Every workflow has a bottleneck, but finding it often requires expensive process mining tools or weeks of manual observation. The data to identify bottlenecks usually already exists in your systems - it just needs to be analyzed.

The Problem

Work items flow through multiple steps and teams. Delays accumulate at different points depending on workload, staffing, and dependencies. Without systematic analysis, teams optimize the wrong steps - making a fast step faster while the actual bottleneck remains untouched.

The AI Approach

An LLM can analyze workflow timing data (timestamps for each step completion) to identify where work items spend the most time waiting, which steps have the highest variance, and which handoffs create delays. It produces specific, actionable recommendations rather than abstract process maps.

Three-Step Build

Step 1 - Data extraction. Pull workflow event data from your ticketing system, project management tool, or process automation platform. Each record needs: work item ID, step name, start time, end time, and assignee.

Step 2 - Bottleneck analysis. Feed the data to an LLM with instructions to identify: the step with the highest average wait time, the step with the most variance, the handoff with the longest delay, and any time-of-day or day-of-week patterns.

Step 3 - Recommendation report. Generate a report with specific findings and ranked recommendations. Include estimated time savings for each recommendation to help prioritize.

Where It Breaks

Workflow data often has gaps - manual steps may not be timestamped, and work done outside the system is invisible. The model cannot distinguish between productive work time and idle wait time within a step. Organizational politics may prevent acting on the findings.

The Production Path

Start with one high-volume workflow where timing data is complete. Validate findings with the team that owns the process before presenting recommendations. Measure cycle time reduction after implementing changes to demonstrate value.