Risk & Resolve
The Risk & Resolve Podcast is your go-to resource for insightful conversations at the intersection of leadership, business ownership, and the insurance industry. Hosted by Ben Conner and Todd Hufford, this podcast dives deep into the challenges and opportunities that leaders face in an ever-changing world.
Each episode features candid discussions with business owners, industry experts, and thought leaders, exploring topics like innovation, risk management, and the strategies that drive success. Whether you’re an entrepreneur, executive, or insurance professional, you’ll gain actionable insights and inspiration to navigate today’s complex business landscape.
Tune in to Risk & Resolve—where leadership meets resilience.
Risk & Resolve
James Paden - NextGen Healthcare Summit 2025 Recording Series
Artificial Intelligence is reshaping the way businesses operate, and Large Language Models (LLMs) like ChatGPT are at the forefront of this transformation. In his presentation at the NextGen Healthcare Summit, James Paden explored how AI-powered tools can enhance productivity, streamline decision-making, and create a competitive edge.
Through this session, attendees gained valuable insights into how organizations can effectively integrate AI—starting with understanding the strategic risks and opportunities, and then evaluating the best approach to adoption: whether buying off-the-shelf solutions, building custom tools in-house, or partnering with AI specialists.
Key points from James’s talk include:
- AI is Your New Competitive Advantage – LLMs like ChatGPT are more than just tools; they act as highly skilled digital assistants, capable of research, writing, and decision-making.
- Adoption Strategies Matter – Choosing the right path—buy, build, or partner—can determine the success of your AI journey.
- Data Drives Success – The success of AI depends heavily on data availability, quality, security, and compliance.
- Learn from the Past – Just as the internet and smartphones transformed industries, AI is poised to do the same. Companies that fail to adapt risk being left behind.
- Infrastructure & Security – A strong technical foundation, privacy safeguards, and access controls are essential for responsible AI implementation.
This audio clip captures the highlights of James’s forward-thinking approach to AI in business—delivering a powerful message about the importance of embracing AI now to stay competitive in the future.
You're listening to Risk and Resolve, and now for your hosts, ben Conner and Todd Hufford. Today I want to talk to you a little bit about practical strategies for leveraging AI. I've been working in technology, building software companies, for about 25 years now and I've never been more excited about technology than I am today. What we're facing, as Ben mentioned, is an enormous shift in the capabilities of technology, what it can do for us, what it can do for our businesses, and today I want to share a little bit about that. So we're going to go over why AI matters now. Why now? Why not before? Why is everyone talking about it? We'll talk about what it can do, a little bit about what it can't do, and we'll spend most of our time talking about how to adopt it in our orgs, where are the opportunities to use it and how do we go about doing that strategically, intelligently, not just, you know, willy-nilly all over the place, but how do we do it with some thought, and then we'll end with just a tad on where to go from here. When you walk out of this room today, what should you be thinking about? What are the next steps? So I'll start with a little brief history lesson. You've probably heard about AI for decades now. Right, it's been a buzzword, everyone's making it. It's been around. Why is everyone talking about it? Why is it, you know in presentations? Why are we giving speeches on it? Why is everybody raising hundreds of millions of dollars?
Speaker 1:So in the past, we had AI models. We used computers, we trained them. They could learn things. They were fantastic at identifying a stop sign or identifying a mustache or identifying if someone's carrying a weapon or not. They're sort of single purpose models. They were good for one or two things and they took millions of dollars to build. They took engineers, phds. They were sort of out of the grasp of companies like yours and mine. That's not true anymore. The current AI models are completely different in their capabilities and in their cost structure. So about 2017, google invented a new way to train an AI model called Transformers. This model got combined with chips from NVIDIA that were used for Bitcoin mining. So these two sort of developments basically made possible for the first time to imagine not a small model, not a model that knows what a mustache is or where a stop sign is, but a model that is large and that created this thing called a large language model. And so to build these large language models, they sucked up the entire knowledge of humanity, everything that humanity has ever created in any digital form, and even some non-digital forms. They sucked it all into this big model and trained it on everything. So the word large implies, contrary to the old models that were good at small tasks and trained on small sources of data, this was trained on a large source of data, the entire Internet. So all the books, wikipedia, articles, movies, youtube, research papers it has it all in this giant model.
