How companies can identify suitable applications, prepare their data, and bring AI into day-to-day operations
There is no longer a shortage of potential applications for AI in logistics. Forecasting order volumes and staffing requirements, automating document processing, accessing process knowledge, or supporting decisions during live operations all show just how broad the spectrum has already become. The more difficult question often begins after the first use case has been identified: How can a promising idea become an application that works with real-world processes, existing systems, and the demands of ongoing logistics operations? This is where technological possibilities meet operational reality. Data is distributed across WMS, WCS, TMS, ERP, and other applications, while decisions are based partly on defined rules and partly on experience. AI can make a significant contribution in this environment if companies start not with the technology, but with the decisions and processes they actually want to improve. The report “Your Practical Guide to Implementing AI: Logistics Edition” outlines six steps, from assessing the current situation to measuring success.
Many AI projects still begin by asking which technology should be used. For implementation, however, it is more important to first identify where decisions regularly take too much time in day-to-day operations, where information is missing, or where data from several systems has to be considered together. These are precisely the areas where potential use cases emerge. The following six questions help companies systematically assess the path from an initial idea to a reliable AI application in live operations.
1.) What problem should AI in logistics actually solve?
The starting point should not be a technology that still needs a use case. It is more useful to examine the existing process: Where do workflows regularly slow down? Which decisions do employees have to make under time pressure? And where does information from multiple sources first have to be brought together manually? Situations in which the same decision has to be made frequently and multiple factors need to be considered are particularly relevant. In the warehouse, for example, this could involve deciding whether an order should be prioritized, employees reassigned, or action taken in response to a developing backlog.
Before discussing specific AI capabilities, companies should therefore examine what actually happens in their operations. High volumes, missing information, or several simultaneous exceptions often reveal the decisions where AI can ultimately make a tangible difference. Implementing AI in logistics therefore does not begin with selecting a model. It begins with a clearly defined operational task.
2.) Which AI use case fits the task?
Once the problem has been clearly defined, it becomes much easier to determine which type of AI is suitable. Different approaches are designed for different tasks. Generative AI can make knowledge accessible, summarize information, or generate work instructions. Predictive AI uses historical and current data to forecast factors such as workloads, delays, or staffing requirements. Vision AI processes visual information from the operational environment. Agentic AI becomes relevant when data from multiple systems needs to be combined, evaluated, and translated into concrete next steps.
The objective is therefore not to start with the most complex approach possible. The technology and use case need to fit. A company that wants to give employees faster access to process knowledge needs a different application than one that wants to coordinate operational actions across multiple systems. For AI in logistics, the goal is not to deploy as many AI capabilities as possible, but to select the right approach for the task at hand.
3.) Is the data foundation for AI in logistics reliable enough?
Even a well-chosen use case will only work if the underlying information is reliable. In established logistics environments in particular, data often comes from multiple applications that do not necessarily use the same identifiers, timestamps, or process statuses. An order may already have a new status in the WMS while an older update is still being reported by the automation system. Employees may already understand and account for these discrepancies. An AI application, however, needs a clear and consistent basis if it is expected to generate reliable recommendations.
This does not mean that companies first need to clean up their entire data landscape. A more practical approach is to start with the specific use case: What information is needed, which systems does it come from, and does it accurately reflect the actual process at the moment when a decision needs to be made? For AI in logistics, data quality is therefore best assessed in the context of the specific decision the application is intended to support. The pilot phase can already reveal where data needs to be standardized, responsibilities clarified, or interfaces added.
4.) Is the data foundation for AI in logistics reliable enough?
Even a well-chosen use case will only work if the underlying information is reliable. In established logistics environments in particular, data often comes from multiple applications that do not necessarily use the same identifiers, timestamps, or process statuses. An order may already have a new status in the WMS while an older update is still being reported by the automation system. Employees may already understand and account for these discrepancies. An AI application, however, needs a clear and consistent basis if it is expected to generate reliable recommendations.
This does not mean that companies first need to clean up their entire data landscape. A more practical approach is to start with the specific use case: What information is needed, which systems does it come from, and does it accurately reflect the actual process at the moment when a decision needs to be made? For AI in logistics, data quality is therefore best assessed in the context of the specific decision the application is intended to support. The pilot phase can already reveal where data needs to be standardized, responsibilities clarified, or interfaces added.
5.) How much responsibility should AI in logistics take on?
Once AI begins recommending specific actions, the question becomes how much authority it should have. There are several useful stages between providing information and taking fully autonomous action. Initially, a system may simply monitor operations and flag expected deviations. The next stage can include concrete recommendations that employees review. Once the application and its decision logic have proven reliable, defined actions can be executed after human approval. Only then does it make sense to consider controlled autonomous execution for selected processes within clearly defined boundaries. This gradual approach is particularly important in logistics because individual decisions often affect adjacent processes. The appropriate level of autonomy therefore depends not only on what is technically possible, but also on how well the consequences of a decision can be understood and controlled.
6.) How can companies determine whether AI is successful?
Introducing an AI application is not a measure of success in itself. What matters is whether the process being addressed improves in measurable terms. The right metrics depend on the use case. For exception handling, the time required to resolve an issue may be relevant. For workforce forecasting, utilization during a shift could be an appropriate measure. Other applications can be evaluated based on throughput, error rates, service levels, or reductions in processing time.
These criteria should ideally be defined before a pilot begins. At the same time, the application also needs to prove itself outside normal operating conditions. Order peaks or delayed system updates, for example, can reveal how reliable the results really are and where data, rules, or decision boundaries still need to be adjusted.
Conclusion: AI readiness becomes visible in operational processes
The path to meaningful AI adoption does not begin with the question of which model is the most powerful. It starts with a concrete problem and moves from there through the right use case, a reliable data foundation, and clearly understood decision paths to the question of how much responsibility a system should take on and how its contribution can be measured. This is particularly important in established system landscapes. An AI project does not require companies to completely rebuild their WMS, WCS, TMS, or ERP. It does, however, require a clear understanding of which information is needed and how an application will be integrated into existing processes and responsibilities.
The next step: Assess your AI readiness
Which processes are suitable for an initial AI pilot? Is the required data sufficiently prepared? Which decisions offer enough potential for AI support, and where should human approval remain in place initially? “Your Practical Guide to Implementing AI: Logistics Edition” takes these questions further. It includes an AI readiness assessment, an overview of different AI categories and logistics use cases, a data quality checklist, and exercises to help identify suitable decisions and define appropriate boundaries for agentic and autonomous applications. The practical guide is complemented by current Gartner research on key AI developments in warehousing.
