A benchmark report based on observations from deployments across P&C carriers, MGAs, and brokers.
18
months of data
17
pages of benchmarks
6
key metrics tracked
What’s inside
1
Typical Phases in AI Deployment
The four-phase arc every deployment follows, from pilot to steady state, and what to plan for at each stage.
2
How Metrics Move
How containment rate, handle time, transfer volume, cost impact, and CSAT evolve across the deployment arc, and what drives each.
3
What Good Looks Like
The decisions and practices that consistently separate best-in-class deployments from those that stall, with real-world examples.
4
Case Study
How a specialty property insurer reached 98% containment and disabled human transfers entirely within three months of go-live.
Key findings
Deployment is phased, not instantaneous.
Four stages, ten weeks or more to full production. The timeline varies, but the sequence does not.
Operational involvement drives performance.
Metrics do not improve on their own. Active collaboration between operations and technical teams during stabilization is what moves containment, handle time, and cost impact.
Steady-state benchmarks are predictable.
75%+ containment, transfer rates below 25%, and CSAT at or above human-agent levels at steady state.
A small number of decisions determine outcomes.
Use case precision, operational ownership, scope discipline, and phase-calibrated oversight separate deployments that reach their potential from those that stall.
75%+
Containment at steady state
Predictable performance once the deployment reaches steady-state operations.
80+
Agent-hours saved
Agent-hours saved per 1,000 weekly calls through conversational AI containment.
98%
Best-in-class containment
Containment achieved by the report’s featured specialty property deployment.
About Strada Signals
Strada Signals is a series of benchmark reports drawing on operational data from AI agent deployments across P&C carriers, MGAs, and brokers. Each issue documents what deployment actually looks like, phase by phase, metric by metric, for operations leaders making decisions about conversational AI.
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