Speaker 1:The word language here is important as well. It's not language in terms of English or grammar. It's sort of at that root definition of what is a language. It's for communicating concepts and understanding for us working as humans. And the large language model, or LLM, does the exact same thing. It understands concepts, so it computes, takes all these concepts, all this knowledge, and boils it down to math and at a mathematical level it's sort of like taking the number for the word king and you add the number for the word woman and it understands that you're talking about a queen. So before now this wasn't possible at this scale. We didn't have the understanding of these concepts. You take the word child and you add the number for the word time and out pops the number for the word adult. So for the first time, we have universal knowledge and understanding that can be applied in any concept at any business. It's not something that has to be specially trained for your business. It's available off the shelf. It did require tens or hundreds of millions of dollars to train, but now it is so generally applicable that I can access it for pennies. You can access it for pennies. So this is the foundational shift is that it doesn't take millions of dollars to make a specialized model, but for pennies and for dollars we can all do things that were previously impossible. So this opens up an enormous amount of opportunity for all of us.
Speaker 1:On the capability side, what do you do with a model that knows everything and understands what you tell it? What can you do with it? Right? What can it do for our orgs? I think, first and foremost, you can take nothing and turn it into something. You can do research. You can ask a question. We've been using Google for years, right? Google is keyword-based. You type in you know what movie did Matt Damon star in and you get a list of Matt Damon movies. These large language models. Tools like ChatGPT can be concept-based. You can just talk to it about something. You vaguely remember what the protagonist did, and it can find that information.
Speaker 1:So at a business level, we can research and find all sorts of things. We can look up research papers. We can find the latest trends in HR. We can create something out of nothing. Then we can automate that. So all of that is possible. We can take a giant mess and turn it into something nice and clean and organized Again. This wasn't possible before without a lot of money and a lot of time specialized to your particular use case. But this strategy can be applied to any use case. Ai, current AI models, understand those concepts, that language within our data. So if you have a giant pile of something, it previously may not have had a lot of value and now it can have a lot of value. So if you have a giant pile of resumes or employee descriptions, you can analyze those and extract information from those notes, from that free form document, maybe skills. If you've got medical records or doctor's notes, you can extract coding from that right Anytime. You have information that's not organized now we can organize it. We can take audio records and transcripts and organize those at scale. All because the model understands the language being used, understands what we're saying.
Speaker 1:You can take a bunch of something and make it into a smaller something. You can condense information. You can extract the essence of meaning out of a document. So if you have a bunch of employee feedback surveys right, you could squeeze that information down into the sentiment analysis. What is that person thinking or feeling when they say this large block of text, right? What is the core meaning that we're extracting? Large block of text, right? What is the core meaning that we're extracting?
Speaker 1:If you take, you know, you can summarize a transcript of a call. If you have a sales call or a customer service call, condense that down. I use this a lot. Where I'll take a sales call and I'll be hey, what are the key pain points that were referenced there? What are the stories, what are the things that were listed and said? What are the action items those of you who may have experienced a lot of the meeting bots on Zoom or Fathom I've probably seen this in action where it can take a call and then outline here's the topics discussed, here's the action items. Right, you can take that same concept of extracting and condensing information down at any part of your business. Now and again it's available for pennies, for dollars.
Speaker 1:You go the other way. You can take a little bit of something and you make it into something bigger. You can take an idea, something you just built and you want to promote it. Take that idea, that concept, and turn that concept into a tweet. Turn that tweet into a LinkedIn post. Turn that LinkedIn post into a series of blog posts. You can generate HR policies and documentation, all with just a few sentences. So you can take a little bit of something, combining it again. You have a piece of software behind you with the universal knowledge of the world. It knows what a blog post looks like or HR documentation looks like. It has a thousand examples sitting in its math code. Right, you can make these things. You can generate something and expand upon it.
Speaker 1:My favorite you can transform it into something completely new and different. So if you've got a document that's full of medical jargon, you can be like hey, get rid of that medical jargon, give it to me in plain English now, right, like a normal person would. If you haven't used ChatGPT and asked it to talk like a normal person, try that. It's very good at just talking like a normal person. I think we could use more of that in the business world. You can take and translate, obviously, into different languages, but you could also translate all sorts of things and transform documents. One of my favorite examples is you take a piece of support documentation and transform that into sales material. You can take the same core nuggets of information but change the intent of how we're communicating with them. Are we communicating to sell, to convince, to train, to support? Do we want to do it in a concise, easy-to-read format that's scannable, or do we want to use more words? That would be more explanatory? So all of these things are available again at scale across a wide variety of use cases, wide variety of industries.
