Over the past two years, enterprises have rapidly adopted artificial intelligence (AI), deploying generative AI tools across customer service, software development and business operations. But as organisations move beyond pilots to large-scale deployment, many discover that success depends on more than choosing the right AI model. Reliable AI requires accurate and real-time data, yet many enterprises continue to struggle with fragmented databases, legacy systems and poor data quality. As a result, the focus is shifting from AI models to the data infrastructure that powers them.

This shift is reflected in Confluent’s 2026 Data Streaming Report, in which 79 per cent of Indian respondents consider inadequate real-time data infrastructure the major obstacle to scaling AI, while 72 per cent say poor data quality and fragmented systems are slowing the adoption of agentic AI. The findings suggest that enterprises increasingly view data readiness, not AI investment, as the defining factor in the next phase of AI adoption.

Rubal Sahni, AVP – India and Emerging Markets, at Confluent, said, “AI can only make reliable decisions when it has continuous access to fresh, trusted and contextual data. When that data arrives late, lacks context or sits across disconnected systems, the AI cannot be relied on, regardless of how advanced the underlying model is.”

Why data infrastructure matters

Generative AI has dramatically lowered the barrier to accessing advanced AI capabilities. Today, organisations can choose from numerous proprietary and open-source models without building one from scratch. As a result, the competitive advantage is gradually shifting away from the AI model itself towards the quality of enterprise data.

AI models depend entirely on the information they receive. If customer records are outdated, inventory databases are incomplete, or financial information is spread across multiple disconnected systems, AI-generated responses become inaccurate regardless of how powerful the underlying model is.

This challenge becomes even more significant as enterprises move beyond chatbots and content generation towards business-critical AI systems. Customer support assistants need live account information, fraud detection systems require continuous transaction updates, and supply chain AI depends on real-time inventory and logistics data. Delayed or inaccurate information directly affects AI performance.

Confluent’s findings indicate that Indian enterprises recognise this gap. While AI investments continue, organisations increasingly view modern data infrastructure as a prerequisite for scaling those investments rather than an optional technology upgrade.

What’s slowing AI adoption?

One key challenge highlighted in the report is fragmented enterprise data. In many organisations, customer, finance and operational data are spread across separate systems such as CRM and ERP platforms. Without proper integration, these systems create data silos, preventing AI applications from accessing a unified, real-time view of business information.

Data quality presents another major obstacle. Duplicate records, inconsistent formats, missing information and outdated databases reduce the reliability of AI outputs. Large language models may generate convincing responses, but they cannot determine whether enterprise data itself is accurate.

Governance also remains a challenge. Organisations need clear policies defining who can access data, how sensitive information should be protected and whether data complies with industry regulations. Without proper governance, enterprises risk exposing confidential information or producing AI outputs based on unreliable datasets.

According to the Confluent report, 72 per cent of Indian IT leaders say poor data infrastructure and data quality are slowing the deployment of agentic AI systems, highlighting that these issues become even more important as AI applications begin making autonomous decisions.

On legacy systems and infrastructure investment vs. AI investment, Sahni said, “Indian enterprises have spent decades building IT environments designed for periodic, batch-style reporting rather than continuous, real-time intelligence. Making these systems AI-ready requires substantial work, because it is not simply a matter of layering AI on top of existing infrastructure, but of fundamentally changing how data moves through the business.”

Why agentic AI raises the importance of real-time data

The report comes at a time when enterprises are increasingly exploring agentic AI, systems capable of carrying out multi-step tasks with limited human intervention. Unlike traditional generative AI tools that answer questions or generate content, agentic AI can interact with enterprise systems, retrieve information, execute workflows and make operational decisions.

For these systems to function effectively, they require continuous access to accurate and up-to-date information. For example, an AI agent managing supply chains needs current inventory levels, supplier updates, shipping information and demand forecasts simultaneously. If any of this information is delayed or incomplete, the AI may recommend incorrect purchasing decisions or disrupt operations.

The Confluent report shows that only 37 per cent of Indian organisations have agentic AI in production. While interest in autonomous AI systems is growing, many enterprises are still addressing data challenges before deploying them at scale.

Speaking on India’s AI readiness and data maturity, Sahni said, “Across nearly every measure, Indian enterprises are investing in real-time data infrastructure at a high intensity. Sectors that already operate at digital scale—banking and financial services, retail, e-commerce, quick commerce and telecommunications—are leading this shift, as they already process millions of real-time events daily, from payments to transactions to network activity. That positions Indian enterprises well to extend this advantage rather than simply close the gap with more mature markets.”

The growing role of cloud and governance

Cloud infrastructure has become another important component of enterprise AI strategies because it enables organisations to consolidate data from multiple business systems while supporting large-scale analytics and AI workloads.

However, cloud migration alone does not solve data problems. Organisations must also establish governance frameworks that ensure data remains accurate, secure and compliant throughout its lifecycle.

Effective governance includes standardising data formats, monitoring data quality, defining access controls and maintaining audit trails for AI systems. These measures become increasingly important as regulations around AI transparency and responsible AI continue to evolve globally.

The road ahead for enterprise AI

Enterprise AI is entering a new phase where success will be measured not by the sophistication of the AI model but by the strength of the data ecosystem supporting it. As organisations move from experimentation to large-scale deployments, reliable data pipelines, governance frameworks and real-time information are becoming essential for delivering accurate AI outputs. For Indian enterprises, improving data quality and breaking down silos may prove to be as important as investing in AI models themselves.

The shift also signals a change in enterprise technology priorities. Instead of focusing solely on adopting the latest AI models, businesses are increasingly investing in modern data architectures, cloud platforms and real-time data streaming to build AI-ready organisations. As AI applications become more autonomous and integrated into business operations, data infrastructure is likely to emerge as the defining factor that determines which enterprises can successfully scale AI and which will remain confined to pilot projects.