📝 usncan Note: The AI Bottleneck No One Really Talks About: Real-Time Data Agility
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Enterprises are learning the hard way: Real-time data agility isn’t a luxury for AI. It’s the backbone of anything that actually works.
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AI keeps making headlines, with billion-dollar investments and valuations. But behind the curtain, most enterprise deployments are hitting the same wall. According to a recent report by MIT’s NANDA initiative, nearly 95% of corporate AI projects fail to deliver measurable impact.
Even after spending a lot of money, many businesses still have trouble getting the correct data to their AI systems at the right time. Customer support tools, supply chain assistants and pricing engines depend on up-to-date information. Yet in most organizations, that data is either delayed, disconnected, or difficult to trust.
Real-time data agility — the ability to get the right information into your AI system at the exact moment it’s needed — is fast becoming the difference between AI that works and AI that just demos well.
Why AI Breaks In The Wild
AI models don’t fail because they’re stupid. They’re failing because they’re not getting enough food. In a lot of businesses, data is stored in separate systems that don’t communicate with each other. Or it comes too late to be helpful. People want voice assistants to answer right away, while their backends are sometimes working with reports from the day before. Predictive tools are supposed to see problems before they happen, but the sensor data lags behind.
As Tom’s Hardware pointed out, AI projects often falter not because of weak models, but because the data pipelines supporting them can’t keep up with real-time demands. The companies that succeed usually start small, focus tightly, and build their systems to pull from clean, current data sources — not outdated snapshots.
That difference becomes quite important when AI goes from the lab to the front lines. And that’s where most deployments go wrong.
Real-Time Data Agility
It’s not enough to train a model and bolt it onto your workflow. For AI to be useful in real-world settings, it needs up-to-date information — like current inventory levels, recent transactions, or the latest customer activity. But in many companies, that kind of data is hard to reach. It’s often stored in disconnected systems, updated too slowly, or stuck behind layers of outdated software.
“The silent killer,” said Oren Eini, cofounder and CEO of RavenDB, “is treating AI like it’s separate from your operational data.” When every query has to pass through a maze of services, queues, and transformation layers, he explained, “you’re basically playing broken telephone with your most important business decisions.”
That lag is where things fall apart. If a customer asks, “Where’s my order?”, an AI system can’t rely on yesterday’s batch-processed data. It needs to reason with current information, directly from the systems that run the business.
To close that gap, Eini believes AI should be guided by infrastructure, not burdened by it. “Instead of making the AI deal with data scraps, we tell the model what questions it can ask us and apply its intelligence to figure out what’s needed as part of the agentic process itself.”
That shift — giving AI secure, structured access to real-time data — is what real-time data agility looks like. And it’s what separates working systems from ones that only work in demos. Tools like RavenDB’s newly launched AI Agent Creator are built around that idea. They let developers set clear parameters for what an agent can access and control, enabling production-ready features in days, not quarters.
But RavenDB isn’t the only one building for this future. Cockroach Labs, InfluxData and Redis are all pushing toward the same goal: Infrastructure that doesn’t just allow AI to function but is designed with AI at the center.
Where AI Is Actually Working
At companies where AI is actually working, the difference isn’t just the technology. It’s how they’ve redesigned their data systems to support it. These organizations aren’t just layering AI on top of existing tools. They’re rebuilding the foundation so that information flows more easily, decisions happen faster, and models stay connected to real-world conditions.
“Don’t start by asking, ‘How do I integrate AI everywhere? Start by asking, ‘What does my user actually need?,’ Eini told me. “Most of the time,” he continued, “they just need to be able to get to the data they might already have permission to see more easily.”
In practice, this means you should “inject” AI as a smart assistant that operates inside the rights that users already have. The access restrictions and security boundaries stay the same, but the AI should be able to get information faster and easier and maybe even do things with it that don’t breach the rules. “This user-first approach cuts through corporate complexity and actually ships results,” Eini explained.
In many cases, the path to success has started small. Instead of launching sprawling initiatives, teams focus on one clear use case. They choose a problem they understand well and build an AI system around it. Once the system is running smoothly, they expand. This approach avoids the chaos that often comes with trying to solve everything at once.
Another pattern is how these companies treat AI as part of the core business, not as an experiment. The models don’t rely on delayed reports or outdated snapshots. They work with the same real-time data that human teams use every day. That kind of access changes what AI can actually do. It helps the system learn faster, respond more accurately and deliver results that matter.
What sets these teams apart isn’t a secret strategy. It’s the way they integrate AI into the business itself, aligning tools with real workflows and making sure the infrastructure supports it from day one. That’s what turns a promising pilot into something that lasts.
Making AI Work
There is a lot of talk about new models and better hardware, but the main job of making AI useful is happening somewhere else. How data is kept, how quickly it travels, and whether systems are made to support intelligence that works in real time are all important.
Some businesses are already making that a priority and revamping their infrastructure so that it can keep up with AI and be reliable. Eini thinks that the people who move this way will be in charge. As he put it, “the winners will be the ones who can quickly test ideas, get real feedback to make them better, and deploy them without breaking everything.” That kind of flexibility is what makes the difference between pilot experiments that don’t go anywhere and AI systems that do.
The noise around AI keeps growing. But the companies that are actually making it work are the ones fixing the foundation first, so the rest of it doesn’t fall apart later.
