AI Wisdom Ep. 40: The Future of AI, Wearables, and Mobile Apps in Workers’ Comp

James shares his perspectives on how the pandemic accelerated digital transformation in the insurance industry and the leading factors impacting workers’ compensation insurance today. Benham explains, “COVID introduced a whole slew of tracking and monitoring that people were absolutely against beforehand.” He goes on to outline how technology can help keep workers safer and reduce comp premiums.

As Benham puts it, we will continue to see a much more rapid adoption of tracking, sensor, network, check-in, check out, proximity, density tracking, and health check along with much faster adoption of these technologies than had COVID never happened. He also highlights how forward-thinking organizations approached technology in the construction space during the pandemic to move forward and save lives through the use of AI contact tracing.

Just like Bill Pieroni at ACORD sees that access to data improves the free flow of information and raises the ability to act on it quickly and accurately for value. Benham sees the biggest trend today over on the broker and carrier side is dramatically streamlining the amount of data that an insured has to enter to get a price. Then dramatically streamlining the amount of time it takes to bind that policy. James goes on to state the three biggest trends in Workers’ Comp today are automation, use of big data and machine learning models.

James highlights how Excel is still a major hurdle as it still powers a substantial portion of underwriting, claims, and policy management and it’s not centralized, not standardized, not secure and not uniform. If you took away Excel, underwriters could not do their job. Benham illuminates another big challenge is people think going paperless means going digital and it’s not. Scanning paper in and routing it presents another set of hurdles. Frank Sentner of Sentwood Consulting concurs: As long as the regulators and our industry attempt to solve our business challenges using forms (electronic or paper), we will fail. Plagued with legacy technologies and approaches, carriers, brokers and TPAs take a long time to roll out new lines of business. Benham goes on to say, “sometimes it'll take three months to roll a new state on one line, one state. Which means that if they want to roll a new line out to 50 states, it's going to take years. I think that's the big hang-up, and I still don't feel like I'm seeing enough forward movement in that area.”

Known for frequently using the term “cost of inefficiency”, Benham explains how organizations bury buckets of gold every day on their worksites and shares an example of how UPS dialed in the process of driving the cost of inefficiency down to the tune of more than $10 million a year.

To hear more great insights, examples, and how AI, predictive analytics, fraud detection and more are impacting Workers’ Comp, listen to the full episode. Click below to listen to the full episode (listen time 41 minutes) or read the full transcript.

Full Transcript

Ron: Hello and welcome to AI Wisdom – Talking Innovation in Insurance. On this podcast we talk to business and insurtech leaders about how artificial intelligence is transforming the way we buy and sell insurance. I am your host Ron Glozman, Founder and CEO of Chisel AI and a strong believer in the power of AI to help people work smart and enrich their lives. So, let us get into it! 

I am very excited to be doing one of our first video podcasts and with me, I have a very special guest. We've been on his podcast, which is a wonderful podcast, the JBKnowledge podcast, and now he's on our podcast. So, I'm very pleased to have with me James Benham, CEO and co-founder, JBKnowledge, join me today, as we talk about technology and the impacts that it's going to have on workers over the next 5 to 10 years in the insurance industry. James, thanks for having us.

James: Man, I love guesting on other people's podcasts. I've been a podcaster for six years now, and it is super fun to get to visit on other people's shows. I'm excited to be here today, Ron, and good to see you.

Ron: Awesome. So, why don't you give us a little bit of a background, aside from the fact that you're a pilot and you have a great radio voice, how did you get into insurance?

James: I am from South Louisiana, Baton Rouge, and just fell in love with technology when I hit about 11 and they started teaching computer science in school, and my dad got me a computer when I was 12 and changed my life. That was it. I mean, nothing was the same after that and I really never cared about games or anything like that. I always loved coding, surprisingly. I thought that building my own apps was way more fun than playing somebody else's game. So, got super involved in that, went to the world's finest institute of higher education, Texas A&M University in College Station, and started in comp sci, and actually was kind of burnt out, because I'd been coding all through... I went to an engineering high school, that was all focused-on engineers, so it was, like, super nerdy high school. No football, baseball, or basketball. It was just like... seriously, they said they were distractions to academics. So, it was really amazing.

