Download the full 2026 Supply Chain Trends Report here.

This free trends report outlines industry perspectives and expert advice from our team of supply chain consultants. You can view an excerpt of the report below, and if you’d like to discuss any of the trends or other challenges in the supply chain space, connect with our team today.

Key Supply Chain Trends

In 2026, supply chain transformation will accelerate through rising AI adoption and deeper integrations across core technologies. Organizations will experiment with and hone in on specific AI uses cases within supply chain planning and execution to drive cost savings and efficiency within the supply chain. Ongoing supply chain uncertainty and increased cybersecurity risk will continue to challenge organizations, highlighting the importance of assessing supply chain network and optimizing supply chains to be agile and adaptable, while emphasizing the importance of risk management within daily operations. 

Clarkston’s supply chain consultants have highlighted the top trends that businesses should consider and keep top-of-mind throughout the year:

1. Expansion of AI use cases within supply chain
2. Managing supply chain uncertainty with network design & optimization
3. Leveraging integrated technology to drive supply chain outcomes

Trend 1:

Expansion of AI use cases within supply chain 

In 2026, we anticipate the focus of AI utilization to rapidly expand beyond planning to include greater emphasis on execution. As agentic AI matures, organizations are starting to venture into autonomous supply chain execution, often with minimal or no human intervention required. 

Within the planning function today, AI is primarily leveraged to enhance decision-making: improving the quality of forecasts, identifying trends, and recommending actions or exceptions for teams to evaluate. Going forward, we can expect to see organizations experiment with agentic AI to directly make business decisions and act vs. recommending actions for teams to implement. Rather than relying on planners to interpret insights, revise orders, and develop production schedules, agentic AI can assess all available information, determine the best course of action, and autonomously carry out these tasks. However, achieving this vision requires organizations to shift their approach across several key areas. 

Data quality and analytics maturity play a foundational role in adopting an AI-driven approach. Organizations with high levels of maturity in this area can implement faster and extend AI across a wider set of use cases, but that doesn’t mean others can’t start realizing the value of AI within strategic areas and focused use cases. Investing in data engineering, data governance, and advanced analytics to ensure foundational data is consistent and reliable drives the potential for unlocking further AI capabilities. As part of that foundation, organizations also need to evaluate how they’re factoring in today’s rapidly changing environment and the relevance of historical data given the constant change in recent years (e.g. COVID impacts, tariffs, purchasing pattern changes) as advanced models and data tools are heavily reliant upon the accuracy of this data.   

Governance frameworks and guardrails are equally critical to setting agentic AI up for success and aligning the utilization approach across the organization. Clear boundaries need to be defined for when agentic AI can act independently, when decisions need to be escalated, and where human approvals remain essential. Doing so helps ensure autonomous execution stays aligned to business strategy, customer commitments, and the organization’s overall risk appetite. 

Cross-functional alignment and business process redesign are also needed to support an AI-driven approach. Enabling agentic AI across business functions (e.g. planning, sourcing, production, logistics, and customer operations) requires new ways of working, along with mechanisms to integrate decisions across various functions so organizations can optimize solutions and create a more adaptive, resilient supply chain. 

Finally, successful adoption depends on closing the gap between AI literacy and business expertise, supported by key layers of talented individuals with skillsets that span both areas. In parallel, performance measurement should evolve to evaluate the effectiveness of AI-driven models.  

Organizations should monitor the learning rate at which models incorporate new data, adjust to changing conditions, and refine the logic used to make decisions. They should also assess the time taken to detect issues, which helps clarify how quickly the system can identify anomalies or supply chain disruptions, enable quicker intervention, and reduce downstream impacts. Advanced planning tools are incorporating AI capabilities to enhance planning efficiency and drive the organization to focus on the true exceptions that need to be managed.  

Continue reading by downloading the full report below.

Download the Full 2026 Supply Chain Trends Report Here

Contributions from Kate Poknis