Poor data infrastructure, not AI budgets, is slowing enterprise AI in India

Confluent's latest research suggests enterprises are shifting attention towards real-time data infrastructure as they move AI projects from experimentation into production.

Poor real-time data infrastructure, rather than a lack of AI investment, is emerging as one of the biggest barriers to enterprise AI adoption in India, according to new research from Confluent.

The company's 2026 Data Streaming Report found that 79 percent of Indian IT leaders say inadequate real-time data infrastructure is slowing their ability to scale AI initiatives, highlighting a growing focus on the data foundations required to move AI from pilot projects into production.

The findings suggest that as enterprises expand AI deployments, attention is shifting beyond models and applications towards the infrastructure needed to deliver trusted, real-time data across business systems.

The research also found that organisations continue to face challenges around data quality, fragmented data ownership and real-time data processing, making it harder to deploy AI at scale.

These issues are also delaying the adoption of agentic AI, with many organisations yet to move such deployments into production.

Confluent’s AVP – India and Emerging Markets, Rubal Sahni, said AI success is tied to an organisation's ability to manage and use data in real time.

"AI adoption has reached a point where success is no longer determined by access to technology alone. As organisations look to scale AI across business functions, the ability to move, govern and act on data in real time is becoming increasingly important," Sahni said.

He added that enterprises are no longer treating AI and data infrastructure as separate investment priorities.

"As AI applications and agentic systems become more deeply embedded into business operations, organisations need data that is continuously available, trusted and discoverable," he said.

Data infrastructure moves up the investment agenda

The report indicates that enterprises are investing in data streaming technologies alongside AI, reflecting a broader shift towards building the infrastructure needed to support production-scale AI environments.

According to Confluent, most IT leaders believe data streaming platforms help improve the quality, availability and accessibility of enterprise data, making it easier to deploy AI applications and agentic systems across business operations.

Confluent chief product officer, Shaun Clowes, said organisations have already committed significant budgets to AI, but the underlying data infrastructure has not kept pace.

"Most organisations do not have an AI investment problem, they have a data problem," Clowes said.

He said AI systems depend on fresh, accurate and contextual information, while many organisations continue to rely on fragmented data environments and legacy processes that were not designed for continuous intelligence.

The findings point to growing customer demand beyond AI platforms themselves.

As enterprises look to operationalise AI, partners are likely to see increasing opportunities around data integration, governance, real-time data architectures and modern data platforms that can support AI workloads at scale.

The report is based on responses from 4,625 IT leaders across 14 countries, including 650 respondents from India.