For logistics and supply chain leaders, demand fluctuations and disruptions have become increasingly structural rather than incidental. The question now becomes how to anticipate and prepare for them before they cascade across the supply chain. Most recently, AI has been positioned as the answer.  

Yet many organizations are discovering that AI alone is not enough. Models trained on historical and siloed data struggle when conditions across the logistics ecosystem change faster than the data can be updated. In logistics, where outcomes are shaped by thousands of actors across borders, AI can’t reach its full potential without awareness of the full logistics ecosystem.  

AI must be continuously informed by real-time data from supply chain partners, carriers, and the broader logistics ecosystem. 

Traditional logistics systems were designed to optimize what a company could directly control: contracts, inventory, routes and costs. That model made sense when supply chains were relatively linear and predictable. Today, however, performance is increasingly determined by external, multidimensional dependencies such as carrier capacity, services subcontracting, port congestion, regulatory changes, and upstream supplier disruptions that sit outside an enterprise’s purview. Furthermore, today’s high-performing logistics operations rely on digitalized collaboration for accuracy and agility, including electronic bills of lading (eBL) or electronic consignment notes (eCMR). 

This is where network intelligence becomes foundational. By connecting shippers, logistics providers, carriers and suppliers on a shared network, companies gain visibility into what is happening on the ground. Real-time signals like status updates, delays, capacity changes, and exceptions provide the context AI needs to move from reactive analysis to proactive insight. 

Without this shared data layer, AI models are forced to infer risk and propose decisions based on incomplete information. With it, they can detect patterns and insights as they emerge across the ecosystem, enabling earlier warnings, more accurate forecasting, and more efficient orchestration. 

AI Is Only as Good as the Data It Sees 

Logistics disruptions manifest as ripple effects, where the root cause is often far removed from the businesses affected. A port strike triggers carrier schedule changes, which drive transportation delays and inventory imbalances that lead to production and delivery escalations when supply expectations go unmet. AI systems trained on siloed data often struggle to process these cascading effects in real time. 

Network-fed AI, by contrast, learns continuously, across organizational borders. As new events unfold, from sudden governmental regulatory changes to weather-driven reroutes, AI models can recalibrate predictions based on live inputs from multiple partners when fed with data from a business network. This allows teams to shift from asking, “What just happened?” to “What comes next, and how can we prepare?” 

This represents a significant shift. Supply chain management has long been focused on cost optimization and supplier relationships. Today, it is increasingly responsible for securing continuity of supply through volatile conditions in a complex global economy. AI informed by logistics network data enables supply chain leaders to assess risk alongside cost, evaluating suppliers and service providers not just on price, but on reliability, flexibility and performance. 

The most powerful outcomes of network-powered AI begin with improved collaboration and lead to optimized supply chain orchestration. To enable this, supply chain and logistics teams rely on technology to connect systems, metrics and analyses, not just within their own organization, but across ecosystem partners too. When disruptions occur, any disconnect can slow decision-making when speed across the chain is what matters most. 

Shared network data creates a common language for true process integration that transcends simple, old fashioned data exchange. Supply chain teams can see the downstream impact of upstream sourcing decisions, while logistics teams gain insight into external service provider challenges and constraints. With inter-company intelligence, AI helps translate this shared visibility into real-time recommendations, adjusting order quantities, shifting lanes, or engaging alternative carriers in the process.  

All of this leads to stronger carrier collaboration, enhanced orchestration, and better supply chain performance. Supply chain relationships are no longer about negotiated rates; they enhance data sharing and responsiveness for the betterment of the supply chain. When carriers contribute timely status updates into a shared network, AI can more accurately predict delays, recommend proactive rerouting, and support fairer, more transparent allocation of capacity during periods of constraint. The adage “a rising tide lifts all ships” holds true for logistics collaboration technology.   

AI That Responds at the Speed of Disruption 

Looking ahead, the next evolution of AI in logistics will be agentic systems that autonomously monitor conditions across the network, identify emerging risks, and orchestrate activity with increasingly reduced human oversight. 

For example, if an AI agent detects increasing dwell times at a port combined with tightening carrier capacity, it could automatically propose alternative routes, alert supply chain managers to potential supply impacts, or initiate replanning with logistics partners. Humans remain in control of strategy and governance, but AI handles the speed and scale of operational response. 

This capability is valuable in environments where disruptions unfold outside traditional business hours and across global time zones. Agentic AI helps ensure that early warning signals are not missed and that response time is measured in minutes, not days. To reach such heights in innovation, a business network is the foundation.  

Resilience is often discussed as a steady-state destination, when in fact, it is an active capability that must be continuously exercised. AI plays a central role in achieving resilience, but only when it is grounded in the reality of the network it serves. 

For logistics and supply chain leaders, the path forward is clear. Investing in AI without a foundation in network connectivity limits their potential. In a world where uncertainty is the norm, true resilience comes from an understanding that we are in this together, and combining intelligent technologies with shared, real-time data across partners, carriers, and suppliers is the key to unlock supply chain potential. 

Ralf Hierzegger is chief product officer, SAP business network for Logistics.