Before choosing an AI tool, a logistics team should identify the decision it wants to improve. Summarising a shipment’s exception history, suggesting a route and authorising a carrier payment are different tasks. Each needs different evidence, evaluation and authority.
NIST provides resources for applying its AI Risk Management Framework. The framework offers a reference point for considering trustworthiness throughout an AI system’s lifecycle. For Indian logistics operators, our practical interpretation is to bound the workflow before connecting a model to operational systems.
Define what the system may do
Write a short task statement that names the user, input and permitted output. For example, a customer service assistant might assemble verified movement events and draft a response for review. It should not invent a missing milestone, promise an unconfirmed delivery or alter a shipment record merely because the draft sounds plausible.
Specify which source is authoritative for each fact. The transport system may hold the assignment, while the warehouse system confirms loading. If those sources disagree, the assistant should surface the disagreement. Asking the model to choose a confident narrative can conceal exactly the uncertainty a person needs to resolve.
Give the reviewer access to the supporting records and a clear route to reject or correct the output. Human review should be a usable operating step, not a checkbox attached to a decision that is already effectively committed. The colleague must have enough time, evidence and authority to change the outcome.
Evaluate the entire workflow
Use anonymised examples that represent the awkward situations: incomplete tracking, contradictory events, changed instructions and unfamiliar customers. Agree the acceptance criteria before running the pilot. A fluent summary is insufficient if it omits an unresolved exception or attributes one shipment’s event to another.
Measure the effort required to check and correct the output as well as the time spent generating it. An assistant that produces an attractive draft but demands extensive verification may transfer work rather than remove it. Keep a record of recurring errors so that data, prompting and workflow problems can be distinguished.
Only expand permission after the bounded use is dependable under the agreed evaluation. A tool that drafts text does not automatically deserve the power to send messages, amend bookings or release payments. The useful question remains the same at every stage: which decision improves, what evidence supports that improvement and who remains responsible when the system reaches the edge of what it knows?
Sources
- NIST AI Risk Management Framework resources Source accessed 2026-10-13.
Illustrative photograph: Adrian Sulyok / Unsplash.
