Challenge

NAES, a global energy services firm, wanted to improve the efficiency of its order-to-cash (O2C) billing operations. Each invoice required the finance team to painstakingly match hundreds of time and expense line items to backup documents such as timesheets and receipts. This manual reconciliation drained resources, delayed billing cycles and often led to last-minute rejections by operations teams.

The process lacked early-stage quality control and real-time visibility, which meant issues were typically caught only during final approvals. Late-stage breakdowns forced rework and slowed productivity. Operations teams, brought in only at the tail end of the workflow, frequently rejected proposals for mismatches or missing documentation, creating a frustrating cycle of delay and revision. With no analytics or reporting tools in place to identify the root causes of these failures, the organization remained reactive and inefficient. Billing cycles were stretched, cash flow was hindered and operational confidence suffered. To break this cycle, the firm partnered with EXL to implement a multi-pronged solution.

EXL helped global energy services firm NAES transform its financial operations for early-stage quality control, visibility, and scalable ongoing process improvement.

Solution

Leveraging agentic AI, automation, and workflow optimization, EXL’s transformation team began a sweeping redesign of the client’s billing process, aimed at improving accuracy, visibility, and speed.

At the heart of the transformation was an agentic AI system that autonomously extracted invoice line-item details and their supporting documents. Using contextual logic based on resource identifiers, amounts, project codes and dates, intelligent agents matched entries automatically. Each invoice proposal was generated with a match confidence score and full audit trail, allowing only exceptions or low-confidence matches to be routed for human review. This human-in-the-loop model slashed the manual burden while maintaining quality control.

Simultaneously, the approval workflow within the enterprise resource planning platform was restructured to engage operating teams earlier—during timesheet approvals—rather than after invoice generation. AI-driven quality control agents flagged anomalies early in the process, enabling rapid resolution and reinforcing ownership and accountability among the team. This proactive involvement allowed them to validate billable entries upfront, reducing end-stage rejections.

To maintain performance visibility, the team implemented dynamic dashboards with real-time tracking of proposal status and rejections. Agentic analytics powered root cause analysis, uncovering systemic issues like missing timesheets or misallocated expenses. These insights enabled upstream fixes in time tracking and expense processes, preventing recurring errors and reinforcing a culture of continuous improvement.

Behind the scenes, a suite of automation platforms, intelligent content extraction tools, enterprise workflow systems and user-focused interface enhancements facilitated the transformation. Together, these technologies laid the groundwork for a scalable, repeatable billing process that could handle high volumes without sacrificing accuracy or speed.

Outcomes

The transformation led to faster, more accurate billing cycles, stronger operational oversight and measurable improvements in productivity. By combining intelligent automation with early-stage quality control and analytics, the organization turned a historically manual and error-prone process into a scalable, insight-driven engine for financial operations.