Network complexity is quietly stalling AI initiatives. New IDC research finds that network security concerns, automation gaps, and staffing shortfalls are the top network-related reasons AI projects get delayed or abandoned, with nearly a third of enterprises citing network security alone. This new IDC report, Navigating the Shift to Autonomous Networking, examines why manual network management can no longer keep pace with digital business and AI-driven demand, and lays out what's actually required to build a trustworthy autonomous network.
At the center of that shift is the network digital twin: a complete, mathematically precise behavioral model of a network's configuration, state, and security posture across every vendor, domain, and cloud. IDC identifies four requirements every autonomous networking strategy must meet and explains why a digital twin foundation is what makes safe automation possible in the first place. The report also quantifies the payoff: organizations with strong network visibility and change validation report up to 89% less unplanned downtime, 44% faster mean time to resolution, and 71% faster service deployments.
The report also looks at how Forward Networks operationalizes these principles in production, multi-vendor and multi-cloud networks. Forward Enterprise builds the digital twin itself; Forward Predict validates network and security changes against that twin before they ever touch production; and Forward AI provides a natural-language, evidence-backed interface to the twin for engineers, operators, and autonomous agents alike.
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