Banyan Technology has launched an AI-powered capability that flags shipments likely to miss their delivery windows before a service failure occurs, moving the exception-management process from reaction to prediction.

The tool, branded Deliveries at Risk, expands the Cleveland-based freight management software provider's predictive freight intelligence suite. It evaluates incoming shipment tracking updates in real time and scores each load's probability of on-time arrival.

The model draws on four data points per shipment: current location, destination information, expected delivery timing, and the destination dock close time. Shipments scoring below an established confidence threshold are automatically labeled "At Risk," pushing them to the top of transportation teams' queues while there is still time to intervene.

The commercial logic is straightforward. Transportation teams today track more data than ever, yet most still assign people to watch every load manually. Banyan's pitch is that machine-scored risk rankings let teams concentrate on the freight most likely to require intervention, rather than spreading attention across the entire shipment file.

Once a load is flagged, AI-assisted workflows guide users through next steps — carrier follow-up, customer communication, delivery planning, and service recovery. That sequence matters for shippers and forwarders managing over-the-road freight, where a missed dock close time often cascades into detention charges, rescheduled appointments, and strained customer relationships.

"Transportation teams have more data available to them than ever, but the real value comes from turning that data into better, more proactive decisions," Banyan CEO Brian Smith said in the announcement. "Our focus is on building intelligence into freight management so clients can identify opportunities faster, reduce manual work and take action with greater confidence."

The launch extends Banyan's existing predictive freight intelligence portfolio, positioning the company alongside a broader industry push to embed machine learning directly into transportation management systems. For carriers, the tool effectively raises the bar on communication: shippers using predictive exception flags will likely expect earlier and more precise answers on at-risk loads, not post-failure explanations.

For shippers, the near-term benefit is labor efficiency. Automated risk screening reduces headcount hours spent monitoring routine shipments and reallocates that effort to loads where a phone call to a carrier or a dock can still change the outcome. For third-party logistics providers, earlier visibility into probable service failures creates a window to protect service-level commitments before they are breached.

Banyan has not disclosed pricing or customer adoption figures for the new capability. The company said the tool is available as part of its freight management platform.