Everyone says they are powered by AI. The reality inside Indian marketing teams is more specific and more practical. The tools that get funded, implemented, and measured tend to fall into a clear stack that starts with data, moves through identity and modeling, and ends in activation and measurement. Adoption is no longer fringe. McKinsey’s 2025 survey found 78 percent of companies use AI in at least one function, with marketing and sales among the top users.
India’s market context is tilting the table toward faster adoption. A Nasscom and BCG analysis projects India’s AI market to reach 17 billion dollars by 2027 at 25 to 35 percent annual growth, supported by a large domestic talent base. That scale is pushing brands to rebuild their stacks around AI ready data and repeatable use cases.
At the same time, Indian marketers are honest about the maturity gap. The Mobile Marketing Association’s India study reports that 42 percent of teams are still in the experimentation phase and 54 percent feel AI adoption in marketing is not effectively understood in their organisations. That gap explains why many pilots stall unless leaders pick narrow, measurable problems first.
Another shift is happening on priority and planning. An AWS linked study covered by Economic Times said 64 percent of Indian companies are making generative AI a priority in 2025, but three in four lack structured change management plans. Budget is moving. People and process are lagging.
A final external pressure comes from search and discovery. Gartner predicted that by 2028 brands could see organic search traffic fall by 50 percent or more as consumers adopt generative AI search. If even part of that plays out, marketers will need stronger first party data and AI driven orchestration to maintain reach and conversions.
The core layers of the Indian AI marketing stack
Most working stacks look like this in the wild.
1) Data and identity foundation. The data lake or warehouse feeds the rest of the system. HDFC Bank is a good example of how a large Indian enterprise is consolidating analytics on a modern platform. Its credit analytics group migrated to the Databricks Data Intelligence Platform on Azure to centralise data, standardise pipelines, and accelerate campaign and analytics use cases. This is not a marketing tool by itself. It is the pipe that makes segmentation, propensity scores, and next best action models possible across channels.
Identity, consent, and event collection sit on top. Many Indian enterprises pair the warehouse with a customer data platform to resolve profiles and stream segments. HDFC has described work with Adobe’s Real Time CDP, Analytics, and Experience Manager to unify experiences across digital and branches, which is a pattern we see in other regulated industries as well.
2) Models and decisioning. This is where predictive and generative capabilities live. In India, two families of models dominate. Predictive models drive classic jobs like churn scoring, lifetime value, and product propensity. Generative models now write, summarise, and guide conversations. McKinsey’s latest global data shows the share of companies regularly using generative AI nearly doubled within a year, and marketing and sales is one of the most active functions. Indian teams are following the same curve.
3) Orchestration and activation. These are the systems that turn a scored segment or conversation into messages across email, push, SMS, in app surfaces, the web, and call centre scripts. India has a strong local ecosystem alongside global suites. Airtel’s CPaaS unit launched Airtel IQ Reach as a self serve marketing communications platform to help brands target audiences and track effectiveness across channels. That kind of network embedded delivery is one reason marketers can move from pilots to scaled reach without stitching ten vendors.
4) Measurement and learning. Marketers are refreshing their experimentation culture for AI. As search and media dynamics shift, teams are returning to marketing mix modeling and incrementality tests to isolate the lift of AI decisions. The practical constraint in India is reskilling analysts to manage new data flows and to design tests that regulators and auditors can understand. Industry research and CMO surveys highlight this skills and governance gap even as budgets rise.
Four living examples inside Indian brands
These are not press friendly proofs of concept. Each example shows a layer of the stack doing real work.
MakeMyTrip’s conversational layer. MakeMyTrip began introducing generative AI for voice assisted booking and trip planning with Microsoft’s Azure OpenAI Service in 2023. In August 2025 the company expanded to a multilingual trip planning assistant aimed at conversational discovery, booking, and post booking support. Product features like these often start for customer experience, but they flow back into marketing because they generate first party intent data, capture language preferences, and produce summaries that can drive personalised campaigns without long copy cycles.
HDFC’s data and CDP backbone. HDFC’s move to Databricks for analytics and its use of Adobe’s real time stack to connect digital and assisted journeys show the pattern large Indian BFSI firms are taking. The lesson for marketers is simple. Without a governed data layer and a CDP that can push segments to channels in minutes, AI becomes a deck, not a driver of revenue.
