Ask the import operations team to track one week of portal checks, inbox searches, status reconciliation, release chasing, and preventable escalations. That measured baseline is more useful than assuming a generic split between reactive and proactive work.
The reactive work looks like this:
Checking carrier portals one by one to see if releases dropped. Logging into terminal websites to check availability. Scrolling through email looking for customs release notifications. Calling the drayman to confirm they're actually going to make the pickup. Explaining to a client why their container has been sitting for three days.
Why the ratio is so bad
The root cause is often system fragmentation. The information needed for proactive work may be spread across several categories:
- Carrier portals (Maersk, MSC, CMA CGM, each with their own login)
- Terminal websites and appointment systems
- TMS (CargoWise, Magaya, GoFreight)
- Customs broker status feeds
- Email inboxes (individual, not shared)
- Spreadsheet trackers built by whoever was here before you
Nobody has a unified view of "which containers can actually move right now, and which ones are about to cost us money." So the team builds that view manually every morning, and it's outdated by lunchtime.
What proactive actually looks like
A proactive ops team starts the day knowing: which containers are actionable right now, which are approaching free-time expiry, which are blocked and why, and who needs to do what to unblock them.
That doesn't require replacing your TMS or buying a new platform. It requires connecting the status information that already exists into a single prioritized queue, ranked by cost exposure, not by who happens to yell loudest.
The potential saving should come from the baseline: time spent checking portals, reconciling conflicting statuses, rebuilding trackers, and chasing events that could have triggered an alert. The work does not disappear, but the team can stop recreating the same picture from scratch.
In one engagement, a regional forwarder shifted to proactive exception detection and caught 85% of the defined exception set before those events became charges. That is a specific result with its own workflow and baseline, not a general freight benchmark.
If this sounds familiar, the logistics operations AI agents use case shows exactly how to implement this, and the demurrage leak breakdown explains where the money is slipping today.
