Calculating ROI for an AI voice agent with Brilo AI starts by measuring the change in cost and value after you deploy the Brilo AI voice agent versus your baseline (staffing, call volume, and current tools). Key inputs include total answered calls, call deflection (containment) rate, average handle time (AHT) changes, live agent hours recovered, and the cost per contact before and after deployment. Combine recovered labor cost and avoided outsourcing or overtime with any incremental revenue (better conversion or faster resolution) to estimate return on investment (ROI). Use Brilo AI call transcripts and analytics to validate assumptions and track ongoing performance.

How do you measure ROI for voice automation? — Compare baseline costs (labor, outsourcing, call transfers) to post-deployment costs and value, then express the net gain as a percent or payback period.

What’s the ROI of an AI answering system? — Calculate labor hours saved plus avoided costs (outsourcing/overtime) and any revenue lift, then divide net benefit by total Brilo AI program cost.

How to prove value from Brilo AI agents? — Use call-level metrics (containment, AHT, transfers) from Brilo AI analytics to quantify time saved and quality improvements, then translate time into cost savings.

Enterprise buyers need a repeatable, auditable way to justify investments in automation. Procurement, finance, and operations teams ask for an ROI approach because AI voice agents change both cost structure (fewer routine calls to humans) and revenue/experience metrics (faster resolution, higher conversion on retention or collections). For regulated sectors like healthcare, banking, and insurance, buyers also need clear evidence that automation reduces workload while preserving escalation paths and audit trails.

Brilo AI captures the inputs you need for an ROI calculation by logging call transcripts, intent tags, sentiment signals, confidence scores, transfer metadata, and timestamps. A simple ROI model uses:

In Brilo AI, call deflection is the percentage of inbound calls fully resolved by the Brilo AI voice agent without human transfer. Average handle time (AHT) is the mean total duration a caller spends in a Brilo AI interaction or a human-handled call, including transfers and wrap-up. For examples of the analytics and transcripts Brilo captures, see the Brilo AI call intelligence solutions: Brilo AI call intelligence solutions.

Steps typically include exporting a baseline period of call data, running a controlled pilot, and measuring delta on containment, AHT, transfers, and conversion or resolution rates.

Brilo AI should not be measured on ROI alone without accounting for safety and escalation. Guardrails include confidence thresholds, handoff triggers, and topic limits so the Brilo AI voice agent never attempts regulated tasks or sensitive disclosures beyond its training. A confidence score is the system’s internal estimate of how accurately the agent understands the caller’s intent; low-confidence events trigger human handoff or scripted clarifications. For recommended fallback and uncertain-call handling, review Brilo AI’s escalation guidance: Brilo AI uncertain-call handling & escalation.

Do not count ambiguous savings: exclude calls flagged for repeated clarifications or those routed by policy to human-only workflows. Track handoff quality separately to ensure ROI gains do not come at the cost of customer experience.

Healthcare example

Banking / Financial services example

Insurance example

Note: Do not assume regulatory compliance (HIPAA, SOC 2) unless you have validated certifications with Brilo AI security and compliance documentation.

When Brilo AI reaches configured boundaries (low confidence, caller request for a human, or flagged sensitive topics), the Brilo AI voice agent passes context to the receiving human. That handoff includes the recent transcript snippet, identified intent, sentiment flag, and any captured form fields so agents don’t repeat questions. You can configure warm transfer (immediate live transfer) or callback scheduling in routing rules. Monitoring handoffs is essential in ROI because transferred calls still consume human time; measure the percent of transferred calls and transferred-call duration separately.

For guidance on enabling long-call and transfer behavior during setup, see Brilo AI long-conversation handling: Brilo AI long-conversation handling. For voice and naturalness tuning (which affects containment and customer acceptance), consult the voice naturalness guide: Brilo AI voice naturalness guide.

Avoid overstating outcomes; tie all projected savings to verifiable metrics you collect during a pilot.

How do I convert time saved into dollar savings?

Multiply recovered agent hours by fully loaded labor cost (wages + benefits + overhead) for the same period. Add avoided overtime or third-party costs and subtract Brilo AI program and service expenses to find net savings.

Which metrics matter most for ROI?

Containment (calls resolved by Brilo AI), average handle time (AHT) changes, transfer rate, and conversion or resolution rate are primary. Also track quality metrics like repeat call rate and customer satisfaction to guard against negative side effects.

How long should a pilot run to produce reliable ROI signals?

Run a pilot long enough to capture normal weekly cycles and variations—commonly multiple weeks—so you have stable averages for call volume and AHT. Ensure the pilot sample includes peak and off-peak periods.

Do I need system integrations to calculate ROI?

Integrations help because CRM or ticketing timestamps allow you to match Brilo AI interactions to downstream work and revenue events. If integrations aren’t available, use call logs and manual sampling, but expect higher measurement uncertainty.

What if automation increases transfers for complex issues?

Measure transferred-call duration and total human time (triage + transfer + resolution). If transfers increase, refine intents and handoff rules so Brilo AI contains only where reliable and routes earlier on complex intents.

If you’d like, start with a scoped pilot that targets a single use case (scheduling, balance inquiries, or FNOL) and use the steps above to produce an auditable ROI model you can present to procurement or finance.