But I ended up getting an accounting degree, which has helped me so much in insurance, and got a master's in business and a Master of Science in Management Information Systems in the business school at A&M, and then did a couple internships at a big consulting accounting firm, Price Waterhouse Coopers, and did six months of internships with them as an undergrad and grad student, was like, ahh, not my thing. Great firm, great people, not my speed. So, I started JBKnowledge 20 years ago out of my dorm room, when I was wrapping up my master's and undergrad. And fell, like many, fell into insurance. There was no major for insurance. There was no undergrad or master's program in insurance. There was no minor in insurance. We didn't have an insurance class, which is just mind-boggling. Now that I understand the industry, there should at least be one class on risk management and insurance in business school. There was nothing.

And built a lot of websites and software for people in the early days, 20 years ago, basically anything that would pay the bills, because we were bootstrapped. We started with $5,000.00 and a dream, and never raised a round in 20 years, and went out and got a lot of business. One of my friends was an M&A guy and he bought an insurance vendor that did inspections for property underwriting for commercial and personal lines, underwriting inspections. And so, we ended up building a bunch of software for them to integrate them with some carriers, State Farm, Nationwide, USAA, all these different carriers. And that's what got us in the business, was just, by chance, a friend of mine was an M&A guy, and by chance, he bought an insurance company. By chance, they needed help with software development in 2004, 17 years ago now.

I ended up being their interim IT Director for a period of time, helping them transition IT directors, and ended up rebuilding a lot of their tech and their infrastructure to the point where I was in the server room, physically pulling old machines out and replacing them. I'm an old hardware network guy. I used to be a sysadmin. I used to sysadmin some FreeBSD boxes and some RS6000s back in the '90s, and super nerdy stuff, Windows NT systems. So, I just jumped into the deep end of the insurance pool, and I remember going to my first carrier, and I was like, "Man, this is crazy. They have an incredible amount of data, and they need so much help." I just kept thinking, "They need so much help." Even the really big companies needed a lot of help.

In 2008, a good friend of mine got me involved in work comp, and the rest is history. I went from being a property specialist to being a hardcore work comp guy. I'll fast forward to today. We have 250 employees, we're still bootstrapped, and we work almost entirely in the work comp space. We have a pretty good footprint in surety and construction risk, but we're heavy, heavy, heavy in comp. We still do home and commercial property software. We do services and we have our own two products, we have a claim system called TerraClaim. We've got a compliance system that, COI compliance, called SmartCompliance. And then we have a whole outsourcing division for carriers and brokers and TPAs. So, we're insurance nerds, man. I mean, that's it, and totally didn't intend to be, but fell in love with the business.

Ron: So, before we jump into the heavy on the insurance and technology side here, tell us about your very first application, the one when you wrote when you were, like, 12 or 14, or whatever. What was it?

James: I was 12. It was a true business application, because it was solving a business purpose, and it was from my own business. You have to understand, everybody in my family was in business. I mean, I didn't know someone who worked for someone else. My aunts, uncles, they all had their own businesses. And so, growing up, I saw that, and I was like, well, when I was 12, I wanted to make money. I mean I wanted to go make some money. So, what can a 12-year-old do that actually makes money? Well, paper routes, at the time, didn't make much. So, I started a lawn business, cutting grass. It was a racket. I mean, it was great. I would make between $ 25.00 - $40.00 a yard, in '92.

Ron: Back then. That's, like, $100.00 today.

James: It's nuts. Well, I mean, and some of them were really big yards, right? I mean, the $40 yard was pretty big.

Ron: That's a lot of money though.

James: But still, $ 20.00 - $ 25.00, and I'd be done in an hour, and it was great, 12 years old. My dad lent me the money to buy a lawnmower, and then I had to pay him back before I could keep any money. It's the first lesson of bootstrapping. Get out of debt fast. Then I realized I was having a hard time keeping up with who owed me money. It was '92. There was no internet. And it wasn't like there was accounting software growing on trees back then, like there is now. So, I built a really cool accounts receivable application, and I want to say I built it in QBasic. It was either that or Pascal, because at the time, I was coding in Pascal and QBasic, GW-BASIC. So, I built this really cool app that all it did was track who owed me money, and it would print an invoice. Then I could mark it as paid. I didn't understand that I was building an AR app, but I was. I just needed to know who owed me money. It was funny because my first customer I used it on got really ticked off because he's like, "What 12-year-old sends invoices?"

Ron: I love it.

