While generative AI has been a phenomenon over the past couple of years, most uses have stayed pretty close to home — gathering information to make underwriters, adjusters, and agents more efficient, producing first drafts of reports or communications to clients, that sort of thing.
But some recent articles suggest that AI may be getting ready to break out into much more sophisticated uses, including showing up as part of insurance products.
The article that most caught my eye was one in Fortune about how the CEO of Honeywell intends to use AI for competitive advantage. I had just finished another article in Fortune about a long list of impressive things that Honeywell was doing with generative AI at the operational level and was startled that the CEO belittled the effort. He said the work had to be done, because all his competitors were doing it, but said all the gains in efficiency would be competed away. To gain a sustainable advantage, he said, Honeywell needed to be bolder.
The CEO, Vimal Kapur, mentioned three areas where Honeywell is focusing, all of which strike me as being opportunities for the insurance industry, as well.
First, he talked about using AI to address his industry's talent gap. That issue sound familiar to anyone in insurance?
Kapur said, "If historically somebody said, ‘This job requires 12 years or 15 years experience,’ well, maybe you’re going to be able to have someone do it with seven years experience, and a supplementer [based on generative AI].... What’s a Plan B? There is no Plan B. There’s no humans to replace the humans who have left the workforce.”
He's talking mostly about engineers, so a different pool of talent than the insurance industry requires, but the issues strike me as very similar. AI can provide tremendous aid to help newer employees operate at more sophisticated levels.
Second, he talked about the area that intrigues me the most: actually adding AI to products, not just using it in the background.
The article says:
"He gives examples of supermarket checkout scanners, of which Honeywell is a major producer. Today, these scanners work well for bar-coded products. But if you get to the counter with an individual piece of fruit or vegetables where the bar-coded sticker hasn’t been applied or has fallen off, then the cashier must manually look up the price, or the customer is forced to go and weigh the product individually on a separate digital scale, often holding up the line. Kapur says that integrating cameras and computer vision directly into Honeywell’s checkout scanners would enable the scanner to recognize the item and charge the customer appropriately, without delaying the process."
Again, not an insurance example, but it illustrates the kind of thing that's possible for insurers, especially as more companies adopt a Predict & Prevent business model.
AI is already getting built into insurance offerings: the computer vision that monitors the road and the driver and offers warnings in real-time that can prevent accidents; the sensors that detect water leaks and alert homeowners before major damage can occur; Whisker Labs' Ting, which plugs into a wall socket and detects electrical anomalies and warns policyholders before a fire can start.
But I can imagine a host of other opportunities, particularly in risk management. Who wouldn't like to have a smart adviser whispering in their ear that a risk is developing. Generative AI can be that adviser on issues as complex as cyber or as mundane as the approach of a hail storm, some crime trend, the need for a roof inspection, or certain home maintenance.
You don't just sell a policy. You provide some continuing intelligence based on AI that is constantly learning.
If you do this right — delivering insights that are truly smart, that are useful, and that are delivered in the way that policyholders want to receive them — then you open up the sort of line of communication with customers that insurers have long craved. Insurance companies complain that they only interact infrequently and in cursory ways with most customers, when they make their monthly, semi-annual, or annual payments. Adding an AI channel would change all that.
Finally, the article says another area where Kapur will use AI to Honeywell's advantage "is in providing engineering solutions to customers. In many cases, Honeywell doesn’t just sell an off-the-shelf product. It sells a system incorporating several of its products. These are usually built to a customer’s specifications, a task that requires a significant amount of time from the 5,000 engineers Honeywell employs for this work. 'We write the solution or spec for the project every time and we do tens of thousands of projects in our business every year,' he says.
"Kapur says Honeywell wants to build a large language model that can streamline this spec writing process, so that what currently takes as long as a month could be completed in just minutes—with engineers checking the output for perhaps a few additional days to guard against the risk of AI 'hallucinations.'”
What he's describing sure sounds like it could be applied to the writing of complex insurance policies.
Another recent article tees up the possibilities of what's sometimes called agentic AI. which I think is a bit further out than the sorts of things Honeywell is pursuing but which could certainly be a profound advance for generative AI. The basic idea is that the AI wouldn't just have the authority to gather and work with information. The AI would also have the ability and authority to work with apps and have them execute tasks on behalf of the user.
In other words, you wouldn't just tell the AI to gather tips on how to build a website. You'd tell the AI to build a website, and it would.
The article's author, Bernard Marr, offers a range of areas where agentic AI could make a big difference. For instance:
"Business Operations: Agentic AI could revolutionize how businesses handle day-to-day operations. These AI agents could autonomously manage supply chains, optimize inventory levels, forecast demand, and even handle complex logistics planning. By processing vast amounts of data and making real-time decisions, they could significantly improve operational efficiency and reduce costs."
Or:
"Healthcare: Agentic AI could revolutionize patient care by serving as round-the-clock health assistants. These AI agents could engage with patients daily, monitoring their mental and physical health, adjusting treatment plans in real-time, and even providing personalized therapy support. By analyzing vast amounts of medical data, they could also predict potential health issues before they become serious, enabling truly proactive healthcare.
He also describes opportunities in software development, cybersecurity, human resources, scientific research, and finance.
Again, I think agentic AI will take a while to take hold, just because a hallucination could lead to such big problems if the AI has the authority to act on its own. But the premise is certainly provocative and should be watched.
We're still in the very early innings in terms of what generative AI will do.
Cheers,
Paul