In today’s supply chains, delivering on the customer promise has become both more critical and more challenging. Demand fluctuations, shifting customer expectations, and ongoing disruption are putting pressure on organizations to align what they plan with what they can actually deliver.

These challenges were a central focus at the recent customer forum co-hosted by Maersk and MIT’s Center for Transportation & Logistics, “Supply Chain of the Future: Methods, Models and What’s Next in the Age of AI.” Across industries, leaders explored how artificial intelligence (AI) can address one of the most persistent gaps in supply chains by enhancing human decision making: the disconnect between demand signals, fulfillment decisions, and real-world outcomes.

A consistent theme across both the roundtable discussions and pre-event survey was that while data availability has improved, confidence in how that data is used has not kept pace.

The Hidden Friction in Demand and Fulfillment

Demand and fulfillment decisions sit at the intersection of planning and execution, relying on inputs from sales, inventory, and logistics. Misalignment across these functions often leads to inefficiencies that compound downstream.

Forum participants highlighted that these inefficiencies are less about missing data and more about how data is interpreted. Demand forecasts are frequently adjusted across functions, introducing bias and inconsistency. At the same time, local teams apply their own buffers—whether in forecasts, safety stock, or allocation—to manage uncertainty.

While these actions aim to reduce risk, they often distort demand visibility and create excess cost, inventory imbalances, or service gaps.

This reflects a broader challenge: improving demand and fulfillment performance requires not just better models, but better alignment and trust across decision-makers.

Strengthening Signals Through Demand Sensing

Traditional forecasting approaches rely on periodic updates and static assumptions. In a dynamic environment, these models can quickly fall out of sync with reality.

Leaders at the forum emphasized the shift toward demand sensing, or using AI to continuously integrate and validate signals across the network. This includes reconciling inputs such as order patterns, inventory availability, and changing demand conditions.

By doing so, organizations can detect changes earlier, reduce reliance on manual overrides, and respond more quickly to evolving demand.

Recent Maersk customer survey insights point in the same direction. Participants indicated that AI is already being applied in demand forecasting, planning, and inventory optimization, though most organizations are still scaling these capabilities toward a more reliable foundation for decision-making.

Connecting Demand to Fulfillment Outcomes

Improving signals alone is not enough; the real value comes from linking demand insights directly to fulfillment decisions.

Participants identified several areas where AI can strengthen this connection:

• Inventory deployment and order prioritization, ensuring constrained supply is used where it delivers the most value
• Delivery promise and ETA prediction that reflect real-time conditions
• Adaptive fulfillment strategies that adjust sourcing, routing, and capacity as demand shifts
• Integrated demand–inventory–fulfillment decision support across the network

These capabilities are increasingly important as customer expectations rise and vary by product, channel, and market.

Rather than relying on standardized rules, organizations can begin to tailor fulfillment decisions dynamically, balancing service, cost, and complexity more effectively.

Bridging the Gap Between Models and Reality

Despite strong interest, adoption challenges remain. Participants pointed to data fragmentation, system constraints, and evolving governance as key barriers.

However, trust remains the most significant constraint.

Demand and fulfillment decisions are highly visible and directly tied to customer outcomes. As a result, organizations are cautious about relying on AI-generated recommendations without transparency and clear guardrails.

To move forward, AI must be embedded directly into workflows—supporting planners and operators in real time, rather than existing as a separate analytical layer.

What It Means for Leaders

For leaders, unlocking AI’s value in demand and fulfillment requires rethinking how decisions are made across functions:

• Align teams around a shared, AI-validated demand signal
  Reduce conflicting inputs and duplicate buffers by creating a single, trusted view of demand across commercial, planning, and operations teams.
• Use AI to detect and correct signal bias early
  Identify where manual overrides or siloed adjustments are distorting forecasts and reconcile differences before they affect inventory and fulfillment decisions.
• Link demand signals directly to fulfillment actions
  Enable real-time assistance with inventory allocation, order prioritization, and routing decisions based on current demand and network constraints.
• Integrate AI into customer promise setting
  Ensure delivery commitments and ETA predictions reflect actual network conditions, improving reliability and reducing costly exceptions.
• Shift from buffer-driven to scenario-based decision-making
  Replace reactive safety stock increases and expediting with proactive, AI-supported trade-off decisions across cost, service, and risk.
• Build trust through transparency at the point of decision
  Make AI recommendations explainable and actionable for planners, addressing one of the primary barriers to adoption.

The Path Forward: From Accuracy to Reliability

The discussions at the Maersk–MIT customer forum in North America highlight a clear evolution in demand and fulfillment.

The focus is shifting from improving forecast accuracy in isolation to strengthening the reliability of how decisions are made, aligned, and executed.

AI plays a central role in this transition—not by replacing human judgment, but by reinforcing it. By improving visibility, reducing bias, and linking decisions more closely to real-time conditions, AI enables a more consistent and responsive supply chain.

The result is not only better planning, but better outcomes—where organizations can deliver on their customer promise with greater confidence, even in an environment defined by uncertainty.

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