Uber Freight Debuts Agentic AI Network Unifying TMS, Procurement, and Finance
Uber Freight introduces AI across TMS, procurement, and finance to cut manual work. Teams get faster decisions, shorter dispute cycles, and automated scheduling.
Uber Freight launches scaled AI logistics updates for operations leaders
Uber Freight introduced AI-driven capabilities that cut manual work across transportation management, procurement, and finance. The goal: move from a system of record to active management-so teams act faster, resolve issues sooner, and keep freight moving.
"From procurement to payment, shippers face constant complexity across the freight lifecycle," said Steve Barber, vice president of product at Uber Freight. "Through continued investment in platform innovation, Uber Freight delivers the tools, automations, and integrations that simplify the work and unlock meaningful outcomes such as faster decisions, smarter cost control, and more resilient supply chains."
What's new in the TMS financials module
Shippers can manage the full order-to-cash flow inside the TMS-no more hopping between disconnected systems. Bulk processing tools, redesigned workflows, and real-time dashboards aim to shorten dispute cycles and speed up AR/AP.
- End-to-end visibility of invoices, payments, and disputes within the TMS portal
- Bulk approvals and streamlined workflows to reduce cycle time
- Dashboards for live tracking of exceptions and cash movement
Uber Freight also built this module to support upcoming AI-native functions that will:
- Flag payment risks earlier
- Improve freight spend forecasting
- Automate dispute resolution
Procurement: faster scenario analysis and smarter awards
Uber Freight Exchange now supports side-by-side scenario modeling, allowing teams to compare costs, service levels, and carrier mixes in minutes instead of weeks. Once awards are final, results export directly into any TMS to connect planning with execution.
Uber Freight plans to tie this to its Insights AI engine to surface recommendations on underperforming lanes, targeted mini-bids, and carrier selection.
Agentic AI for repetitive ops work
Agentic AI is already automating thousands of scheduling actions daily. The system captures arrival and departure times from carrier emails, corrects data errors in less-than-truckload shipments, and improves shipment tracking accuracy.
Impact reported by the company: shorter scheduling time, fewer reschedules, and better data quality-freeing teams to focus on exceptions and high-value work.
Why this matters for operations
Uber Freight is building a unified logistics platform that ties transportation management, procurement, and finance into one AI-supported system. For operations leaders, this points to faster decisions, clearer network visibility, and stronger resilience under volatility.
"Ultimately, we're building a unified logistics ecosystem where AI and human expertise work hand in hand to drive efficiency, reliability and adaptability," Barber said.
How to put this to work
- Map your order-to-cash process and identify handoffs to move into the TMS financials module.
- Define dispute reason codes and auto-rules to enable straight-through processing where possible.
- Pilot scenario analysis on a subset of lanes; compare outcomes (cost, OTIF, carrier mix) to current awards.
- Set target KPIs: dispute cycle time, AR days outstanding, reschedule rate, and email-to-schedule latency.
- Create exception playbooks for agentic AI handoffs-what the agent handles vs. when humans step in.
- Tighten data inputs: standardize carrier email formats and LTL data fields to boost automation accuracy.
- Integrate outputs directly into your TMS to avoid spreadsheet drift between planning and execution.
Upskilling your ops team on AI
If you're ramping AI skills across your operations, explore practical training and playbooks built for day-to-day workflows:
