In this episode of Executive Connect, Melissa Aarskaug sits down with Hunter Jensen, founder and CEO of Barefoot Labs, to talk about why public AI tools can create serious security, compliance, and trust issues for businesses. Hunter explains the hidden cost of relying on third-party platforms, why secure self-hosted AI is becoming a real advantage, and how companies can automate, scale, and grow without handing over sensitive data or adding headcount. He also shares how Compass was built, where companies are already seeing measurable results, and why leaders need to stop treating AI like a side experiment.

This episode is for founders, executives, and operators who want to use AI in a smarter, safer, and more strategic way. Press play before your team adopts AI faster than your company can govern it.

Chapters:
(0:20) The hidden cost of public AI
(4:31) Where compliance risk shows up
(6:09) Why Barefoot Labs was built
(10:42) What makes Compass different
(14:04) Practical use cases across industries
(17:58) What surprised him most about AI
(20:15) ROI, adoption, and measurable outcomes
(25:08) Why private AI is the future
(32:44) Stop treating AI like an expense
(34:48) Final warning for the naysayers

Hunter

(0:00) You have to stop thinking about this as an expense and start thinking about it as an investment, right? (0:05) It’s not just, okay, we’ll just buy everybody a $20 a month license to this thing, but rather we’re investing in the future of this company by building out something that people will use, people will like to use, and will supercharge our business.

Melissa

(0:20) Everyone’s talking about AI like it’s a magic bullet, but let’s be real, handing over your company’s data to public platforms is basically like leaving your front door wide open. (0:33) My guest today, Hunter Jensen, has a different approach. (0:38) He’s the founder and CEO of Barefoot Labs, building secure, self-hosted AI that companies can actually trust.

(0:47) After two decades in digital innovation and working for brands like Microsoft, Samsung, and Salesforce, he’s now focused on helping companies automate, scale, and grow without adding headcount. (1:02) If you’ve been wondering how to make AI work for your company without selling your soul or your data, this episode’s for you. (1:10) Welcome, Hunter.

Hunter

(1:13) Thank you so much for having me on, Melissa.

Melissa

(1:15) Now let’s get started with the elephant in the room. (1:18) Public AI tools are everywhere, but you said they come at a hidden cost. (1:26) What risks are business overlooking when they rely on these platforms?

Hunter

(1:32) Yeah, I mean, so really what we’re talking about here is third-party large language models. (1:38) So that would be ChatGPT, or Claude, or you name it. (1:43) You pick your poison there.

(1:46) And what we’re seeing is that folks, they want to use these tools. (1:53) It can really accelerate their work. (1:56) It can improve the quality of their output.

(1:59) It’s all very understandable why they want to use something like that. (2:03) Now the problem is that if you don’t give your team the tools to do it securely, then they’re just going to upload stuff into these platforms. (2:12) They’re just going to use ChatGPT anyway.

(2:16) And what they typically will need to do to make it effective is share a context. (2:23) That’s how these things work. (2:25) And often that means telling these large language models confidential, private information about your company, about your clients, about company finances, you name it.

(2:38) And there’s two issues with that. (2:41) One is if they’re using a free account or a cheap account, then somebody like OpenAI is very open about the fact that they’re going to use that information to help train their models and make them better. (2:55) Now, if you are paying for a more expensive license for this employee, then in their terms of service, they say that they’re not going to do that.

(3:04) One, do you trust that? (3:08) Some of these companies have made moves and made statements that for me personally, I don’t trust it. (3:16) But two, that means that your proprietary, sensitive, confidential company data is living on their servers.

(3:24) Even if they’re not using it to train their models, it’s on their infrastructure. (3:28) And they have what is probably the largest target on their back for cyber attacks of any company in the world. (3:36) And you have to think about it like they’re a startup.

(3:40) They’re not Microsoft. (3:42) Microsoft has been hardening their security for decades. (3:47) These large language model platforms have not.

