2026: The age of the AI supply chain

This year has added a new chapter to the evolving story of supply chain resiliency, confronting leaders in the manufacturing and automotive sectors with a fresh set of challenges. As 2026 inches closer, we are wading further into the waters of artificial intelligence (AI), finding businesses at an inflection point defined by both unprecedented opportunities and significant hurdles.

Indeed, the coming years are poised to be defined as the era of AI in supply chains, representing the next big cycle for leaders who are increasingly focusing on data governance and process standardization as a precursor to fully embracing AI and the agentic future. However, this next big cycle is not just about integrating the latest technology; it also involves a critical focus on upskilling the workforce and implementing effective change management. Manufacturers are balancing ambitious organizational goals while trying to lead their staff forward into a new technological frontier.

A foundation to build from

The consensus is growing: the future of supply chain management is inextricably linked with AI. After nearly two decades of discussion of global supply chains, the next generation of supply chains will center on AI. While the mantra of "AI first" looks truer with each passing day, the practical application within supply chains follows a more measured and strategic path. The sentiment is one of caution and preparation, not a blind rush to adopt. Having standard processes and data is foundational for organizations as they start to weave AI into their efforts. Clients we speak with are diligently strengthening their fundamentals around planning, logistics, and, crucially, risk management. As uncertainties over geopolitical dynamics persist and economic headwinds swirl, laying the proper groundwork for AI to build upon is essential for resilient and scalable supply chains.

Another critical element of the supply chain story is the divide between AI-driven productivity gains and tangible enterprise value. Bridging this gap requires a new way to measure success that moves beyond traditional ROI metrics. The key is to adopt a portfolio-view of value creation by tracking dynamic indicators across the business, connecting immediate efficiencies to long-term outcomes. Understanding how initial efficiencies create major improvements such as better cash management from an accelerated financial close or a more resilient supply chain is how organizations can link AI adoption to true enterprise growth.

The upskilling imperative

Equally important as a solid foundation are comprehensive upskilling programs for employees while simultaneously addressing concerns over job loss due to intelligent automation. Manufacturers across sectors, notably automakers, are trying to evolve the skill set of supply chain team members to extract the value from AI and grow their ability to use the new digital tools. For example, data analytics training programs that feature data scientists training supply chain analysts. Ultimately, effectiveness often depends on how quickly employees can engage with and apply these new learnings in their daily routines. Leaders know they need to get the equation right between balancing workforce shape, size and capabilities, but uncertainty on the outcome remains. What is clear is that AI agents will become embedded team members across organizations and thus, the imperative to upskill workers and have processes in place to govern, manage and develop agents.

Navigating the nuances of change management

The dichotomy of skill versus will perfectly captures the current balancing act for supply chain leaders. It’s a constant challenge of executing on the organization’s will to innovate while simultaneously improving people’s skills to thrive in the new environment. Given the enormity of the AI transition underway, robust change management is not just a recommendation; it is a necessity. Organizations must clearly articulate the reasons behind AI adoption and how it benefits both the company and its employees. Executing on proven change management techniques, however, can be problematic. The trust factor between the worker and the company is paramount.

But how do you build that trust?

A good place to start is from a solution perspective. Be transparent with data, facts and results. Change is always something that individuals, both personally and professionally, can be reluctant to embrace. Through demonstrating outcomes and being honest, leaders can drive effective change management in the era of AI supply chains.

The strategic shift to local for local

Amid compounding volatility and evolving economic challenges, another powerful strategic shift is underway: the rise of local for local manufacturing. Companies are increasingly placing a premium on shortening their supply chains and locating sources of supply as close as possible to their customers. The principle of “buy where you make and make where you sell” is no longer a niche strategy but a mainstream approach to building resilience and agility. This local for local strategy for both production and the supply chain has proven to be remarkably effective in a sea of uncertainty, providing a stable physical counterpart to the digital transformation being driven by AI.

The road ahead

The last half-decade, beginning with the COVID-19 pandemic, has brought upon a myriad of challenges and strained supply chains in ways that were nearly impossible to foresee. As 2025 comes to a close, the coming year presents challenges and opportunities as noted for supply chain leaders as they seek to effectively embed AI in their efforts while upskilling employees and scaling efforts appropriately. Those who can solidify their foundations, especially processes and data management, while engendering trust among their employees will be best positioned to continue to navigate the turbulent macroeconomic landscape confronting businesses.

The views expressed are those of the authors alone and do not necessarily represent those of KPMG LLP.

About the authors

Brian Higgins is the KPMG U.S. Manufacturing Sector Leader. He is a principal in KPMG’s Advisory Services practice focused on Strategy and Operations management consulting. He has deep experience with competitive strategy and operational design and brings nearly 20 years of industry and consulting experience.

Lenny LaRocca is the KPMG U.S. Automotive Leader.  He has over 20 years of automotive experience and has led or been involved in some of the most important and transformational transactions in the industry.

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