Implementing AI in your insurance agency is a significant step that requires careful consideration and planning. It is crucial to ensure that the implementation aligns with your agency’s budget and objectives and to be fully aware of the potential risks and challenges involved.

What’s the Difference Between Generative AI and Traditional AI?

The Key Difference

The main difference between traditional AI and generative AI lies in their capabilities and applications.

Traditional AI systems are primarily used to analyze data and make predictions. Imagine you’re playing computer chess. The computer knows all the rules; it can predict your moves and make its own based on a pre-defined strategy. It’s not inventing new ways to play chess but selecting from strategies it was programmed with. That’s traditional AI – it’s like a master strategist who can make smart decisions within a specific set of rules. Other examples of traditional AI include voice assistants like Siri or Alexa, recommendation engines on Netflix or Amazon, or Google’s search algorithm. These AIs have been trained to follow specific rules, do a particular job, and do it well, but they don’t create anything new.

Generative AI, on the other hand, can be thought of as the next generation of artificial intelligence. It’s a form of AI that can create something new. Suppose you have a friend who loves telling stories. But instead of a human friend, you have an AI. You give this AI a starting line, say, “Once upon a time, in a galaxy far away…”. The AI takes that line and generates a whole space adventure story with characters, plot twists, and a thrilling conclusion. The AI creates something new from the piece of information you give it. Today’s generative AI can create not only text outputs but also images, music, and even computer code. Consider GPT-4, OpenAI’s language prediction model, a prime example of generative AI. Trained on vast swathes of the internet, it can produce human-like text almost indistinguishable from a text written by a person.

Take a look at the McKinsey chart: A Gen AI Risk Assessment. A bubble heat map shows the severity of the potential risks associated with deploying generative AI in various use cases across different risk categories. The highest risk use case, “detect/prevent fraud by aggregating/interpreting payment documentation,” scored high in the risk categories of impaired fairness, data privacy and quality, third party, and performance, and explainability. The second-highest risk use case, “AI bot for businesses to track targets,” scored as having high risk in the data privacy and quality, third party, and performance and explainability categories. At the opposite end of the spectrum, “mining financial reports to derive important insights” ranked as having the lowest risk of all use cases, with a high score in the single category of potential intellectual property infringement.

Traditional AI

Before making a decision, assess your current operations and identify areas where AI could provide the most value. Integrating AI into your insurance agency can offer numerous benefits, but it depends on your specific needs and goals.

It is worth noting that AI can streamline processes, enhance customer service, improve data analysis for risk assessments, and automate tasks such as claims processing. Learn practical strategies for preparing your agency for AI, from data collection to choosing the right AI solutions.

IIABA ACT (Agents Council for Technology) has been hosting webinars monthly on trending topics. They had an interesting conversation with Chris Cline and Margeaux Giles, CEO of IRYS Insurtech, about reframing our thoughts on AI.

Key Takeaways:

1. Understanding the Data Imperative: Grasp why data management is a precursor to effective AI integration in your insurance business.
2. Navigating the AI Landscape: Gain insights into the evolving world of AI and how it applies to insurance operations and customer engagement.
3. Practical Strategies: Learn practical strategies for preparing your agency for AI, from data collection to choosing the right AI solutions.
4. Empowering Decision-Making: Equip yourself with the knowledge to make informed decisions about AI and technology in your insurance practice.

These potential benefits could significantly transform an agency’s operational efficiency.

Aim Higher: 8 Ways Independent Agents Can Use AI to Increase Productivity and Profitability

IAMagazine.com article outlines some cases and cautions. Every AI solution has some caveats for use in an agency. “One concern is data security and privacy,” says Sean Erikson, vice president of enterprise architecture, IT strategy, emerging tech, and enterprise automation at Grange Insurance. AI relies heavily on data, and with the increase in digital reporting and processing, there’s a greater need to safeguard sensitive customer information.”

Everyone loves to say the emerging technology du jour will be the extinction event for agents, even as direct writers that attempted to eliminate the role of agents have failed to take over the world. Despite this, the independent agency channel continues to make steady gains in lines of business penetration, according to the Big “I” 2023 Market Share Report.

Hutsenpiller Insurance began implementing AI in 2018. “Our first AI tool was chatbots, but they were very basic,” Hutsenpiller says. “They had a bit of conditional logic and knowledge but nothing like what is available now.”

8 Ways AI Can Supercharge Your Agency

Agencies that proactively learn about and incorporate AI-enabled tools into their tech strategy will have the edge. With caveats and cautions firmly in place, here are eight ways the independent agency channel can harness AI for productivity and profitability.

Click here to read the full report: Aim Higher: 8 Ways Independent Agents Can Use AI to Increase Productivity and Profitability.

Review your current operations and identify areas where AI could provide the most value before making a decision. Integrating AI into your insurance agency can offer numerous benefits, but it depends on your specific needs and goals. AI can streamline processes, enhance customer service, improve data analysis for risk assessments, policy comparisons, and automate tasks such as claims processing.

Insurance conversations often involve addressing worst-case scenarios, and trust enables clients to tell their agents about their fears and concerns. AI may provide optimal coverage but cannot convey the reassurance and emotional support clients seek during such conversations about what coverage they have and what they may need.

AI relies heavily on data, and with the increase in digital reporting and processing, there’s a greater need to safeguard sensitive customer information and privacy. Learn practical strategies for preparing your agency for AI, from data collection to choosing the right AI solutions. There is a lot of information out there, and it can take time to figure out what it all means and what is best for your agency. Attend classes and webinars, learn, and ask the right questions.

Legal Disclaimer: This material is intended to provide you with general background and insight. The material does not constitute, and should not be regarded as, legal advice regarding any particular facts, circumstances, or issues. This material is not intended to serve as a substitute for legal counsel, and we advise you to contact legal counsel for specific analysis, drafting, and advice.

More Information: Seek your trusted advisors Attorney, Banker, and CPA to ensure that your legal and financial interests are adequately protected. The information provided in this publication is not intended to be a substitute for legal advice. You should consult your legal counsel and ensure that you are in compliance with state law. These laws and rules are subject to change.

Cited Sources

1. The Difference Between Generative AI And Traditional AI: An Easy Explanation For Anyone – Bernard Marr, Forbes, Jul 24, 2023.
2. A Gen AI Risk Assessment – McKinsey, April 10, 2024.
3. IIABA ACT Webinar with Chris Cline and Margeaux Giles, CEO of IRYS Insurtech, about Reframing Our Thoughts on AI.
4. A Real Example of the Power and Limitations of Generative AI – Annemarie McPherson Spears, IA Magazine, Oct 1, 2023.
5. Why Predictions That Insurtech Would Replace Agents Have Flopped – Ryan Mathisen, IA Magazine, Jan 1, 2022.
6. Aim Higher: 8 Ways Independent Agents Can Use AI to Increase Productivity and Profitability – Annemarie McPherson Spears, IA Magazine, Oct 1, 2023.