(3:50) They’ve only been around for a few years. (3:52) And so their security posture is just not hardened enough to be able to trust that. (3:57) So even if you trust that they’re not willingly and knowingly using your data, they’re putting it at risk for a breach and for attack.

(4:06) And so that should also be a concern, particularly for companies that are dealing in confidential proprietary information all day. (4:16) You think about law firms or accounting firms or defense contractors, pharmaceutical companies. (4:23) There’s a whole segment of industry that needs much better data protection than these third-party platforms can provide.

Melissa

(4:31) Yeah. (4:31) And where do the compliance and regulatory risks surface most clearly during these discussions?

Hunter

(4:39) Yeah. (4:40) And so you start thinking about maybe the SEC as an example, which is the governing body of a lot of financial services companies and banks and all the rest of it. (4:51) And they have pretty stringent data security requirements.

(4:57) One of which is you need to disclose all the third parties that may be hosting your information. (5:04) And they in turn need to also pass various security checks. (5:10) And if your team members are using it without it being governed at a company level, it is likely falling out of compliance.

(5:21) And so you may unknowingly be falling out of compliance depending on who your regulatory body is, because you have folks on your team that are sharing information in ways that they should not be. (5:32) And so it’s a big risk for these regulated companies.

Melissa

(5:37) Now you’ve spent years building barefoot solutions, delivering enterprise software for global brands. (5:45) What made you shift from services into creating barefoot labs and focusing on secure AI infrastructure? (5:54) Ready to lead smarter and invest wiser?

(5:57) On the Executive Connect podcast, we unpack executive strategies for wealth and influence. (6:05) Hit the subscribe button now. (6:07) Don’t just watch, act.

Hunter

(6:09) It’s a good question. (6:10) So a little bit of backstory, barefoot solutions started, yeah, 20 years ago. (6:16) We started off building WordPress websites in 2005.

(6:20) We got into mobile app development very, very early. (6:23) We had one of the first hundred apps in the app store once it came online. (6:27) And then we got into the internet of things and connected devices, medical device software, all the rest of it.

(6:33) Then we had a fun little run in blockchain and cryptocurrency. (6:39) And then we got into data science after that. (6:42) So it comes in about four-year cycles that we’ve had to, I wouldn’t say reinvent, but change the trajectory of what we’re doing.

(6:51) But that whole time, we had the same business model. (6:55) And that was selling software development hours, selling engineering time. (7:02) Then come these large language models.

(7:05) And one of the things that they’re great at is writing code. (7:10) Code is a language. (7:11) Code is actually a much more precisely defined, less ambiguous language than any spoken language.

(7:18) And so it was an obvious place to start for these AI companies. (7:26) The people building them, they’re all coders themselves. (7:29) So it was like, well, let’s teach these things how to write code.

(7:31) And man, they have gotten great at that. (7:35) And so all of a sudden, my business model is at risk. (7:40) These large language models are an existential threat to the business model of selling engineering time.

(7:46) What used to take 100 hours might only take two or three, and you might not even need an engineer to do it anymore. (7:54) And so with that as the backdrop and a gray cloud over the business, what does the future of software look like? (8:04) I had a buddy come to me who is a patent attorney.

(8:08) And he said, gosh, I wish I could use chat GPT for my job, but I can’t. (8:14) I said, why not? (8:15) And he rattled off all the reasons I just talked about.

(8:18) He’s looking at inventions that he’s writing patent applications for. (8:23) There’s no way he can trust these third party platforms to host them securely. (8:27) It’s just a non-starter.

(8:29) And he would fall out of compliance without express written permission from all of his clients. (8:33) And so I said, well, let’s see what we can do about that. (8:37) And I duct taped together some open source software and I put it directly on his laptop.

(8:42) And he went away and used it for a couple of months. (8:44) He came back and said, man, this saves me eight hours a week. (8:48) I said, eight hours a week?

(8:50) How much do you build per hour? (8:53) And we did the math there and we looked at it like, what if we were to deploy something like this firm wide and the value creation is nine figures in a year. (9:06) And around that time, I start reading about GSAI, which is Goldman Sachs AI platform.

