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Home » Do Clients Actually Want to Deal with Your AI Agent?

Ask a brokerage owner whether their clients want to interact with an AI agent, and most will say no. Their instinct is that insurance is a relationship business, that clients want a human, and that deploying a chatbot signals you’re cutting corners.

The data says otherwise. The better question isn’t “do clients want AI?” It’s “for what?

 

The Numbers Have Shifted — Fast

In 2025, only 20% of policyholders said it was a good idea for their insurer to use AI to improve services. One year later, that figure is 39% — nearly double — according to Insurity’s 2026 AI in Insurance Report. In the same period, the share of consumers who said they were less likely to buy from an AI-using insurer fell from 44% to 36%. The trend line is steep.

Consumers are becoming more comfortable with AI because they’ve experienced it working. Fast answers. Simple questions resolved without being put on hold. Documents retrieved at 11:00 PM without waiting until the next business day. The bar isn’t “better than a human.” It’s “better than waiting.” The 9-to-5 service model creates a gap — and clients have noticed.

 

After-Hours Lead Capture: First to Respond, First to Win

JD Power’s 2025 study found that 47% of auto insurance shoppers now make their first contact through digital channels — which means evenings, weekends, and the Friday of a long weekend when your team has long since gone home. The first brokerage that engages a prospective client usually wins the business. Not the best brokerage. The fastest one.

The After-Hours Intake: An AI agent that qualifies a lead, collects contact information, and confirms that a broker will follow up by 9:00 AM Monday keeps the prospect in your funnel. Without it, that lead doesn’t wait — it calls someone else. Brokerages running AI-powered “always-on” intake are reporting a 15–20% increase in lead conversion, not because the AI is closing deals, but because it’s stopping the bleed that happens when no one answers.

The Late-Night Pink Card: The client who needs proof of insurance at 10:00 PM on a Tuesday doesn’t want to wait until morning. They want the document. An AI agent connected to your BMS can deliver it in under a minute. That’s a service win — and it costs your team nothing.

 

Routine Service Requests: Where AI Actually Belongs

Consumer acceptance for AI in insurance is highest for transactional, low-stakes interactions: certificate of insurance requests, proof of insurance documents, billing questions, basic policy lookups. These tasks don’t require a licensed broker. They require fast, accurate retrieval — which is exactly what a BMS-connected AI agent does well.

What Your CSRs Get Back: The “pink card” calls your team handles between the work that actually needs them are time leaks, not value-adds. When AI absorbs the routine service queue, your CSRs get that time back for the interactions that require a licensed professional: the commercial renewal with a changed risk profile, the client navigating a claim, the new business owner who doesn’t realize they’ve outgrown their personal lines coverage.

 

Where Client Tolerance Drops Off

The readiness data has a ceiling. Clients are comfortable with AI handling tasks. They get significantly less comfortable when AI starts making decisions — particularly coverage decisions. Guidewire’s 2026 European Insurance Consumer Survey found that 26% of consumers remain unconvinced by AI in insurance regardless of safeguards, with discomfort rising sharply for AI-led underwriting or claims adjudication.

Know Where the Line Is: Insurance renewals, coverage disputes, and FNOL conversations are still human territory. That’s not a permanent state — tolerance is rising year over year — but it’s the reality for most of your book today.

Disclose, or Pay for It Later: Consumers who discover after the fact that they were talking to an AI agent without being told feel deceived — regardless of how good the interaction was. A straightforward disclosure (“You’re chatting with our AI assistant — a licensed broker will follow up if your question needs a human touch”) costs nothing and protects the trust of the 36% of clients who remain skeptical. Don’t hand them a reason to leave.

Summary: Deploy Where Clients Are Already Ready

Routine service and after-hours intake: deploy AI, capture the wins, free up your people. Coverage conversations, renewals, and claims: keep a licensed professional in the chair. The brokerage that maps this correctly runs leaner, answers faster, and retains clients better — because their brokers are actually spending time on the work that earns loyalty.