James: I don't know what he was mad about because I put it under his windshield. He wasn't paying me on time and then when I was 15, I kept writing a lot of code, just anything I could think of. Mind you, no internet, no code samples. I mean, books came with a floppy disk that had a minimal amount of code samples. You had to read books for everything else. And then, when I was 15, they made us...and I'm so thankful. So, this was such a big life event, and you never know when big life events are happening, right? Like, sometimes you do, but a lot of times you don't. They made us do science fair. So, I was like, "Well, what the hell do I do? Do I make the volcano crap that just spews...? No, no, no, no. So, I was like, "I'm going to write a piece of software for science fair." So, everybody else did the volcanoes and the earthworms and the ants and all the other stuff and I was like, I wrote code. Well, what could I do? I was super fascinated with the Enigma machine from the World War II and encryption, and so I went to the library, this thing where there's a room with, filled with books, called a library.

Ron: I heard of it.

James: I checked out every book on encryption, and then I coded my own cipher. I built a rotating cipher, and I built an addition cipher, and then...it was a rotating key cipher as well. So, you put a password in, then it would hash the cipher, and then it would mash up the cipher with the code, and then it would rotate, so that it really garbled the hell out of the message. And if you got just one character wrong in the password, nothing made sense, right? So, I built, like, this eight-step encryption algorithm. Then I was like, "Well, how do I use it?" So, I wrote a word processor. There was no... it was 1995. Like, there was hardly anything.

Ron: I know. I just love the 15-year-old being like, "I'm just going to build a word processor."

James: I did. I mimicked MS-DOS edit. If you remember the old MS-DOS edit, I mimicked that. There was no intrinsic functions, so at the time, I had to build all the dropdowns, by hand and I had to build the File, Save, Exit, all that was by hand. Cursor navigation, all by hand. You had to hand-code everything. Then I baked the encryption hash into the editor, so that you could type a message, save a message, you could encrypt it and decrypt it all inside the app and save it and load it. Then to open and save different files. I built all of that. I won, like, everything. I mean, it was crazy. I won my school science fair. I went to regional; I won the regional science fair. The Navy and Army gave me science awards, they gave me money and calculators, and bags. And I went to state, and I got second place only to another encryption project from a different city in Louisiana. So, number one and number two are both encryption projects. And his was really good. I mean, he legitimately won. Then I got more military science awards, and I was like, "Dude, I can make money writing code." That was it. That was... I'm sorry for the long explanation.

Ron: No, I love it.

James: That was the game-changer for me and ever since then, it's all I wanted to do. That was it.

Ron: Love it! Congratulations to you on the growth of JBKnowledge and being such a premier provider of all these insurance rate services, as well as technology. I'd love to get your opinion - where do you see the common challenges that organizations are facing today?

James: Well, it's like my favorite movie, my favorite movie of all time, and it's going to sound so stupid. It's a Bill Murray movie, called Groundhog Day. And love Groundhog Day. Technology feels like Groundhog Day, because you wake up and it's like the same music's playing, and you still have to hit the same clock and we're still facing the same challenges. I mean, people think that going paperless means going digital, and it's not. And so, they scan paper in and route it. So that's a big challenge. Number two, Excel is still a major hurdle. I mean, Microsoft Excel still powers a substantial portion of underwriting, claims, and policy management. When you really dig, and you go to a carrier, and you go to the desktops of the people actually doing the work, if you took Excel away, they could not do their job.

I think that's a really big problem, because Excel is not centralized, it's not standardized, it's not secure, and it's not uniform. So, you end up with a lot of really challenging results and scalability issues. I think it still takes way too long for people to roll out new lines of business and you see this in particular carriers, but brokers and TPAs struggle with this, too. We're entering this amazing, creative phase in insurance, where we're coming up with totally new lines of business, and totally new ways of charging for it.

When you're talking about pay-per-use insurance, that's a major thing for underwriters and for policy management and claims handling to get their hands around because you have to change the way you bill and the way you collect and commissions. I mean, everything changes. And a lot of folks are going direct write as well. That's all super challenging and I still see a lot of hurdles, where sometimes it'll take three months to roll a new state on one line, like one line, one state. Three months. Which means that if they wat to roll a new line out to 50 states, it's going to take years. So, I think that's the big hang-up, and I still don't feel like I'm seeing enough forward movement in that area.

Ron: For sure. Would you say that the pandemic has accelerated the digital transformation? In your interactions with companies in the industry, have things been moving faster than before COVID? And then, how well do you think we're doing as an industry when it comes to responding?

