Why 95% of enterprise AI projects fail

The Hidden AI Bottleneck: Real-Time Data Agility

Earlier this month, Forbes interviewed Oren Eini, CEO of RavenDB, spotlighting a silent—but all too common—AI showstopper: real-time data agility. The article lays bare a harsh truth: almost 95% of enterprise AI projects fail to deliver real-world impact—not because models lack power, but because they lack fresh data when it matters most.

Why AI Stumbles Outside the Lab

AI doesn’t break down from lack of intelligence—it crashes from data starvation. When your AI systems rely on yesterday’s snapshots, siloed storage, or outdated batch feeds, they starve:

  • A voice assistant cobbled together with stale data looks impressive…but fails miserably in today’s context.

  • Supply chain tools that lag on sensor updates can't foresee disruptions.

  • And when your e-commerce bot can't tell a customer “Where’s my order?” in real time, you lose trust—and potentially revenue.

As Eini warns, treating AI as separate from operational data is like playing corporate decisions through broken telephone.

Real-Time Data Agility: What it Means

To succeed, AI needs data that’s clean, current, and always available—in other words, real-time data agility. That requires:

  • Direct, secure access to live systems—not stale stand-ins.

  • AI embedded in workflows, not perched on top as an experiment.

  • A user-first design where the AI works within existing permissions to fetch data faster and smarter.

The most successful deployments start small, tackle a well-understood use case, and expand—laying a foundation, not chasing speculative moonshots.

Infrastructure That Puts AI First

It turns out that the future of AI isn’t just about bigger or faster models—it’s about agile infrastructure that supports real-time intelligence.

RavenDB’s AI Agent Creator, mentioned in the interview, exemplifies this shift. Instead of wrestling with stale data scraps, AI agents can now ask the right questions and securely access live operational data—with guardrails in place. Results? Production-readiness in days, not quarters.

We’ve embraced this at Strife: we use RavenDB and its agent-driven architecture to power our AI workflows. The result is a fast, reliable CMS where AI isn’t an add-on—it’s part of the core.

Why This Matters (And What You Can Do)

Lots of companies talk about models and compute power; the winners are those tackling the real problem: data access and freshness.

If you want AI that sticks, you should:

  • Rebuild infrastructure to support real-time data flows.

  • Pilot with purpose: start with one use case, insist on speed and adaptability.

  • Integrate AI into business as a responsive agent—not a side project.

Because ultimately, AI is only as powerful as the data you feed it and how fast it gets there.

Read the full Forbes article here.

Want to learn more about what an AI agent is at a high level? Take a look at our post What Is an AI Agent? for a quick overview of the concept and how these agents can help streamline digital workflows.