Run SLA and backlog reviews tied directly to live work queues
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.
Track approval aging, escalation rates, and throughput across core processes
Monitor how new AI-enabled workflows affect speed, consistency, and rework
Typical outcomes
What teams actually see.
01 / Outcome
Faster improvement cycles and better optimization decisions
02 / Outcome
Clearer ROI and performance visibility for operational AI programs
03 / Outcome
Better accountability across teams and workflows
04 / Outcome
More confidence in what to scale, change, or stop
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Build on Opal
Measure operational AI in terms the business can act on.
See how Opal helps you connect workflow performance to business outcomes.