James: I know that COVID accelerated certain digital adoption. I know it because I observed it. I mean, I don't need to rely on anecdotal evidence. I definitively observed that companies who still had these remnants of on-prem computing, that got blown out of the water. I mean, when you can't go to your office for seven months, and all your stuff's on-prem, you got a big problem. In my opinion, I have two daughters and I remember when I was trying to get one of my daughters to jump off a diving board for the first time. You know the kid that starts crying and not wanting to jump that was my older daughter. Just did not want to do it.

I feel like COVID took all of those people that were staring there at the edge of the diving board, looking at the digital divide and crying about it, proverbially, not, like, actually crying, but I think it just pushed them off the diving board.

You had to deal with off-prem. You had to just come to grips with cloud. You had to deal with virtual meetings, which is a big deal. Then you had to deal with digital workflow and routing. The companies and people that were not prepared for that really struggled in the first 6 to 12 months. I think one of the reasons you're looking at a lot of labor shortages now, too, is there's people that just literally were close to retirement and did it. They just retired. Because they just didn't want to deal with it. I mean, it just wasn't a workforce they wanted to deal in anymore. So, I know it accelerated it. We saw it accelerate it.

It also because everyone was so uncertain around money... I mean, there was a legitimate fear that the entire economy was going to completely collapse. Global. With good foundation, like, there was a good reasoning behind it, that spending on discretionary contracts dried up for several months. So, for those of us who were selling product, it got really hard to sell for a while. I mean, it got really hard to move units, because there were a lot of companies that just put a complete freeze on new spending. So, that was difficult.

Ron: For sure. What do you think are the biggest factors that are going to have an impact going forward on workers' comp? Has COVID had any differences on workers' comp? I know some people talked about business interruption, which is obviously not related to worker's comp, but there have been other things that COVID has impacted.

James: Thinking at it from a technologist's perspective, and trying to look at what about the interaction between technology and workers will impact risk? COVID introduced a whole slew of tracking and monitoring that people were absolutely against beforehand. So, when you look at temperature checks, wellness checks, biometric scans, infrared cameras with scans, and cameras in general, looking at proximity and distancing, there was an enormous amount of resistance to doing proximity, distance checking, worker tracking, biometric and health scans. And this was ripping the giant band-aid off of that entire category of technology that could help keep workers safer and would tie directly back in with comp premiums.

I think that we will continue to see a much more rapid adoption of tracking, sensor, network, check-in, check out, proximity, density tracking, health check. I think we're going to see a much faster adoption than had COVID never happened.

Because now we know what a pandemic looks like. We know the kind of tech that we have to have. The reality is that the proximity tech that was implemented, and there was quite a few of them out there, and we covered it on both my podcasts. You can take something like Genda, or there's three or four platforms that would be good examples here.

Ron: Sure. I'm sure people can Google them.

James: Genda's the one that comes straight to mind, because they really made a major play in both construction and insurance. Triax is another one. They're in Connecticut and their Spot-r platform. The really neat thing is, when you track proximity and density, you're also tracking worker movement. You can track lifting patterns, for lifting injuries. You can track slip, trip, fall, for slip, trip, fall detection and prevention, and notification. You can do evacuation facilitation and Spot-r cut evacuation times down by half.

Ron: Wow, that's impressive.

James: An average of 13 minutes down to 6 minutes, by using tech. So, this technology that could be implemented to deal with another version, because we're seeing the Delta variant really spread across the country, and the anti-vaxxers that aren't getting vaccinated are getting infected a lot faster right now. This tech is going to help with that. But the other area that it really helps with, that helps the bottom line of the insured, is productivity tracking. Because productivity tracking is the golden goose, in all industries. So, the ability to really apply lean principles, and eliminate waste and reduce the number of steps in a task, well that's directly tied to technology that can track worker proximity and contact tracing. So, that's what got me excited. I mean, there wasn't a lot to be excited about in the pandemic, especially for a Type A extrovert who likes to be around people, it was hell. But that was an unexpected benefit.

Ron: For sure and I think it's interesting because it ties into inefficiency. You have this saying that you're known for, which is the "cost of inefficiency." Can you explain to the listeners what you mean by it, and what the implications are when it comes to the insurance industry?

James: The cost of inefficiency?

Ron: Yes, that's right.

