The logistics industry is undergoing a dramatic transformation as artificial intelligence (AI) and machine learning (ML) move from trendy concepts to essential business tools. Companies are now using these technologies to revolutionize how they manage supply chains, make critical decisions, and stay competitive on a global scale.
A team of researchers recently dove into this transformation, examining how AI and ML are reshaping business-to-business (B2B) logistics—where companies serve other companies—and business-to-government (B2G) logistics, where businesses work with government clients. Their study focused on the United States and Canada. Walton College’s Janeth Gabaldon worked alongside Vipul Garg (Texas A&M-San Antonio), who led research, and Suman Niranjan and Timothy Hawkins (University of North Texas). Their research, “Impact of strategic performance measures on performance: The role of artificial intelligence and machine learning,” dug into how these technologies are changing the way organizations track and achieve success.
The Rising Stakes in Modern Logistics
Getting the right goods to the right place at the right time has always been the heart of logistics, but today's supply chains have grown exponentially more complex. Companies like Walmart have already shown what's possible by using AI and ML to optimize inventory, streamline transportation routes, and predict customer demand with remarkable precision. And the payoff? Lower costs and smarter, faster operations that make a real difference financially.
The numbers tell a compelling story. The market for logistics AI is projected to explode, reaching over $31 billion by 2028. Yet despite this rapid growth and widespread adoption, researchers have found a surprising gap: very little empirical research exists on how these technologies actually impact the strategic performance measures that matter most to firms. This gap is particularly pronounced in B2B and B2G sectors, where business relationships span years or decades and decisions carry complex, far-reaching consequences.
Filling the Knowledge Gap
Recognizing this critical blind spot, the research team embarked on an ambitious mission to understand how AI and ML technologies work alongside traditional business capabilities to drive success. Rather than looking at technology in isolation, they focused on three fundamental performance indicators that form the backbone of successful logistics operations.
The first is information sharing—essentially how well data and insights flow both within organizations and between business partners. In today's interconnected world, the ability to share relevant information quickly and accurately can make or break supply chain efficiency. The second indicator, decision synchronization, captures how well companies align their choices and actions across the entire supply chain network. When everyone is working from the same playbook and toward common goals, results improve dramatically. Finally, logistics efficiency processes measure how effectively a company delivers products and services while keeping costs low and minimizing environmental impact.
What really caught the researchers' attention was how AI and ML interact with existing company systems. They weren't just asking whether these technologies work—they wanted to know if adding ML to an already strong information-sharing system creates exponential benefits, or if AI actually performs better when it operates independently. Basically, the study was trying to uncover whether AI and ML are team players that amplify what companies already do well, or solo performers that work best on their own.
A Theoretical Foundation
To tackle these questions, the study used Dynamic Capabilities View (DCV), a strategic management theory that's gained momentum recently. This framework focuses on how companies adapt, innovate, and restructure when markets change. The difference is simple: ordinary capabilities help companies run day-to-day operations and serve current customers. Dynamic capabilities let them spot new opportunities, grab them fast, and reinvent their approach to stay ahead long-term.
Within this theoretical lens, AI and ML represent powerful dynamic capabilities—sophisticated tools that don't just help organizations keep pace with change but position them to stay several steps ahead of competitors. This perspective shifts the conversation from viewing these technologies as simple automation tools to recognizing them as strategic assets that can fundamentally reshape how businesses operate and compete.
There's Nuance in Research
The study used Partial Least Squares Structural Equation Modeling (PLS-SEM)—a statistical technique designed to untangle complex cause-and-effect relationships—to reveal a nuanced picture: companies successfully integrating AI and ML showed obvious superior organizational performance—more competitive, agile, and better positioned long-term. However, direct operational impacts were less dramatic, suggesting these technologies excel at strategic positioning but need specific conditions to deliver operational benefits.
Logistics efficiency was the clear winner. Companies combining strong logistics practices with AI and ML saw remarkable results, with the technologies acting as accelerators that amplified existing strengths. ML optimized routes, AI improved inventory management, and predictive analytics enhanced delivery scheduling, creating compounding effects on business success.
Information sharing and decision coordination told a subtler story. While these capabilities didn't drive major improvements alone, they became tremendously valuable when paired with AI and ML. AI systems coordinated real-time decisions across partners, while ML could identify patterns in shared data that humans might miss. The magic happened when combining traditional capabilities and new technologies, sparking results that wouldn’t have been possible with just one or the other.
Further analysis using Fuzzy-set Qualitative Comparative Analysis (fsQCA)—a method that identifies specific combinations of conditions leading to success—confirmed this collaborative theme, showing that high performance doesn't follow a single blueprint. The most successful companies combined strong logistics efficiency with robust information sharing and advanced AI/ML capabilities. This underscores a crucial point: technology investments alone won't guarantee success. Companies must also cultivate collaborative cultures and processes that support seamless information flow and coordinated decision-making across their networks.
The Hidden Costs of Strategy Blind Spots
For business leaders navigating this technological transformation, the research offers clear strategic guidance. AI and ML should be seen as more than just automation tools—they’re strategic capabilities that strengthen a company’s ability to adapt, innovate, and stay competitive over time.
The data strongly suggests that the greatest returns come from applying these technologies to enhance logistics efficiency, but success requires building robust foundations in information sharing and decision synchronization. This means adopting a holistic approach that goes well beyond technology acquisition. Companies need to invest not just in sophisticated software and hardware, but in developing people and processes that support collaboration, adaptability, and continuous learning.
The organizations that will thrive in this new landscape are those that can successfully blend advanced technology with sound strategy and collaborative culture into a cohesive, adaptive system. This integration requires thoughtful planning, sustained commitment, and willingness to evolve organizational practices alongside technological capabilities.
Limitations and Future Research
Every research study has boundaries, and this one is no exception. The focus on U.S. and Canadian B2B and B2G firms means the findings may not translate directly to business-to-consumer (B2C) environments or to companies operating in other regions with different regulatory, cultural, or technological contexts. With AI and ML evolving so quickly, ongoing research is crucial to keeping up with emerging trends and fine-tuning best practices across industries and regions.
This study does offer a solid, evidence-based starting point for understanding how AI and machine learning are reshaping supply chains. The takeaway is clear and practical: these technologies make the biggest difference when they’re part of a broader strategy—one that also prioritizes information sharing, aligned decision-making, and overall logistics efficiency.
For companies willing to invest not just in technology, but also in the cultural and structural shifts needed to support it, the payoff can be substantial. AI-powered logistics isn’t on the horizon—it’s already here. The real challenge isn’t whether to adopt it, but how quickly and effectively organizations can weave it into their larger strategic goals. Those that do will be better positioned to gain (and sustain) a serious competitive edge.
