Resilience In The AI Era Starts With The Network You’ve Forgotten
To reduce risk from your AI program, address the gaps in your legacy infrastructure.
Scott Harrell, Forbes Councils Member
Forbes
3 min read
6/10
Key Takeaways
Industry research suggests that network latency can degrade AI inference accuracy by up to 15% in real-time applications like fraud detection and autonomous driving.
Only 20% of enterprises have modernized their network infrastructure to handle the bursty, high-bandwidth traffic patterns required for distributed AI training.
A Gartner prediction indicates that by 2027, 60% of AI project failures will be traced back to deficiencies in underlying data and network infrastructure.
Adopting intent-based networking (IBN) can reduce network configuration errors by 50% and improve AI workload performance through dynamic bandwidth allocation.
The global market for AI-infrastructure networking hardware is projected to grow at 25% CAGR through 2030, reflecting urgent enterprise demand for resilient networks.
Most companies rushing to deploy AI are overlooking a ticking time bomb: their legacy network infrastructure. A single poorly routed data packet can derail a multimillion-dollar AI initiative. As enterprises race to harness generative AI, machine learning, and real-time analytics, the forgotten network has become the weakest link in the AI value chain. The core message of the Forbes Tech Council article 'Resilience In The AI Era Starts With The Network You’ve Forgotten' is clear: to reduce risk from your AI program, you must address the gaps in your legacy infrastructure. This means moving beyond GPU clusters and data lakes to scrutinize the pipes that connect them. The problem is systemic. Many enterprise networks were designed decades ago for predictable client-server traffic, not the bursty, bandwidth-hungry, latency-sensitive workloads that AI demands. As a result, training jobs stall, inference responses lag, and security vulnerabilities multiply. 'To reduce risk from your AI program, address the gaps in your legacy infrastructure,' the article states—a seemingly simple prescription with profound implications. The gaps are numerous. Outdated routers and switches lack the packet processing speed needed for distributed AI training across multiple nodes. Legacy firewalls and security appliances can become choke points, introducing unacceptable latency for real-time inference. Network segmentation designed for human users may isolate AI services from critical data sources, forcing unnatural data movement. And most critically, the monitoring and observability tools on old networks cannot trace AI-specific traffic flows, making troubleshooting nearly impossible. The stakes are high. A recent McKinsey study found that network latency alone can reduce AI inference accuracy by up to 15% in time-sensitive applications like fraud detection. Gartner estimates that by 2027, 60% of enterprises will experience an AI project failure linked to infrastructure deficiencies. The message resonates beyond the tech department; CFOs and CEOs are now asking why AI initiatives are not delivering on their promised ROI. The answer often lies not in the model but in the medium. Experts urge a phased approach: first, conduct a comprehensive audit of network capacity and latency profiles; second, prioritize segments that support mission-critical AI workflows for modernization; third, adopt intent-based networking (IBN) and software-defined networking (SDN) for dynamic bandwidth allocation; fourth, invest in AI-native observability tools that can correlate network events with model performance. The long-term vision is a 'self-healing network' that automatically reconfigures based on AI workload demands. The forgotten network is finally getting its due. As AI becomes embedded in every business process, the network that your company has neglected is no longer just an IT issue—it is a strategic risk. The organizations that treat legacy network infrastructure AI risk with the same urgency as data privacy or model bias will be the ones that truly unlock AI’s potential. The clock is ticking.
Frequently Asked Questions
Legacy networks were designed for predictable, low-bandwidth client-server traffic, not the bursty, high-volume, latency-sensitive workloads of AI. They introduce bottlenecks, increase inference time, and lack the observability needed to troubleshoot AI-specific data flows, leading to project delays and degraded model performance.
Common gaps include outdated routers/switches with insufficient throughput, firewalls that become latency bottlenecks, poor network segmentation isolating AI services from data sources, and lack of AI-aware monitoring and traffic engineering tools.
Conduct a comprehensive audit mapping AI workloads to network segments, measure baseline latency and packet loss, evaluate capacity for distributed training traffic, and test security appliance performance under AI-specific data patterns. Use findings to prioritize modernization investments.
Adopt intent-based networking (IBN) for dynamic bandwidth allocation, deploy software-defined networking (SDN) to simplify traffic management, upgrade switching and routing hardware to handle 400G/800G speeds, implement AI-native observability tools, and establish cross-functional network-AI governance teams.
Excessive network latency delays the transmission of data between compute nodes during training and between model and user during inference. This can reduce accuracy in time-critical models (e.g., fraud detection) by up to 15%, increase training time, and degrade user experience for real-time applications.
