Paul Carroll
You’ve long been my go-to guy on the business implications of artificial intelligence. To start us off, how would you set the table for insurance agents and brokers in terms of how they should think about how AI will play out?
John Sviokla
Well, first, I think it's important to reassert that salesmanship is going to continue to be needed—probably even needed more. How salespeople serve, what segments they serve, and so forth will be different, but the role remains.
The people who go too far toward automation are going to lose. The people who don't automate enough are going to lose. There's going to be a new balance point.
AI will not change the fact that a very small number of salespeople sell most of the stuff. It's not going to democratize selling, I don't think. In fact, the distance between the top people and the average is going to increase, not decrease.
Technology is an amplifier. Getting a bigger amplifier does not make you a better guitar player. But if you're a really good guitar player, you'll likely have a bigger audience. Right?
Paul Carroll
You and I have talked about a few areas where agents and brokers should be adopting AI. Would you walk us through those?
John Sviokla
The critical things a salesperson needs to understand in the early going are account planning, product and service knowledge, and sales skill. AI can help in each of those areas.
It can help you in account planning in terms of finding people and profiling them. It can help you with sales skill through simulation. You can actually tell an AI, "I'm going to sell this person. Here's their background. Go find out about them. Here are my products. Help me understand what's right for them." Then you can actually simulate the sales process. AI is a flight simulator for selling if you do it right.
The third thing is product and service knowledge, and AI is miraculous in this area. Even the general models know a ton. But certainly, if you have any kind of custom setup, it's trivially easy for you to load up all your product information, compliance forms, all that other stuff in a local database.
AI can be a big cost driver because of the turnover rate in distribution channels that have high turnover rates. LIMRA says, for instance, that as many as 80% of new life insurance agents quit in the first 12 to 18 months. You sell to your relatives, and then you're out of the business.
Even if an agency doesn’t get better at keeping people, you’ll materially reduce your training costs, and you’ll be giving new agents tools and training that should make them more successful sooner. Will you go from 80% churn to 65%? No, but will AI take it from 80% to 75%? Yeah.
Most insurance companies and general agencies stink at onboarding. They think they're good because they spend money, but they aren’t. In an hour with the proper documents, I bet I could create a robot that would be much better than your best trainer on understanding product features, product regulations, and product documentation.
The other thing is that training is once-and-done, even though the half-life of the knowledge that I've been given at the beginning is short. Well, AI is there all the time. You can always ask, “Hey, I'm going to go pitch Paul. Tell me again about how to think about this product. What are the regulatory things again? Does this product work in this IRA, and all that?” That's just not happening right now. Insurance companies and general agents can't afford to keep staff around to answer those questions, to do the genius bar. AI is a genius bar for everybody all the time.
For account management, you look for people in your area, and you put names into the AI. When I pitch top management, there’s enough information out on the internet that I can actually do informal psychological profiles of the individuals and then of the whole team. I ask what points in my presentation are most important for each person, then ask the AI to simulate our interaction. Right now, I’m doing that with text, but in time I’ll be loading pictures of the team and will be talking with them.
Paul Carroll
Going back to our work together in the early internet days, we saw how it takes a while for people to figure out how to adopt something that's radically new. What tips would you offer agents and brokers on AI? Should they take a top-down approach, bottom-up, or both?
John Sviokla
It's absolutely both. But you have to understand something first—and I'm going to make a blanket proviso about something I know is more complicated than the way I'm expressing it.
AI is not a technology. AI is a capability: a capability that you have to grow, that you cannot buy.
You buy the beginning of it, but it's like buying seeds or buying bulbs. You've got to grow them. The people need to learn, the machine needs to learn, and then that group together—the people and machines—needs to learn.
The proper way to think about it is what we call our RISE adoption model, which is the old S-curve adoption model with some tweaks.
The R part is research and education, and that's a pure investment. The proper measures of that stage—do you succeed or not—are two: Do people use it, and do they feel confident while they're using it? You bring people in and train them up.
The I is islands of automation. You invest in a portfolio of innovations. That cost should be net zero because you lose a little money here and make a little there as you build a capability.
Then you go to scaling, the S part. You manage that the same way as any other initiative. Here's the time frame. Here are the inputs. Here are the outputs. Here's the accountability. Here are the goalposts. All that stuff.
This is where lots of organizations fall off, for three reasons. One, a lot of executives basically want you to do the scaling work off the side of your desk. "Hey, Paul, that looked great. Why don't you scale it?" No, to scale, I have to deal with data, process, security, scale, updates—all that stuff.
If you want people to reinvent things, organizations need to give people a way to raise their hand and note that they don’t have the necessary time and resources. Most don’t. That’s the second reason for failure.
The third reason—this is the most complicated—is that organizations in general no longer have the people who actually know how to do process redesign, who can do real R&D, and who know how to buy small. Very, very few large companies know how to buy small. How do you work with small vendors? Nobody knows how to do that.
Finally, the E is emergent intelligence. This is where AI capability becomes self-sustaining and strategically differentiating. AI models improve autonomously. Agentic systems coordinate across the enterprise without constant human direction. Hybrid human-AI decision-making becomes the operating norm.
Paul Carroll
What should someone reading this do tomorrow morning to get started?
John Sviokla
First, assess where you are on the RISE framework.
If you're at islands of innovation, what's the process you're going to use to figure out what you're going to scale? If you're at scaling, do you have the three competencies I talked about in enough supply to succeed?
The measurements of success don’t have to be that complex, by the way. Once you’re scaling, you should see revenue per employee increasing. When you get to emergent intelligence, the revenue per employee should be increasing at an increasing rate.
Paul Carroll
This is great, John. For those who want to learn more, here is a link to your big AI conference this month in Boston, the 4th Annual GAI World 2026. Want to give us the 25-second pitch?
John Sviokla
Thanks. The confererence is about helping business leaders in financial services and healthcare understand where they are, what they need to do next, and how they scale for economic value. The first two days are speakers and some hands-on activity. For those who want to stick around on the third day, we go deep on how to use Claude in your personal and professional life. All of GAI World 2026 takes place Sept. 28-30 at the Hynes Convention Center in Boston. We would love for members of the ITL audience to come and learn from practitioners who are scaling and getting value.
Paul Carroll
Super. Thanks, John.
About John Sviokla
Dr. Sviokla is the chairman and co-founder of GAI Insights. Throughout his career, he has explored the practical implications of leading technologies. He is widely published and was a partner at PwC, vice chairman of Diamond Technology Partners, and a professor at Harvard Business School, where he pioneered AI research and AI courses. Dr. Sviokla has his doctorate, master's, and BA from Harvard University. He was named an executive fellow at Harvard Business School to develop cases for the MBA and executive ed programs and is a Forbes contributor.
