When food delivery app Zomato revealed that 4,940 people searched for “girlfriend” on its platform in 2024 (with only 40 searching for “dulhan,” meaning bride), it showcased how quirky consumer behavior data can reflect cultural trends. This light-hearted insight is backed by serious technology: across India, companies are harnessing artificial intelligence to analyze millions of such data points and forecast what people want, how they feel, and even how culture is evolving – all in real time. From e-commerce giants and FMCG conglomerates to food delivery apps and influencer marketing platforms, AI is increasingly being used to predict cultural and consumer trends in India. The goal is simple: stay one step ahead of India’s fast-changing market and diverse consumer base.
Decoding Trends with Indian Data and AI Tools
Behind the scenes, Indian firms are deploying advanced AI models – from natural language processing (NLP) to predictive algorithms – to make sense of cultural signals. For example, ITC, one of India’s largest consumer goods companies, has built an internal AI platform leveraging NLP and machine learning to sift through vast public data and consumer interactions. Key applications include sentiment analysis on customer care calls and trend tracking on social media, helping ITC identify emerging patterns in real time. By mining public conversations about topics like protein diets or gut health, ITC’s AI can classify trends as emerging, mainstream or waning, giving brand teams a data-driven map of cultural shifts. It’s a far cry from traditional surveys – AI can now scan everything from Google searches to social media chatter in multiple languages, pinpointing the “what, when, why, and who” of consumption patterns across India’s diverse regions.
Natural language processing is especially crucial in India’s context. With dozens of languages and dialects, AI models must grasp local nuance to gauge sentiment accurately. This is why industry leaders and policymakers alike emphasize cultural context. Union IT Minister Ashwini Vaishnaw has argued that India needs AI models “built on domestic languages, culture, conditions, nuances, and social norms” rather than importing one-size-fits-all algorithms. In practice, this means training AI on Hindi, Tamil, Bengali or Marathi content to truly understand regional sentiment – whether it’s excitement for a cricket win or backlash to a film. Sentiment analysis tools powered by NLP comb through tweets, product reviews, and call transcripts to take the pulse of the people. At ITC, for instance, AI-driven transcription of 300–500 daily customer calls (previously an untapped resource) now enables automated sentiment tagging of feedback in multiple languages. These insights alert the company to consumer pain points and shifting preferences much faster than manual methods ever could.
Trend prediction engines are another piece of this puzzle. Machine learning models can ingest sales data, web searches, and social trends to extrapolate what might be the next big thing. In India’s dynamic food delivery market, this can mean forecasting a surge in biryani orders during festival season, or identifying an upcoming craze for Korean cuisine by analyzing order patterns. Zomato’s new Food Trends platform openly shares some of this intelligence with restaurant partners, crunching millions of transactions across hundreds of cities to show demand spikes for certain dishes and gaps in supply. By looking at what people search for, what they order, and when they order, such platforms help businesses anticipate consumer behavior. The data underscores how quickly tastes change: in 2024, Delhi-NCR alone logged 12.4 crore orders on Zomato – more than some neighboring states combined – and Bengaluru out-ordered Mumbai by 30 lakh, even though Mumbai spent more on food. AI parses these patterns to tell companies not just what is popular, but why – linking spikes to events, seasons or social buzz.
On the cutting edge are startups like Stylumia, which uses AI to forecast fashion trends by analyzing images and sales data. Stylumia’s system studies millions of product photos and shopping patterns to predict which colors, cuts or styles will be in vogue next season. According to the company, clients have improved the accuracy of style and color trend predictions by up to 30% using AI. In a country where fashion preferences can vary hugely from Mumbai to Meerut, such AI-driven foresight helps brands stock the right inventory and avoid overstocking unpopular designs. These examples highlight a common theme: AI’s ability to crunch eclectic data – text, images, transactions – is giving businesses unprecedented visibility into India’s cultural zeitgeist.