(9:13) They’ve been building it for the last 18 months. (9:15) They rolled it out late last year. (9:17) And I think, wow, this is an awful lot like what we were going to build for this law firm.

(9:22) And then I dig in a little further and it’s like, oh, wow, JP Morgan has one too. (9:26) And so does Citigroup and so does BAE Systems and so does Omega Healthcare. (9:30) And wow, all of enterprise has already built this stuff for themselves.

(9:35) Mid-market is going to come next and they’re going to need help. (9:38) They don’t have the resources of Goldman Sachs. (9:41) And so that is when I realized that rather than a one-off custom software project, I had a product on my hands here.

(9:50) And so I went to the law firm. (9:52) I said, hey, rather than build this just for you, how about we build it on our own dime and we license it to you and then we go take it to market? (9:59) Because I think there’s a real need here for mid-market companies.

(10:03) And that’s how Compass was born, which is the name of the product that we’re putting out through Barefoot Labs, which is, like you said, a self-hosted, it’s a large language model, plus a rag database. (10:15) And it connects to various internal and external systems at a company to really be able to leverage this new technology in a way that’s safe and secure and compliant, not sending it out to some third party. (10:28) So we have both an existential threat to my old business model, but I’m an entrepreneur and I see opportunity, not risk.

(10:36) And so we decided to go all in on Compass and that’s how we got here today.

Melissa

(10:42) So the way I see Compass is really AI on your terms. (10:46) So can you walk us through or maybe explain how Compass is different and the way it works versus leveraging tools like ChatGPT?

Hunter

(10:59) Certainly. (10:59) So the number one most important thing is that it’s not SaaS. (11:04) It’s not software as a service like ChatGPT is, right?

(11:08) This is installed on your infrastructure. (11:10) So your Azure cloud or your AWS infrastructure or what have you. (11:16) So you actually maintain control.

(11:17) It stays in your firewall. (11:20) The second biggest differentiator is that it’s fully customizable. (11:25) And I don’t mean there’s a few knobs that you can turn.

(11:27) I mean that you have the code and we can connect it to any system that you may have. (11:32) Some old legacy database that nobody supports anymore or spreadsheets or you name it. (11:38) And we can get it all integrated and we can build out custom workflows for your processes, right?

(11:46) Because every business is a bit like a snowflake, right? (11:48) They all have their weird little standard operating procedures. (11:51) And we can codify that and even almost enforce standard operating procedures when people are using a tool like this.

(11:59) So between being self-hosted and fully customizable, I love that AI on your terms. (12:05) It’s really AI for your business. (12:07) It’s specifically configured and built and customized to work for your business, not just a tool for the masses.

Melissa

(12:18) I’m thinking about all the different industries that this could support. (12:23) And how does this product balance security with usability is kind of one thing that comes to mind. (12:30) And then what level of customizations do enterprise actually need if you’re talking about a law firm versus like maybe, I don’t know, a hospital or other businesses?

Hunter

(12:45) Yeah. (12:45) So security comes first. (12:48) This whole tool is about security at its core.

(12:54) Now, with that being said, the usability and the user experience is also critically important because if you can’t get your users to adopt a tool like this, you don’t get a return on investment. (13:05) It will be a failure. (13:06) You have to get people to use it or it’s a waste of money.

(13:11) So it’s a constant weighing of security and user experience. (13:20) But at the end of the day, if it’s one or the other, security wins, basically, because that is what this tool is really for. (13:32) Sorry, there was a second part to that question.

(13:35) What was the second part?

Melissa

(13:36) No, I was just thinking, customizing it for different industries. (13:41) And you work with companies in healthcare and finance and legal and government contracting, industries that can’t afford to make major mistakes in life. (13:55) And so what are some practical ways the clients are using AI to scale without hiring people?

(14:01) Because those are very different kind of companies.

Hunter

(14:04) Yeah. (14:05) So each kind of industry has their own needs. (14:09) As an example, law firms, they use case management software.