Your clients aren’t afraid of your AI agent. Most of them just want their certificate of insurance — and they’d prefer not to wait until tomorrow morning to get it.

 

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AI-Powered Client Onboarding & Data Intake

Technical Requirements

BMS Integration: Integration with BMS for secure data capture and storage.

Applied ARS Integration: Integration with Applied ARS to enable automated quote generation.

Dedicated Parsers for Renewal: Document parsing capability for 5–6 carrier renewal documents with high accuracy.

Extensibility Framework: Modular architecture to support future enhancements and additional automation.

Functional Scope

AI Chatbot for Client Onboarding: Collect client information, answer onboarding questions, and guide users through the onboarding process.

Data Collection & Storage: Capture and store collected data directly in the broker’s BMS.

Document Processing: Enable clients to upload renewal documents and extract key data to accelerate the onboarding process.

Quotes Generation: Generate quotes based on collected information.

Aligning with Regulatory Frameworks and Standards

Principles will align with international AI guidelines (e.g., ISO 42001, OECD frameworks) and industry-specific regulations (e.g., RIBO, OSFI, AMF) upheld by stakeholders. Adherence to legal standards will help organizations navigate jurisdictional requirements, promote sustainable practices, and prevent misuse of AI data

Encouraging Responsible Al Innovation

Principles will encourage the broker community to innovate responsibly by developing AI systems that prioritize consumer well-being, inclusivity, and fairness, while also assessing the societal, environmental, and economic impacts of their AI solutions

Promoting Accountability in AI Oversight

AI principles will reinforce accountability across all levels of organizations and third-party collaborators. By defining roles, ensure human oversight in AI processes, this enhances traceability, enables informed decision-making, and embeds mechanisms for ethical redress when errors or adverse outcomes occur

Ensuring Consumer Trust and Fairness

AI governance principles support commitment to transparency, fairness, and accountability. By requiring explainable outcomes and proactive consumer communication, it fosters trust among the broader broker community, their clients, and external stakeholders

Supporting Ethical Standards and Stakeholder Collaboration

By incorporating AI governance principles, broker members can align with its mission of fostering an ethical culture among its stakeholders Address biases, safeguard consumer protection, and promote inclusivity, which will reinforce commitment to ethical AI practices in collaboration with industry stakeholders, regulators, and third-party solution providers

PoC Use Case Overview: Al-Assisted Coverage Discovery & Gap Analysis

Technical Requirements

AI models with context on industry benchmarks and policy structures to interpret existing policy terms, endorsements, and clauses.

Integration with BMS to retrieve client profiles, exposure information, and historical policy data.

Data ingestion and continuous updates to ensure alignment with typical coverage patterns and industry guidance.

Data security and compliance features to protect client information.

Functional Scope

Analyze client-submitted data, including exposures, business context, and other relevant information.

Extract and interpret existing policy terms (e.g., endorsements, exclusions, clauses).

Benchmark against typical coverage patterns and industry guidance to identify coverage gaps.

Prioritize identified coverage gaps and provide rationale based on considerations such as industry standards and risk.

Recommend relevant products and coverage options tailored to the client profile, and generate summaries for client discussions.

AI-Powered Client Onboarding & Data Intake – Overview:
Proof of Concept (PoC) Use Case

Technical Requirements

BMS Integration: Integration with BMS for secure data capture and storage.

Applied ARS Integration: Integration with Applied ARS to enable automated quote generation.

Dedicated Parsers for Renewal: Document parsing capability for 5–6 carrier renewal documents with high accuracy.

Extensibility Framework: Modular architecture to support future enhancements and additional automation.

Functional Scope

AI Chatbot for Client Onboarding: Collect client information, answer onboarding questions, and guide users through the onboarding process.

Data Collection & Storage: Capture and store collected data directly in the broker’s BMS.

Document Processing: Enable clients to upload renewal documents and extract key data to accelerate the onboarding process.

Quotes Generation: Generate quotes based on collected information.