1. Database without temporal relevance

More than 85 percent of all AI initiatives fail in practice. The reason for this is incomplete, fragmented, or simply data that arrives too late in the system. To make precise predictions—such as calculating the estimated time of arrival (Estimated Time of Arrival; ETA), real-time information is absolutely essential. If logistics data isn’t available until hours after the actual event, the technological advantage is lost. Without a timely digital representation, however, AI operates in a state of logistical uncertainty.

2. Problematic Legacy Integration

Much of a company’s mission-critical information resides in legacy systems that were never designed to interact with modern applications. Such data is therefore structurally inaccessible: stored in proprietary formats, lacking standardized interfaces, and often accessible only through manual exports. For AI applications, these data points effectively do not exist. Instead of costly replacements for functioning legacy systems, companies should rely on an integration platform that reliably connects even historically developed systems and makes their data usable for AI.

3. Lack of Governance Due to Uncontrolled Proliferation of Interfaces

Companies use numerous different data sources throughout the entire supply chain. If new AI applications or partner interfaces are connected directly to the core systems without proper oversight, an opaque network of connections results. These uncoordinated point-to-point connections result in a complete loss of control over data flows. Without a central governance layer, it becomes nearly impossible to track which information is flowing where.

4. Failure Due to Forced ERP Replacement

Vendors often promise to solve existing IT problems by completely upgrading the core system. In reality, such a replacement of the ERP system usually takes 18 to 36 months. Furthermore, a large proportion of these migration projects go significantly over schedule and budget. Modernization is far more efficient when existing, functioning core systems remain untouched and are instead made AI-capable through a flexible integration layer.

5. Manual B2B onboarding as a drain on resources

In practice, onboarding new suppliers or logistics partners often takes weeks. Delays usually arise not from the actual system configuration, but from asynchronous coordination via email and phone. Questions about firewall ports, IP addresses, or security certificates tie up valuable IT resources. Without automated self-service portals for partners, every new onboarding process inevitably turns into a drawn-out project.

“Successful digital transformation in the supply chain is not purely an IT task, but a strategic architecture issue,” explains Tim Srock, CEO of Lobster. “Artificial intelligence can only realize its full potential when built on a foundation of real-time data and seamless integration of all partners.”