Speaker 1:We can also use AI to help us make decisions. To go, what should we do about that? And again, we have a giant pile. That's the history of the world, all of humanity's knowledge Doesn't mean it's perfect, doesn't mean it's going to do exactly what we would do, doesn't mean we can't have a human in the loop, but we can ask it. Hey, pretend to be an expert in this subject matter area. What would you do? Pretend to be an angry customer. How would you react.
Speaker 1:What would you do in this scenario? Give me a list of five different frameworks I can use to analyze a decision. It's like having an MBA in your back pocket, ready to help you analyze any decision, and then you can build automations on top of that. You can connect all of these concepts together, or you can start with a giant pile of data, clean it up and then transform it, then build a decision. It can loop on this over and over and over again. Never before have we been able to do this.
Speaker 1:I've worked on, you know, like I said, technology for 25 years now, and I've never had this kind of capability at my fingertips where, in seconds, I can take any document, any piece of data, and make it into something else, and then I can automate that, and it cost me practically nothing. And then I can automate that, and it costs me practically nothing. So this is the sort of the transformative nature of AI is. It allows all of us to do this and we all have the same exact opportunity, right? Every business, every department is looking at this and thinking what should I do about it? What can I do about it? What are the opportunities that exist? So that's why everyone's talking about AI right now is that we're all facing these same questions and these same opportunities, the same challenges. So hopefully I give you a few more answers here as we go along. But this is new. We've never had this capability before at this scale or at this price.
Speaker 1:So a few case studies of people who have done things with it Commerce Bank in Germany. They're a bank and when they talk to you, they have to. You know they do some money transfer for you over the phone and something like that. They have to log that. They take all those notes, write all that down. What did they do? What did they talk about? What happened? They're a bank. They have an audit trail. Well, now they put that transcript into AI and the AI generates all that. They still have an employee review it. They're still an employee in the loop, but now it's minutes instead of lots of minutes or an hour. Details are available on Google.
Speaker 1:Bloomreach, big e-commerce company, doubled their blog posts, with a 40% increase in traffic, by using an AI tool called Jasper for their content marketing efforts. So expanded their efforts, 40% increase in traffic. A lot of these case studies are based off, let's say, a year ago's AI capabilities as well. So companies today are doing even more powerful things, getting even more results. I used AI to find all these case studies. You can too. So a company called 123RF reduced by 95% their content translation. So they're a multilingual company and they translated it all now using generative AI tools, and it's not like they weren't using computer translation before. They were right, but the newer models are just that much cheaper, that much faster. So when you think about at your org, your department, your team, what could you do if you reduced the cost of doing something by 95%? That's what we can do with a lot of these tools and a lot of these use cases.
Speaker 1:Thinkbridge, as a consulting company, it turns out they hire a lot of people, so they build a chatbot system to help them I think mainly internally access their recruiting and HR information better, access their recruiting and HR information better. So that system led to a 35% increase in recruiter productivity and that led to a 40% increase in candidate engagement. By having a better, faster communication path, they increased their ultimate goal, which was engagement. So those are again just a brief. There are tens, hundreds of these kinds of case studies out there. You can ask ChatGPT to help you research them and you'll find a bunch more, perhaps more applicable to your industry.
Speaker 1:But if that's what's possible, right? How do we adopt it strategically? How do we think about it at a higher level and make smart decisions? So, first thing you can do is you can just sprinkle a little AI on top. Nope, I'm lying, don't do that. Do not do that. Do not be the person in the room who says, just well, just AI can do it. Right, let's think smart. The first way to think smart is to understand how they work, to use it at a personal level. Right, if you're not using chat, gpt, the tools provided by your organization, whatever it might be, get some experience with these tools.
