Artificial intelligence has never looked more promising—and yet, across enterprises, it remains frustratingly stuck in pilot mode.
At Confluent’s Data Streaming World Tour in Mumbai, Andrew Sellers, VP of Technology Strategy and Enablement, offered a clear diagnosis of the problem. After speaking with more than 200 customers globally, his conclusion was blunt: the issue isn’t the sophistication of AI models. It’s the data feeding them.
To make the point, Sellers posed a simple question to the audience: would you cross a busy Mumbai street based on where traffic was ten minutes ago? Or an hour ago? The answer is obvious. But that, he argued, is exactly how most businesses still operate—making decisions based on delayed, outdated snapshots of reality.
For years, this approach worked. In the era of business intelligence, batch processing and periodic dashboards were enough to guide strategy. But AI changes the equation. It doesn’t just generate insights; it drives decisions in real time. And decisions, by definition, can’t afford to lag behind the moment.
This shift also exposes a deeper vulnerability. Traditional systems relied on human judgment to interpret data. Experienced professionals could spot inconsistencies, adjust for gaps, and apply context instinctively. AI systems, however, don’t have that luxury. They don’t question the data—they trust it. Completely.
That trust becomes dangerous when the underlying data is fragmented, outdated, or stripped of context.
Most enterprises, Sellers explained, were never designed for real-time intelligence. Over decades, they evolved into a patchwork of systems—each optimized for a specific function, each holding a piece of the data puzzle. Moving data across these systems is often slow and complex, involving multiple transformations before it becomes usable. By the time it reaches an analytics layer or an AI model, it has already lost its immediacy.
In controlled environments, this problem is easy to overlook. AI models trained on clean, static datasets often perform impressively in proof-of-concept stages. But the real world is far less forgiving. Data is messy, constantly changing, and deeply interconnected. When these models encounter live environments, the cracks begin to show.
That’s why so many AI initiatives stall before reaching production.
What’s needed, Sellers argued, is not just better models but a fundamental rethink of data itself. Enterprises must stop treating data as a passive byproduct of operations and start treating it as a living, evolving asset. One that is continuously updated, contextualized, and governed.
This is where data streaming enters the picture.
Unlike traditional batch systems, streaming architectures allow data to move in real time, capturing events as they happen and making them instantly available across the organization. The impact is profound. AI systems gain access to fresh, relevant, and trustworthy data, enabling them to operate with far greater accuracy and confidence.
The difference, Sellers noted, often comes down to context. Give an AI system the right information at the right time, and it can deliver remarkable outcomes. Starve it of context, and it can just as confidently produce the wrong answer.
In sectors like banking and financial services—where Mumbai sits at the heart of innovation—this shift is already becoming critical. Use cases such as fraud detection, risk scoring, and personalized customer engagement depend on immediacy. A delay of even a few minutes can render an insight useless.
Real-time data changes that dynamic. It allows institutions to detect anomalies as they occur, respond to risks in the moment, and create customer experiences that feel truly responsive. Just as importantly, it brings governance closer to the source, ensuring that decisions remain transparent and compliant in highly regulated environments.
For Sellers, the message is clear. The future of AI isn’t about building smarter algorithms in isolation. It’s about creating the conditions in which those algorithms can thrive.
Because in the end, the world doesn’t operate in batches. Events unfold continuously, decisions happen instantly, and value is created in motion.
Enterprises that align their data with that reality will be the ones that finally move AI from promise to production.