Modern supply chains are extremely complex, intricate, and expansive, comprising many parties (like brokers, shippers, and warehouses) that must communicate and operate in a timely and organized manner. Like any ecosystem, one small disruption can affect the larger environment in unexpected and ruinous ways. Consequently, many enterprises have incorporated artificial intelligence (AI)-powered systems and applications to more effectively facilitate their ever-expanding supply chains.
AI has had an extraordinary impact on supply chains and logistics. For starters, AI systems can analyze real-time data and compare those insights with historical data much faster than humans. This unprecedented speed and accuracy allow supply chain managers to perform data-driven decision-making and engage in forecasting, demand planning, and predictive warehouse management. AI can also help automate documentation and other data entry tasks, saving time for short-staffed teams. AI can even examine weather forecasts and traffic patterns to optimize routes for truckers.
Experts expect the global AI in logistics market size to grow exponentially. In fact, Precedence Research estimates that it will increase from USD 26.35 billion in 2025 to around USD 707.75 billion by 2034, accelerating at a CAGR of 44.40% from 2025 to 2034. While it is a business imperative that enterprises implement AI into their logistics and supply chain processes to stay competitive, they cannot overlook the necessity of network resiliency.
The Consequences of Outages and the Unexpected Risks of Increased AI Usage
Supply chains need a resilient network underpinning their AI-enabled applications to ensure business continuity, even during a disruption. Without such a network, unexpected outages, misconfigurations, and security vulnerabilities could compromise AI performance. Should AI-reliant logistics systems stop working, businesses will face consequences ranging from minor inconveniences to significant disruptions and financial losses. For example, if crucial AI tools aren’t working, demand forecasts will be inaccurate, meaning resources will get incorrectly allocated, resulting in delayed deliveries and, ultimately, unhappy customers.
Something worth noting about increased AI usage in supply chains is that as AI-enabled systems become more complex, they also become more delicate, which increases the potential for outages. Something as simple as a single misconfiguration or unintentional interaction between automated security gates can lead to a network outage, preventing supply chain personnel from accessing critical AI applications. During an outage, AI clusters (interconnected GPU/TPU nodes used for training and inference) can also become unavailable. Worst of all, administrators could find themselves locked out of the network and unable to troubleshoot the issue.
Another challenge is that AI workloads demand specialized network considerations. Unlike traditional enterprise workloads, AI traffic involves high-volume data transfers, burst traffic patterns, and frequent synchronization. AI traffic is also sensitive to delays, meaning even small delays can significantly affect performance. Without a resilient network, the traffic from AI applications, especially those requiring real-time processing and large data transmission, could overload network infrastructure, causing bottlenecks, latency, and even outages.
Bolstering Network Resilience with Out of Band Management
Businesses must increase network resiliency to ensure their supply chain and logistics teams always have access to key AI applications, even during network outages and other disruptions. One approach that companies can take to strengthen network resilience is to implement purpose-built infrastructure like out of band (OOB) management.
With OOB management, network administrators can separate and containerize functions of the management plane, allowing it to operate freely from the primary in-band network. This secondary network acts as an always-available, independent, dedicated channel that administrators can use to remotely access, manage, and troubleshoot network infrastructure. Even if the primary network suffers an outage (whether from intense AI workloads, cyberattacks, or misconfiguration), OOB management enables administrators to access infrastructure for management purposes, maximizing the uptime of critical AI applications.
Organizations can further augment OOB management by combining it with a network technology like Failover to Cellular, where a cellular backup connection (3G, 4G, or 5G) automatically activates if the primary connection fails. As another safeguard for business continuity, Failover to Cellular helps administrators maintain visibility of the entire network, permitting them to manage and access all infrastructure remotely.
Along with being invaluable for troubleshooting during outages, OOB management can help proactively prevent issues through continuous monitoring, logging, and security oversight. OOB management is also incredibly useful for administrators who work with distributed networks, as is the nature of today’s sprawling supply chains. Specifically, OOB management empowers administrators to perform remote firmware updates, system resets, and security policy enforcement without interfering with AI workloads. These remote capabilities save time because companies don’t need to send technicians to visit every device in the field.
The Necessity of Network Resilience in Light of Digital Transformation
As supply chains continue to become more sophisticated thanks to AI and other digital transformation technologies like machine learning, IoT, cloud, and blockchain, it is paramount that businesses safeguard their systems from disruption through solutions like OOB management. Enterprises must plan beyond initial deployment and focus on day-two operations, including remote troubleshooting, diagnostics, and data collection when issues arise.