AI projects in logistics rarely fail because the technology “doesn’t work”. They fail because the organisation can’t explain, in operational terms, what an AI output is allowed to do.

Is it a summary? A recommendation? Or a commitment that changes cost, timing or compliance exposure? When that boundary is fuzzy, teams either ignore the tool or – worse – treat fluent answers as authority.

Learn more at www.sterdts.co.za

For South African logistics and international household relocations, the path from pilot to production needs to be designed like any other critical operating capability: with evidence standards, decision ownership and escalation paths that survive exceptions.

Sterdts has found that the fastest wins come from using AI to reduce coordination friction while keeping accountability with people.

1. Data and context: what “truth” looks like in the real world

Across the freight forwarding sector, the biggest surprise for business and data leaders is how much operational truth sits outside the “system of record”. Milestones and charges may be captured in structured tools, but the reasons behind changes often live in e-mails, scanned documents, supplier notes and short messages that never become fields. If an AI layer is trained on the clean slice only, it will be most confident precisely when the missing context matters.

International household moves amplify this. Scope is conditional: access constraints, packing assumptions, inventory detail and timing dependencies change as the job becomes clearer. An AI assistant can help structure information and highlight gaps, but it cannot infer what was never captured – or what changed since the last confirmation.

The governance move here is practical: define a minimum “evidence pack” for key decisions and make it easy to assemble. Evidence can be lightweight – latest milestone proof, the current document set/version and the assumptions that drive cost or timing – so long as it is visible and current.

2. Control layers and approval gates: separating drafts from decisions

In the sector generally, AI becomes risky when outputs slide from “draft support” into “implied promise” without a named owner. The antidote is to classify what the system produces and attach gates.

A useful rule is: information can be shared widely, recommendations must be editable drafts with an explicit “accept/revise/escalate” flow, and commitments require a gate where a person confirms the evidence and owns the decision.

The design detail that matters is timing. Gates should sit at points where reversals are expensive: before communicating a firm date, before accepting a charge or scope change, before triggering third-party actions that create cost and before sending messages that could be interpreted as undertakings.

3. Workflow integration: avoid the ‘extra dashboard nobody trusts’

Across Southern Africa’s logistics operations, many AI initiatives die when they become a parallel universe: another interface, another queue, another place to check. Adoption improves when AI outputs appear where teams already work and map to real handovers – operations to customer service, customer service to finance, sales to execution.

A workable pattern is to position AI as a coordinator’s leverage, not a replacement. Let it compress time by doing the repetitive parts: assembling what changed since the last update, flagging missing inputs that block progress, drafting updates that clearly state conditions and highlighting anomalies in documents or charges. Then design the next step as a human-owned action with a clear audit trail.

4. Implementation pattern: thin slices, clear metrics, exception-driven learning

Sterdts’ implementation bias is to start with narrow workflows where feedback is fast and mistakes are visible: document completeness checks, exception summarisation and quote/charge sanity checks. These projects “survive” imperfect models because humans remain the final owners and the success metrics are straightforward: fewer missing inputs, faster resolution, fewer preventable rework loops.

Run the AI in shadow mode first. Compare its suggestions to what the team actually did, then catalogue the disagreements. The edge cases – where the output sounded plausible but missed the detail that mattered – become the training data for governance. Each exception should tighten one of three things: the required inputs, the gate condition or the escalation path.

A short controls summary CIOs can insist on:

Provenance visible: Source, recency and assumptions are obvious
Decision classes defined: Draft support vs commitments are clearly separated
Gates enforced: Commitments require evidence and a named owner
Exception logging: Misses are captured and reviewed
Workflow-native delivery: Outputs live inside existing work queues

Closing reality check

AI won’t stabilise infrastructure, eliminate corridor volatility or resolve policy complexity. What it can do – when the operating design is right – is make decisions sharper: earlier detection of gaps, clearer handovers, faster coordination and fewer avoidable errors. The prize for business-tech leaders is not “automation”; it’s a more disciplined evidence-and-decision system that scales under pressure.

About Sterdts
Sterdts is a South African freight forwarder and international household moving company based in Johannesburg, serving clients across South Africa. Learn more at www.sterdts.co.za.