AI in supply chains is becoming increasingly important in many companies’ strategies, but despite its potential to improve day-to-day operations, many businesses are still struggling to move beyond early stages. Rather than technology itself, the main reason for this is the difficulty of gathering, connecting and aligning useful data as a foundation for AI deployment. This article sheds light on shifting priorities and the challenges of implementing AI, and explains why cooperation with logistics partners becomes critical to turn fragmented data into more effective day-to-day decisions.
Increased efficiency, fewer manual errors and productivity beyond standard business hours: the list of goals that companies hope to achieve by investing in technology and AI in supply chains is long. However, priorities for deploying technology have recently shifted, as reflected by logistics executives in the EU Supply Chain Tech Survey 2025/26 by Oliver Wyman and Prequel Ventures1. Cost reduction has become the main objective of technology investment, ahead of other goals more closely linked to innovation or growth. Operational agility, improved lead times and greater transparency around supply and availability are also gaining importance.
However, the benefits of application of AI in supply chains can only be realized if they are based on complete, up-to-date information that does not overlap with other operational layers. Trying to automate without fully understanding what is happening across the supply chain can create more friction than efficiency.
For companies looking to use new tools and AI to improve their logistics processes, a strong data foundation is a prerequisite. But as supply chains have become more complex in recent years, the depth, diversity and precision of necessary data – such as cost information – has equally increased.
While many companies have made significant progress in digitalization, information is often scattered across diverse systems and interfaces: Enterprise Resource Planning systems (ERP), Warehouse Management Systems (WMS), Transportation Management Systems (TMS) but also other planning tools, supplier portals and external systems. The challenge today is to regain control over this data before it can be effectively utilized for deploying AI in supply chains.
In the Supply Chain Tech Report 2026, two thirds of surveyed executives report that this fragmentation and low quality of information is one of the main barriers to adopting AI across organizations. Meanwhile, 61% point to integration issues with existing systems, with most use cases still stuck in early stages and far from full implementation.
In other words, the problem is not so much about finding a better AI solution, but about knowing whether the structure it depends on is ready to support it.
67% of surveyed executives cite low-quality or fragmented data as the main barrier to deploying AI.
61% point to integration issues with existing systems.
Only around 15% say they have fully deployed AI applications.
One of the report’s ideas explains why many AI initiatives are currently struggling: AI does not create new problems, but it does make existing ones more visible.
Many organizations are discovering that their limitations are not primarily linked to a lack of technology, but to years of working with legacy systems, loosely defined data structures and fragmented processes. This helps explain why so many initiatives fail to move beyond the pilot stage. For many companies, the next step is therefore not adding more tools, but structuring and aligning the data they already have.
This is where collaboration with logistics partners becomes even more relevant, because they often hold a more complete view of how supply chain data translates into real performance.
In many cases, operators end up handling even more customer data than the customer itself.
This highly valuable information often includes supply chain insights such as:
If available in one consolidated view, this allows companies to identify patterns, detect inefficiencies and better understand how their operations perform in reality. However, as information is often spread across departments, suppliers, internal tools and processes with only limited communication, providing this knowledge is becoming far more valuable.
For Gutiérrez, this shift is already visible in the increasingly analytical relationship between customers and logistics partners: What was once primarily focused on execution tracking – shipments, incidents, timelines or deliveries – is expanding beyond operational tasks. Today, logistics partners can offer businesses insights into the actual performance of their supply chains and help them interpret that data to make better decisions.
AI in supply chains can accelerate that process, but it does not replace a collaborative foundation. Instead, it makes the need for clearly defined, integrated and shared data even more visible.
For many companies, the rise of AI in supply chains is opening a broader discussion about the kind of relationship they need with their logistics partner. What becomes increasingly important is not only execution, but a shared understanding of what is happening across the supply chain – from lead times and incidents to capacity and service levels. This kind of collaboration creates a more consistent data foundation and enables tangible improvements across operations, such as:
At Rhenus this is exactly the logic we work from. Our combination of operational knowledge, customer proximity and ability to interpret logistics flows allows us to support companies not only in day-to-day execution, but also in understanding and using that data to make the most of AI in supply chains.
AI in supply chains offers huge potential in the coming years, but its success will depend less on the tools companies adopt and more on the quality of the data they work with and analyze together with their logistics partners.
The Oliver Wyman and Prequel Ventures report highlights data fragmentation and system integration challenges as the main barriers. In that scenario, closer collaboration between customers and logistics partners becomes essential to improve visibility, enable better decisions and build more efficient supply chains.
Discover how Rhenus can help you gain control, agility and efficiency across your supply chain.
Learn more1 Source: Supply Chain Tech Report 2026,
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