James: I've said it on my other podcast and on a lot of speeches I've given, that companies bury buckets of gold every day on their worksites, whether it's, like, a field-related industry, like construction, or transportation. They just bury buckets of gold. Just buckets and buckets and buckets of gold. They spend so much money. A really good example, an eight-figure example, UPS has really dialed in the process of driving the cost of inefficiency down. One of the things they realized was that buckling and unbuckling seat belts was costing them a lot of money. So, they started doing a half-day to full-day training on how to enter the cab of the vehicle, sit down, close the door, and buckle in the least number of steps.

By cutting that transaction time down, and then the inverse, unbuckling, opening the door, and getting out. By cutting that down, they were able to save, and I verified this with some friends who work at UPS, they were able to save over $10 million a year.

Just wrap your brain around how much money that is. That's, some company's entire top line was tied up in the inefficiency of buckling and unbuckling and entering and exiting a cab. That's really why technology can help us. Now, you can also eliminate that inefficiency just through the power of observation. But technology is a great enabler when you're having to observe at scale. And that's what we're talking about. It's easy to solve all those problems when you got one site and one worker, and one observer. What happens when you have 100,000 workers, and 200,000 sites a day they visit? It becomes impossible to physically observe all of them. So, I think that's what's so cool about tech.

The vast majority of risk-related technology can really help with productivity, which means that it can reduce losses, but it can also drive top-line revenue, because the more productive your workers are, the more billings you get out of them.

Hopefully, you're not billing by the hour. Hopefully, you're billing by the unit or something like that because that's really where you realize the productivity gain.

Ron: That example is so crazy, because where I thought, you were going to go with it is the left turn, which is a very famous example. Left turns cause a lot of accidents, and all trucking companies, as far as I understand, now program three right turns. Not only does it save gas, because left turns take a long time, but it reduces accidents.

James: Correct.

Ron: Just these small, small things can have such an outsized impact. It's crazy to me. So, if you had to think of three trends, let's say, what are some of the biggest trends you're seeing right now when it comes to workers' compensation?

James: Well, the first big one that's being talked about a lot, but it's actually being implemented, is definitely predictive analytics. Actual real predictive analytics. There's a lot of fake AI out there. There's a really great cartoon where someone took a Scooby-Doo cartoon, one of the Scooby-Doo characters lifting a sheet off someone's head and underneath the sheet, it says, "If-then..." So, they're basically saying that AI is just if-thens.

Ron: I love it. I love it.

James: That's how I feel like most AI is just pull the sheet off and it's just a bunch of if-then statements. I remember, when I went to an InsureTech conference a few years ago, and the really hot thing at the time was chatbots, really super-hot. Turns out people don't really like chatting with chatbots. Almost none of them can legitimately get close to passing the Turing test, so, humans can tell they're chatting to a chatbot, and they don't want to. But, like, the chatbots, Ron, I kid you not, were literally like the old software I used to use from ANSI terminals, because it was basically just, "Here's your options. Pick one, two or three." I'm like, "Oh, my gosh. I did that application in 1992 in GW-BASIC." Like, with spaghetti code.

Ron: Sounds familiar.

James: This is not a chatbot. This is a text terminal interface that's five times less efficient. So, I saw a lot of the fake AI garbage. Now I'm seeing legitimate, real, predictive analytic solutions that are going way beyond conditional if-thens, that are not even using public machine learning models. They're actually authoring their own ML models. They're training their ML models, and now they have tens of, or hundreds of millions of claims to train them on. So, you're starting to see the results of that in the ability to predict outcome, i.e., how many days open do you think this claim is going to be? What do you think the total incurred is, and what's your recommended reserve? Which is the big things you want to know when you're looking at predictive analytics, is what should I set the reserve at? How long is this going to be open? Then ultimately, what steps can we take to try and cut down our time to work because you want to get everybody back to work. Sooner they get back to work, the less money it's going to cost.

Some machine learning models that are getting really good at predicting fraud and detecting fraud. Because before, when we said predictive analytics in work comp, we're mainly talking about humans assigning a multiple to a comorbidity factor. Like, if they're diabetic, then, you notice, if-then...if they're diabetic, then triple the cost of the claim. If they're a smoker, then double the cost of claim. If they're obese, then triple the cost of the claim. The interesting thing, and one of the interesting things we found as we started digging into tens of millions of claims, was that the attorney representation had just as big of an impact on claim cost as comorbidity factors. And not even if they're represented by an attorney, which attorney represents them has a big impact. So, that's some stuff you find out once you train an ML model to look at claims.

