Indian firms race toward AI customer support despite readiness gap

Indian companies are betting heavily on artificial intelligence to transform how they handle customers. Nearly four out of five businesses surveyed expect AI agents—systems capable of making decisions and taking action with minimal human oversight—to manage customer support conversations within the next eighteen months. The ambition is clear and widespread. Yet the ground beneath these plans is shakier than the numbers suggest.

Adobe's 2026 AI and Digital Trends Report, which gathered responses from three thousand customer experience leaders and four thousand consumers worldwide, reveals a chasm between what companies want to do and what they're actually equipped to do. Only sixteen percent of organizations have rolled out agentic AI across their operations today. The rest are planning, hoping, preparing—but not yet delivering at scale.

The vision itself is coherent. Companies see AI agents handling far more than just support tickets. Seventy percent expect these systems to manage what happens after a purchase is made. Sixty-nine percent anticipate AI managing the sales conversation itself. Sixty-three percent foresee it handling account management, and sixty-two percent predict it will power customer engagement activities. The technology promises to free human workers from repetitive interactions and let them focus on problems that require judgment, empathy, or creativity.

But infrastructure tells a different story. While eighty-nine percent of companies have built cloud systems capable of supporting generative AI—the kind that writes, summarizes, and creates—only half that proportion, fifty-one percent, have equivalent infrastructure for agentic AI. The gap widens further when you look at customer data platforms: just thirty-nine percent possess the systems needed to feed agentic AI the information it requires to function well. Data itself emerges as the central problem. Seventy-five percent of respondents identified data integration and quality as their primary obstacle to deploying these systems. More than half said their existing data structures are already holding back AI progress.

There is another gap, perhaps more consequential. Forty-nine percent of businesses believe customers will eventually prefer interacting with AI agents rather than humans. Only nineteen percent of consumers agree. When asked whether customers would trust AI to make complex purchasing decisions, thirty-six percent of companies said yes. Twenty-one percent of consumers did. The trust asymmetry is stark: businesses are more optimistic about AI's role in customer relationships than customers themselves are willing to be.

Yet companies are seeing real returns from the AI they have deployed. Seventy-six percent report that AI has increased the volume of content their teams produce. Sixty-nine percent say employee productivity has risen. Sixty-five percent link the technology to revenue growth. These gains are real enough to justify continued investment, even as the harder work of building trustworthy, data-rich systems lies ahead.

What emerges from the data is a picture of an industry in transition, caught between genuine progress and genuine constraint. The next phase of AI adoption, Adobe suggests, will not be about experimentation or pilot projects. It will require something more foundational: robust data governance, frameworks that earn customer confidence, and the kind of infrastructure that can support autonomous systems operating at meaningful scale. Companies are racing toward that future. Whether they can build the foundations fast enough to match their ambitions remains an open question.