Consider a modeled regional insurance agency with 30 producers and service representatives. Customers call about policy documents, payment dates, coverage questions, certificates, claims, renewals, appointments, address changes, adding or removing vehicles, and billing issues. Some calls are straightforward. Others require licensed professionals, claims staff, or underwriting judgment. That makes insurance an especially interesting environment for voice AI.
The National Association of Insurance Commissioners (NAIC) reports that 92% of surveyed health insurers and 88% of surveyed auto insurers said they currently use, plan to use, or are exploring AI or machine learning in their operations — already used across insurance for customer service, claims, underwriting, pricing, marketing, and fraud detection, while insurers remain responsible for applicable consumer-protection and insurance requirements.
The opportunity is therefore not to automate insurance decisions. It's to automate the communication surrounding them.
A modeled call distribution
Imagine the agency receives 4,000 inbound calls per month. For illustration: 30% are routine policy-service questions, 20% involve billing, 15% involve certificates or documents, 15% involve claims, 10% involve appointments, and 10% require complex human handling. These are assumptions for modeling purposes. The AI agent can focus on the predictable portion.
What the voice agent can do
A customer calls: "When is my premium due?" After appropriate authentication, the system could retrieve approved policy information and provide the answer. Other permitted workflows could include scheduling an appointment, confirming office hours, collecting information for a service request, requesting documents, routing a claim to the appropriate team, providing approved policy information, sending a customer to the correct department, or creating a structured callback request.
The key phrase is approved information. The agent shouldn't improvise coverage interpretations.
Claims are where the boundary becomes important
An AI voice agent could potentially assist with first notice of loss by collecting structured information: policyholder identity, date of incident, location, basic description, whether anyone is injured, whether emergency services are involved, and relevant contact information. But collecting information isn't the same as deciding a claim — the claim still requires the appropriate human and organizational process.
That distinction matters because regulators are actively examining how insurers use AI and how governance and risk controls should apply. NAIC's ongoing work includes an AI Systems Evaluation Tool intended to help regulators assess AI use, governance, risk mitigation, and data inputs.
The human remains accountable
NAIC explicitly notes that AI may support insurance workers but does not eliminate the role of human judgment — insurers remain responsible for complying with insurance laws and consumer-protection requirements. A good voice architecture routes every call through intent identification and approved-action handling, with judgment calls escalated to a human by design, rather than an AI system attempting to decide everything itself.
Measuring the opportunity
The agency could track: percentage of calls answered, average time to resolution, routine requests completed, claims successfully initiated, calls transferred, callback requests, customer satisfaction, and staff time spent on routine service. If 1,500 monthly calls prove suitable for automation and the system successfully completes 900, the agency has a measurable operational improvement — but the actual percentage should come from production data, not be invented before deployment.
Insurance is moving toward AI — but trust remains the constraint
With AI/ML adoption or exploration already widespread across insurance lines per NAIC's surveys, the interesting question isn't whether insurance will use AI. It's where AI can be introduced without compromising trust, accuracy, or accountability. Voice agents fit particularly well at the communication layer — making the agency easier to reach, collecting structured information, and removing repetitive work — while underwriting decisions, coverage interpretation, claims decisions, and other high-stakes judgments remain governed by the appropriate human and regulatory processes.