One of my favorite features in Power BI is their intelligent sense. What they call it? I'll remember. Smart Trends or something. There's some feature name inside Power BI, where it will automatically generate widgets for you based on correlation it thinks that it sees inside the data. And that's legit. That's legitimately a machine learning model trying to identify things you're not even asking for. So, I think that's a big trend.

When you look at claim origination, or, in particular, the big trend, over in the broker and carrier side, is dramatically streamlining the amount of data that an insured has to enter to get a price. Then dramatically streamlining the amount of time it takes to bind that policy. Because now, when you look at what Lemonade has been able to do on property...this will be a little harder to do on work comp, but they're tapping into public data sources, and they're pulling 90% of the data they need from people other than you. So, that's one thing I think is really, really cool, is the ability to dramatically reduce all the questions. If you've ever had to fill out an underwriting questionnaire, they suck.

Ron: For sure.

James: It's like drilling a hole in your head.

Ron: Yeah, I know.

James: I think I would say automation, the use of big data, and true machine learning models, are the three biggest trends I'm seeing right now, actually being implemented.

Ron: I love the distinction between true AI with that Scooby-Doo example. That's an amazing example. But I also think you hit the nail on the head there with the automation piece, and having it be true AI in the background there, like that Microsoft example. I love that.

James: You're looking for correlation, and then you're...really looking for causality. I'm a Star Trek fan. We all know that if someone walks out in a red shirt, they're going to die. But the red shirt doesn't cause them to die anywhere else other than Gene Roddenberry's head. The red shirts don't cause death, they just happen to be correlated. So, that's what I think the real difference in machine learning is, it looks for causality, not just correlation.

Ron: I love that. That's a great clip. So, we're going to take a quick 20-second break to tell you where you can find more information and insights about insurance innovation. We'll be right back.

[If you liked this episode of AI Wisdom, subscribe to our blog, Writing the Future: AI in Commercial Insurance at www.chisel.ai/blog for feature articles, interviews, opinions, and more.]

Ron: We're back with our featured guest, James Benham. I'd love for you to share a little bit with our listeners about some of the mobile and wearable applications that you guys are working on for insurance, some of the use cases, and how do you think these technologies are going to go about delivering efficiency?

James: Well, wearable tech has gone through a lot of iterations, and certainly, it's gone through the hype cycle. We've built applications for HoloLens. We've built applications for the original Epson Moverio BT-200, which was a predecessor to the HoloLens. I even went to Consumer Electronics Show and co-exhibited in the Epson booth with them, with an app that we've built years ago. It was an underpowered device. Now it's actually being primarily used by drone pilots to have a transparent display. They can pilot and see the drone, and then see the screen all in the same transparent OLED. I think it's a transparent OLED. Maybe it's a transparent LED.

So, I've built a bunch of stuff there. I built a bunch of VR apps. A lot of that has peaked and valleyed already, as far as interest, because people just don't want to wear something that big and unwieldy all the time, is really what it comes down to. I'm wearing readers right now because my LASIK has worn off after nine years on reading. Until visual wearables get this small, lightweight, and easy to use, we're going to have some lagging on adoption. The main wearable that, of course, has gotten tons of adoption is the Apple Watch, followed pretty closely by Samsung's watches, I think, is the main area, because Apple, did an amazing job with HealthKit, of tying into all of your biometric data. So, it's wild, Ron, because when I go to the doctor now, I buy a lot of my own medical devices, and they all have to be HealthKit enabled. So, I got a blood pressure meter, I got a thermometer. I got a scale to weigh myself on my Apple Watch. And I use Strava and then I have some other, Peloton as well, is tied in with HealthKit. So, all those things integrate with HealthKit. I think HealthKit is one of the coolest innovations that we could use in comp, because we could pull all the workers' health data, up to and during and after the incident.

I think that's the best opportunity, because you don't have to build all the infrastructure around that. You don't have to build it. You can leverage the millions of developer hours that have been put into collecting, and storing, and processing all that data. So, it's really pretty neat to see what they're doing there. When I think of wearables, honestly, I think Apple's really continuing to lead the way on this and what I also love about what Apple's done, and yes, I am a Steve Jobs fanboy, and yes, I am Apple fanboy. I am. I'm just going to admit it.

Ron: That's okay.

