It’s growing difficult to ignore the expensive truth about AI. Companies are spending millions on the newest models, but McKinsey’s 2025 State of AI survey found that more than 80% of companies aren’t seeing any real impact on their EBIT from using generative AI. Only 1% of executives feel their AI rollouts were “mature.”
Models are smart, but the data they use is old. That’s a big problem in a world that changes every minute.
Why Batch Processing Fails Modern AI Systems
Platforms like Ververica’s Unified Streaming Data Platform, built by the creators of Apache Flink®, are solving this bottleneck by enabling enterprises with real-time AI data pipelines that operate in the moment, not after the fact. With the ability to process billions of records per second and deliver millisecond-level latency, Ververica provides the scale and responsiveness that today’s AI systems demand.
Traditional batch-based systems are a thing of the past. What was sustainable then is no longer adequate for the modern world. In fraud detection, this means catching threats before losses or data security is compromised. Many companies invest in models without fully modernizing the data delivery systems that power them – which often results in underperformance and unmet expectations of AI rollout.
Consider this: 92% of organizations plan to increase AI spending in the next three years. But data center vacancy is just 1.9% in major markets, and 59% of firms report rising bandwidth issues. We’re forcing next-gen AI through last-gen pipes.
What Is Real-Time Infrastructure and Why It Matters for AI
When companies address the infrastructure gap, the difference is dramatic. Take KartShoppe, as a use case, a retail brand that moved to real-time feature engineering. Instead of relying on historical data, its system now analyzes every customer interaction in real time.
This enables up-to-the-minute suggestions: “items viewed in this session,” “time since last purchase,” and more. Conversion rates improved, and abandoned carts dropped significantly.
Why Agentic AI Needs Real-Time Data to Succeed
The next wave of AI, agentic systems that act autonomously, makes this infrastructure gap even more critical. These aren’t your typical AI models that just provide answers. Agentic AI systems make independent decisions, adapt to new information, and solve complex problems with minimal human intervention. Unleashing them with stale datasets is suboptimal at best, and irresponsible at worst.
Without real-time data infrastructure, these systems become slow, blind, or reactive. The recommendations are like having a brilliant detective who only receives case updates once a week. By the time they figure out what’s happening, the criminals have moved on.
A good example of how to truly leverage AI is ING Bank, who uses real-time infrastructure based on Apache Flink to power its fraud detection system, processing over 1 million transactions per day and streaming machine learning (ML) model updates live to adapt to emerging threats in real time.
Agentic AI needs an event-driven architecture that can manage multiple jobs simultaneously while maintaining context across different systems and complex workflows. It requires the infrastructure that can process several billion transactions per second while ensuring zero downtime and zero data loss.
This kind of transformation is made possible by Ververica’s Unified Streaming Data Platform. Built by the creators of Apache Flink and 100% compatible with open-source Flink, the platform processes billions of records per second with millisecond-level latency. It also integrates with modern ML frameworks, analytics tools, and data sources, making real-time AI workflows easier to deploy and manage.
How does Real-Time Feature Engineering come into play?
Real-time feature engineering is what enables AI to adapt in the moment. It’s especially critical for agentic systems because they need to make decisions based on what’s happening now, not last week.
Traditional models are trained on batch-derived features like “monthly average spend” or “weekly login frequency.” Those signals are static and stale. This is fine for dashboards, but useless for real-time decision making.
In contrast, real-time feature engineering powers context-aware features like “past hour transaction velocity” or “this session behavior.” These are the signals that make fraud detection instant, personalization precise, and autonomous actions safe and relevant.
This is where agentic AI and feature engineering truly become powerful together:
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Agentic AI acts as the “brain” – capable of deciding what to do.
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Real-time feature engineering is the “senses” – think of it like feeding that brain the freshest signals.
Without this pipeline, agentic systems are operating blind.
Ververica’s software enables enterprises to build these features directly into the data stream, transforming lagging pipelines into dynamic, decision-ready architectures. It is what makes real-time AI viable at scale; not just in theory, but in production.
Why Real-Time Infrastructure Is the Competitive Advantage in AI
The infrastructure divide is becoming the clearest line between AI leaders and laggards. Companies that modernize now are seeing real business outcomes: better personalization, proactive fraud prevention, and smarter decision-making.
The others? They’re still wondering why their expensive models don’t deliver.
You’ve already invested in AI. The only real question is, have you invested in the infrastructure that lets it work?
In this era, infrastructure isn’t just a technical improvement. It’s the strategy.
