One thought stayed with me this week: AI in logistics may fail because the work itself is unclear.
That is uncomfortable, but it offers a practical starting point. Conversations about AI often begin with platforms, strategy, transformation and roadmaps. Daily operations reveal much smaller gaps.
A document arrives. An order changes. A permit contains conditions. A customer asks for a status update. A dispatcher must decide what matters now. The useful question is whether a system can help at that point without creating new problems.
1. AI skills become a management responsibility
A recent Gartner analysis describes growing demand for AI skills in supply-chain and logistics job profiles. Operational managers need to be able to manage work supported by AI; they do not all need to become developers.
A dispatcher need not know how a model is trained. They do need to judge whether an answer is usable, identify missing information and spot assumptions hidden in an apparently prepared decision.
For me, AI competence begins with clarity about the work.
2. Agents need clear roles
Research increasingly describes agentic systems for supply-chain workflows. One agent might find data, another read documents and another prepare a next step. A person or system coordinates the work.
This requires a clear definition of who should do what. An agent can speed up unclear work without resolving its ambiguity.
The first step is to break down the work: what needs reading and checking? What may be suggested? What must a person decide? What needs to remain documented? An agent becomes useful once these questions have answers.
3. Start with a bottleneck
McKinsey’s State of AI study describes greater success where companies rethink workflows alongside introducing tools. Anthropic gives similar advice from a developer’s perspective: start with the simplest solution that works, then add complexity when the process requires it.
This matches what we see at ViMoKi. A useful starting question is where a team loses time every week manually interpreting documents, emails, permit conditions, transport data and discussions.
Consider a transport permit. Its conditions, times, routes and restrictions create operational work. Experienced staff may recognise the importance of a small phrase immediately.
AI should prepare that work: which details matter? Where is the risk? What must reach dispatch? What needs checking? Where must the system stop?
A practical idea for the week
Choose a recurring logistics task that is frustrating but seems unremarkable: a permit, a status email, a transport request or a customer question. Describe the work before writing a prompt:
- What is the input?
- What should exist at the end?
- Which three things must be identified reliably?
- Which decision must never be made automatically?
- How can a person tell whether the result is useful?
Clear answers to these five questions give you a stronger basis for AI work than opening ten tools in a browser.
Why we pursue this at ViMoKi
We are building ViMoKi to provide concrete help within logistics workflows: permits, transport documents, dispatch and coordination.
People retain responsibility. AI can take on preparatory tasks that currently consume too much time and remain hard to see.
You can find a first impression at https://vimoki.ai.