James: But it's their maniacal obsession with privacy that I think is going to allow them to win over enterprise, and win over the general public, because the public's grown weary, and I know I have grown weary, of selling my data. So, I've been unplugging everything I can from different ecosystems that ship my data around, and Apple's given me a private alternative. So, when we talk about wearables, I think that's really the big area of opportunity. The HoloLens 2's an amazing device. I mean, it's ridiculous. I own HoloLenses. I own multiple HoloLenses. I love them. They're fun. They're fun to game on. They're fun to build specific apps, but I'm just not going to wear it around on a daily basis, whereas my Apple Watch is always on, and it's always collecting data and then that and all my other health apps are constantly dumping data. When you look at a comp perspective, medical and return to work and all those things are big watch phrases, and it could really benefit from that kind of data centralization on health data.

Ron: I think that's such a great example, and so tangible, and something that everybody has probably experienced in their day-to-day life. As we wrap up, I'm curious, James, who's a business leader, in any industry, that you admire most?

James: Well, I read Bob Iger's biography, his autobiography, recently. Now he's the chairman of Disney Corporation. I got so much out of it. His real obsession with using design in engineering is something I think everybody in software should pay attention to. And what he did by fusing together the Disney Imagineers and the Pixar creative geniuses, has led to this massive renaissance of this global powerhouse, and it all was his core pillars that he's built Disney on. So, I really wasn't expecting to learn so much out of studying his career and life. I totally did. Certainly, Steve Jobs is someone that all of us in tech look up to. If you don't, you're lying. Like, you're just being a hater or something.

Ron: Yeah, I'm with you 100%.

James: You know what I mean? How can you not admire someone who's radically transformed the way that all of us live every day?

Ron: I think he had his faults, which is I think where most people come, but I think the faults are separate from the contributions.

James: Yes, and I really wish we would stop just ripping apart people, in general. Let he has no sin cast the first stone is all I got to say, because I mean, you better be perfect if you're going to come at somebody these days. You know what I mean?

Ron: I'm with you.

James: I think Jobs really had an amazing career. Elon Musk is a little out of control on Twitter, but he is redefining what a moonshot is. He's really redefining what aggressive goal setting looks like, and he's really redefining the role of a visionary CEO. So, I think he's really an impressive one to watch and there's a bunch in the tech sector that you could look at, just pure-play tech, that are really interesting to study and observe. Whether you're looking at the Google Ventures guys, that wrote some really great books on lean startups, or there's a guy who owns a woodworking manufacturing company called Paul Akers. He wrote my favorite book on lean, called "2 Second Lean." He has developed this amazing, easy-to-use system for eliminating waste and improving efficiency, and what he says, fixing what bugs you. It's my core bible of how I'm always leaning out my workstation in my house, in my car, in my office.

Then, the last one would be Gino Wickman, who invented the EOS system, and we've run on entrepreneurial operating system. It's actually a methodology, not a software, but it's a methodology for running business that's completely transformed my life and my business. So, if I looked at Gino and Paul Akers and Elon and Steve Jobs and Bob Iger, these are some really great folks that it helps to study and read their books and look at their lives, look at their businesses, and not get too critical on their personal everybody's imperfect. Everybody's flawed. Everybody's sinned. Everybody's a little messed up. So, I think as long as you remember that and you study the good things out of them, you can get a lot out of it, and it can change your business. It certainly has changed my business.

Ron: For sure. That's amazing. And Bob is definitely not somebody... I know of Bob, and not somebody that's ever been mentioned. So, I echo that. James, where can people find out more about you and JBKnowledge?

James: Well, you can find out more about my company, JBKnowledge, and our 250 employees at jbknowledge.com. You can find out more about me at jamesbenham.com. Our two Insurtech products are smartcompliance.co and terraclaim.com. You can check them out there. I'd love to chat or engage. I'll be at ITC in Vegas. I hope you will too, Ron. I'll be flying myself out there. I am a pilot. I like to fly to all this stuff. So, I'll be at InsureTech Connect. I'll be at as many conferences as I can get to in the next 12 months, trying to enjoy being around people again.

Ron: Love it! Awesome. Well, James, thank you so much for sharing your knowledge with us. And as always, if you'd like to stay up to date with the latest and greatest in insurance innovation, check out www.chisel.ai. Thank you.

That’s a wrap for this episode of “AI Wisdom” hosted by Chisel AI and me, Ron Glozman. Thanks for listening.

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