Across industries, supply chains are under pressure from changing end-customer demand, supplier uncertainty, and geopolitical disruption. At the recent Maersk Customer Forum in North America, hosted with MIT’s Center for Transportation & Logistics, these challenges were front and center as leaders discussed how artificial intelligence (AI) can strengthen planning and support better decisions.

A consistent theme across the forum discussions and pre-event research was that AI is no longer seen as a standalone technology. Organizations now view it as an important part of the decision-making process that connects signals, tradeoffs, and actions across the supply chain, especially in upstream planning and supply agility.

From Static Plans to Adaptive Supply Networks

Traditional upstream planning, which spans sourcing, production allocation, and inbound logistics, has long relied on static assumptions and periodic updates. But those approaches often struggle to keep pace with disruption, creating excess buffers in some areas and slow responses in others.

What came through clearly in the forum was a shift in ambition: organizations are moving beyond optimizing individual functions and toward coordinating decisions across interconnected networks. AI can support that shift by helping teams sense change earlier, evaluate tradeoffs faster, and respond with more confidence through scenario-based planning.

That move toward more adaptive, network-wide coordination reflects where many organizations are today. Survey feedback from the event suggests companies are actively experimenting with AI, but most are still in the early stages and looking for clearer ways to scale it and embed it into day-to-day decision workflows. This trend was re-confirmed recently through another Maersk study for AI readiness in choosing a 4PL provider.

Where AI Unlocks Value in Upstream Planning

Within upstream planning, both survey findings and roundtable discussions point to several high-impact areas where AI has the potential to create value.

In sourcing and supplier management, AI can support more dynamic decision-making by bringing together supplier performance data, risk signals, and market shifts. Leaders at the forum emphasized that improving supplier reliability, geographic exposure, and routing decisions remains a priority.

Visibility and risk intelligence also emerged as critical enablers. Participants pointed to the need for deeper visibility across supplier networks and inbound flows, including beyond tier-one suppliers. They were equally clear that fragmented data and limited integration remain major barriers, reinforcing that data quality is foundational to AI’s value.

Another key area is scenario-based planning. Rather than relying only on static forecasts, organizations increasingly expect AI to support integrated decisions such as network design, inventory allocation, and sourcing tradeoffs. These use cases ranked among the areas where AI is likely to have the greatest future impact.

Finally, AI is helping enable more adaptive supply strategies. Roundtable discussions underscored the importance of dynamically aligning sourcing, fulfillment, and execution decisions so organizations can better balance efficiency and resilience across products and flows.

The Reality: Progress Is Directional, Not Linear

Despite strong momentum, progress remains uneven. Integration complexity, data readiness, and other factors continue to slow adoption. Trust is still a major constraint, especially for high-stakes decisions involving cost, service, and risk tradeoffs.

As a result, most organizations are not aiming for full automation. Instead, they are prioritizing AI-assisted decision support to augment human judgment, surface tradeoffs, and improve confidence in decisions.

From Insight to Impact: What It Means for Leaders

To translate AI ambition into impact, leaders at the forum highlighted several practical priorities:

• Prioritize AI in high-impact planning decisions, such as supplier allocation, sourcing strategy, and inbound routing, where trade-offs between cost, service, and risk are most dynamic.
• Strengthen upstream visibility and signal integration, ensuring planning decisions are informed by real-time supplier performance, risk indicators, and market shifts.
• Embed scenario-based planning into workflows, enabling teams to evaluate sourcing, inventory, and network trade-offs quickly and respond to disruption with greater confidence.
• Design for adaptability, not just efficiency, using AI to dynamically rebalance sourcing, inventory, and flows as conditions change across regions and suppliers.
• Build trust through decision transparency, making AI-driven recommendations explainable and actionable, especially for high-stakes decisions involving supplier risk and service impact.

The Path Forward: Decision-Centric Supply Chains

Ultimately, the discussions at the customer forum point to a clear conclusion: the real value of AI in upstream planning lies in helping organizations make better decisions under uncertainty.

By improving visibility, strengthening alignment, and increasing confidence in tradeoffs, AI is helping organizations move toward more adaptive and resilient supply networks. The long-term opportunity is decision-centric supply chains, where systems interpret signals, evaluate options, and guide action in real time.

The organizations that will lead are not those that adopt AI fastest, but those that apply it most effectively where it matters most.

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.