiment, topic distribution, and competitive position — gives communications teams something that most PR dashboards can't: a single trackable number that moves with your actual earned media health across the full Indian landscape. Not just English media health. The full landscape.
This matters for AI visibility strategy in a specific way. If earned media is the input into AI-mediated brand perception, then the Media Score is the metric that tells you whether that input is moving in the right direction across all the markets where AI will be answering questions about your brand. A rising Media Score in Hindi and vernacular print is not just a PR win. It is a compound investment in the AI training signal that will shape your brand's discovery for years.
Reporter and publication intelligence — understanding which journalists and outlets are most engaged with your brand, which publications carry the most credibility in which regional markets, which reporters are working on stories in your sector — is directly relevant to GEO strategy. AI systems favor coverage in authoritative sources. Knowing which sources carry authority in which regional markets, and building media relationships with the journalists who drive coverage in those outlets, is now both a PR program and an AI visibility program simultaneously.
Competitive intelligence — tracking competitor media coverage, share of voice, messaging, and narrative patterns across the same multilingual landscape — tells you where your competitors are building the AI training signal that disadvantages you. If a competitor is generating strong earned media in Tamil and Telugu markets while you are not, they are not just winning regional coverage. They are building the AI-mediated visibility advantage that will compound over the next three to five years as AI search becomes the default discovery layer for Indian consumers.
Crisis intelligence — the early warning system that surfaces anomalies in coverage patterns before they become national stories — is doubly valuable in the AI age. A negative narrative that is caught at regional media level and addressed before it scales is a crisis managed. But it is also a negative AI training signal that was prevented. The coverage that never scaled is the coverage that never gets indexed by AI systems as a persistent negative signal about your brand.
The India-Specific AI Visibility Challenge
One thing the NIQ report does not address, because it is focused on the U.S. market, is the specific complexity that makes AI visibility strategy for Indian brands uniquely challenging.
The AI systems that Indian consumers use — ChatGPT, Perplexity, Google Gemini, Google AI Overviews — were trained primarily on English-language web content. Their coverage of Hindi and vernacular Indian media in their training data is, relative to the actual scale of that media landscape, thin. This creates a specific kind of distortion: for queries posed in English about Indian brands, AI systems are drawing primarily on English-language sources, which represent a minority of the actual coverage those brands receive.
But as Indian consumers increasingly use AI systems in their native languages — querying in Hindi, Tamil, Telugu — the systems must draw on whatever indexed content exists in those languages. The brands that have the strongest presence in regionally indexed vernacular media will have stronger AI visibility in native-language queries than brands that are well-covered in English but invisible in regional languages.
Profound's research analyzing 3.25 billion citations across 7 models and 14 countries found that query language is the dominant force reshaping social citation rates across AI models — with Google AI Overviews and ChatGPT responding to non-English prompts in fundamentally different ways. For Indian brands, this means that earned media strategy for AI visibility is not a single strategy. It is a multilingual strategy, calibrated to each language's specific media ecosystem and its representation in AI systems.
This is why an Indian-built intelligence platform with native multilingual coverage is not just a nice-to-have. It is the only tool that can give Indian brands an accurate picture of the earned media landscape that AI systems are actually drawing on when Indian consumers ask questions about their categories.
Monitoring 2,400+ sources across 14+ languages — as Nemi does — is not a feature. In 2026, it is the minimum viable infrastructure for a serious brand operating across India's geographic and linguistic breadth.
What This Means for How Brands Should Think About PR in 2026
The strategic implication of everything above — the NIQ data, the GEO research, the India-specific AI visibility dynamics — is a significant rethink of what PR programs are actually for.
PR has always been described as reputation management. That framing is still accurate but no longer sufficient. In 2026, PR programs are also:
AI training signal management. Every article your brand generates in a credible regional publication is a persistent input into how AI systems describe you. PR programs that understand this will prioritise earned media in the specific outlets that AI systems trust in each regional market — not just the national English press that generates the most shareable coverage.