Axis Bank’s personalisation engine. Axis Bank deployed Moneythor to deliver real time insights and personalised nudges inside digital channels. For marketers this kind of engine shortens the distance between a model and an impression. It also creates on channel measurement, since offers and content are served in the app or site and their lift is observable without relying only on media platforms.
Airtel’s outreach fabric for activation. Airtel IQ Reach gives marketers a self serve way to define segments, control message delivery, and get unified analytics across communications at scale. For Indian SMEs and mid market brands this can serve as the activation layer without building a complex patchwork of gateways and reporting tools. It also hints at a future where the network provides anti spam and verified sender protections by default, which improves deliverability for legitimate marketers.
Five practical data points that explain the shift
Adoption is now mainstream. Seventy eight percent of companies report using AI in at least one function, with marketing and sales near the top of the list. This matters because roadmap decisions are now made against a baseline of active AI use, not hypothetical benefits.
Budgets are moving into India’s AI economy. The AI market in India is projected to hit 17 billion dollars by 2027. The growth rate and talent pool mean more local vendors, more partner capacity, and faster implementations for marketers who do not want multi year lifts.
Marketing maturity is uneven. MMA India reports that 42 percent of teams are still experimenting and more than half do not feel adoption is well understood internally. That is why clear use cases and shared success definitions are critical.
Priority does not equal readiness. Sixty four percent of Indian firms say generative AI is a priority in 2025, yet 75 percent lack change management plans. Marketers who plan training, data governance, and content workflows will ship and scale faster than peers who only buy tools.
Search is changing. Gartner predicts organic search traffic could fall by half by 2028 as users adopt generative AI search. The logical response is to strengthen first party data capture, enrich profiles with consented signals, and activate through owned channels where you can test and learn.
What “inside the stack” looks like on a typical Indian team
A mid to large brand in India that is doing this well usually runs a cloud data platform where marketing can access cleaned events. A CDP resolves identities, applies consent, and exports fresh segments to activation tools every hour or faster. A library of models covers churn, next best product, next best message, and creative recommendations. A content engine pairs templates with generative AI to produce subject lines, variations of push copy, and image crops, then routes those variants into tests. Activation happens through email, SMS, RCS, push, on site modules, telephony prompts, and call centre scripts that are now influenced by AI through CPaaS layers like Airtel IQ Reach. Measurement combines experimentation in channel with periodic mix modeling refreshes to capture off platform effects.
For brands with high service intensity such as travel and banking, conversational surfaces feed the top of the funnel and the CRM. MakeMyTrip’s assistant is an obvious example. Even if the initial intent is service or planning, every interaction is an input for segmentation and creative. When those patterns flow back into the CDP, the next campaign is not a batch email. It is a triggered message in the right language with the next best action and an offer aligned to the stage the assistant already inferred.
BFSI shows how the data foundation pays off. HDFC’s consolidation onto Databricks and Adobe means a branch banker viewing XpressWay can see real time context, while the marketing team sees the same identity graph when planning a cross sell. That is a direct path from analytics to revenue for local teams that used to work in silos.
How to build or refit your stack without stalling
Start with the use cases that fit your data reality today. If your first party data is strong in app but weak on the open web, put your first AI dollars into on channel personalisation where consent is clear and measurement is clean. Axis Bank’s personalisation engine approach shows one way to start where the signal quality is highest.
Invest early in identity and consent. A CDP connected to your warehouse is not optional if you want to turn models into messages without friction. HDFC’s model of a governed data platform plus a real time experience layer is becoming the standard path in India because it fits privacy, speed, and scale.
Connect activation to the pipes you already own. If your customer base is mobile heavy, CPaaS platforms such as Airtel IQ Reach can give you reach, delivery controls, and reporting in one place. This is especially useful for mid market teams that need to ship value while the broader data program matures.
Treat measurement as a product. AI led campaigns need learning loops you can trust. As the search landscape evolves, plan for controlled experiments and periodic mix modeling to attribute lift. Industry signals suggest this is where skill gaps slow teams down, so plan training alongside tooling.
The near term outlook
The lines between product and marketing will keep blurring. Conversational surfaces will double as both service tools and intent capture engines. The vendors behind India’s stacks will keep consolidating. Global suites will absorb agentic features. Local platforms will compete on cost, speed, and India specific channels. If the generative search shift tracks in the direction Gartner outlines, the value of first party data and owned channels will rise further. The winners will not be the loudest AI storytellers. They will be the teams that turned data into decisions, decisions into activation, and activation into provable lift.