The supply chain industry has spent the last several years focused on technology. Companies have invested heavily in transportation management systems, visibility platforms, warehouse automation, predictive analytics, and artificial intelligence. These investments have delivered measurable benefits, including improved efficiency, better shipment visibility, and faster decision-making. AI in particular is transforming how organizations forecast demand, optimize routes, identify disruptions, and analyze operational performance. However, while companies have concentrated on digital transformation, many have overlooked a growing challenge that technology alone cannot solve: the loss of institutional knowledge. Ironically, the effectiveness of many AI initiatives may depend on preserving the expertise that is now at risk of disappearing.
Across North America, a significant portion of the logistics workforce is approaching retirement age. The transportation and logistics sector is experiencing a demographic shift that threatens to create a substantial knowledge gap in the coming years. According to research from the American Transportation Research Institute (ATRI), the average age of truck drivers has increased from 42 years old in 1995 to 47 years old in 2024, with retirements accelerating and fewer younger workers entering the profession. In Canada, industry studies have similarly identified an aging workforce, with the average truck driver age projected to exceed 49 years old and a growing concentration of workers nearing retirement.
While discussions about labor shortages often focus on the number of workers available, the greater concern may be the experience and expertise leaving with them. Dispatchers, customs specialists, freight brokers, transportation coordinators, warehouse supervisors, and operations managers who have spent decades navigating complex supply chains are gradually exiting the workforce. When they retire, they take with them years of practical knowledge that cannot easily be replicated through software, training manuals, or standard operating procedures.
The challenge extends beyond replacing headcount; organizations are losing experienced problem-solvers whose practical knowledge and decision-making skills have been developed through decades of navigating supply chain disruptions.
Anyone who has worked in transportation understands that supply chains rarely operate exactly as planned. Containers miss vessel connections, rail shipments encounter delays, documentation errors hold freight at the border, and severe weather events disrupt schedules. Labor disputes, geopolitical conflicts, and unexpected market shifts can create additional challenges with little warning. In these situations, experience often becomes more valuable than any technology platform.
Veteran logistics professionals possess practical expertise that cannot easily be captured in databases, workflow diagrams, or standard operating procedures. Their experience enables them to anticipate risks, navigate regulatory and customs complexities, leverage industry relationships, and identify potential disruptions before they escalate. Equally important, years of managing real-world challenges have developed the judgment and composure needed to make effective decisions under pressure. While AI systems can identify patterns and recommend actions based on historical data, they often struggle with the ambiguity, exceptions, and rapidly changing circumstances that characterize many supply chain disruptions. Human experience remains essential when decisions must be made despite incomplete information and evolving conditions.
The aging workforce challenge is compounded by ongoing recruitment difficulties throughout the transportation sector. Although trucking employment remains substantial, with more than 3.5 million professional drivers in the United States and over 790,000 workers in Canada’s trucking and logistics industry, the sector continues to struggle with attracting younger workers. Industry researchers have repeatedly identified a shortage of younger entrants, creating concerns about long-term workforce sustainability.
Younger professionals entering supply chain careers often bring valuable technical skills, data literacy, and fresh perspectives that can help organizations modernize. However, many companies have not developed effective knowledge-transfer strategies to ensure critical expertise is passed from experienced employees to the next generation. In many organizations, operational knowledge remains concentrated among a relatively small group of long-tenured employees. As these individuals retire, businesses risk losing decades of accumulated insight. This creates an additional challenge for AI adoption, as the systems organizations are implementing today often require experienced subject-matter experts to validate recommendations, refine processes, and ensure technology reflects operational realities.
The effects of knowledge loss are often difficult to detect during normal operations, when transportation networks continue to function and service levels remain stable. Its significance becomes clear during periods of disruption, when experienced professionals are needed to navigate uncertainty, mitigate risks, and maintain operational continuity.
When unexpected challenges arise, organizations without experienced personnel may struggle to respond quickly and effectively. A delayed shipment that once could have been resolved with a phone call may turn into a costly service failure. A customs issue that a veteran specialist could have anticipated may result in days of unnecessary delay. A transportation manager with limited experience may follow procedures correctly yet fail to recognize alternative solutions that an experienced colleague would immediately identify.
As supply chains become increasingly complex and interconnected, resilience depends not only on technology but also on human expertise. Recent years have demonstrated that disruptions are no longer rare events. Companies must be prepared to respond to uncertainty, and that requires knowledgeable professionals who understand how supply chains function beyond what is displayed on a dashboard.
Some industry leaders believe artificial intelligence will eventually help bridge the experience gap by capturing organizational knowledge and providing decision support to less experienced employees. While there is merit to this view, AI can only learn from the information that organizations preserve. If critical knowledge exists primarily in the minds of retiring employees, companies may lose valuable expertise before it can be documented, analyzed, or incorporated into future systems. In this sense, the retirement wave facing logistics may represent not only a workforce challenge but also a missed opportunity to build more intelligent and resilient supply chain operations.
Addressing the freight knowledge gap requires more than recruitment. Organizations must actively capture institutional knowledge before it disappears. Formal mentorship programs should pair experienced employees with newer team members. Cross-training initiatives can reduce dependence on individual experts and improve organizational flexibility. Companies should document best practices, decision-making processes, and lessons learned from major disruptions. Succession planning should become a strategic priority rather than a last-minute response to retirement announcements.
Technology will undoubtedly continue to play an increasingly important role in supply chain management. Automation, artificial intelligence, and advanced analytics will help organizations improve efficiency and manage growing complexity. Yet the future of supply chain resilience will likely depend on how effectively companies combine technological innovation with human expertise. AI may enhance decision-making, but it cannot fully replace the judgment, relationships, and practical experience developed through years of navigating real-world disruptions. In my experience, the people who solve the toughest transportation problems are rarely the ones following a checklist. They are the people who have spent years building relationships, recognizing patterns, and learning from disruptions that no training manual could fully anticipate.
As the industry looks toward the future, one question deserves greater attention: Are we investing as much effort into preserving knowledge as we are into acquiring technology?
The answer may determine not only how resilient our supply chains become, but also how successfully organizations leverage the next generation of AI-driven tools. Companies that capture and transfer institutional knowledge today will be better positioned to strengthen both their workforce and their technology capabilities tomorrow.
