AI VEndor Evaluation Resources for Independent Agents.

PREPARING YOUR BUSINESS FOR SMARTER TECH DECISIONS

5 Steps To Take Before You Engage With Vendors.

1.Clarify Your Business Goals & Growth Plan

Update your business plan to reflect current and future goals

Set 3–5 year targets for revenue, premium volume, and headcount

Define your target book of business now and in the future (e.g., PL vs. CL, Life, Health, Benefits)

2. Assess Your Current Tech Stack

List all current technologies in use and rate their value to your operations

Identify underutilized tools and areas for improvement

Document costs and note which could be eliminated with better-integrated solutions

Determine your budget for new tech and support— include both upfront and recurring costs

3. Evaluate Internal Readiness

Survey your team to understand what’s working and where pain points exist

Be transparent with your team—share that change may be coming and invite their input

Review KPIs to see where inefficiencies lie (e.g., hit ratios, lead response, hold times, NPS)

Define your target book of business now and in the future (e.g., PL vs. CL, Life, Health, Benefits)

4. Start Thinking About AI

Define your agency’s vision—how do you want to show up to clients and staff in an AI-powered world?

Start educating yourself through podcasts, webinars, your Big “I” state association, and ACT

Be honest about team readiness—are you equipped to adopt AI or still exploring?

Begin drafting internal AI guidelines and involve legal to address risk and compliance

Recognize that team members may already be using AI tools—bring that usage into the open and shape policy accordingly

5. Align Internally Before Moving Forward

Identify which business processes will be impacted or need to change with new tech

Clarify the goals you want the technology to support—avoid “tech for tech’s sake”

Create a simple change management plan to guide employee engagement during future implementation

Begin gathering referrals from peers, state associations, and ACT to inform future vendor conversations

Start thinking about ROI— what outcomes matter most (efficiency, growth, client experience)?

Glossary of Technical Terms.

• Artificial Intelligence (AI): The simulation of human intelligence processes by machines, including learning, reasoning, and self-correction.
• Bias in AI: Systematic errors in AI outputs caused by biased training data or flawed algorithms, which can lead to unfair or inaccurate results.
• Encryption in Transit and at Rest: Encryption in transit protects data while it is being transmitted. Encryption at rest protects data stored on a device or server.
• Explainability: The degree to which an AI system’s decisions can be understood and interpreted by humans.
• Generative AI: AI that can create new content such as text, images, or music based on training data.
• GLBA: Gramm-Leach-Bliley Act, a U.S. law that requires financial institutions to explain how they share and protect customers’ private information.
• HIPAA: Health Insurance Portability and Accountability Act, a U.S. law that protects sensitive patient health information.
• Inference: The process of using a trained AI model to make predictions or generate outputs based on new input data.
• ISO 27001: An international standard for managing information security.
• Large Language Model (LLM): A type of AI model trained on vast amounts of text data to understand and generate human-like language (e.g., ChatGPT, Claude).
• Machine Learning (ML): A subset of AI that enables systems to learn from data and improve performance over time without being explicitly programmed.
• Model Hallucination: When an AI generates incorrect or fabricated information that appears plausible.
• Natural Language Processing (NLP): A branch of AI that helps computers understand, interpret, and generate human language.
• Predictive AI: AI that analyzes data to make predictions about future outcomes.
• Prompt Engineering: The practice of crafting effective inputs (prompts) to guide AI models toward desired outputs.
• Rule-based AI: AI that operates based on a set of predefined rules and logic.
• SLAs: Service Level Agreements, which define the level of service expected from a vendor, including uptime and response times.
• SOC 2 Type II: A certification that evaluates an organization’s information systems relevant to security, availability, processing integrity, confidentiality, and privacy over a period of time.
• Training Data: The dataset used to teach an AI model how to perform tasks or make predictions.

20 Smart Questions To Ask For Independent Insurance Agencies Evaluating AI Tools.