(14:14) That’s kind of where all their documents live and where they manage any client matters. (14:20) And so they need an integration with those. (14:23) Whereas maybe a life science or healthcare organization, they have EMR systems, electronic medical record systems.

(14:32) And so they might need to integrate with those. (14:36) Where we’re seeing a ton of value right out of the gate is lessening the burden of administrative work. (14:46) Just like a really basic example would be anybody that’s gone into a doctor’s office, you’re still filling out forms with a pen.

(14:58) And so the ability to kind of process documents like that, referrals that get faxed over from other doctor’s offices, as an example. (15:11) The administrative work of pushing paper, we don’t need to be doing that hardly at all anymore. (15:18) The technology has gotten to a place in the last two years where it can understand documents.

(15:25) Even those that are filled out by a pen or with illegible handwriting from a doctor, it can decipher those things now and it can process those things. (15:34) And then switch over, you look at maybe the law firm. (15:38) It’s certainly helpful with research and with preparation and administrative stuff.

(15:43) But it’s also helpful at the senior attorney level with help drafting a patent application, as an example, or reviewing contracts for a particular set of red flags. (15:55) It’s a tremendous tool to do that kind of work. (15:58) And at the end of the day, what is the benefit?

(16:01) The benefit is time. (16:03) We’re getting time back for our knowledge workers by using these tools. (16:10) And so the real return on the investment is what you do with that new time.

(16:17) Maybe these attorneys can spend a little bit more time doing business development and finding new clients because they’re doing less rote tasks as part of their day-to-day. (16:27) And so it does vary greatly by industry, but at the end of the day, what it does is it saves time for knowledge workers.

Melissa

(16:36) Yeah. (16:37) And I would also argue that it speeds up the decision-making velocity. (16:42) You’re getting to decisions quicker and you’re getting to answers faster, which of course is then saving you time.

(16:51) And I find I’ve spent a large majority of my career in technology and we have meetings to make decisions, to make decisions, to re-talk about them, re-think about them when we should just be executing more and focusing on what keeps the business moving forward. (17:10) So what surprised you the most about AI, like embedding the compass into organizations properly? (17:21) What was the aha that surprised you the most in working across these different verticals?

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Hunter

(17:58) I think one of the unexpected things for me was when we started, we kind of envisioned it as being able to help with maybe what I would call low-level tasks, tasks that don’t require a lot of skill or knowledge, administrative tasks, maybe things that your paralegal would do, not your senior attorney would do, that kind of thing. (18:26) Maybe somebody that’s working the front desk for a medical facility and not necessarily the doctor. (18:33) And what was surprising is how far upstream it goes and is still very helpful, right?

(18:40) It goes all the way to the senior attorney. (18:43) It goes all the way to the head physician at a clinic. (18:48) And part of this is because we started building it a year and a half ago and the technology has progressed so much since we started, right?

(18:55) When we started, that was about the state of the art at that point. (19:00) It was like, yeah, I can help you with administrative tasks, but it’s improved so much so rapidly that we’re not that far away from it being better than the senior attorney or the head physician at a clinic or whoever. (19:14) That’s probably happening this year.

(19:18) And so just how far upstream and how impactful it is across an entire org is maybe something I didn’t fully anticipate until I saw it in action. (19:30) But it really, if your job is sitting in front of a computer for most of the day, it can probably help you. (19:37) And that’s a lot of people.

Melissa

(19:42) Yeah, well, it’s all of us. (19:43) I mean, who wouldn’t want, you know, eight hours back in their week for tasks that they didn’t want to do or maybe tasks that they were overthinking. (19:51) And so, you know, you talk a lot about AI, not just as a tool, but as a driver of measurable business outcomes.

(19:59) And so what results have you seen outside of just, you know, more hours? (20:05) Is it better compliance? (20:06) Is it cost savings?

(20:08) Is there any other additional kind of outcomes that you’ve seen besides just time?

