SLA and backlog reviews
Run SLA and backlog reviews tied directly to live work queues, not stale dashboards.
By Use Case · AI Observability
Measure the workflows that matter.
Opal helps you connect execution data to business outcomes so you can see where AI and workflow changes are improving throughput, accountability, and operational performance.
Why AI Observability matters
Counting AI usage isn't enough.
Counting AI usage is not enough. You need to know whether cycle times are dropping, bottlenecks are moving, SLA performance is improving, and workflows are actually creating value.
Measuring real outcomes
What Opal helps you do
Performance in business terms.
- 01Track workflow health, exceptions, backlog, and completion performance
- 02Connect execution activity to business outcomes
- 03Surface bottlenecks faster and improve continuously
- 04Measure adoption in the same environment where work happens
Example operational workflows
Patterns teams run on Opal.
Approval and throughput signals
Track approval aging, escalation rates, and throughput across core business processes.
Impact of AI workflows
Monitor how new AI-enabled workflows affect speed, consistency, and rework over time.
Quality and drift reviews
Coordinate periodic quality reviews across AI-drafted decisions with human sampling.
Anomaly-triggered follow-up
Trigger follow-up workflows when queue health, SLA, or quality signals slip out of range.
Process-level dashboards
Give operators, owners, and executives shared views across the workflows they run.
Typical outcomes
What teams actually see.
01
Faster improvement cycles and better optimization decisions
02
Clearer ROI and performance visibility for Agentic Operations programs
03
Better accountability across teams and workflows
04
More confidence in what to scale, change, or stop
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