Speaker 1:They're fallible. They make mistakes At their core level. We're skipping a lot of the technical details here, but this core level, generative AI is a guessing machine. It gets better and smarter at guessing the right answer with every iteration, but it's just guessing, and sometimes it guesses with 90% accuracy and sometimes it guesses with 99.9% accuracy, but ultimately it's a guess. There's an inherent randomness in the system so that it sounds more natural and human, because that's how we talk. What I say, the way I say a sentence, is going to be different than the way Jason, when he speaks next, says that same sentence, and AI does the same thing. So it makes mistakes. So if you want to know how it makes mistakes, what its limitations are, practice, get used to it. Work on these at a personal level so that you're able to apply these automations at an organizational level, so that you can have smarter, intelligent conversations when it comes time to guess well, what can we use AI for? How would this act on this document? It's not just sprinkle AI magic on top, but it's. Well, let's apply this transformation. Let's use this prompt it can handle this use case. Let's use this prompt it can handle this use case.
Speaker 1:So, at a deeper level, though, when I think about when I work with clients and organizations implementing AI, we talk about sort of a three level pyramid. So, at the base level, we have our supporting functions. This is the departments in your organizations. This is HR, engineering, finance. They support the organization. They help the organization do whatever the organization does. In the middle, you have your core processes and experience.
Speaker 1:This is what you do. You know the classic movie quote. You know what would you say you do here? What is your company's answer for this question? This is what you do how data moves through your company, how services and products move through your company. What is it you're selling? That's your core. And then, at the top level, there's that strategic differentiation. What do you do better than anybody else? Why do customers pick your company and not your competitor's company? What is that unique aspect? So, at each of these three levels, ai can assist and do different things right.
Speaker 1:And so what we want to think is sort of level by level where are the opportunities to apply AI in my business? And obviously they have greater impacts the higher you go up. So step one is identify some high-value use cases right. Each one of these levels. We're going to sort of go through this exercise. You're going to think about what is the business impact if I had AI here, if I could transform data or clean data or generate documentation or translation at massive scale and minimal cost? What would the business impact of a shift like that be? Just a theoretical level, right?
Speaker 1:Do I have any data that gives me an advantage in doing that thing, that makes it easier or maybe harder to do that thing? What do I have? How long will it take to implement? How ready am I to implement that? Do I have people that can help me? Do I know consultants that can help me? Do I have an IT team? How ready am I to implement these things? And then, where can they have, apply and create that competitive differentiation? Do you have access to a team that your competitor doesn't, or data that your competitor doesn't, or are you in better shape? So the joke among AI consultants is that every AI project is actually a data project.
Speaker 1:In order to do any of these things, you have to be able to give the machines your data and say, hey, here's a something. Whatever that something is could be a record, notes, transcripts, marketing material you have to be able to give it to the machine right, and do that in a secure and safe format. So when you're talking about your data, first off think about availability. Is it sitting on Joe's computer and when Joe goes home for the day, that's it. The data's gone. Or is it in the cloud somewhere? Is it accessible and available to the AI in a way that can be connected? Is your data of high quality, or is it a mess with lots of duplicate data and incorrect records and some mixture of old stuff and new stuff?
Speaker 1:Ai can help clean up a lot of this right, but that's sort of a separate process than just actually using it. So what state is your data and your documents in? Are they accessible? How secure? Are they right? If you've got a whole list of customer records or financial records, you'll want some employees to have access to some of those documents. You probably have a lot of these controls available today, but if you upload all those documents into ChatGPT or various tools, does it offer that same level of data control? I know we're talking in the healthcare industry. There's probably a lot of compliance privacy regulations at play. How do you continue to follow those regulations within these new tools and frameworks? So, and lastly, do you have the technical infrastructure to manage all this? Is it again in the cloud? Is it available? Where is it at? So these are kind of questions to be thinking about as you process all this.
Speaker 1:You can ask AI to help you with any of these. You know you don't always have to work with a consultant. You can start with just asking AI. It again sort of knows everything. It has all that knowledge. If you use a paid version of ChatGPT or some of these other tools to get started at a personal level. They have a lot of research power. They are up to date.
Speaker 1:I used to have a slide in my decks that says here's the four or five things AI can't do. Here's what you need to watch out for. I had to take that slide out because they've changed so much. Right, they can do most of those things. So if you start using ChatGPT at that personal executive level, sign up for a paid plan. You get access to the latest stuff and then you can just ask it to help you. So the ultimate here is that you sort of get this cross-section between impact and effort, and in the top right you have your strategic investments this cross-section between impact and effort, and in the top right you have your strategic investments. This slide's probably a little boring for most of you because you've seen a thousand examples of it. Which is the point? This isn't anything new.