Brands Bet on AI to Read the Indian Consumer
It’s not just tech startups or IT giants; mainstream B2C brands in India are betting big on AI to decipher consumer sentiment and stay culturally relevant. Tata Digital, the group behind India’s super-app Tata Neu, is one prominent example. Tata Neu integrates everything from groceries and electronics to fashion and finance, catering to 150 million consumers across the Tata empire’s brands. To personalize this vast experience, Tata Digital partnered with Chennai-based AI firm Mad Street Den (MSD). “Our AI solutions absorb multi-dimensional data in real time, responding to customers dynamically and delivering value across the value chain,” says Ashwini Asokan, Mad Street Den’s co-founder and CEO. In other words, Tata’s AI engine is constantly learning from user behavior – what you browse, buy, or watch – to tailor recommendations and content for each customer. If a user’s purchase history suggests a strong tea preference, Tata Neu might highlight new blends from Tata Tea; if another user frequently shops fashion, the app adapts to showcase trending apparel. The AI connects dots across traditionally separate domains to anticipate needs: a traveler who booked a Tata hotel may get restaurant deals on the app, while a BigBasket grocery shopper might see personalized recipe ideas. Early results are promising: by unifying these insights, Tata Neu has driven higher engagement and loyalty, prompting Tata Digital’s CEO Pratik Pal to tout a “relentless focus on ROI” from the AI-driven personalization push.
Food and hospitality companies are also riding this wave. Swiggy, for instance, uses machine learning to forecast demand for its deliveries and even for its cloud kitchens. By analyzing historical ordering data, weather, and local events, Swiggy’s AI can predict which neighborhoods will spike in orders on a given evening – allowing them to pre-position delivery partners or prepare popular dishes in advance. This kind of predictive demand modeling has tangible payoffs: it improves delivery times and reduces food waste by aligning supply with anticipated orders. Swiggy’s rival Zomato, beyond sharing trend insights, introduced an AI-driven customer support chatbot called Nugget to handle common queries, reflecting the broader trend of AI augmenting consumer-facing services. While customer service bots are a form of “narrow AI,” their increasing adoption by Indian apps signals how AI is woven into consumer experiences. Even the quick-commerce boom (for instant grocery and essentials delivery) leans on AI for inventory placement and route optimization. Analysts estimate India’s quick-commerce sector will rocket from just $200 million in 2021 to $35 billion by 2030 – a growth trajectory that would be impossible without AI predicting what consumers will need and where, almost before they know it themselves.
Perhaps nowhere is the cultural element of AI predictions more evident than in marketing and media. Indian advertising has always grappled with the country’s cultural diversity – what clicks with a Gen-Z consumer in urban Bengaluru might fall flat with a middle-aged shopper in Tier-2 towns. Marketing tech firms are now using AI to navigate these nuances. Companies such as Winkl and Qoruz have built intelligence platforms for influencer marketing, a space tightly entwined with pop culture. These platforms use AI to sift through thousands of social media creators, analyzing not just follower counts but audience demographics, engagement quality, and even the tone of comments. The result is that brands can algorithmically find the right influencer whose content style and follower base align with the brand’s cultural image. It’s a data-driven matchmaking game: over 50 brands and agencies in India use platforms like Qoruz to plan influencer campaigns, moving beyond gut feel to evidence-based decisions.
The impact has been significant. “Brands using our platform have seen up to 2.5× improvements in spending efficiency on repeat campaigns,” says Praanesh Bhuvaneswar, co-founder and CEO of Qoruz, which is based in Chennai. He notes that with AI, “campaign planning time drops by 40–50%, engagement relevance increases by 30–35%, and wasteful spends on misaligned creators drop sharply. It’s like switching from a compass to GPS – you still get there, but a lot faster and with fewer wrong turns.” By crunching past campaign data and social media trends, Qoruz’s algorithms can even predict which type of influencer content will perform well, helping marketers forecast sentiment – for example, identifying that Instagram reels about a certain festival are likely to go viral in one region, while Twitter discussions on a social cause resonate more in another. Similarly, rival platform Winkl launched an AI-powered search engine to let brands discover relevant influencers in seconds.
Importantly, these AI tools don’t work in isolation. Marketers combine them with human insight to truly predict “culture.” As Shalini Kumar, Head of Consumer Experience at Kenvue India (formerly Johnson & Johnson), observed, “with 20 to 100 influencers in a campaign, it’s critical to evaluate fit, authenticity and relevance from among millions of creators – that’s where the intelligence platform becomes invaluable.” The AI can crunch reach and engagement stats, but humans still judge the cultural fit. For Indian brands, relevance often means localization – picking up on regional slang in an ad campaign, or understanding the sentiment behind a viral meme. This is why many companies adopt a hybrid approach: AI for large-scale data analysis, people for interpretation and creative strategy.
Promise and Pitfalls of AI Cultural Forecasting
The promise of AI in predicting culture and consumer sentiment in India is vast. Surveys show over 90% of businesses are now monitoring customer behavior with AI, and about 69% use AI specifically for demand forecasting. Across retail, banking, media and beyond, executives see AI as the key to anticipating trends rather than reacting to them. Indian startups, too, are building niche predictive solutions – nearly 69% of AI startups focus on areas like forecasting demand or pricing trends for clients. The results can be impressive: from FMCG firms reducing stockouts by predicting next month’s hot seller, to streaming platforms like Netflix India analyzing viewership data to commission shows that match emerging viewer tastes. AI enables a proactive stance toward cultural currents, which is invaluable in a country as dynamic as India.
However, this new era of cultural forecasting is not without its pitfalls. Indian industry leaders caution that algorithms have their limits, especially in grasping human nuance and “the why” behind trends. “AI tools are useful but they cannot replace cultural instinct,” warns Rohit Sakunia, founder of marketing agency Art-E Mediatech. Sakunia, who helps brands blend data with storytelling, notes that while AI can quantify reach and detect what’s trending, “we focus on relevance... Because this is one marketing area where brands can now chase and get relevance,” he says. In other words, a trend isn’t worth much to a brand unless it aligns with the brand’s soul and the audience’s genuine interests. A meme or hashtag may go viral, but only a human marketer might discern whether it’s ironically trending or a meaningful shift in consumer sentiment. Over-relying on automated trend forecasts could lead companies to jump on every bandwagon, diluting their identity. As Sakunia points out, context matters: “It’s more about matching context with conversation... tracking real sentiment and not just surface-level engagement spikes.” A purely AI-driven approach might misread sarcasm as positivity or fail to see the cultural significance of a minor trend.
There are also ethical and risk considerations in using AI to predict behavior. One concern is privacy: the more data companies analyze (from social media to personal transactions), the more they must safeguard consumer privacy and consent. Another is algorithmic bias. If the AI’s training data isn’t diverse enough, its predictions could be skewed towards the dominant cultural groups online – for instance, overemphasizing English-language urban sentiment and missing rural or non-English trends. Recognizing this, Indian stakeholders stress inclusivity in AI. The government’s IndiaAI program, for example, is pushing for open datasets in Indian languages and cultural contexts so that predictions aren’t blind to large swathes of the population.
Even when the data is sound, transparency and trust remain issues. AI can flag an upcoming trend, but if marketers can’t explain the rationale to management (the “black box” problem), it may breed skepticism. Moreover, as Vaibhav Gupta, co-founder of influencer platform KlugKlug, notes, the rise of AI-generated content itself demands caution. “Labeling AI-generated posts, applying robust fraud detection to weed out bots or fake followers, and maintaining human oversight in creative direction are no longer optional,” Gupta says. In other words, companies must clearly distinguish organic cultural signals from AI-fabricated ones. If an AI system starts auto-generating trendsetting content, it could distort the very culture it’s meant to measure. This blurring of lines raises ethical questions: Will AI predict culture, or begin to create culture? For now, Indian firms are treating AI as an aid, not a replacement – a powerful telescope for seeing further ahead, but not the star of the show.
Crucially, regulators are also watching. A recent Competition Commission of India study flagged emerging risks of algorithmic collusion, opacity in AI decision-making, and unequal access to data and compute if AI adoption goes unchecked. The worry is that big companies with superior AI might shape market trends to their advantage, or that secret algorithms might inadvertently price-fix or discriminate, undermining consumer trust. To counter this, India is talking about “AI compliance culture” – ensuring that AI-driven predictions and decisions abide by fair practices and don’t stifle healthy competition.
In sum, AI has become an indispensable tool for India’s businesses to forecast cultural and consumer trends, but it’s a tool that must be used thoughtfully. The long-term success of cultural prediction will depend on a balanced approach: combining AI’s speed and scale with human creativity, ethics, and cultural intuition. “The future is AI-powered,” concludes ITC’s consumer insights head, after seeing productivity soar with AI in research. Yet, as India’s experience shows, the human touch – understanding the emotions, values and diversity behind the data – remains as crucial as ever. AI may learn fast, but Indian culture isn’t a code to be cracked; it’s a living, breathing narrative, one that technology and people will co-author together in the years to come.
Disclaimer: All quotes are either sourced directly or attributed to public statements.
