Article

Global supply chains are navigating unprecedented turbulence. From port strikes and raw material shortages to extreme weather events and geopolitical conflicts, the frequency and unpredictability of disruptions have exploded. And yet, many supply chain organizations still operate with tools and workflows built for stability – not for speed or flexibility. 

Traditional dashboards and analytics solutions are valuable for visualizing what has happened. But they often fail to provide actionable paths forward. In other words, they show the storm – but don’t offer a way through it. 

Take a typical scenario: a container with critical components is delayed in transit. The delay shows up in the transport visibility system. But between recognizing the issue, notifying procurement, escalating to the planner, and deciding on a response, hours – sometimes days – are lost. By the time action is taken, the production line has already stopped. The data was available. But the steering failed. 

The graphic illustrates how advancing in AI and data maturity transforms decision-making from reactive to fully informed, directly reflecting the statement “Steering without AI is like flying blind through turbulence.” In the early stages, with only raw or basic descriptive data, organizations can sense issues but lack the clarity to anticipate or navigate them effectively. As they progress through predictive analytics and prescriptive modeling toward GenAI business partnering, their “visibility” improves – turning data into foresight and clear direction, much like equipping a pilot with the instruments needed to navigate safely through turbulence.  

Awareness alone doesn’t protect a supply chain – responsiveness does. And in many organizations, the response time to disruptions remains far too slow. Why? Because many workflows are still manually triggered and human dependent. Planners rely on gut instinct, back-and-forth emails, spreadsheets, and unclear ownership of decisions. 

Even when risks are detected, the organization often lacks the capacity to act in real time. For example, a control tower flags a port strike in Southeast Asia. But rerouting decisions require multiple layers of approval, each delayed by time zones, lack of clarity, or conflicting priorities. The opportunity to respond proactively vanishes. Inventory builds up in the wrong place. Customers are impacted. Revenue is lost. This is where AI – especially in the form of intelligent supply chain steering – makes the difference

Modern AI doesn’t just analyze data – it proposes and triggers action. 

Predictive models detect disruption signals before they escalate. Large Language Models (LLMs) can synthesize these inputs and create human-like communication, escalation paths, and decision options. Combined, they form a new digital capability: autonomous or semi-autonomous supply chain steering. 

Let’s take a practical example. A predictive engine detects a severe weather system moving toward a supplier’s region. Based on historical disruption patterns and real-time stock levels, the AI model recommends the following: 

• Pre-emptively shift inbound orders to secondary suppliers 
• Reallocate safety stock from the West to the East DC 
• Alert key customers of potential delays in affected SKUs 
• Draft a supplier communication to confirm delivery options 
• Trigger an internal escalation if the supplier doesn’t respond within 3 hours 

All of this happens within minutes – not hours or days. The planner still has control. But instead of discovering the issue too late, they’re presented with fully prepared, data-driven action paths and communication. 

This is no longer a theory. Major retailers like Walmart and Target already use AI to prioritize stock replenishment by region in near real time. Amazon is integrating generative AI into its last-mile systems to reroute deliveries and predict missed deliveries before they happen. AI is not coming. It’s here. 

To make this shift, companies need more than just AI tools. They need an AI-ready operating model. 

First, real-time data is non-negotiable. ERP, TMS, WMS, demand planning, supplier portals - these systems must be connected in a way that allows AI models to read, interpret, and act. Data silos kill response time. 

Second, decision logic must be encoded. What triggers an escalation? What are acceptable lead time trade-offs? Which customers get priority in a constraint scenario? These rules – often tribal knowledge – must be made machine-readable. 

Third, governance is essential. Not all AI recommendations should run fully autonomously. Organizations need “human-in-the-loop” frameworks for critical or high-risk decisions. But for the 80 percent of routine disruptions, AI should act independently within pre-agreed boundaries. 

A concrete use case: A dynamic ATP (Available-to-Promise) engine powered by AI reprioritizes orders in real time based on customer value, SLA (Service Level Agreement) risk, warehouse availability, and last-mile delivery conditions. No manual rebooking is required. 

The biggest mindset shift in modern supply chain management is this: from sensing to steering. It’s no longer enough to know what’s happening. Competitive advantage comes from responding faster, smarter, and earlier than the competition. 

AI doesn’t make supply chains perfect. But it makes them decisive. It reduces the delay between disruption and response. It frees up human planners from constant firefighting. And it enables supply chains to act – not just react. 

So, ask yourself: is your supply chain operating with visibility, or with velocity? 

In today’s turbulent environment, steering is everything. Without AI, you’re flying blind. 

Kröber, J. /  Zeigert, J.