Speaker 1:The way we evaluate these opportunities is not new. It's not groundbreaking. The way we implement them is relatively standard software practices. Jason's been my next speaker. He's been working on this for decades. Right, ai gives him new tools and new advantages.
Speaker 1:With generative AI, the core way it works is the same. The same way we make decisions is the same. I had a potential client I was working with and they asked for a governance framework and we had this whole 10-minute conversation where I tried to get them to tell me what they wanted and I didn't understand. Ultimately, what they wanted was a way to figure out how to make decisions and prioritize AI projects. How do you prioritize projects today? It's the same. What's new is that we can do a lot more things that we could not do yesterday. Right, the possibilities are new and there's a lot more of them coming at us faster than ever before.
Speaker 1:Back when, you know, apple invented the smartphone, over the next few years, every company had to ask themselves do we build an app? Does it make sense for us to have an app? It was sort of a singular decision and we had some years to make it, as everyone got smartphones. That's not the case for AI. There's a lot more decisions to be made. There's a lot more opportunities to be had, to be seized by forward-looking companies, by forward-looking individuals to further their career. There's a lot of opportunities on the table in front of us and everyone has access to them all at the same time. It's not like the smartphone, where it gradually got rolled out to 10% of the population, and now everyone's got a smartphone in their pocket. This is a slightly different ballgame in terms of the opportunities in front of us, but the way we make decisions, the way we think about it and look at those opportunities is the same way we look at business opportunities today.
Speaker 1:So, for each project, how are you going to implement it? Right? Your base options roughly fall into the same three categories most projects fall into. Are you going to buy it off the shelf? Are you going to build in-house? Are you going to partner with a specialist or consultant, such as myself? When we think about the three levels, they roughly break down and this is fuzzy. It's labels, categories, it's all fuzzy, but roughly.
Speaker 1:What I tend to recommend most clients is if you're talking about the base functions, if you're talking about something that your marketing team needs, your HR team, your engineering team, try to find off-the-shelf solutions for those. They're already using software suites, software tools, wait, or look for solutions within those existing suite of tools. I guarantee you, every software company in the world right now is trying to figure out how AI makes sense within their product. You don't have to build it custom. You can use a lot of your existing technical stack to take advantage of the opportunities of AI at sort of that foundational level. Your finance department does not need to reinvent the wheel. It's coming. So if it's not there yet, you can be patient. You can look for a new software vendor. But a lot of the stuff is available off the shelf or with existing software packages. You don't have to build it, you can just go buy it.
Speaker 1:The middle layer, on the other hand, I'd argue that that is the opportunity to work with a consultant or a specialist, someone who does have the groundbreaking latest knowledge of what AI can do, where it can do it, where it can do it most cost effectively. Every few months, ai changes. I mentioned that slide I had to take out of the deck. Every couple months there's a new model, a new way of doing things, and so working with a specialist who has the latest knowledge can get you there faster. Right, if you don't have the in-house team or in-house knowledge to build these things. This is where we believe that there's a lot of opportunity to move. Fast is in that core. What does your company do here? How can you accelerate that change, that with AI At the top level, where we're building that strategic differentiation.
Speaker 1:That's where I'd argue. You make those investments, you build it in-house. What can you do that no one else can do At the AI level? That's a lot about what data do you have that no one else has? We all have access to chat, gpt and the base large language models. That's not unique to my company or your company. They're practically giving it away out there. That's not unique to my company or your company. They're practically giving it away out there. But what do you have inside your walls of your organization that no one else has? And how can you leverage AI to make that more valuable, more effective? And that's where I think there's a lot of opportunities to make some investment to build it in-house. Again, you can work with specialists at any of these levels. If you've got an in-house IT team with spare cycles because I know we're all sitting around bored all day Software guys they can build any of this at any level. But most of the time, I see projects sort of broken down by these categories.
Speaker 1:Lastly, I'll talk just briefly here about where to go from here how to lead, be a leader in your organization, how to equip the rest of your team to help you on this journey to to be forward thinking as well. So, first and foremost, if you don't have an AI policy within your team, within your department, your org, make one Right. Tell your team what they can and cannot do, starting with who's in charge of this thing. Who do they go to when they have a question or want to make an exception? And they will want to make exceptions. There'll be lots of exceptions, of people trying to ideally, push the boundaries, find new tools, to use new ways to do things, but make sure everyone knows who is the decision maker on AI policy in your organization.
