A massive wave of AI investment is coming, but a startling number of business leaders are feeling unprepared. While global AI funding is set to hit a staggering $1.5 trillion in 2025, nearly half of executives lack confidence in their company’s ability to handle a crisis, control costs, and maintain security.
The root of this anxiety? The very complexity of the next generation of AI. “Agentic AI” systems, which can make autonomous decisions and interact directly with critical infrastructure, introduce unprecedented challenges. To harness their power without courting disaster, we need to completely reimagine what digital resilience looks like.
The solution gaining traction is an information fabric—a smart, integrated layer that connects and governs data across the entire business. By breaking down data silos, this fabric gives both human teams and AI agents the real-time insight they need to sense risks, prevent problems, recover quickly from disruptions, and keep operations running smoothly.
The New AI Doesn’t Speak Human—It Speaks Machine
Early AI models learned from the same information we do: text, audio, and video. But the new, autonomous AI needs to understand the digital world on its own terms. Its lifeblood is machine data: the constant stream of logs, metrics, and telemetry generated by every server, application, and device in your organization.
For agentic AI to be a true partner in resilience, it needs seamless, real-time access to this data stream. Without it, these powerful systems can miss critical anomalies, make flawed decisions, or their capabilities can be severely limited.
As Kamal Hathi, a senior vice president at Splunk (a Cisco company), puts it, agentic AI relies on machine data to understand context, simulate outcomes, and adapt. “We often describe machine data as the heartbeat of the modern enterprise,” Hathi says. “Agentic AI systems are powered by this vital pulse.”
The High Stakes of Getting It Wrong
Most companies haven’t yet achieved the level of machine data integration needed to fully empower these AI agents. This isn’t just a missed opportunity; it’s a genuine risk.
Early language AI often struggled with ambiguity and bias. Similarly, if agentic AI isn’t built on a foundation of fluent, real-time machine data, we can expect similar “misfires”—but with far greater consequences for security and stability.
“The speed of this innovation is starting to hurt us because it creates risks we’re not ready for,” Hathi warns. Relying on AI trained only on human-language data simply doesn’t work when your goal is a system that is secure, resilient, and always available.
Building a Smarter Foundation: The Resilience Fabric
So, how do we build a safer future with AI? The answer lies in designing a new kind of data infrastructure.
The goal is to move to an information fabric—a connected architecture that weaves together fragmented data from security, IT, and business operations. This fabric breaks down the silos that hide risks and enables real-time analysis and danger management across the entire digital landscape.
It’s the essential foundation that allows AI to not just be intelligent, but to be trustworthy and resilient.