Hunter

(20:15) Yeah. (20:15) So I’ll give you kind of a few examples here. (20:22) One is kind of unusual and unexpected, but, you know, I had a CEO tell me, you know what, we’re going to be able to give every other Friday off to our team now.

(20:37) Right. (20:38) And that was an interesting one to me, because it doesn’t necessarily directly impact your P&L, but indirectly, it sure does. (20:49) Right.

(20:50) Retention. (20:51) Turnover is expensive. (20:53) Hiring is expensive.

(20:55) If you’re able to save the time in your team and give them every other Friday off, that’s a win. (21:01) Right. (21:01) Even though it’s not directly driving revenue.

(21:04) Now, I’ll give you a example. (21:06) A defense contractor, they say they’re going to be able to put out twice as many responses to RFPs. (21:16) Now, if they have a similar win rate, that doubles revenue in a year.

(21:23) Doubles. (21:24) That’s staggering. (21:25) Right.

(21:26) So there is the kind of, you know, well-being of your employees. (21:32) There’s revenue. (21:34) And then there’s also cost cutting.

(21:36) And I want to be clear about this, because there’s a lot of fear out there right now that it’s like coming for your job. (21:45) But layoffs are not really a growth strategy. (21:47) That’s not what I suggest.

(21:49) What I suggest is you can supercharge the output of the people that you have. (21:55) Right. (21:56) And you may, like this clinic I mentioned, the idea here is that they can grow the number of patients they’re seeing without needing to hire another administrative front desk person.

(22:11) That’s what they think they can accomplish with that. (22:13) Right. (22:13) And so it’s about being able to grow without also increasing your expenses, like kind of in tandem, but rather keeping the resources you have and still enabling you to grow.

(22:26) So there’s the intangible, there’s the top line and the bottom line. (22:30) Right. (22:30) And so those are all real examples of where, you know, you can actually see the impact across these different organizations because it really can be all over.

Melissa

(22:43) Yeah. (22:43) Well said. (22:44) So what KPIs matter the most in AI-driven automation?

Hunter

(22:49) User adoption. (22:51) User adoption is the number one most important metric that we that we track. (22:56) And the reason is I am banging my fist on the table.

(23:01) ROI. (23:03) Yes. (23:04) ROI.

(23:05) That is the most important thing. (23:07) How do you get ROI? (23:10) Well, if you have one person and it’s saving them eight hours a month, that’s nice for that person.

(23:16) But as a business, you are not getting a return on the investment. (23:21) If you have 400 people, then you are getting a 100x return on that investment. (23:29) And so you have to get users to adopt.

(23:32) And that’s where, as you mentioned earlier, usability comes into play. (23:36) It needs to have an excellent user experience. (23:39) It needs to connect to the systems that they need to be able to do their job.

(23:45) In order for them to actually get value out of it, they have to get value or they’re not going to use it. (23:51) If they don’t use it, then you as a company are not going to get a return on your investment. (23:56) So that’s the number one thing that we track is just user adoption.

(24:01) How many at your company are using it? (24:03) How often are they using it? (24:05) How much time do they think it saves them?

(24:07) Those are the things that we look at.

Melissa

(24:10) Yeah, I would absolutely agree with the usability. (24:12) I think in all companies, in all industries, there’s those that don’t want to use it. (24:19) They fear it or whatever their reasonings are, and they slow down the progress.

(24:25) And I keep hearing these staggering numbers of how many of these projects actually fail. (24:32) And it’s exactly what you said. (24:33) It really is.

(24:34) If you’re not adopting and people aren’t using it and it’s costing you money, then people are going to stop using it, not invest in it. (24:42) And it’s going to just be kind of a waste of everyone’s time. (24:45) So I love that you mentioned that.

(24:47) Now, when we look ahead, and where do you see the biggest opportunities for secure self-hosted AI? (24:56) And then kind of the second part, what’s the one thing that you want everybody listening today to take away about using AI responsibly?

