Eight in ten Indian marketers now use artificial intelligence in their operations, but fragmented customer data and disconnected systems are holding back the technology's ability to deliver real-time, personalised engagement. The findings come from Salesforce's tenth State of Marketing report, which surveyed 250 marketing decision-makers in India and found that AI adoption has become mainstream while the data foundations required to support it lag behind.
Nishant Kalra, Vice President - Sales, Salesforce South Asia, said data quality has emerged as the defining factor for successful AI-led marketing. "The biggest barrier to personalization today isn't AI, it's the quality and connectedness of the data that powers it. As AI agents become the next frontier of marketing, organizations need a trusted, unified view of the customer to deliver contextual, real-time engagement at scale," he said.
Data silos slow response times
Customer expectations have shifted sharply toward two-way conversations. Nearly 92% of marketers said consumers now expect brands to engage in dialogue rather than broadcast one-way promotional messages. Yet 71% admitted they struggle to respond quickly because they lack access to complete customer context across different business functions.
Cross-functional data access remains patchy. Only 60% of marketers reported full access to customer service data, while 61% have complete access to sales data and 58% to commerce data. Organisations with unified customer data are already seeing measurable advantages: those satisfied with their data foundations are 1.4 times more likely to regularly respond to customers and 1.6 times more likely to use AI agents to scale engagement compared with those operating on fragmented data.
AI trust grows despite implementation barriers
Marketers are increasingly willing to rely on AI for customer-facing activities. Around 86% said they would trust AI to respond to customers to help scale engagement. At the same time, 83% said they require more personalised content than they can currently produce, and 81% are already using AI to bridge that content gap - with content personalisation emerging as the most common use case.
Implementation challenges remain widespread. The report found that 98% of respondents encounter barriers to personalisation. Regulations and privacy concerns, poor-quality or unstructured data, and limited technical expertise ranked among the biggest obstacles. AI for Marketing Courses cover practical skills in personalisation, campaign optimisation, and AI agents - areas where many teams are working to close capability gaps.
Marketers using AI agents report stronger data connectivity across customer touchpoints. Around 76% of organisations using AI agents said they were satisfied with their ability to connect customer interactions across channels, compared with 55% among those not using AI. High-performing marketers were also 1.7 times more likely to have unified customer data and to use that information to create relevant customer experiences.
Generative AI reshapes search and discovery
The research points to changing customer discovery patterns as generative AI becomes more prevalent. Nearly half of marketers said they have yet to adapt their strategies to widespread AI adoption, while 66% reported struggling to keep pace with evolving customer behaviour.
Search strategies are shifting in response. According to the report, 91% said AI is reshaping their search engine optimisation strategy, and 92% have already started optimising content for AI-generated responses on platforms such as ChatGPT and Google's AI Overviews. High-performing marketing teams were 2.2 times more likely than underperforming organisations to have adopted Answer Engine Optimization strategies.
Why this matters for marketers
The report makes clear that AI adoption alone does not guarantee better customer engagement. The gap between deploying AI tools and having the unified data infrastructure to power them is where most teams stall. Marketers who invest in connecting data across service, sales, and commerce functions see measurable gains in response speed and the ability to scale personalised interactions. For teams evaluating AI agents or content personalisation tools, the starting point is not the AI itself - it is the quality and accessibility of the customer data feeding those systems.
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