Over the past two years, companies have rapidly adopted artificial intelligence (AI), deploying generative AI tools across customer service, software development, and business operations. But as organizations move beyond pilots to large-scale deployments, many companies realize that success depends on more than choosing the right AI model. Reliable AI requires accurate, real-time data, but many companies continue to suffer from fragmented databases, legacy systems, and poor-quality data. 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, where 79 percent of Indian respondents believe inadequate real-time data infrastructure is the primary barrier to AI expansion, and 72 percent say poor data quality and system fragmentation are slowing the adoption of agent AI. This finding suggests that companies are increasingly viewing data readiness, rather than AI investment, as the determining factor for the next stage of AI adoption.
Rubal Sahni, AVP, India and Emerging Markets, Confluent, says, “AI can only make reliable decisions if it has continuous access to fresh, reliable, and contextual data. If that data arrives late, lacks context, or exists across disconnected systems, AI cannot be trusted, no matter how sophisticated the underlying model.”
Why data infrastructure matters
Generative AI has dramatically lowered the barrier to accessing advanced AI capabilities. Organizations can now choose from a number of proprietary open source models without having to build a model from scratch. As a result, competitive advantage is gradually shifting from the AI models themselves to the quality of enterprise data.
AI models are completely dependent on the information they receive. If customer records are outdated, inventory databases are incomplete, or financial information is spread across multiple disconnected systems, the responses generated by AI will be inaccurate, no matter how powerful the underlying model.
This challenge becomes even more important as companies move beyond chatbots and content generation to business-critical AI systems. Customer support assistants require live account information, fraud detection systems require continuous transaction updates, and supply chain AI relies on real-time inventory and logistics data. Delays and inaccuracies in information directly impact AI performance.
Confluent’s findings show that Indian companies are aware of this gap. While investments in AI continue, organizations are increasingly recognizing a modern data infrastructure as a prerequisite for scaling their investments, rather than an optional technology upgrade.
What is slowing down AI adoption?
One of the key challenges highlighted in the report is fragmented enterprise data. Many organizations have customer, financial, and operational data spread across separate systems, such as CRM and ERP platforms. Without proper integration, these systems create data silos that prevent AI applications from accessing a unified, real-time view of business information.
Data quality is another major hurdle. Duplicate records, inconsistent formats, missing information, and outdated databases make AI output unreliable. While large-scale language models may generate convincing responses, they cannot determine whether the corporate data itself is accurate.
Governance also remains a challenge. Organizations need clear policies that define who has access to data, how sensitive information will be protected, and whether the data complies with industry regulations. Without proper governance, companies risk having sensitive information compromised or generating AI output based on unreliable datasets.
According to a report by Confluent, 72% of Indian IT leaders say poor data infrastructure and data quality are slowing down the adoption of agent AI systems, highlighting that these issues will become even more important as AI applications start making autonomous decisions.
Commenting on investments in legacy systems and infrastructure and investments in AI, Sahni said, “Indian businesses have spent decades building IT environments for periodic batch-style reporting rather than continuous real-time intelligence. Making these systems AI-enabled will take a lot of work, because it doesn’t just layer AI on top of existing infrastructure; it requires a fundamental change in the way data moves within the business.”
Why agent AI makes real-time data more important
The report comes as companies explore agent AI, systems that can perform multi-step tasks with limited human intervention. Unlike traditional generative AI tools that answer questions or generate content, agent 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, up-to-date information. For example, an AI agent managing a supply chain simultaneously needs current inventory levels, supplier updates, shipping information, and demand forecasts. If any of this information is late or incomplete, AI could recommend incorrect purchasing decisions or disrupt operations.
According to a report by Confluent, only 37% of organizations in India have deployed agent AI in production. While interest in autonomous AI systems is growing, many companies are still grappling with data challenges before deploying them at scale.
Commenting on India’s AI readiness and data maturity, Sahni said, “Indian businesses are investing intensively in real-time data infrastructure in almost every aspect, including banking and financial services, retail, e-commerce, quick commerce and telecom, which already operate at a digital scale. The Indian sector is leading this change, as it already processes millions of real-time events every day, from payments to transactions to network activity. Indian companies are therefore well-positioned to extend this advantage, rather than simply closing the gap with more mature markets.”
The growing role of cloud and governance
Cloud infrastructure has become another critical component of enterprise AI strategies because it allows organizations to integrate data from multiple business systems while supporting large-scale analytics and AI workloads.
However, moving to the cloud alone won’t solve your data problems. Organizations also need to establish a governance framework to ensure data accuracy, security, and compliance throughout its lifecycle.
Effective governance includes standardizing data formats, monitoring data quality, defining access controls, and maintaining audit trails for AI systems. These measures are becoming increasingly important as AI transparency and responsible AI regulations continue to evolve globally.
Industry analysts have consistently argued that successful AI adoption requires improvements across data management, governance, and operational processes, rather than simply deploying more sophisticated AI models. Confluent’s research reinforces this view by suggesting that enterprise AI success depends as much on data readiness as on model capabilities.
The road ahead for enterprise AI
Enterprise AI is entering a new phase where success is measured not by the sophistication of the AI model but by the strength of the data ecosystem that supports it. As organizations move from experimentation to large-scale deployment, reliable data pipelines, governance frameworks, and real-time information are becoming essential to delivering accurate AI output. For Indian businesses, improving data quality and breaking down silos could prove to be as important as investing in the AI models themselves.
This shift also signals a shift in companies’ technology priorities. Rather than focusing solely on deploying the latest AI models, enterprises are increasingly investing in modern data architectures, cloud platforms, and real-time data streaming to build AI-enabled organizations. As AI applications become more autonomous and integrated into business operations, data infrastructure is likely to emerge as a determining factor in which companies are able to successfully scale AI and which remain in pilot projects.
