Global transportation giant drives topline with automated order management

An Operating Model That Could Not Scale

This operations division ran workflows where every incoming transaction required a trained operator to interpret unstructured inputs, cross-reference business rules, make a judgment call, and execute the appropriate action. This was an end-to-end process chain: reading, understanding context, validating against policy, deciding, and routing, repeated across hundreds of thousands of orders in varying formats, languages, and complexity levels.

The cost model was linear: every new transaction required human time. Volume growth, seasonal peaks, and regional expansion all translated directly into headcount pressure. There was no leverage in the system.

Previous attempts at automation had addressed fragments of the workflow, extracting a data field here, routing a notification there, but none had replaced the end-to-end judgment chain. Traditional tools could digitize inputs but could not interpret, decide, or act. The operational bottleneck remained human capacity, and no amount of point automation changed that.

The pressure was compounding: volumes would not decrease, SLA expectations from internal stakeholders were tightening, and the division could not keep adding headcount proportionally to transaction growth. Incremental automation had reached its ceiling. The division needed operational workflows that could execute autonomously, end to end, with human involvement only where genuine judgment was required.

Autonomous execution, deployed by the business

The operations division deployed autonomous AI agents using Otera, replacing a manual, human-dependent workflow with end-to-end autonomous execution, built and owned entirely by the internal business team.

This was not a sandbox or a simplified pilot. The agents processed live transactions at production volume under full enterprise governance, including audit trails, role-based access controls, and compliance-grade data handling.

The deployed agents operated as a coordinated autonomous workflow:

1. Intake and interpretation: Agents ingested incoming RFPs, service and parts orders across unstructured and semi-structured formats, understanding content, context, and intent regardless of layout variations, input quality, or formatting inconsistencies. This was not template-based extraction. The agents read and interpreted each case the way a trained operator would, but at machine speed and consistency.
   
   
2. Decision execution: Each interpreted case was evaluated against business rules, cross-referenced for consistency, and acted on. Agents did not flag items for human decision. They made the decision and executed it: validating, categorizing, enriching, and routing each transaction through to completion. Cases meeting the governance-defined confidence threshold were processed without human intervention at any stage.
   
   
3. Governed exception handling: When agents encountered genuinely ambiguous inputs, conflicting rules, or edge cases below the confidence threshold, the case was escalated to a human reviewer, not as a blank referral, but with the agent's full interpretation, confidence assessment, and flagged discrepancies. The human resolved the exception with complete context, and the system incorporated the outcome to sharpen future execution.

Right from the initial rollout, the agents achieved over 90% autonomous execution, processing the vast majority of cases end to end without human involvement, while routing only the residual exceptions to human review under full governance.

A New Operating Model for the Division

The impact was not incremental. The division shifted from a model where every transaction required human processing to one where autonomous execution is the default and human expertise is reserved for the cases that genuinely require it.

• 90%+ of operational volume executed autonomously. Agents handle end-to-end process execution, from intake through decision and action, without human involvement. The operations team shifted from processing every order and RFP to governing the agents that process them.
  
  
• Production-grade reliability from day one. The agents met the threshold required for autonomous operations in a compliance-sensitive enterprise environment out of the box. This was not a pilot accuracy score. It was production performance on live transactions.
  
  
• Deployed by the business, without engineering dependency. The operations division owned the implementation end to end and launched the production agents, demonstrating that autonomous execution at enterprise scale does not require months of technical integration or dedicated engineering resources. 

Operational capacity redirected to higher-leverage work. With routine process execution running autonomously, the division reallocated capacity toward exception management, process optimization, and cross-regional coordination, functions that directly drive service quality and throughput rather than repeating known workflows.