For more than two decades, digital marketing has been built around keywords. Search and performance teams argued over exact match, phrase match and bid prices. Today that world is starting to change. As consumers move to AI assistants, conversational search and chat based shopping, they are using full sentences and questions, not just chopped up phrases. Discovery is slowly shifting from keywords to natural language.
When a user types “best washing machine under 25,000 for a small family in a hard water area” or asks an assistant “where should I go for a three day trip near Bengaluru with my parents”, the system is not only matching words. It is trying to understand intent, context, constraints and preferences, then generate an answer that feels complete. That is the environment brands are now entering.
Industry analysts describe this as the rise of answer engines and generative engines. Instead of just ranking pages, AI systems decide which sources to trust, summarise them and then reply in natural language. For marketers, that means content has to make sense both to people and to models that are scanning for clarity and usefulness rather than just the presence of a specific keyword.
Natural language begins at the platform layer
The shift to natural language marketing starts with the big platforms that shape discovery.
Meta has been explicit that AI sits at the heart of its feeds. “The entire algorithm or the backbone of why your Instagram feed is different from mine is because of AI,” says Arun Srinivas, Director and Head of Ads Business in India at Meta. The comment underlines how Instagram and Facebook increasingly show people content based on behaviour and inferred interests, not on what they typed into a search box.
On the search side, Google is also moving beyond traditional keyword logic. Shekhar Khosla, Vice President Marketing for Google India, recently said, “Speed and scale are the biggest advantages AI brings to advertising,” while explaining how AI powered tools help create hyper personalised experiences and new visual and conversational search formats. Features such as visual search and circle to search allow users to point a camera or mark an object on screen and then ask natural questions about it. Those journeys are much closer to conversation than to classic SEO.
As AI driven feeds and search results become the default, marketers are realising that the way they describe products, explain benefits and structure information has to match how people actually talk. That is where natural language marketing comes in.
Indian examples: Travel, Ecommerce and The Move Beyond Keywords
Some of the clearest examples of natural language marketing are now visible in Indian consumer apps.
MakeMyTrip has rolled out an AI powered semantic search for hotels and homestays that lets users type queries such as “family friendly homestay near a quiet beach with vegetarian food” and receive contextual results, instead of filtering only by city and price. The company has also introduced Myra, a multilingual trip planning assistant that supports English and Hindi, allowing travellers to ask full questions about where to go, what to do and how much it might cost, then refine their plans in a chat like interface. In both cases, the surface language is conversational, but the underlying marketing is still about presenting the right hotels and packages.
In ecommerce, Indian marketplaces are experimenting with conversational shopping. Flipkart has launched Flippi, a generative AI powered chat assistant that helps users discover products through natural language dialogue inside the app. Customers can describe what they are looking for in plain terms, such as “a waterproof smartwatch for my mother who walks in the morning”, and the assistant guides them through options, filters and offers. Flipkart has also invested in multimodal search features that combine text and images so that users can show and tell what they want.
Recent reports also suggest that Amazon India and Flipkart are optimising product listings so that large language model chatbots can understand them better. That work focuses less on stuffing titles with keywords and more on writing clear descriptions, specifications and benefits that AI tools can safely recommend when users ask detailed questions in chat interfaces.
These examples show a pattern. The campaigns are still performance driven, but they are designed so that natural language questions about family needs, budgets, locations and occasions lead smoothly to the brand’s catalogue.
Global Examples: Assistants as The New Front Door
Globally, brands are using natural language interfaces to make service and discovery more conversational.
Fintech company Klarna has deployed an AI assistant that now handles around two thirds of its customer service chats across multiple markets. The assistant has had more than two million conversations, does the equivalent work of hundreds of full time agents and resolves issues in under two minutes on average, compared to 11 minutes earlier. Although the use case is service rather than advertising, the experience is entirely natural language based. Many of the questions customers ask about payments, refunds and offers are moments where Klarna’s brand promise is reinforced.
In travel, Expedia has integrated conversational trip planning into its app so that users can ask open ended questions about destinations, budgets and dates, then receive recommendations and links to book without leaving the dialogue. Similar ideas are now appearing in other travel and hospitality platforms where chat based planning is becoming a parallel track to traditional search and filter interfaces.
