Before launching an AI voice agent pilot, enterprises should define measurable success metrics that align with business outcomes: conversion or resolution rates, contact and response timing (speed-to-lead), quality indicators such as first call resolution and transcription/intent accuracy, and operational metrics like call deflection and escalation frequency. Brilo AI’s AI voice agent pilot should measure both customer-facing KPIs (e.g., conversion rate, CSAT proxy) and internal efficiency KPIs (e.g., agent handle time reduced, handoffs avoided) so you can compare pilot performance against a clear baseline. Track a small set of primary KPIs plus 2–3 secondary measures, then iterate after the pilot cohort completes. Use these metrics to decide whether to scale, optimize call flows, or expand integrations.

Enterprises ask this because pilots must justify full deployment costs and operational change. An AI voice agent pilot touches CRM routing, live callers, and human teams; without clear metrics you can’t tell whether Brilo AI improved outcomes or introduced new risk. Procurement, compliance, and operations teams need replicable KPIs to compare pilot results to current human-run processes.

Clear metrics also help legal and risk owners understand when to approve scaling and when to require more guardrails.

Brilo AI voice agent pilots run a controlled set of calls against predefined call scenarios and measure outcomes against your baseline. Typical pilot workflow: define pilot cohort and call scripts, route a fraction of inbound or outbound traffic to the Brilo AI voice agent, capture structured call telemetry (transcriptions, intent labels, timestamps), and evaluate conversion, deflection, and escalation outcomes. Brilo AI captures speech-to-text, intent recognition, and routing events so teams can correlate customer outcomes with agent behavior.

In Brilo AI, a pilot cohort is a defined group of numbers, users, or call types routed to the Brilo AI voice agent for test evaluation and comparison against baseline performance.

In Brilo AI, a primary success metric is a named KPI (for example, resolved rate or conversion rate) that you will use to decide whether to scale the voice agent after the pilot.

Common technical terms used during analysis: conversion rate, call deflection, first call resolution (FCR), speed-to-lead, transcription accuracy, intent recognition, sentiment detection.

Define what the Brilo AI voice agent must not do during the pilot and when it must escalate. Set explicit thresholds and human-in-loop conditions: maximum acceptable intent-confidence threshold, maximum allowable transcription error for certain flows, and escalation conditions for sensitive or ambiguous intents. Use conservative routing for high-risk callers (e.g., finance or medical queries) by defaulting to human handoff unless confidence is high.

In Brilo AI, an escalation threshold is a configured confidence score or rule that triggers a handoff to a human or alternate workflow when the voice agent cannot safely or reliably resolve the caller’s intent.

Do not use pilot data to claim legal or compliance suitability—treat pilot outcomes as operational performance indicators, not formal certification.

Healthcare example:

Banking / Financial services example:

Insurance example:

Brilo AI voice agent workflows can be configured to hand off to a human, a specialist queue, or a different automated workflow when configured policies trigger. Common handoff triggers include low intent confidence, caller request for human, detection of sensitive keywords, or business rules (e.g., high-value accounts).

During a pilot, route handoffs through your existing queues or webhook endpoint so humans see the Brilo AI transcript and intent context. Record handoff timestamps and the reason for escalation to analyze common failure modes and refine the Brilo AI conversation flow.

Required inputs from your side typically include access to routing controls (SIP or existing call routing), your CRM or webhook endpoint, sample data for intent mapping, and a business owner to approve KPI thresholds.

A well-designed Brilo AI voice agent pilot helps you make evidence-based scale decisions. Expected outcomes include clearer visibility into which call types can be automated, reduced manual touches for routine tasks, and early identification of failure modes that require guardrails.

The pilot also surfaces integration requirements (CRM fields, routing rules) and human training needs before company-wide rollout. Use pilot results to prioritize workflows that deliver reliable customer outcomes and minimal compliance risk.

Which KPIs should we prioritize for a four-week pilot?

Prioritize one customer outcome (for example, resolved or conversion rate), one quality metric (transcription or intent accuracy), and one operational metric (call deflection or escalation rate). Keep targets realistic and derive them from a baseline period.

How large should the pilot cohort be?

Choose a cohort large enough to produce statistically meaningful results for your primary KPIs but small enough to limit exposure—often a percentage of traffic or a defined call type that represents frequent, low-risk interactions.

How do we measure intent recognition quality?

Compare Brilo AI intent labels against human-verified annotations on a sample of calls to calculate precision and recall for each intent. Review false positives and false negatives to refine utterance training.

Can we pilot inbound and outbound flows simultaneously?

Yes. Treat inbound and outbound as separate pilot tracks with their own KPIs and thresholds, because caller behavior and success criteria often differ.

What should trigger an immediate stop to the pilot?

Define stop conditions in advance, such as frequent incorrect disclosures of sensitive information, high escalation for safety-critical intents, or systemic failures that affect a large percentage of calls.