Speaker 1:And then, what can they do?
Speaker 1:What tools can they use? Can they use ChatGPT? Can they use Cloud or Copilot? Can they use something that's baked in their software? Can they turn on that AI toggle in the settings? What can they do? You need to evaluate the risks involved in that and tell them what they can use. I will say if you tell them they can't use anything, they're just going to go to ChatGPT behind your back and start using those tools right, guarantee it. So figure out what they're allowed to use, how they can use it. If you don't know, chatgpt will take your data and use it for training the next model by default. The default setting on ChatGPT is we're going to use your data in our next training model, so it is important to have trained employees, to be trained yourselves and know how do I use these tools securely, so they protect privacy.
Speaker 1:When configured properly, I believe these tools are just as safe as using Salesforce, hubspot any number of the software systems we use today. Right, we treat those, we upload all sorts of documents into those systems, and I think we can do the same with many AI tools. But it's only if they're configured properly and you've had the training and education to know how that works. What data can be uploaded within these tools? Is it anything? Is it financial data? Is customer data okay? What if it's anonymized? Be specific, provide guidance to your team.
Speaker 1:And then, is it necessary to disclose the AI usage? Generally, I'd argue no. Maybe if the AI built this thing completely with an automation, maybe you put a disclaimer that AI made that, but this is one of these rapidly changing systems, and why you need to update your AI policy all the time is because people's expectations around AI usage has shifted so much over the last couple of years. I would expect it's rare in a professional setting for me to see a document that hasn't been touched by an AI model. I certainly used it to help make my presentations, to write my proposals, to review all my legal documents, right. But you know you decide what's best for your business and your industry.
Speaker 1:The important thing is that you communicate and again, this isn't groundbreaking. This isn't a new way of doing business. It's the same old business. It's the same old. You know change management techniques. Same old communication techniques. It's just a whole new area that's changing rapidly and we need to stay on top of it. You can lead by example. Be public about your usage, the fact that you're using AI, that it's okay to use AI.
Speaker 1:I think the ideal situation is you have a whole team of people who feel empowered and excited by the opportunities in front of them with AI. That's the whole thing we've talked about today is that, at a strategic level, this is entirely new capabilities. We never could do this before. So lead, show them it's okay. Show them it's okay to do these things. Get them trained. We talked about how that's important.
Speaker 1:There's a big difference between someone who's using AI and not trained and someone who is trained. The prompts, the instructions we provide AI. There's specific techniques. You can use a lot of tips and tricks you'll get from either personal experimentation or with training, so make sure your people are equipped. It's the same as anything else. Right? You want to have trained, educated people, that's just. The entire world is having to learn at the same time how to use these tools. So don't leave your people behind. Get them trained, standardize and share best practices. If you find something that works well for your organization, a use case that works well, share that. Show your own internal case studies.
Speaker 1:The most impact comes when we start to automate processes, when we build these things into our core processes. But that starts by saying hey, I uploaded this survey into ChatGPT and I was able to find insight, I was able to condense results, I was able to do something five times faster by using these tools and when you share and document these things with your org, that's how you find these opportunities and these use cases across the org, across departments, by standardizing and sharing. So, that said, I'll wrap up here Points. Main points are AI has unlocked new capabilities. Main points are AI has unlocked new capabilities. Never before. I keep saying it, this is why we're talking about it. It is a monumental shift in what is possible and what we could do.
Speaker 1:Consider the use cases for each level. You have your supporting functions, your supporting teams, you have your core processes and teams and then you have that little nugget of what makes my company unique. So take the time to think about how AI can be applied at each of those levels within your organization. Decide how to develop that use case. Does it make sense to build? What's the ROI impact standard? How do I prioritize this project kind of stuff? Is this something I'm going to work with a consultant on? Am I going to try and build it in-house? What's my capacity for building these projects in-house? How many can I tackle?
Speaker 1:And then, lastly, lead your organization into this future. You know I'm excited about it. I do this all day, every day. This is the most fun I've ever had in technology. Like it's like a kid in a candy shop out here for an engineer uh, all sorts of groundbreaking new things that were not possible, um. But I think the same applies at the organizational level. As you're leading a team, there's all sorts of new possibilities, new ways of doing things, and by equipping yourself to lead into that future, your team will see the most results. So thank you guys. Thanks for tuning in to Risk and Resolve. See you next time.
