With decades of experience navigating the complexities of supply chain and delivery, Rohit Laila has a unique perspective on the intersection of logistics and technology. He joins us today to unpack the findings of a recent survey on Agentic AI, a technology poised to reshape the industry. The research reveals a stark divide: while some leaders are pushing ahead, a significant portion remain on the sidelines, grappling with concerns over cost, transparency, and a clear path to value. We’ll explore this hesitation, the practical steps for de-risking pilot projects, and what it truly takes to prepare an organization for an autonomous future.
With a significant number of logistics organizations, 42% to be exact, not yet exploring Agentic AI, what specific competitive disadvantages might they face over the next few years? How can leaders of these firms justify a continued reliance on more traditional tools, and what is the tipping point for them to reconsider?
The competitive gap will widen faster than most people think. We’re not just talking about incremental improvements. The firms embracing this technology are looking at a potential 30% reduction in fuel and mileage costs. Imagine your competitor suddenly having that much more capital to reinvest in service, talent, or expansion while you’re still wrestling with legacy systems. Beyond costs, it’s about agility. The survey showed 22% expect stronger operational resilience. When the next disruption hits, those with Agentic AI will adapt in minutes, while others will be scrambling for days. Leaders justify inaction by pointing to the familiar devil—the high integration costs and the 21% who see an unclear ROI. The tipping point will be when those pilot programs from the 23% planning to start in 2026 begin publishing their results. Once a direct competitor demonstrates a clear, measurable advantage, the pressure to act will become immense.
High integration costs, cited by 32% of leaders, and a lack of transparency in AI decision-making at 26% are clearly top frustrations. Could you provide a step-by-step approach for a company to de-risk a pilot project and build a clearer, more measurable case for its return on investment?
Absolutely. The key is to think small and focused. First, don’t try to boil the ocean. Pick one high-impact area, like the first- and final-mile routing that 35% of executives identified as a priority. This is a contained environment where you can measure everything. Second, establish a clear baseline. Before you turn on any AI, you need to know your current fuel costs, on-time delivery rates, and planner workload down to the decimal. Third, run the pilot in parallel. Don’t rip out the old system. Have the AI make recommendations while your human planners do their work. This builds trust and addresses that 26% who are worried about the ‘black box’ nature of AI. Finally, you measure relentlessly. If the AI’s routes are consistently saving miles and its plans are more resilient, you now have a powerful, data-backed case for ROI that directly addresses the skepticism.
First- and final-mile route scheduling is a top priority for Agentic AI implementation for 35% of companies. What makes this specific area so ripe for autonomous decision-making, and what key operational metrics—beyond fuel costs—should a company track to prove the success of an initial pilot?
That final mile is a hornet’s nest of variables. You have traffic, delivery windows, vehicle capacity, driver availability, and unexpected customer requests all changing in real time. A human planner, no matter how skilled, simply can’t compute all those permutations continuously. Agentic AI thrives in this kind of dynamic complexity; it can re-optimize hundreds of routes in the time it takes a planner to answer a phone call. Beyond fuel savings, the metrics that truly prove its worth are driver utilization and on-time delivery rates. Are your drivers completing more stops per shift without feeling rushed? That’s a massive efficiency gain. Is your on-time delivery percentage climbing, leading to happier customers and fewer support calls? That directly impacts customer retention and brand reputation. I’d also track planner intervention rates—how often does a human have to override the AI? A successful pilot will see that number steadily decrease as trust in the system grows.
Many leaders, 42% in fact, believe that supporting autonomous AI requires redesigning core business processes. What does this redesign look like in practice for a logistics planner’s daily workflow, and how can companies manage this transition to keep humans in control while still gaining efficiency?
It’s a fundamental shift from reactive problem-solver to strategic overseer. Today, a planner spends most of their day putting out fires—rerouting a truck stuck in traffic, reassigning a load. With Agentic AI, the system handles that repetitive, complex decision-making. The planner’s new role becomes managing the parameters, reviewing the AI’s performance, and handling the true exceptions that require human ingenuity and empathy. The transition is managed through a phased approach. Initially, the AI is a co-pilot, suggesting optimized plans that the planner must approve. As the system proves its reliability and the team builds trust, you can gradually grant it more autonomy, allowing it to execute decisions within pre-defined boundaries. This keeps the planner firmly in control, evolving their role from a tactical dispatcher into a strategic manager of an autonomous system, which is a much more valuable position for the company.
What is your forecast for Agentic AI in the logistics sector over the next five years?
Over the next five years, Agentic AI will move from a competitive edge to a baseline operational requirement. The 23% of companies planning to pilot the technology in 2026 will be the trailblazers, and their success stories will trigger a massive wave of adoption by 2028. We’ll see it become standard for complex tasks like network design and last-mile delivery. The conversation will shift from “Should we use it?” to “How deeply can we integrate it?” Companies that fail to adapt won’t just be less efficient; they’ll be fundamentally unable to compete on the levels of speed, cost, and resilience that customers will come to expect as the norm. It will be the dividing line between the logistics leaders of the next decade and those who get left behind.