Multilingual narrative governance. The story your brand tells in English is not the only story being told about you. In 14+ languages across India's media landscape, journalists, bloggers, regional broadcasters, and social media commentators are continuously constructing narratives about your brand that may or may not align with your intended positioning. Knowing what those narratives are — accurately, in real time, in the languages they are being expressed — is the prerequisite for managing them.
Competitive visibility intelligence. Share of voice in the AI age is not about volume of coverage. It is about presence in the sources AI systems trust, across the language-market combinations where your competitors are most active. A brand that knows its competitor is building strong regional vernacular coverage in Maharashtra can respond strategically. A brand that doesn't know it is losing ground in AI-mediated discovery in that market has no opportunity to respond.
Crisis prevention at the regional layer. The negative narrative that starts in a regional publication and gets addressed before it scales is the negative AI training signal that was prevented. This is a new argument for crisis intelligence that goes beyond the traditional "respond before it goes national" frame — it is "catch it before it gets indexed at scale."
All of this requires intelligence infrastructure that can see India's full media landscape, in all the languages it operates in, with the accuracy and speed that 2026's information environment demands.
The Moment We Are Actually In
The NIQ report's framing — "a moment of tension" — is exactly right.
The tension is between the speed at which AI is inserting itself into product discovery and the speed at which brands are adapting their earned media strategies to account for it. Most brands are in the second category. They are running PR programs designed for a media environment that predates AI-mediated discovery. They are measuring success in impressions, clippings, and AVE scores. They are monitoring their earned media with tools that cover English content comprehensively and regional language content badly.
Meanwhile, the AI systems that will describe their brands to hundreds of millions of Indian consumers are already indexing the content that exists right now. The earned media footprint a brand has today is partially the AI training signal it will have tomorrow.
The brands that understand this are building intelligence infrastructure that maps the full multilingual earned media landscape, measures narrative health across all the language-market combinations that matter, and manages their PR programs as both reputation strategy and AI visibility strategy simultaneously.
The platform that makes that possible for brands operating in India's multilingual complexity — with ten years of operational depth, ISO 9001:2015 certification, 2,400+ sources across 14+ languages, Nemi AI-powered multilingual sentiment, the Media Score as a unified performance metric, and real-time crisis intelligence — is Nemi Insights.
NIQ's report is a signal about where brand discovery is heading globally. In India, the destination is the same — but the route runs through Dainik Jagran and Dinamalar and Eenadu and Ananda Bazar Patrika as much as it runs through the Economic Times or NDTV. Any intelligence platform that can't read all of those simultaneously is not giving you a map. It's giving you a corner of one.
Key Data Sources:
NIQ Consumer Life Analysis, May 2026 — Visibility in the Age of AI: How Product Discovery Is Being Rewritten · Superlines AI Visibility Study, March 2026 (34,234 AI responses, 10 platforms, 30 days) · Profound Citation Research, 2026 (3.25 billion citations, 7 models, 14 countries; query language impact on AI citations) · Edelman AI Citation Research, 2025 (90% of AI citations from earned/owned media) · Princeton GEO Study (Aggarwal et al.) — original coining of Generative Engine Optimization · upGrowth AI Traffic Report Q1 2026 (India and Southeast Asia AI search adoption growth) · Perplexity India market data, Q2 2025 (Airtel Pro partnership, India surpassing U.S. as largest Perplexity market) · Google AI Mode — 100 million users, U.S. and India · Ahrefs, August 2025 (80% of AI-cited URLs do not rank in Google top 100) · Nemi Insights Platform Documentation, 2024–2026 · ABC India Print Circulation, April 2026
Nemi Insights is a New Delhi-based media intelligence and PR monitoring platform — ISO 9001:2015 certified, founded 2016, 60+ team members, 200+ clients served globally. Real-time monitoring across 14+ languages and 2,400+ sources, covering print, broadcast, online, and social media across India's full multilingual ecosystem.