Digital now accounts for nearly half of India’s advertising expenditure. Marketers have access to more customer signals, platforms and automation capabilities than ever before. Yet for many organisations, the MarTech stack still resembles a collection of tools rather than a connected growth engine.
The challenge is no longer whether brands should adopt artificial intelligence. A large majority of Indian marketers have already done so. The harder question is whether their data, workflows, content systems and measurement models are ready to make that investment useful.
The gap between ambition and readiness is becoming visible across sectors. Banks are trying to connect service and marketing journeys. Retailers are working to link store, app and commerce data. Consumer brands are under pressure to produce more relevant content across languages, formats and platforms. At the same time, AI-led search is changing the way customers discover products and brands.
Here are the 10 biggest gaps in India’s MarTech stacks.
1. Customer data exists, but it is not truly unified
Most large Indian brands do not suffer from a shortage of data. They collect signals through apps, websites, stores, call centres, loyalty programmes, payments, delivery networks and social platforms.
The issue is that these signals frequently sit in separate systems. A customer who browses a product on an app, asks a question on WhatsApp, buys in a store and contacts customer care can still be treated as multiple different customers.
That is why personalisation often breaks down at the most important moments. A customer may receive a promotional message immediately after registering a complaint. A loyal buyer may be treated as inactive because their store transaction is not visible to the app team. A customer can be offered a product they already own.
Only 60% of Indian marketers say they have complete access to service data, 61% have access to sales data and 58% have access to commerce data. The numbers point to a basic disconnect: brands want a unified customer experience while their data is still organised around internal departments.
Nishant Kalra, Vice President, Sales, Salesforce South Asia, has said, “The biggest barrier to personalisation today isn’t AI, it’s the quality and connectedness of the data that powers it.”
2. Too many tools, too little integration
Indian brands have added specialised platforms for CRM, advertising, analytics, email, push notifications, social listening, customer service, loyalty and content. The result is often a stack with impressive logos but weak interoperability.
Integration remains the most persistent operational problem in MarTech globally, with 65.7% of respondents in a recent industry survey identifying it as their biggest stack-management challenge. More than six in 10 respondents said they were using more tools than they did two years earlier.
The problem is visible in sectors such as retail, BFSI and travel. One team may own acquisition data, another owns transactions, a third controls communications, while agencies hold media dashboards. Every team has metrics, but no one has a complete view of what the customer has experienced.
The result is overlapping licences, duplicated audiences, disconnected reporting and dependence on fragile point-to-point integrations. The solution is not always another platform. Often, it is a clearer architecture that identifies the systems of record, the data layer, the decisioning layer and the tools that can be retired.
3. AI adoption is moving faster than AI readiness
Around 81% of marketers in India say they have adopted AI. But the definition of adoption varies widely.
For some teams, it means using generative AI for copy drafts, image variations, presentation summaries or campaign ideation. For others, it means using AI for segmentation, personalisation, customer responses, forecasting and journey orchestration. These are very different levels of maturity.
The next wave of AI will depend less on the model and more on the quality of the information available to it. If an AI system has incomplete customer histories, outdated product information, inconsistent consent data or unclear approval rules, it will not improve the experience at scale.
HDFC Bank’s EVA illustrates the value of focused service automation. The bank’s intelligent chatbot helps customers find information across banking products and services. But the broader challenge for brands is more complex: enabling AI to make context-aware decisions without compromising accuracy, privacy or brand safety.
Nearly 86% of Indian marketers say they would trust AI to respond to customers to help scale engagement. The readiness gap lies in whether organisations have the trusted data, human oversight and governance mechanisms to support those responses.
4. Personalisation is still campaign-led, not journey-led
Indian brands have become much better at targeting. They can segment audiences, trigger abandoned-cart reminders, recommend products and tailor creative by customer profile.
But most personalisation still happens campaign by campaign. It does not consistently follow the customer across the full journey.
A true journey-led system takes account of what has already happened. It knows whether a customer has purchased, complained, renewed, returned a product, engaged with support or opted out of a particular channel. It changes the next interaction accordingly.
This is especially important for businesses with high-frequency, multi-channel customer journeys. Nykaa operates across e-commerce, mobile commerce and physical retail. Tata Neu brings together shopping, travel, financial services and loyalty rewards through NeuPass. Reliance Retail runs a large omnichannel network across stores and digital platforms.
For such businesses, relevance depends not on sending more messages but on deciding when not to send one. The strongest customer experiences are often created through suppression, timing and context rather than volume.
5. Measurement is fragmented and overly channel-focused
Marketing teams have abundant dashboards, but not always a unified view of effectiveness. Media teams track reach and clicks. CRM teams track opens and conversions. Commerce teams monitor sales. Finance teams look at revenue and margin.
