Lower average handle time
Compared with the client's pre-Addie average for the same call categories.
Measured deployment case study
20,000 calls. Seven months. Measured operational results.
Across 20,000 calls from February 1 through August 30, 2026, one managed Addie deployment produced measurable changes in handling time and call-handling overhead while operating with no reported caller or internal complaints. This is one deployment, not a guarantee of identical outcomes.
Anonymous client deploymentFebruary 1–August 30, 202620,000 calls
Caller or internal complaints reported
No deployment-related caller or internal complaints were reported during the measurement period.
Callers who asked or commented about Addie being AI
This measures explicit caller comments or questions, not whether callers understood Addie was AI.
Lower total call-handling overhead
Compared with the client's prior call-handling model using the defined overhead categories in the methodology.
Measurement scope
The measurement period ran from February 1 through August 30, 2026 and covered exactly 20,000 calls handled in the deployment. The client is intentionally not identified, and the results are not presented as a healthcare, restaurant, property-management, or other industry-specific case study.
Average handle time
The 40% reduction compares the client's pre-Addie average handle time with Addie's average handle time for the same call categories. Matching the call categories keeps the comparison focused on handling efficiency rather than mixing different types of work.
Caller and internal response
No deployment-related caller or internal complaints were reported during the measurement period. Separately, 1.8% of callers explicitly asked or commented about Addie being AI. That 1.8% figure should not be read as the percentage of callers who knew or understood that Addie was AI; it measures only callers who explicitly raised or commented on it.
Call-handling overhead
The 75% reduction compares the total call-handling overhead of the prior model with the measured deployment. The baseline includes the operating categories that were part of the client's prior call-handling model; it does not add estimated lost revenue or other unmeasured upside.
- Labor and benefits: the staffing cost associated with the prior call-handling model.
- Training and turnover: the recurring burden of bringing people into and through the prior operating model.
- Seats, subscriptions, and licensing: the tools and access required to support that model.
- Missed-call recovery: the work required to recover calls that were not handled when they first arrived.
- Related prior-model overhead: other directly associated call-handling costs included in the client's baseline.
How to interpret the results
The useful takeaway is not that AI automatically lowers costs or shortens every conversation. The result came from one managed deployment with its own call mix, baseline, workflow design, integrations, operating rules, and human handoff paths.
Results reflect one measured deployment and are not a guarantee of identical outcomes for every business.
Measure the operation first
A useful deployment starts with the baseline you want to improve.
ATC can map the call categories, current handling model, connected systems, human handoffs, and measurement plan before implementation.