Large retail platforms are taking a similar route. Detailed reports highlight how Amazon has launched an in app shopping assistant that answers natural language product questions and offers suggestions in line with the user’s browsing history and preferences. In such journeys, marketing no longer stops at the banner or the search ad. It continues inside a chat window where the assistant is effectively the brand’s voice.
Data Points that are Nudging Marketers
Several data points are pushing marketers to take natural language seriously.
Research on AI maturity in Asia shows that Indian brands are at the front of the curve. One recent study on generative AI in marketing found that 66 percent of Indian brands regularly use generative AI, compared with 44 percent in Australia and 22 percent in Japan. Another India focused report on AI driven consumer value found that a majority of Indian consumers now expect brands to use generative AI to enhance value and experience, provided it is done transparently and responsibly.
At the same time, there is a gap between experimentation and strategy. A white paper on AI usage in communications across Asia reported that 92 percent of Indian communications and marketing teams use AI for content, but only a minority have formal guidelines or a clear plan for generative and answer based optimisation. That means many teams are writing prompts and testing tools, but fewer are redesigning their content libraries and data structures for AI driven discovery.
Indian marketing leaders see this both as a risk and an opportunity. Chetan Mahajan, founder and CEO of The Mavericks, has called out generative engine optimisation directly. “GEO represents the single biggest growth opportunity for India’s integrated marketing communications industry. When communication becomes AI discoverable, personalised and anchored in ethical clarity, PR does not become less relevant. It becomes even more essential,” he says. His emphasis on ethical clarity is a reminder that natural language at scale can amplify both good and bad information.
On the brand side, Adobe’s India research has shown how quickly AI is moving into the content workflow. One Adobe study found that more than half of Indian brand executives already rely on data and algorithms to deliver personalised website experiences, and a large majority see clear benefits in using AI for content creation. Commenting on this shift, Anindita Veluri, Marketing Director at Adobe India, said, “Generative AI represents a transformative shift in how brands connect with consumers. It goes beyond mere automation and is the key to unleashing creativity, achieving hyper personalisation, and productivity in marketing - a win win for brands and their customers.”
What Changes for Marketers When Keywords are not Enough
In practice, the end of the keyword era does not mean keywords disappear. They still matter inside media buying tools and analytics. What changes is how marketers think about the journey.
On search and content, natural language marketing means building pages, help centres and knowledge hubs around real questions and tasks. For example, a financial services brand might prioritise explainers that answer “How do I choose a credit card if I travel abroad twice a year” instead of optimising only for “best travel card”. An insurance company might write guides around “what to check before taking health insurance for ageing parents” instead of focusing solely on high volume generic terms.
On platforms like Instagram and short form video, it means captions, titles and even on screen dialogue should sound like something a person would actually say or ask, so that models can map them more easily to user interests and intent. Meta’s Srinivas points out that more than half of the content people see on Instagram today is recommended by AI rather than coming from accounts they already follow. That makes every piece of content a potential answer in someone’s personalised feed.
In ecommerce, natural language journeys mean product catalogues with clean, structured attributes and descriptions that can be read by both humans and AI. That is why marketplaces are investing in rewriting product titles, bullet points and descriptions so that assistants like Flippi, Rufus or external chatbots can safely pull from them when users ask about specific features or use cases.
A Gradual Shift, not an Overnight Reset
For now, the rise of natural language marketing is gradual. Many brands still run classic keyword based campaigns alongside their experiments with chatbots, AI assistants and semantic search. But the direction is clear. As AI systems get better at understanding plain speech, the advantage will lie with brands whose content, data and governance are ready for that world.
Indian marketers, in particular, have to manage this shift across many languages and levels of digital comfort. That makes the work harder, but it also means the benefits of getting natural language journeys right can be significant, both for discovery and for loyalty.
The practical work looks less glamorous than the headlines. It involves mapping real customer questions, cleaning product and policy data, training teams on how to brief and review AI tools, and putting ethical guidelines around what should and should not be automated. When that groundwork is done, AI can help make brand interactions feel more like a conversation and less like a search query.
The end of the keyword era is not about abandoning performance thinking. It is about recognising that performance now depends on how well a brand shows up in conversations that people have with machines. As AI becomes another interpreter between consumers and content, natural language marketing is emerging as the way to make sure the brand’s side of that conversation remains clear, trustworthy and useful.
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.
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