How are chief supply chain officers (CSCOs) and supply chain leaders anticipating disruptions and creating resilient, technology-driven operations for advanced decision-making in supply chain? 

The Gartner Supply Chain Symposium/Xpo™ 2026, themed “Dynamic by Design: Renew, Rethink and Recode Supply Chains for the Autonomous Era,” highlighted the industry’s need to move past rigid, reactive supply chain models in favor of more agile networks built to handle ongoing disruption.  

Across sessions, leaders explored how to design organizations that can sense and respond in real time, combining advanced analytics with human judgement to make faster, more informed decisions. Discussions highlighted the importance of improving end-to-end transparency to better manage geopolitical shifts, trade changes, and environmental challenges, while also maintaining margins amid cost pressures alongside evolving AI talent strategies. 

Clarkston’s Wesley Ange and Sebastian Valencia attended the 2026 conference and have outlined their takeaways below. 

Key Takeaways from the 2026 Gartner Supply Chain Symposium/Xpo™ 

Achieving Advanced Decision-Making in Supply Chain 

The premise and major takeaway from the 2026 Gartner Supply Chain Symposium last week was that organizations will not transform simply by deploying AI. They will transform by improving how decisions flow across people, processes, data, and technology.  

Sessions ranged from companies sharing case studies and vendor demonstrations to how to best prepare for change over the next five years. Many of these sessions had familiar themes, such as understanding a clear business problem, starting with clean data, iterating and learning fast, etc. But what stood out this year was an emphasis on the human role and judgement in the AI process. In some situations that included managing a team of AI agents or a virtual AI team, whereas in other situations, the focus was on being able to contextualize data and create a narrative to help drive decisions.  

The most important shift we saw at the conference is that companies are no longer just talking about AI; they’re actively using it. In doing so, they’re beginning to define where the human role needs to sit in this evolution. 

Compared to past conferences, where the conversation centered almost entirely on AI itself, this discussion felt more mature. The focus has moved toward the role of the human, the importance of context, and the practical realities of applying AI within large companies. 

Improving Operational Response 

It’s clear that many companies have already invested heavily in visibility platforms, dashboards, and analytics capabilities to gain a closer look into what’s happening in the supply chain. Disruptions are being identified, but many companies are still struggling to act quickly enough to minimize or create advantage. The challenge is improving the operational response.  

Several software companies highlighted new frameworks that involve an AI team that identify the disruption, begin working on the issue, and present potential solutions. These AI teams are “run” by a goal-oriented AI agent that work alongside a human team member to find the most appropriate solution.  

Redesigning Operations & Decision-Making Models 

The need for change when it comes to AI initiatives is highlighted with Gartner’s research, stating that 77% of organizations believe their current operating model is inadequate for competing in an AI-driven environment. Operations and decision structures designed around human interactions rather than real-time execution limit the opportunity to seize competitive advantages in the supply chain. Whether that is from unexpected disruption or a spike in demand because of social media, the tools available today have the potential to unlock new value for companies that change their approach.  

A great example was given by AstraZeneca discussing their vision for a self-healing supply chain. The company described their progression from foundational data and visibility toward predictive planning, autonomous orchestration, and eventually, automated decision-making. Their roadmap anticipates automating up to 50% of decisions within five years. This is a great example of a new operating model for decision-making at scale.  

Transforming for the Supply Chain of the Future 

Discussions at this year’s conference were not just on AI. Many presentations covered the work needed to transform an organization to better operate in today’s environment and prepare for the future.  

Columbia Sportswear, for example, had a wonderful presentation on transformation. It focused less on technology and more on governance, alignment, and disciplined execution. They emphasized that AI was a component of their solution, but it wasn’t the reason for the transformation. Their approach centered on scalable growth, capital efficiency, and customer experience, supported by structured program management, measurable value delivery, and enterprise-wide engagement. The end result was a transformed organization that realized more than $100M in savings and is prepared for the new challenges of the future.  

Reframing Your Data 

Redesigning how decisions get made is only part of the equation when it comes to advanced decision-making in supply chain. Even with strong data and process alignment, decision-making slows when stakeholders cannot quickly understand what the data means or what action is required. Oftentimes, the response is to add more data, but that typically creates confusion rather than clarity.  

The issue is not the data itself, but how it is framed and delivered. It is and will continue to be the human teams that are helping leadership understand and drive decisions. These teams must give the data context, create a narrative around what happens with and without action and why that matters, and finally, remove ambiguity with clear actions and clarity around what decisions are needed. Without the last piece, decisions can stall even with great context and explanation. If the goal is faster, better decisions, then the conversation must shift from presenting data to telling a clear story that drives action. 

Final Thoughts

Despite the buzz and multitude of AI use cases, there is still plenty of work companies need to complete to realize the true potential of AI. Gartner repeatedly cautioned against treating AI as a guaranteed value driver, with overspending on compute infrastructure, disconnected pilot programs, and low AI readiness among supply chain leaders creating significant risk.  

The recommended path was more pragmatic: build foundational capabilities first, pilot with purpose, and then shift conversations away from tools and toward decision-making. Organizations will not transform simply by deploying AI. They will transform by improving how decisions flow across people, processes, data, and technology.  

Gartner also noted that CEOs tend to place CSCOs lower on the trust scale compared to other C-level executives, even though CSCOs are arguably among the best-positioned leaders to help companies navigate uncertainty and support CEOs through complex change. 

That creates an important tension: how can CSCOs excel at leading change and supporting their CEOs if they are not yet top of mind as trusted strategic partners?  

This is where Clarkston’s recommendations become especially relevant. By acting on them, CSCOs may be able to strengthen their role, increase executive confidence, and move higher on the CEO trust scale. If a discussion sounds valuable, we would love to connect.