FEATURE Industrial AI
AI DRIVES SMARTER, FASTER LOGISTICS Dennis Schmitz, Managing Director
of Arvato in the UK, discusses how AI is powering smarter, more connected logistics operations
W
hen the world’s first industrial robot hit factory floors in 1961, it marked the beginning of an industrial revolution. Since
then, the world of logistics and manufacturing has transformed beyond recognition, shaped by globalisation, e-commerce, supply chain disruption, and growing economic volatility. Yet through every wave of change, automation and robotics have remained a constant, helping businesses drive speed, efficiency, and resilience across warehousing and fulfilment operations. Today, in an increasingly unpredictable environment, it’s no longer about deploying more robots; it’s about deploying them in smarter, more adaptable ways. AI is supporting this shift, working as the intelligence layer behind modern logistics operations. AI empowers logistics teams to make faster, smarter decisions and respond more dynamically to changing operational demands, in turn, helping clients gain greater control in an era shaped by complexity and chaos. For years, logistics automation performed best in highly structured settings where products, workflows, and demand remained relatively stable. Today, however, UK businesses are operating in a landscape defined by disruption and uncertainty: volatile demand, tighter fulfilment windows, and rising expectations for speed, accuracy, and transparency.
It’s the same story in different forms across sectors. Retail is shaped by promotional and peak-driven spikes, healthcare by critical, time- sensitive supply needs, and technology by rapid product cycles and global dependencies. Against this backdrop, AI is shifting the conversation beyond traditional automation toward operational resilience, helping logistics teams move from reacting to disruption to anticipating and adapting to it in real time. Here are some of the most significant ways AI is transforming modern logistics operations: 1. Solving Logistics’ Variability Problem Historically, variability has been one of
28 June 2026 | Automation
the biggest barriers to scaling automation in logistics. Conventional robotic systems struggled whenever processes became inconsistent or environments changed unexpectedly. AI is now helping overcome that limitation.
By combining machine learning, computer vision, and real-time operational data, automated systems are becoming significantly more flexible. Instead of relying on rigid, pre-programmed instructions, AI- enabled technologies can adapt dynamically to changing products, workflows, and demand conditions.
This is critical because variability has become the norm in modern logistics operations. E-commerce is a strong example of this shift. Retailers are no longer managing a small number of predictable stock-keeping units, but thousands of products across multiple fulfilment channels, often with rapidly changing inventory profiles and increasingly fragmented order patterns. AI allows automation to respond to that complexity rather than being constrained by it. For businesses, this makes large-scale automation more commercially viable, not because it removes humans entirely, but because it enables operations to scale without sacrificing agility.
2. The Rise of Physical AI The emergence of “physical AI”enables machines and robotics to interact more effectively with unpredictable, real-world environments.
In practice, this can be as simple as using vision to verify that an order is right before it leaves the building. For example, AI-powered order verification systems can inspect photos of cartons or totes as they move along a conveyor and flag likely
quantity or quality errors for review. In our Arvato facility in Hamm, our system SPOT that we’ve implemented together with our technology partner Nomagic, has been used to perform millions of order verifications with roughly 95% of orders cleared automatically, leaving just 5% flagged for targeted human checking. As a result, teams can focus their time and expertise on resolving genuine exceptions and improving operational quality, rather than manually checking every order. This saves time and allows us to operate at high speed without slowing down for quality control tasks. The broader point isn’t the technology for its own sake, it’s that AI can reduce avoidable mistakes while focusing human attention where judgement actually adds value. This matters because many logistics
processes are inherently messy. Returns processing, for example, remains one of the most operationally complex areas in ecommerce. Products arrive in varying conditions, packaging may be damaged or incomplete, and until recently, manual inspection was the only way to determine whether items can be restocked, repaired, or rejected. AI is now transforming this process, enabling faster, more accurate assessments that reduce manual intervention while improving consistency and decision-making. The result is not simply greater efficiency.
It is greater operational resilience. Businesses can process higher volumes, adapt more quickly to changing conditions, and reduce bottlenecks during peak periods, all while improving consistency and visibility across and maintaining speed and quality. 3. Powering More Connected Logistics Operations
Adding isolated AI tools into fragmented
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