Artificial intelligence is moving into practical logistics workflows, particularly in transport planning and fleet safety. The technology can use historical delivery information to improve route assumptions and analyse operational data for more focused driver support.
Route plans commonly rely on standard estimates for the time required at each delivery point. Machine learning can build a more specific picture by comparing previous jobs and considering the goods, vehicle, customer and earlier delivery performance. More realistic stop times can help operators create workable schedules and support stronger on-time delivery performance.
Safety teams face a different but related challenge: data is spread across telematics, cameras, compliance systems and spreadsheets. AI can help identify recurring patterns in that information, rather than treating every event as equally significant. This may highlight drivers who could benefit from coaching and allow managers to monitor whether the intervention is improving behaviour.
The approach is intended to give transport managers and drivers more context. Distinguishing persistent behaviour from an isolated incident can make coaching more relevant while supporting efforts to reduce accidents, compliance concerns and operational risk.
Future applications could include agentic AI that learns how transport planning teams work and takes on routine tasks. For businesses considering investment, the central issue is not whether a system carries an AI label. It is whether the technology addresses a defined planning, safety or compliance problem and produces an outcome that can be measured.
A multilingual professional experienced in Europe, Canada, and China, Herbert has developed invaluable networks in the automotive and energy industries. He has led high-profile projects involving ENBW, Mercedes-Benz Group, Siemens Group, and the Fraunhofer Institute.