In today’s supply chains, execution is where value is either delivered or lost. As disruptions become more frequent and harder to predict, the ability to respond in real time has become essential to maintaining reliability and customer trust.

These themes came through clearly at this year’s Maersk North America Customer Forum in partnership with MIT’s Center for Transportation & Logistics, “Supply Chain of the Future: Methods, Models and What’s Next in the Age of AI.” Leaders discussed how AI can strengthen execution and disruption response, helping companies manage uncertainty more effectively.

Many organizations are already using AI in planning and forecasting, but fewer have embedded those capabilities into real-time execution. That leaves a gap between what is planned and what actually happens when disruption hits.

The Execution Gap: Where Plans Meet Reality

Even the best planning systems cannot account for every real-world variable. Port congestion, capacity constraints, weather events, and shifting customer priorities all create volatility at the point of execution.

Forum participants stressed that disruption is no longer the exception; it’s now the norm. In a world where in the past 10 years, we’ve seen as many global supply chain disruptions as in the previous 50, many operational decisions still rely on static rules or manual intervention, which limits how consistently companies can respond at scale.

The result is often reactive firefighting, inefficient use of capacity, and inconsistent service. The challenge is not just seeing disruptions but turning that visibility into coordinated action quickly enough to matter.

From Visibility to Actionable Intelligence

Over the past decade, organizations have invested heavily in visibility platforms. These tools make it easier to track shipment status and exceptions, but they often stop short of helping teams decide what to do next.

At the forum, leaders pointed to a shift toward AI-enabled execution, where insights are translated into clear, prioritized actions.

Key applications included:

• Real-time disruption detection and impact assessment
• Dynamic rerouting and capacity allocation
• Prioritization of exceptions based on customer and business impact
• Automated recovery recommendations

These capabilities help organizations move beyond monitoring and toward active supply chain management, balancing cost, service, and risk in real time.

Coordinating Response Across the Network

One of the hardest parts of execution is coordination. Responding to disruption often means working across carriers, warehouses, suppliers, and customer teams that operate in different systems and with different priorities.

Forum participants noted that AI can help bring those decisions together. By connecting data across the network and applying more consistent decision logic, AI can support a more coordinated response.

For example, a delay in one leg of the journey can trigger downstream adjustments such as rerouting inventory, updating delivery commitments, or reallocating capacity.

That kind of coordination reduces manual escalation, improves resilience, and helps ensure that decisions in one area do not create problems elsewhere in the supply chain.

Embedding AI Into Operational Workflows

Adoption challenges remain, especially in execution environments where decisions are time-sensitive and carry real consequences. Embedding AI into workflows is essential. Rather than sitting in a separate analytics layer, it needs to support planners and operators at the moment decisions are made. That requires strong data, clear governance, transparency, and a process for learning from outcomes over time.

What It Means for Supply Chain Leaders

For leaders, the implications go beyond adopting new tools. Capturing value from AI in execution and disruption response requires changes in how decisions are made, coordinated, and acted on across the network.

• Move from reactive issue management to proactive disruption response. Supply chain leaders need operating models that can anticipate and prioritize issues earlier, before they escalate into service failures or customer-facing problems.
• Turn visibility into action at the point of execution. Real-time data only creates value when it leads to faster decisions, which means embedding AI-generated recommendations directly into day-to-day workflows.
• Support faster, network-wide decisions. AI can help organizations make better trade-offs across routing, inventory, capacity, and recovery actions, rather than optimizing one issue at a time in isolation.
• Prioritize based on customer and business impact. Not every exception matters equally, so disruption response should be aligned to the outcomes that matter most for service, cost, and the customer promise.
• Build trust through transparency and governance. Adoption will depend on whether operational teams understand the logic behind recommendations and can see that the system improves performance over time.

The Path Forward: From Resilience to Agility

Maersk North America’s Customer Forum points to an important shift in how organizations think about execution.

The focus is moving from simply managing disruptions to shaping outcomes in real time. AI can support that shift by helping organizations respond with more speed, precision, and coordination.

Ultimately, the goal is not just resilience, but agility. By embedding intelligence into execution, organizations can deliver more consistent service in volatile conditions and better keep the customer promise when it matters most.

Read more about AI’s potential to aid supply chain leaders in upstream planning and aligning demand and fulfilment.

Disclaimer: The views and considerations in this piece are a synthesis of the discussions from Maersk’s MIT Customer Forum only. They should not be interpreted as the direction or intention of Maersk’s AI product or service developments in the future.