The rapid emergence of generative AI tools, such as ChatGPT and Microsoft Copilot, has introduced a wave of transformation across nearly every industry. For independent insurance agencies, the implications are significant. From streamlining internal processes to enhancing client communication, AI presents enormous potential.

Yet, AI’s promise is matched by risk. To harness its power responsibly, agencies must proceed with a thoughtful, structured approach.

Risks To Understand Before Deployment

Most agency leaders have already heard about the common risks of generative AI: privacy concerns, hallucinated answers (or answers that are nonsensical or made-up), and legal ambiguity around usage rights. Rather than rehash the headlines, let’s briefly recap what matters most in the context of insurance operations.

First, never input sensitive client data — such as names, policy numbers or claim details — into public-facing AI tools. These platforms may retain and use inputs for training, which can jeopardize confidentiality and compliance. Even seemingly harmless details, like agency workflows or internal notes, can pose a risk if disclosed inadvertently.

Second, remember that AI isn’t a licensed insurance professional. It can make persuasive arguments and cite imaginary facts. Any client-facing output must be reviewed, verified and approved by a qualified individual.

Finally, agencies must resist overreliance. While AI is a powerful assistant, it is not a replacement for critical thinking. It’s most effective when paired with subject matter expertise, process controls and human oversight.

Why Free Tools May Not be Worth the Cost

The accessibility of free AI platforms makes them tempting, especially for smaller agencies with more constrained budgets, but these tools come with significant tradeoffs.

Free, public-facing AI tools often retain and learn from user inputs. That means any client names, policy details claims information, or internal notes submitted could be stored or repurposed by the platform, creating a serious privacy risk. Moreover, free tools offer limited guarantees around data residency, encryption or access controls. All which are essential for maintaining regulatory compliance.

Free tools can be appropriate for experimentation and individual learning, particularly when used with nonsensitive prompts or general education. They are not suitable, however, for building business processes or handling non-public information. Agencies must clearly differentiate between personal experimentation and approved business use.

Professional AI use demands secure, auditable platforms. Agencies operating in regulated environments, including those subject to 23 NYCRR 500 or MDL-668, must ensure any AI tool used for business purposes meets security and compliance requirements. That includes support for administrative controls, audit logging, data protection commitments and access governance. Even for non-sensitive tasks, platform selection should involve IT and compliance to ensure the tool aligns with the agency’s security obligations.

While they may be fine for learning, free AI tools should not be used in production workflows, especially when client data is involved. This isn’t just a recommendation; for regulated entities, it’s a legal requirement.

Best Practices for Responsible AI Usage

To ensure responsible AI usage, agencies must create clear policies and enforceable boundaries for AI usage. An acceptable use policy (AUP) is the first step.

An AUP is a document that should clarify where AI can and cannot be used, outline prohibited content types and define review processes. A strong AUP will include:

• Purpose and Scope: A clear statement about why the policy exists, what tools it covers (e.g., ChatGPT, Copilot) and who is expected to follow it.
• Permitted Use Cases: Describe acceptable uses of generative AI, such as content drafting, internal documentation or marketing support, with an emphasis on human review.
• Prohibited Use Cases: Detail what staff should avoid, such as entering PII, using AI to answer compliance questions or treating AI output as legally or contractually binding.
• Data Handling Requirements: Reinforce that sensitive client data should never be entered into AI tools unless the platform has been vetted and secured.
• Review and Approval: Outline when AI output must be reviewed before use, who is responsible for oversight and how final content should be approved.
• Accountability: Define user responsibilities, including reporting misuse, following data governance policies and staying current on training.

All employees should be trained on this policy and given examples of acceptable use. Periodic reviews can help the agency adapt the policy as technology evolves.

And most importantly, human review should be required for all AI-generated content before it reaches clients. This ensures both factual accuracy and brand alignment. AI may generate the words, but humans must provide the judgment.

Training the Team to Use AI Effectively

Successful AI adoption is not just a technology rollout — it’s a cultural shift within the agency. Staff need to understand not only how to use AI tools, but also how to think critically about the output they receive.

Training should include the basics of "prompt engineering," or how to ask AI the right questions to get useful results. Effective prompts often include specific context, a defined goal and instructions on tone, format or audience. For example, “Draft a client-facing email explaining cyber liability coverage in plain language” is more effective than “Write about cyber insurance.”

Another helpful technique is using personas, imaginary roles that guide how AI responds. Agencies might build prompts around personas such as “a seasoned account manager,” “a claims adjuster” or “a cybersecurity compliance officer.” These personas help the AI adopt the right tone, level of detail and perspective. For instance, a persona-infused prompt like, “Act as a licensed insurance advisor preparing an FAQ for homeowners in Florida,” helps tailor the response to your target audience.

Agencies should provide teams with prompt templates that reflect real-world needs, such as crafting marketing messages, summarizing client meetings or generating internal documentation. A good prompt template includes space for context (e.g., "What is the task or scenario?"), tone (e.g., "How formal or casual should the output be?"), audience (e.g., "Who is this being written for?") and format (e.g., "Should the result be a bulleted list, a paragraph, or a script?"). For example:

• "Write a {format} for {audience} that explains {topic}, using a {tone} tone. Include key points about {optional context}."

These templates help standardize usage while still allowing room for creativity and adaptation. The more specific and grounded the prompts, the better the results.

Getting Started: A Practical Approach

To begin adoption in a meaningful way, agencies should consider taking the following structured, achievable first steps:

• Identify two to three internal, non-client-facing workflows that are time-consuming but not sensitive. Good candidates might include drafting initial drafts of internal SOPs, summarizing meeting notes or assisting with onboarding documentation. These would be tasks that are essential but repetitive. Use these as pilot programs to evaluate how AI can save time, improve consistency and enhance team productivity.
• Assign a small team to experiment with AI tools, document results and share learnings with the rest of the organization. As part of this effort, they can help define agency-specific AI use case. Tasks like prospecting email drafts, blog post outlines or internal SOPs that align with existing processes and show clear benefits.
• Draft an acceptable use policy using real examples. Include what is allowed, what is not and who is responsible for review.
• Provide prompt templates and training for common tasks to build employee confidence and consistency.

These early efforts create momentum, surface practical insights and build buy-in across departments.

Generative AI is not a shortcut to success, nor a threat to be feared. It is a force multiplier—a tool that, when used responsibly, can help agencies operate more efficiently, serve clients more effectively, and innovate with greater speed.

By recognizing both the opportunities and the risks, and by putting the right safeguards in place, agencies can unlock the benefits of AI without compromising their values or their clients' trust.

Learn more strategies on data migration in the ACN Learning Center, a members-only resource! Available resources include webinars Client Service Reimagined: How your agents can team up with AI in Applied Epic and Now Hiring: Digital Workers of Tomorrow!