An example from US yard logistics captures what interests me about AI in logistics.

Lazer Logistics has built an internal AI system called “Uncle Phil AI”. It is named after Phil Newsome, the company’s COO, who brings 36 years of operational logistics experience.

The system translates an experienced operator’s knowledge of recurring patterns into support for site managers at many locations.

Lazer Logistics runs yard operations for retailers, manufacturers and logistics sites. Trucks arrive, trailers need moving, loading bays get blocked and drivers wait. Data sits in different systems, yet someone must quickly decide what happens next.

The AI system brings together data from telematics, driver checks, maintenance, staffing and yard workflows. It then helps with questions such as:

  • What is actually happening right now?
  • Why is a process backing up?
  • Which decision would an experienced operator examine now?
  • What information is missing before someone can act sensibly?

This is a US example and a company story. Its relevance lies in making operational experience available beyond the individuals who hold it.

Operational judgement often relies on many small signals: a missing status, an unclear order, an unread attachment, a permit condition, a waiting vehicle or a question that was never asked in time. This is where the clarification burden arises.

AI know-how for logistics professionals in 2 minutes

What is an agent?

An agent works within a clearly bounded digital workflow. It receives a goal, uses permitted data and tools, checks intermediate steps and prepares an action. Its boundaries need to be defined.

Examples in logistics include:

  • Reading a transport permit and marking relevant conditions.
  • Comparing transport data to identify missing details.
  • Identifying questions that remain open before dispatch.
  • Suggesting a next action.
  • Writing a draft that requires approval before sending.

People remain responsible for the goal, decisions and guardrails. AI performs part of the preparatory work.

ViMoKi aims to reduce clarification work for operational teams, giving people more time for the decisions themselves.