BLOG | Sep 25, 2025

Is AI really new—or just automation with better branding?

Is AI really delivering something new—or just making long-standing automation goals more accessible? In this episode of Discovering Disruptions in Tech, Forward Networks CTO, Brandon Heller and GigaOm CEO, Howard Holton explore what AI can (and can’t) accomplish on its own. Their conversation cuts through the hype to examine why clean, contextual data—not just algorithms—is the real key to meaningful enterprise outcomes.

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Dawn Slusher
Senior Manager, Content Marketing and Analyst Relations

Who should read this post?

Enterprise CIOs, CTOs, and CISOs navigating AI integration
Network and Security Operations leaders at complex enterprises
IT architects tasked with automating legacy infrastructure

What is covered in this content?

Why generative AI alone isn’t enough without reliable infrastructure data
How a network digital twin makes your network understandable—and accessible—for AI-driven operations
What it takes to safely embrace AI at enterprise scale

“AI is just automation by a different name.” It’s a bold claim—but one that Brandon Heller, CTO and co-founder of Forward Networks, and Howard Holton, CEO of GigaOm, unpack in a way that will make you think. In their recent conversation on Discovering Disruptions in Tech, they make the case that artificial intelligence, especially generative AI, is not delivering brand-new capabilities. Instead, it’s accelerating access to outcomes that were previously achievable only through years of custom development.

AI’s real value lies in its ability to reduce friction. With a natural language interface and broader data accessibility, AI reduces the dependence on rare engineering talent and accelerates insights. However, this value only materializes when the underlying data is trustworthy, especially when AI is applied to highly complex systems, such as enterprise networks. You still need a reliable data foundation. And that’s where a network digital twin comes in.

Can enterprises succeed without AI?

Perhaps. But in practice, few enterprises have the internal bandwidth or budget to build bespoke tools to automate network workflows. Even companies with deep technical resources, like Goldman Sachs, eventually hit a wall. As Heller recalled, Goldman initially built its own tool to automate firewall rule analysis, but maintaining it required specialized knowledge and constant attention. What changed with AI is not the possibility, but the practicality.

For AI to be safe and effective, engineers need precise, always-current data about the network; without it, AI tools are prone to making inaccurate assumptions, risky recommendations, or dangerous mistakes. Modern network digital twins solve this problem by providing always-accurate, structured, and queryable data across the entire hybrid network—spanning vendors, clouds, and platforms. This turns AI from a promising concept into a production-grade reality.

What is a network digital twin and how does it enable AI in enterprise IT?

A network digital twin is not just a data visualization tool—it’s a comprehensive, mathematically accurate software model of your entire hybrid, multi-cloud network. It enables AI to reason over the current and historical state of your network with precision. Unlike homegrown systems or narrowly scoped vendor solutions, a digital twin makes infrastructure data accessible to teams across NetOps, SecOps, and CloudOps.

As Heller puts it: “Think of it like Google Maps for your network.” A network digital twin is not a static map; it’s precise network data with context that delivers insight. You can simulate paths, validate connectivity, and even test configuration changes before deploying them. This capability is critical for enabling agentic AI, where systems need to analyze real-world data and simulate actions before taking them. AI without a digital twin risks hallucination and blind spots. AI with a network digital twin becomes a strategic enabler.

Why has the network been left behind—and what can we do about it?

While application development and security have embraced automation and AI, the network has remained largely manual. That’s not because it’s less important, but because it’s more fragile and more complex. It works, but it’s risky to touch.

A network digital twin changes that. By providing a verified single source of truth about your infrastructure, it allows teams to apply AI without worrying about bad outcomes based on inaccurate data. It’s a critical foundation for AI-readiness, enabling safe, scalable adoption of intelligent systems.

As both Heller and Holton emphasized, AI can’t succeed without data—and in the enterprise, the most critical data is locked in the network. The network digital twin is the key to unlocking this data.Watch the full discussion here.

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