By Rahul Jha, VP of Cloud, GenAI & Cybersecurity, Visionet Systems
Today, enterprises are sitting on more data than they have ever had access to: transaction records, customer interactions, supply chain signals and years of unstructured documents. But for most organisations this wealth of information has not been matched by a similar increase in business intelligence. Data is being collected at an unprecedented rate, but too little of it is being translated into decisions, action or results. The gap between having data and using it well is slowly becoming the defining line between AI leaders and AI laggards.
The scale-up era has arrived, and it is exposing a data problem
AI is moving beyond experimentation for many enterprises. In EXL’s 2026 U.S. Enterprise AI Study, which surveyed 322 executives, 96% of respondents view scaling AI as very or extremely important and about 40% of organisations have already taken their agentic AI initiatives beyond the pilot stage.
However, it is the scale that also shows the cracks. The same study found a striking 76% of respondents think they are ahead of competitors on AI, while only about one in ten meet the study’s criteria for AI Leaders. A key difference between the two groups is the state of their data, not just the sophistication of their models. Data infrastructure was the most cited barrier to scaling AI, with 70% of respondents reporting data challenges. The vast majority of laggard organisations still operate with data locked away in functional silos, unable to move fluidly across the enterprise.
This is not an isolated case. Dun & Bradstreet’s July 2026 AI Momentum Survey of ten thousand businesses found that although nearly all respondents have active AI initiatives, only 6% say their data is fully ready to support AI at scale. Limited access, inconsistent quality, and weak system integration can hinder this readiness.
The pattern is consistent across industries and geographies: the desire for AI is there, but the data foundation underneath is not keeping up.
Why “more data” was never the goal
For years, digital transformation conversations centred on collecting more data: more sources, more sensors, more systems of record. What enterprises are learning now is that volume without cohesion creates noise, not intelligence.
Three things need to work together to turn data into intelligence: a common and trustworthy data foundation, the AI and analytics capability to extract meaning from it and a governance layer that ensures the insights generated can actually be trusted and acted upon. This is where purpose-built Data and AI services are most valuable, not as an add-on to existing IT estates, but as the connective layer that makes every other digital investment more productive.
Building the enterprise-wide data foundation
For many enterprises, the starting point is data modernisation: breaking down silos between legacy systems, cloud platforms, and third-party sources to build a unified view of the enterprise data that all departments can rely upon. This involves ensuring that the data is clean and standardised, defining clear data ownership, and building pipelines that move information without losing integrity.
This can include adopting Data-as-a-Service (DaaS) solutions that provide access to managed data, rather than building everything from scratch. This can help address the cost and scalability constraints that have traditionally limited data strategies, allowing internal teams to focus on acting on data rather than managing it.
From clean data to contextual intelligence
Once the foundation is in place, AI services help determine how much value an enterprise can actually extract from it. Data pipelines that include ingestion, transformation, enrichment, and orchestration can enable AI systems to use real-time data rather than rely only on historical reports. This can help a fraud detection system flag anomalies as they occur. The value lies in what happens next: a suspicious transaction is routed for review, a stock shortage triggers replenishment, or a customer-service team receives the information needed to resolve an issue faster.
None of this works without context. Enterprise AI is only as useful as its grasp of how a specific business actually operates: its processes, regulations, and even the expertise of the employees. AI that lacks context and relies on disconnected data can fail in production even after successful initial pilots. The organisations pulling ahead have adopted data quality and governance as foundational capabilities rather than afterthoughts.
“Trusted data gives AI its foundation. Business context gives it purpose.”
Reimagining, not just automating, the operating model
The enterprises seeing the strongest returns aren’t using AI to merely speed up existing processes; they are redesigning how work gets done around AI and data as foundational elements. This involves creating a governance structure with clearly defined executive ownership, evaluating progress based on adoption and results rather than the number of pilots, and developing interoperability so that AI-powered agents in finance, operations, and supply chain can leverage the same data foundation. Success should be measured through outcomes such as shorter decision cycles, fewer errors, improved customer experience and lower cost to serve.
For enterprises in India and globally, this is a moment of both opportunity and urgency. The technology already exists. What remains is the discipline to treat data as a strategic asset rather than an operational byproduct, and to invest in the services that turn that asset into a durable advantage.
The organisations that internalise this mindset now won’t just adopt AI faster; they will extract meaningfully more value from every dollar invested in it. As the gap between AI leaders and laggards widens, that head start can become harder to close.
“The real measure of enterprise intelligence is how effectively information changes what the business does next.”