AI adoption in Indian marketing rises, but full-scale integration remains a challenge: EY

Artificial intelligence (AI) is increasingly finding its way into India's marketing operations, helping brands accelerate content creation, automate reporting and improve customer engagement. However, despite widespread adoption, most companies are yet to integrate the technology across their marketing functions, limiting its potential to influence business strategy, customer acquisition and revenue growth, according to a new study by EY.

The study, based on a survey of more than 60 senior marketing leaders across 11 sectors in India, found that 84-88% of marketers who have adopted AI across the four key marketing capabilities surveyed reported medium to high business impact. Yet, the depth of adoption varies considerably, with many organisations continuing to use AI through standalone tools rather than integrating it into their broader marketing and decision-making systems.

The report, The AI Marketing Maturity Study: From Campaigns to Connected Intelligence, identifies four key areas of AI adoption: content and creative, measurement and insights, propensity modelling, and customer and sales journeys.

While content and creative have emerged as the most widely adopted applications, propensity modelling has recorded the highest level of full-stack AI integration among marketers who have started using the technology. Measurement and insights are also seeing a shift towards in-house capabilities, while customer and sales journey applications continue to face integration challenges.

The findings suggest that the next phase of competition among brands will be determined less by the number of AI tools they deploy and more by their ability to connect data, insights, decision-making and execution into a continuous system.

"AI has moved from promise to performance," the report noted, highlighting that the advantage is shifting towards organisations that embed AI across enterprise workflows instead of deploying isolated solutions.

Content creation leads AI adoption, but strategic use remains limited

Content and creative have emerged as the most mature area of AI adoption, with applications such as advertisement creation, video editing, creative copywriting, blog generation, audio generation and multilingual video production becoming increasingly common.

However, only 35% of marketers have evolved to full-stack AI use cases in this category, while 65% continue to depend on standalone tools.

The report found that 49% of marketers have adopted full-stack AI applications for video editing and advertisement creation. Adoption is relatively lower in multilingual video generation, blog posts and audio generation, where standalone solutions continue to dominate.

The limited integration is also reflected in the proportion of marketers developing their own technology. Only 27% are building in-house capabilities for creative copy, while 26% are doing so for advertisement creation. The corresponding figures are 24% for blog posts, 20% for video editing, 16% for multilingual video and 11% for audio generation.

Despite the fragmented nature of adoption, the business impact has been significant. Around 85% of marketers reported medium to high business impact from AI integration in content and creative.

Advertisement creation, video editing and creative copywriting emerged as particularly strong applications, with approximately 91% of respondents reporting medium to high impact across these use cases.

The report, however, argues that the next stage of maturity will require marketers to move beyond using AI to produce more content and begin integrating consumer and performance signals into creative decisions.

In other words, AI needs to evolve from being a production assistant to becoming an input into creative strategy, helping brands determine what content to produce, which audiences to target and how messaging should change based on consumer response.

Measurement and insights emerge as an area of in-house investment

Measurement and insights are emerging as another important area of AI investment, particularly as brands look to establish greater control over customer data, campaign attribution and customer lifetime value (CLTV).

The study found that 43% of marketers have progressed to full-stack AI integration in measurement and insights, while 57% continue to operate through standalone solutions.

About 50% of marketers using AI in this area are building capabilities in-house, reflecting the strategic importance of the underlying data.

The proportion of marketers developing in-house technology is particularly high for competitor analysis at 61%, followed by customer lifetime value at 57%, campaign performance at 52% and report automation at 47%. Sentiment analysis stands at 32%.

The report points out that measurement capabilities increasingly involve confidential and strategically important information, encouraging brands to develop greater internal ownership.

Customer lifetime value has the highest full-stack adoption within the measurement category, at 48%, followed by report automation at 44% and campaign performance at 44%.

However, adoption remains incomplete. The study found that around a quarter of marketers have not started their AI journey in measurement and insights, with non-adoption particularly high in CLTV and competitor analysis.

Despite these gaps, approximately 84% of marketers reported medium to high business impact from AI integration in measurement and insights.

The report argues that brands that develop proprietary CLTV and attribution models could build a sustained competitive advantage by using customer intelligence to make better investment and marketing decisions.

It also anticipates that by 2028, marketing organisations could have dedicated data and AI intelligence hubs, bringing marketing analytics, commerce intelligence, customer intelligence and AI-led automation into a more integrated structure.

Propensity modelling could become the next revenue engine

Propensity modelling has emerged as the most significant indicator of AI maturity in the study, although adoption remains highly polarised.

The applications include predicting customer acquisition propensity, identifying opportunities for upselling and cross-selling, anticipating churn and recommending the next best action for individual customers.

The study found that only 52% of marketers have started using AI for propensity modelling, meaning approximately 48% have yet to begin adopting it.

