Philipp Pfister, Sector Vice President of Transporeon, discusses scaling agentic AI beyond traditional automation in logistics
As supply chains grow more complex and the pace of decision-making accelerates, logistics leaders are increasingly looking to artificial intelligence not just as a support tool, but as a strategic partner. From autonomous decision-making to agentic AI systems capable of managing entire workflows, the conversation is rapidly shifting from experimentation to execution.
In this interview, Philipp Pfister, Sector VP at Transporeon, explores how agentic AI is reshaping transportation management, why governance and data infrastructure are critical to success, and what organisations must do in 2026 to stay on track for widespread autonomous adoption by the end of the decade.
Philipp, what is the biggest challenge logistics managers are facing in 2026?
The biggest challenge logistics managers are facing in 2026 is that they have too many decisions to make, and not enough time to make them in. But what if AI could ease the burden, handling the routine decisions like carrier selection, route optimisation and exception management, freeing humans to focus on strategy and relationships?
This isn’t a distant future. In fact, Gartner predicts that by 2030, 50% of supply chain solutions will incorporate autonomous decision-making. This is a significant shift from executing tasks to pursuing outcomes.
But we’re not there yet. While 36% of shippers have moderate or basic AI capabilities in their transportation management systems, only 1% currently use advanced autonomous decision-making. But momentum is building, with 23% of organisations already scaling agentic AI systems and another 39% experimenting.
What makes agentic AI different from traditional automation?
Traditional automation follows pre-programmed rules; for example, if X happens, then do Y. Agentic AI is different. These autonomous systems plan and execute multiple workflow steps on their own. They’re goal-oriented and monitor situations to ultimately make decisions and take action within the boundaries you set for them.
So, where are shippers looking to put agentic AI to use? Spot buying, carrier vetting, and real-time ETA monitoring and disruption management top the list of priorities. But once organisations prove that it works in these areas, it’s unlikely any part of the supply chain will remain untouched.
How is the perception of AI changing within the supply chain workforce?
The expression “AI as tool” used to be commonplace in the workplace, but it’s being replaced by “AI as colleague.” This boost in confidence is evidenced by the fact that two-thirds of shippers and more than half of carriers see AI’s primary role as automating repetitive tasks and thereby freeing people for higher-value work. This shift is already tangible. Agentic AI systems are becoming fully-fledged parts of the workforce.
As a result, companies are no longer asking whether AI can help. Instead, they’re increasingly asking: “Can AI do it and how quickly can it deliver?”
However, like with any new hire, AI needs clear job descriptions, continuous feedback and ongoing evaluation to become effective, reliable workers and partners. This means dispatchers and planners are shifting from handling every task manually to overseeing intelligent agents, still responsible for the decisions, but with AI handling the execution.
What infrastructure challenges must organisations address before scaling agentic AI?
It’s no surprise that data quality remains the biggest obstacle to adoption. It has been our industry’s most talked-about topic for years. More than half of both shippers and carriers cite it as their primary barrier. But quality data alone isn’t enough if it stays siloed. Network connectivity is critical, as it amplifies AI’s potential: systems learn faster when connected across trading partners, drawing insights from shared real-time information rather than isolated datasets.
Modularity also matters. Companies must be able to integrate agentic AI into what they already have, not rebuild everything from scratch. This approach lets organisations adopt agentic capabilities incrementally, matching their pace to their resources and technical readiness.
Why is governance essential as AI becomes more autonomous?
The more decisions AI makes on its own, the more critical governance becomes. This means setting clear boundaries: what can your AI agents do and what’s off limits? Those guardrails enable safe AI use that stays perfectly in line with your intentions.
The key is to establish those guardrails before you scale, not after things break down. You need to track how agents perform at each step of the workflow and not just examine the final results. This enables you to catch errors early and keep refining, giving you a level of visibility that becomes critical as you move beyond pilots. Working with market-validated platforms and a trusted network can help you keep your deployments on target.
