The AI Shift in Transportation Management

Artificial Intelligence has quickly become one of the most talked-about topics in supply chain and logistics. While opinions vary on how quickly it will transform the industry, few doubt that transportation management is entering a period of significant change.

The Pulse Report 2026 explored this shift through survey data from more than 230 shippers, carriers and logistics service providers across Europe and North America. When respondents were asked, for example, what benefits they expect Agentic AI could bring to transportation operations, “reduced manual work” topped the list at 66%, followed closely by cost savings and faster decision-making.

Those findings framed a recent webinar conversation I moderated, hosted by Transporeon, featuring Lucia Danisova from Bekaert, Carlo Pinamonti from FERCAM, Denys Kochurin from Hegelmann Express, Stefan Kremsner from S2data and Bernhard Schmaldienst from Trimble.

Here are the key themes and takeaways from the discussion.

AI is already reshaping transportation management

The panelists agreed that AI is already reshaping transportation management, although organizations remain at different stages of adoption.

Carlo Pinamonti summarized the business value well. “Our customers expect speed, transparency and flexibility,” he said. “Using AI together with more than 80 years of transportation experience allows us to respond quickly to market demands and make smart operational decisions.”

That reflects where many companies are today. AI is already helping transportation teams work faster and make better decisions, even as many organizations remain in the early stages of adoption. As Lucia Danisova noted, many companies are still taking “baby steps”, while Denys Kochurin argued that the shift is already underway and will only accelerate.

The conversation also made clear that AI should not be viewed as simply another technology implementation. As Bernhard Schmaldienst emphasized, organizations making the most progress are treating AI as a business transformation initiative. Success depends not only on the technology itself, but also on data quality, solving tangible business problems and securing stakeholder buy-in. As he put it, “AI is both overhyped and underestimated”.

That observation captures today’s reality well. Some organizations overestimate what AI can accomplish in the near term, while others underestimate how profoundly it may reshape transportation management over the longer term. As I noted during the discussion, “we don’t know what we don’t know”. Five years from now, transportation leaders will likely be discussing AI capabilities that are difficult to imagine today.

Another recurring theme was connected data. Transportation depends on information flowing across shippers, carriers, logistics providers, customers and technology platforms, yet much of that information remains trapped in disconnected systems.

Stefan Kremsner described this as one of the biggest barriers to realizing AI’s full potential. “The more information we have, the better we can plan,” he said. “The more information is available for the AI to make decisions, the better they can be.” By connecting data across ERP, TMS, warehouse, production and partner systems, AI can help organizations make smarter planning decisions and optimize operations across the broader transportation network.

AI will change jobs; it won’t eliminate the need for people

The panelists agreed that AI will reshape transportation roles, but they did not see it eliminating the need for people.

Danisova reiterated a point she made during an earlier interview: AI will not necessarily replace people, but people who know how to use AI may replace those who do not. That requires both training and a mindset shift. As she noted, “getting a new tool and not being able to use it is just as ineffective as not having a tool”.

Kremsner expects transportation professionals to spend less time on administrative work and more time applying business judgment as AI becomes embedded in everyday operations.

Kochurin offered a more disruptive perspective. “Anything that can be automated will eventually be automated,” he said, predicting that activities such as quoting, procurement, dispatching and tracking will become increasingly automated. Even so, he emphasized that human expertise will remain essential for supervising critical operations, responding to unexpected disruptions and maintaining business continuity.

Practical use cases are already delivering value

While much of the AI conversation focuses on the future, the panelists highlighted several applications already creating measurable value.

At FERCAM, for example, Pinamonti described how the company uses Trimble’s AI-powered Autonomous Procurement solution to predict transport prices by combining market conditions with rate benchmarks. The goal is to provide customers with the right rate at the right moment while ensuring capacity is available when needed.

The real value, he said, comes from combining AI with decades of transportation expertise. “We are trying to mix everything.” Rather than replacing know-how, AI helps organizations capture, scale and apply it more effectively.

Schmaldienst noted that many transportation AI capabilities are already well established. “Estimated time of arrivals, rate predictions, dynamic time slots, demand forecasting -- this has been running in production for many years,” he said.

The newest advances are in generative AI, large language models and agentic workflows, which are extending automation into processes that previously depended on human interpretation.

From reactive firefighting to predictive decision-making

Another major theme was AI’s ability to help transportation teams shift from reactive execution to more proactive planning.

Kremsner explained that AI expands planning horizons by helping teams understand how today’s decisions will affect capacity, costs and service levels in the days ahead. It also enables companies to evaluate “what if” scenarios much faster, giving planners the ability to make more informed decisions before problems occur rather than simply reacting to them.

Trust, governance and explainability are essential

For AI to influence transportation decisions, companies must trust it. That is especially true when those decisions affect cost, service, pricing, capacity and customer commitments. This trust comes from clear controls and guardrails, verifiable outputs and humans in the loop. 

Bernhard Schmaldienst argued that the most effective AI deployments in transportation right now are not the ones maximizing autonomy as a single goal, but the ones that are designed with the clearest boundaries. "The goal isn't to remove humans from the loop," he said. "It's to make sure humans are in the right part of the loop — focused on judgment, not on repetitive execution." 

Start with the business problem

When asked where companies should begin, the panelists consistently pointed to business needs rather than technology.

Kremsner recommended prioritizing use cases based on business impact, effort and stakeholder buy-in. “It’s important to prioritize what’s good for the business and not what’s being hyped,” he said.

Danisova offered similar advice. Before selecting a TMS or AI-enabled tool, companies need to understand their requirements. “If we don’t know what we are looking for, then we will never find it,” she said.

The road ahead

AI will not transform transportation management overnight, but the shift is clearly underway. Some capabilities are already mature, while others are still emerging. The companies that make the most progress will likely avoid both extremes: ignoring AI or chasing the hype.

Pinamonti encouraged transportation leaders to start now rather than wait for perfect clarity. “Things are moving very quickly,” he said, warning that organizations that move too slowly may find it increasingly difficult to remain competitive.

Danisova offered an equally important reminder: as AI takes over more routine work, transportation professionals should spend more time building relationships, negotiating with partners, and applying human judgment where it matters most.

The panel’s overall message was clear: start building the right foundations today. Technology will continue to evolve, but long-term success will depend on combining AI with high-quality data, sound processes, skilled people, effective governance and trust. AI can generate insights, but people will remain responsible for turning them into decisions and results.

The conversation around AI in transportation management is far from over. If the ideas and perspectives shared by the panel sparked new questions or opportunities for your organization, I encourage you to continue the discussion at Trimble Insight Europe 2026, September 22–23 in Brussels. I’ve had the pleasure of attending this event over the past few years, and it has consistently been a great opportunity to learn from industry peers, hear firsthand how companies are approaching these challenges and opportunities and make valuable new connections. I hope to see you there!