However, among those who have adopted AI in this category, 57% have progressed to full-stack integration, the highest level recorded across the four marketing capabilities surveyed.

Around 36% of marketers are also building in-house technology for propensity modelling, indicating growing interest in developing predictive capabilities within marketing organisations.

Full-stack adoption is particularly high for next-best-action applications at 67%, followed by upselling propensity at 59%, cross-selling propensity at 55% and churn propensity at 55%.

The study also found that 88% of marketers using AI for propensity modelling reported medium to high business impact, the highest among the four pillars.

Upselling and churn propensity applications recorded 90% medium-to-high impact, while acquisition propensity and cross-selling propensity also delivered substantial reported business benefits.

The findings indicate a shift in the role of AI from improving marketing productivity to directly influencing commercial outcomes.

By identifying which customers are most likely to purchase, upgrade, disengage or respond to a particular offer, AI-powered propensity models can help brands allocate marketing resources more efficiently and improve conversion opportunities.

However, the report cautions that the gap between early adopters and organisations yet to begin their AI journey remains substantial. It expects propensity modelling to become a mainstream marketing capability by 2028.

Customer journeys: Full-stack integration improves, but barriers remain

AI adoption across customer and sales journeys is also progressing, particularly in e-commerce recommendations, predictive sales lead scoring, inbound customer call routing and the application of AI to sales processes.

The report found that 54% of marketers who have adopted AI in this category have achieved full-stack integration, while 46% continue to use standalone solutions.

Full-stack adoption stands at 55% for AI applications in sales processes and 54% each for e-commerce recommendations and predictive sales lead scoring. Inbound customer call routing stands at 52%.

However, the study found that non-adoption remains relatively high in several applications, indicating that integration into live customer interactions and enterprise systems continues to be a challenge.

Around 40% of marketers have not started using AI for inbound customer call routing, while non-adoption stands at 39% for predictive sales lead scoring, 35% for AI applications in sales processes and 33% for e-commerce recommendations.

Only 26% of marketers are building in-house technology for customer and sales journey applications, suggesting continued dependence on external technology partners.

Nevertheless, approximately 86% of marketers reported medium to high business impact from AI integration in this category.

The report identifies the ability to deploy AI in real-time customer interactions and decision-making as a key competitive differentiator. The next phase will require brands to connect customer data, sales processes and AI-driven recommendations to ensure that consumer signals can translate into timely action.

CMOs expected to move beyond brand management towards business growth

The findings also point to a fundamental shift in the role of chief marketing officers (CMOs).

According to EY's assessment, marketing leaders will increasingly be expected to operate as growth owners with greater responsibility for business performance, partnerships and customer intelligence.

The report projects that the time CMOs spend on performance and growth marketing could triple by 2028, while the time devoted to media and agency management is expected to decline.

It recommends five priorities for marketing leaders: restructuring their responsibilities around growth, treating measurement infrastructure as a strategic product, moving AI upstream into planning and strategy, making propensity modelling a revenue priority, and developing greater internal ownership of AI capabilities.

The report also suggests that the traditional agency relationship will evolve. Agencies and external technology partners will continue to play an important role in developing and deploying AI solutions, but marketing organisations will need to strengthen their internal capabilities to identify business problems, interpret data and own strategic decisions.

By 2028, the CMO's organisation could be structured around three broad functions: growth, strategic partnerships and insights.

The growth function would encompass performance marketing, online retail media, commerce, digital shelf management, creative performance and lifecycle marketing. Strategic partnerships would focus on agency governance, platform and retail alliances, and marketing technology innovation. The insights function would bring together marketing analytics, commerce intelligence, customer intelligence and AI-led automation.

Brands increasingly looking at enterprise data to build AI advantage

Industry leaders contributing to the study also highlighted the importance of connecting AI applications with existing enterprise data rather than simply investing in new technology platforms.

Saurabh Jain, CMO of The House of Abhinandan Lodha, said the competitive advantage lies not necessarily in the AI model a company purchases, but in the data and intelligence it already owns.

He highlighted the use of enterprise resource planning (ERP), customer relationship management (CRM) and accounting platforms as sources of valuable business information that AI can analyse to identify patterns, uncover problems and improve decision-making.

At The House of Abhinandan Lodha, the company's AI ecosystem includes a WhatsApp bot, an AI voice bot and an intelligence-agent system supporting online sales teams.

The system evaluates customer interactions across 12 dimensions, identifies buying-intent signals and provides real-time cues to sales managers. These signals include customer reactions to pricing, amenities, competitor comparisons and other aspects of the buying journey.

Jain also emphasised that improving the customer experience and creating a frictionless buying journey are as important as deploying the technology itself. He highlighted the importance of consumer data protection, noting that India's Digital Personal Data Protection (DPDP) framework and the European Union's General Data Protection Regulation (GDPR) should be viewed as trust infrastructure rather than merely compliance requirements.

