Poor data infrastructure is slowing enterprise AI across India

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Nearly eight in 10 Indian IT leaders say inadequate real-time data infrastructure is slowing the scale-up of artificial intelligence, shifting the enterprise AI debate from model access to the quality, speed and governance of the data feeding those systems. The finding matters now because companies are moving generative and agentic AI from demonstrations into customer service, fraud detection and operations, where stale or fragmented information can turn automation into an operational risk.

The Confluent 2026 Data Streaming Report surveyed 4,625 IT leaders across 14 countries, including 650 in India. Among Indian respondents, 78% cited insufficient infrastructure for real-time data processing, while 73% flagged uncertainty over data lineage, timeliness and quality; another 73% reported fragmented data ownership. Only 37% said agentic AI was in production, and 72% linked adoption barriers to data infrastructure or data-quality problems.

“AI adoption has reached a point where success is no longer determined by access to technology alone,” said Rubal Sahni, Confluent’s India and emerging markets executive. He said enterprises must be able to move, govern and act on data in real time.

The report’s commercial provenance deserves attention. Its publisher sells data-streaming technology, so the findings should be read as vendor-sponsored research rather than neutral market measurement. Even so, the global survey reports that 72% cited inadequate real-time processing infrastructure in 2026, up from 61% in 2025.

“Most organisations do not have an AI investment problem, they have a data problem,” said product chief Shaun Clowes.

For Indian enterprises, the immediate priority is measurable data readiness: assign ownership, document lineage, improve data freshness and test governance before autonomous systems receive wider permissions. Companies that fix those foundations first should scale enterprise AI faster, with fewer costly failures.