These measurements frequently do not connect.
The danger is that marketers end up optimising what is easiest to count. A campaign may produce cheap clicks but low-quality leads. Retargeting may be credited for conversions that would have happened anyway. A discount may boost transactions while reducing margin and encouraging customers to wait for offers.
As digital accounts for a larger share of Indian advertising expenditure, the need for stronger attribution, incrementality testing and customer lifetime value measurement becomes more urgent.
MarTech should not merely report campaign activity. It should help marketing teams understand whether an action changed customer behaviour and created commercial value.
6. First-party data strategies are not matched by consent design
First-party data has become a strategic priority for marketers. As third-party identifiers become less dependable and privacy expectations increase, brands are looking to their own customer relationships for insight.
But collecting data and earning the right to use it are not the same thing.
A strong first-party-data strategy requires clear consent, transparent communication, preference management, security and a visible value exchange. Customers are more willing to share information when it leads to a faster service interaction, better product recommendations, meaningful rewards or a smoother purchase journey.
The gap appears when consent records remain disconnected from campaign systems. A customer may opt out through one platform and still receive messages on another. Preferences may be collected but never used. In such cases, data collection becomes a compliance exercise rather than a trust-building mechanism.
Privacy concerns and regulations remain among the leading barriers to personalisation. The brands that handle them well will see privacy not just as a legal requirement, but as part of the customer experience.
7. The content supply chain cannot keep up with demand
Personalisation creates a content problem. Every audience, region, product category, channel and moment may require a different version of a message.
Globally, 71% of marketers expect demand for content to grow fivefold or more by 2027. For India, the pressure is even more pronounced because of language diversity, regional consumer behaviour, festival-led demand cycles and a mobile-first media ecosystem.
A national campaign may need dozens of creative adaptations across video, social, commerce, messaging and performance media. It may need different cultural cues for different markets. It may require rapid changes when trends move or inventory shifts.
Generative AI can help produce variations faster, but it does not solve the underlying operating challenge. Organisations need clear brand guardrails, approved content modules, product information, localisation capability and workflow systems that allow teams to move quickly without losing consistency.
The gap is often between creative and technology. Automation can identify the right customer moment, but it cannot deliver a relevant experience if the appropriate content asset has not been created, approved and made available.
8. Talent has not caught up with the stack
MarTech today sits at the intersection of marketing, data, technology, design, analytics, customer experience and privacy. Yet many organisations are still structured as if these are separate disciplines.
The talent gap is not simply about hiring data scientists or engineers. Marketing teams need people who understand segmentation, experimentation, customer journeys and commercial metrics. Technology teams need people who understand brand, consumer behaviour and the realities of campaign execution.
A lack of technical expertise remains one of the biggest barriers to personalisation. This is particularly acute for mid-sized businesses that may rely on agencies, standalone SaaS tools and spreadsheets as they grow.
The modern marketer does not need to become a software engineer. But they do need enough technical fluency to ask the right questions about data quality, integration, measurement, model outputs and customer consent.
9. Discovery is changing faster than stack architecture
The search journey is no longer limited to keywords and blue links. Customers are increasingly discovering brands through marketplaces, creators, video platforms, social feeds, AI assistants and AI-generated search responses.
More than nine in 10 Indian marketers say AI is reshaping their SEO strategy, while a similar share say they have already begun optimising for AI-generated answers. High-performing marketers are significantly more likely to have started this work.
This changes how brands need to think about visibility. The goal is not only to rank for a keyword. It is to ensure that product information, expert content, reviews, pricing, availability, customer support and brand credibility are consistent across the sources that AI systems and consumers use.
For categories such as consumer electronics, travel, beauty, financial services and automobiles, a recommendation from an AI interface may become an increasingly important entry point into the purchase journey.
Yet many stacks still treat search, content, product data, public relations, reviews and customer service as separate functions. In an AI-led discovery environment, they are part of the same answer.
10. Ownership remains unclear at the top
The final gap is governance. Who owns the MarTech roadmap? Who decides which tools stay or go? Who is responsible for data quality? Who is accountable when an automated journey creates a poor customer experience?
In many companies, responsibility is divided between the CMO, CIO, chief digital officer, chief data officer, sales leadership and agency partners. Collaboration is essential, but unclear ownership can turn critical decisions into slow committee processes.
The strongest MarTech stacks are not necessarily the largest or most expensive. They have a clear business owner, common customer-data standards, defined priority use cases and regular reviews of tool usage, integration health and commercial outcomes.
India’s MarTech industry is entering a more demanding phase. The differentiator will not be the number of tools a brand owns or the scale of its AI announcements. It will be the ability to connect data, content, decisioning and measurement into a system that makes every customer interaction more 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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