For years, marketing followed a familiar direction. Brands spoke, algorithms amplified, and consumers responded. That structure is quietly changing. As artificial intelligence becomes embedded into search, voice assistants, and product discovery, influence is starting to flow the other way. Consumers are now shaping how AI systems describe, rank, and recommend brands.
This shift, often described as reverse influencing, reflects a deeper change in power. Instead of brands influencing consumers through campaigns, consumers are influencing AI through prompts, reviews, corrections, and behavioural signals. These signals are then recycled back to other users as AI-generated answers.
In this emerging loop, brands are no longer the sole authors of their narratives. AI systems increasingly rely on what people say and do at scale.
How AI Is Learning From Consumers
AI-powered discovery tools such as ChatGPT, Google’s Search Generative Experience, and voice assistants like Alexa do not rely only on official brand information. They synthesise data from public content including user reviews, forums, videos, news articles, and repeated patterns of questioning.
When a user asks which smartphone has the best battery life or which food delivery app is most reliable, the answer is shaped by aggregated consumer experience, not advertising claims. Over time, consistent patterns become AI memory.
Research published in 2024 shows that over 60 percent of users who rely on AI-led search trust responses that feel experience-based rather than promotional. At the same time, organic traffic to brand websites has dropped between 15 and 25 percent in categories where AI summaries satisfy the query without a click. This means brand discovery is increasingly happening without direct brand control.
Arun Srinivas, Managing Director and Head of Meta India, has spoken about how AI-led recommendation systems prioritise community signals over brand intent. According to him, discovery is shifting from what brands push to what people collectively validate.
Indian Consumers as Active Influence Signals
India’s digital ecosystem makes this shift especially visible. The scale of online participation, multilingual reviews, and complaint culture creates dense data for AI systems to learn from.
E-commerce platforms like Amazon, Flipkart, and Meesho host millions of product reviews. Food delivery apps like Zomato and Swiggy generate constant streams of customer feedback. Banking apps and fintech platforms receive public ratings and complaint screenshots across social media.
AI systems absorb these signals. A consistently criticised service delay or pricing issue becomes part of how an AI assistant explains a brand to future users.
Data from 2024 indicates that over 70 percent of consumers trust AI summaries of reviews more than individual brand claims. This makes collective consumer sentiment a stronger influence than traditional messaging.
Sumeet Mathur, Managing Director of ServiceNow India, has noted that AI-driven systems are raising customer expectations. According to him, consumers now expect platforms to understand context and sentiment, not just keywords. That expectation applies equally to how brands are described.
Prompt Culture and the New Form of Influence
Reverse influencing is also visible in prompt behaviour. Users are learning how to ask questions that extract more candid answers from AI systems. Prompts increasingly include phrases such as honest review, real experience, or pros and cons.
When thousands of users repeatedly frame questions this way, AI systems adapt. Responses become less aspirational and more functional. Brand narratives shift subtly as AI mirrors user intent.
Shrenik Gandhi, CEO of White Rivers Media, has observed that discovery is no longer linear. According to him, AI platforms reward relevance and repeated validation, not creative spectacle. Brands that rely only on storytelling without fixing real issues risk losing visibility in AI-driven environments.
Reviews, Complaints, and AI Memory
Reviews have always mattered. What has changed is their persistence inside AI systems.
Negative experiences do not disappear when a campaign ends. They compound. AI systems trained on public data continue to surface those signals long after the incident occurred.
In sectors like travel, fintech, and food delivery, operational friction shows up quickly in AI summaries. A service outage discussed widely online may reappear months later in AI responses to new users.
Deepinder Goyal, CEO of Zomato, acknowledged this risk publicly when the company banned AI-generated food images. He noted that synthetic visuals misled customers and led to refunds and dissatisfaction. The decision followed consumer backlash and reflected how user trust directly influences platform policy.
Global Platforms, Local Signals
Globally, AI companies acknowledge that user interaction shapes output quality. While training methods differ, consumer behaviour influences how responses evolve.
In India, local nuance plays a major role. AI systems learn slang, mixed-language queries, and cultural preferences not from brand campaigns but from how people actually talk online.
A global consumer electronics brand operating in India shared that its biggest concern is silent erosion of AI visibility due to unresolved customer sentiment. The risk is not a trending controversy but gradual disappearance from AI answers.
Brands Are Learning to Listen Differently
Some brands are adapting by monitoring AI visibility rather than only impressions. They track how AI assistants describe them compared to competitors. They analyse recurring attributes in AI summaries.
This has led to the rise of AI reputation management. It is less about optimisation and more about alignment between reality and representation.
Prashant Puri, CEO and Co-Founder of AdLift, has spoken about the importance of authenticity in AI-driven discovery. According to him, inflated claims are corrected faster by AI systems trained on public feedback. Trust erosion happens quickly when experience does not match positioning.
Ethical Concerns and Platform Safeguards
Reverse influencing raises ethical questions. If AI reflects dominant narratives, minority experiences may be overlooked. Coordinated review manipulation could distort perception.
Platforms are responding with safeguards. AI companies are tightening prompt filters, monitoring coordinated behaviour, and limiting direct monetisation of AI answers. Regulators in India have proposed clearer labeling of AI-generated content to reduce deception.
Brands, meanwhile, are focusing on transparency and speed. Some now respond to complaints publicly to ensure accurate context enters the data ecosystem. Others update FAQs and structured data so AI systems pull current information.
A New Reality for Marketing
Reverse influencing does not eliminate branding. It changes its mechanics.
Brands still matter, but narrative control is shared. AI systems reward consistency, service quality, and community validation more than creative ambition alone.
For Indian marketers, this shift creates accountability. Fixing operational gaps is now as important as crafting messaging. Listening has become a visibility strategy.
In this new environment, consumers are no longer just audiences. They are co-authors of brand perception inside machines.
And machines are listening.
Disclaimer: All data points and statistics are attributed to published research studies and verified market research. All quotes are either sourced directly or attributed to public statements.