Fixing Slow, Manual Back-Office Workflows with AI-Driven Process Optimisation

The Problem

A finance and operations team was struggling with slow, manual back-office processes across invoice approvals, vendor onboarding, and claims handling. These workflows relied heavily on handoffs, email approvals, and manual data checks, resulting in long cycle times and limited process visibility. Exceptions frequently absorbed senior reviewers, creating bottlenecks that affected SLAs, delayed payments, and disrupted cashflow. The organisation needed a scalable way to automate routine steps while ensuring oversight of high-risk or complex cases.

The Solution

One of our digital squads implemented an AI-led business process optimisation layer that blended process mining, intelligent decision agents, and RPA. We began by conducting end-to-end process discovery to map high-variance steps, rework cycles, and systemic bottlenecks. Using this insight, we developed a decision agent that applied business rules and ML-based risk scoring to determine whether a transaction could be auto-processed or required escalation.

For deterministic, repeatable tasks, RPA bots executed the transactional work under the guidance of the agent. When exceptions emerged, the agent passed them to human reviewers with full context, recommended actions, and linked evidence. The system included audit trails, manual override controls, and continuous improvement loops tied to operational KPIs to ensure safe and measurable rollout into production.