Rohit Bhasin, President, Head – Business Banking and Chief Marketing Officer at Kotak Mahindra Bank India, said the larger opportunity in AI-powered marketing lies in predictive intelligence rather than simply improving the efficiency of content production.

He pointed out that richer information on customer behaviour, preferences, context and intent can help brands improve segmentation, develop more relevant messaging and move away from broad-based communication.

According to Bhasin, AI's value will increasingly be measured by its ability to improve marketing judgment and create growth rather than simply reduce operational costs.

AI orchestration and external partnerships gain importance

The report also highlights a growing preference among marketers for vendor-supported AI solutions, particularly where developing proprietary infrastructure would require substantial engineering and data science investments.

Tejas Rathod, Founder and CTO of Mobavenue AI Tech Ltd, said that AI maturity depends on integration rather than adoption alone.

He argued that brands need to establish connected workflows across awareness, consideration, acquisition and retention, allowing data signals, decision-making and execution to feed into one another.

Illustrative deployment patterns from anonymised programmatic campaigns across retail, e-commerce, quick-commerce and consumer electronics during 2025-26 show how such orchestration can influence business outcomes.

These include hyperlocal targeting across connected television, streaming and mobile platforms, sequential cross-screen storytelling, cohort-based intent-led acquisition and AI-powered lifecycle segmentation.

The report cites examples of campaigns reaching more than 12 million users across 14 store clusters, brand recall improvements of 8-11% in a quarter, approximately 30% increases in scale alongside around 20% reductions in customer acquisition costs, and median return on advertising spend (ROAS) of 4-8 times in certain acquisition programmes.

In activation and retention, the cited examples include 50% quarter-on-quarter growth and 60-70% growth in repeat-user engagement, while customer acquisition costs remained flat during scale.

The report clarifies that these are illustrative outcomes from anonymised campaigns and that results vary by category, geography and campaign conditions.

Rathod's assessment is that the CMO's role will increasingly involve orchestrating systems across marketing, data and technology, demanding decision intelligence rather than static dashboards and establishing governance mechanisms to ensure that brand judgment remains part of AI-enabled processes.

Sandeep Pandey, CEO and Co-founder of Skewb, also emphasised that the next competitive advantage will come from agility rather than scale. He said legacy enterprise platforms will continue to provide foundational data infrastructure, but brands will need more flexible intelligence layers and specialist solutions to address specific business requirements.

He also identified trust in AI outputs, their reliability and their alignment with business processes as essential conditions for moving from pilot projects to enterprise-wide adoption.

AI search emerges as another marketing battleground

Beyond conventional marketing applications, the study also identifies AI-powered search as an emerging area that could reshape brand discovery and consumer decision-making.

Kirthiga Reddy, CEO and Co-founder of OptimizeGEO, said AI platforms are increasingly bringing discovery, consideration and decision-making together within a single conversational interface.

She highlighted India's growing adoption of AI assistants and noted that brands need to pay greater attention to how they appear in responses to unbranded, category-level consumer queries, rather than focusing only on searches that specifically mention their names.

According to her, brands working with OptimizeGEO have shown an average visibility gap of 40% for queries that matter to them.

The report also cites a case study involving the Business Intelligence Group (BIG), which partnered with OptimizeGEO. Its AI Visibility Score increased from 25% to 75% over 10 months, while AI-attributable traffic rose 151% and revenue from AI-referred customers doubled. Around 58% of these customers were entirely new to the organisation.

Reddy argued that AI search performance could influence a much wider range of marketing decisions, including content strategy, technical website health, paid media, partnerships, channel strategy, packaging, positioning and pricing.

The shift also presents a measurement challenge, as conventional tools designed around clicks and website visits may not adequately capture consumer discovery and decision-making taking place within AI platforms.

The road to 2028: From AI adoption to connected intelligence

The broader message emerging from the EY study is that Indian marketing is entering a new phase in which the ability to integrate AI across business functions will matter more than the number of tools deployed.

The study describes 2026 as a period of widespread adoption but relatively shallow integration. By 2027, organisations are expected to begin restructuring marketing teams and agency relationships to accommodate AI-led intelligence and decision-making. By 2028, the CMO's role could increasingly resemble that of a business growth leader, responsible for a profit-and-loss engine supported by real-time decision-making.

However, the pace of this transition will depend on how effectively organisations address data integration, technology interoperability, internal capabilities and consumer trust.

The survey was conducted between February and March 2026 and covered CMOs, media heads and agencies across 11 sectors. The report notes that the responses were not independently verified and were collated and aggregated for analysis.

For Indian marketers, the challenge is therefore no longer simply whether to adopt AI. It is how to connect the technology with the larger business agenda and turn fragmented applications into a continuously learning marketing ecosystem.