Distribution's AI Leaders Showcase Strategic Execution and ROI Insights

The Distribution Strategy Group (DSG) recently released its AI Top 25 benchmark report, revealing critical insights into how wholesale distributors are leveraging artificial intelligence (AI) to enhance operational efficiency. The study found that leading firms are not necessarily utilizing advanced technologies unavailable to their competitors; rather, they excel through effective governance, robust data management, and disciplined execution. This distinction is crucial as it highlights a widening gap between distributors that have integrated AI into their operations and those still confined to pilot projects.

The report, published in July 2026, surveyed 233 distribution executives, revealing that while 93% regard AI as a strategic priority, only 16% have successfully deployed it across multiple business functions. This 77-point gap signals a significant challenge within the industry, where ambition is not translating into action. DSG's analysis of over 300 North American distributors indicated that the primary barrier to effective AI deployment is not technological but rather organizational. Companies that remain in the pilot stage risk falling further behind as competitors begin to realize compounding operational gains from their AI initiatives.

The AI Top 25 includes 26 distributors that have moved beyond initial trials to demonstrate sustained, measurable business value. DSG identified six key management practices that differentiate these leaders from their peers: executive ownership, a commitment to moving beyond pilot projects, disciplined data management, cross-functional deployment, outcome-based measurement, and long-term investment. Notably, executive ownership emerged as a pivotal factor; successful companies typically assign a single executive to oversee AI strategy and results, ensuring accountability and focus.

For instance, Sonepar has appointed Fabrice del Aguila to lead a €1 billion transformation project aimed at integrating AI into its operations. This initiative exemplifies how dedicated leadership can drive significant change. Similarly, Wesco International has automated a substantial portion of its invoicing processes, showcasing the operational efficiencies that can be achieved when organizations fully commit to AI deployment.

Data quality has surfaced as a critical competitive advantage. Many executives cited skills gaps and organizational resistance as significant challenges, yet companies like Border States demonstrate how disciplined data management can yield impressive returns. Their machine-learning lead-time prediction system has reportedly generated a 976% ROI, underscoring the financial benefits of prioritizing data integrity.

The report also emphasizes the importance of deploying AI across multiple business functions rather than in isolation. Leading companies are integrating various AI applications to create synergistic effects that enhance overall performance. For example, Grainger has developed a shared AI platform that supports both product search and contact-center operations, allowing for seamless data flow and improved customer service.

As AI capabilities continue to evolve, the firms that have laid the groundwork through years of investment in analytics and infrastructure will be better positioned to scale new technologies effectively. This foresight is evident in companies like Cencora, which has established formal governance structures to oversee AI initiatives, ensuring that new tools are integrated thoughtfully and strategically.

The findings of DSG's research send a clear message to the distribution sector: AI leadership is less about the technology itself and more about organizational discipline and execution. Midsize distributors can adopt the practices of the AI leaders by establishing clear executive ownership, enhancing data quality, and measuring success against tangible business outcomes. As the competitive landscape shifts, those who fail to act may find themselves increasingly disadvantaged, while those who embrace a disciplined approach to AI will likely thrive. The path forward is clear: organizations must focus on execution to close the AI execution gap and unlock the full potential of artificial intelligence in their operations.