The Challenge

The organization managed a high volume of service requests and incident tickets across enterprise clients. While ticketing systems and basic workflows were in place, resolution times remained inconsistent.

Cases frequently moved back and forth between teams. Escalations were common, even for routine issues. SLA breaches increased during peak periods, despite adequate staffing.

A detailed review showed that agents were not struggling to resolve issues. They were struggling to decide. Each case required understanding client priority, historical context, system impact, and contractual SLAs. This information existed, but across multiple systems.

As a result, agents spent significant time gathering context before acting. When uncertain, they escalated. Escalation became a safety mechanism rather than a necessity.

Traditional automation had already been applied. Ticket creation, routing, and notifications were automated. Yet decision latency persisted.

Key issues identified:

• Fragmented context across tools and systems

• Inconsistent prioritization across similar cases

• Escalations driven by uncertainty, not severity

• Automation limited to task movement, not decision support

The problem was not lack of automation. It was lack of decision structure.

The Solution

The engagement focused on applying Intelligent Process Automation to decision-heavy parts of the service workflow.

Work began by breaking the resolution process into two layers: execution steps and judgment points. Each judgment point was analyzed to identify what information was actually required to proceed confidently.

Context from ticket history, client SLAs, asset criticality, and past resolutions was consolidated into a single decision view. Instead of agents searching across systems, relevant context was assembled automatically.

Decision logic was then introduced. Cases were scored based on impact, urgency, and confidence.

• Low-risk, high-confidence cases were auto-resolved or routed directly.

• Medium-confidence cases were routed with clear recommendations.

• High-ambiguity cases were escalated with full context attached.

Human involvement shifted from routine triage to focused exception handling.

Core changes implemented:

• Separation of execution and decision layers

• Automated context aggregation across systems

• Confidence-based case scoring

• Guided decision routing with recommendations

• Escalation only for high-ambiguity scenarios

Automation moved from “moving tickets” to supporting judgment.

The Outcome

Within four months, the impact was visible across resolution quality and operational flow.

Average resolution time reduced by 38%, driven primarily by faster decision-making in early stages. Escalations reduced by 54%, as agents received sufficient context to act without deferring.

Overall case throughput increased by 2.4x, without increasing headcount. SLA breaches reduced by 18%, particularly during high-volume periods.

Teams reported lower fatigue and fewer handoffs. Decision consistency improved across similar cases.

No core platforms were replaced. No roles were eliminated. Improvements came from structuring decision-making, not accelerating task execution.