Hunter

(25:08) Yeah. (25:08) So if I look ahead, what I see is a secure self-hosted AI platform. (25:18) It becomes the operating system of your business.

(25:22) It connects all of the other systems and people in one place where you run your operation from. (25:32) It’s kind of like the promise of ERPs that never got delivered because ERPs are notoriously brittle and clunky and have terrible usability and user experience. (25:47) But the idea was it was going to connect all your systems so that you could run your whole business through an ERP.

(25:53) I envision these AI platforms to be just that. (25:59) And if you buy into that, do you really want to rely on a third party to run your business? (26:09) I mean, OpenAI CFO a few weeks ago kind of floated the idea of, yeah, we’re thinking about actually taking a piece of the revenue that’s being generated by Chad GPT at large enterprises about negotiating deals that way.

(26:28) Even if it makes sense for you today, we don’t know what they’re going to do in the future. (26:33) Do you really want to hand over the keys to your whole operation and just rely on a third party for that? (26:40) I just don’t think you do.

(26:44) And so if there’s one thing to take away from this, it is that it is time to innovate. (26:59) It’s now. (27:00) It’s not later this year or Q4 or next year.

(27:06) It is now and it’s yesterday. (27:12) I can’t tell you how many companies have actually just banned all usage of LLMs. City governments, that’s what they do. (27:20) Law firms, I’m seeing that too.

(27:22) I’m seeing that because they’re worried about the risks. (27:25) You’re going to get smoked if you do that. (27:28) You’re toast.

(27:29) I’m dead serious. (27:31) And here’s how I know. (27:33) It already happened to my industry.

(27:36) We were first. (27:38) Custom software development, it hit us first, us and customer support. (27:43) But that’s just first.

(27:46) That doesn’t mean that it’s not coming for law or life science or healthcare or government contracting. (27:53) It’s coming very, very soon. (27:56) My business model in a matter of a year and a half went from tried and true and trusted for 20 plus years to not really viable.

(28:07) Like that. (28:09) Thankfully, we saw it coming and we innovated and we built a product and we have a great future. (28:16) But others didn’t.

(28:18) They had their head in the sand and their business is evaporating in front of them. (28:24) My one takeaway is just that. (28:28) Even if your business model has been working for decades, it is time to, or in the case of law firms, hundreds of years, they’ve been working the same way.

(28:39) You have to innovate, make moves now. (28:44) That’s my big message.

Melissa

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(29:40) I think, you know, whether you like technology or you don’t, or you like AI or you don’t, it’s affecting everyone. (29:48) It’s affecting every person. (29:49) And it’s something that we all need to learn and use because it’s like the internet, right?

(29:59) There were things that people were nervous about when the internet or cell phones, or, you know, there’s so many different technologies that everybody was unsure of. (30:09) Or, you know, I think back to the day, was it Y2K when everybody thought all their money was going to disappear out of their bank account because we were moving into year 2000. (30:19) And so, you know, this just is the same thing over and over and over again.

(30:24) And so, like you said, we need to do it and we need to, you know, get skilled, get tools up, embrace it. (30:33) People don’t like it, understand why they don’t like it. (30:35) Why are they not adopting it?

(30:36) Why are they not using it versus let them not use it? (30:40) And so I’m curious to get, will private AI infrastructure become the default expectation as we move in, you know, to 2026 and beyond and to 2020, well, through 2026, I guess now.

Hunter

(30:56) I think so. (30:59) Yeah. (31:00) You know, we’re all in, we’re betting big on that becoming the case.

(31:04) And, you know, the most innovative companies have already done it. (31:08) And that’s why, that’s what tells me, and not even just big enterprise, I see some very innovative small and medium-sized businesses that have already kind of rolled out, you know, their own private custom AI platforms, and they’re seeing massive gains. (31:26) I think, you know, two years from now, in a job interview, a candidate might say, well, tell me about your AI platform.

(31:37) Like, you know, how does it, how does it work? (31:40) What is it connected with? (31:41) Are people, do people use it a lot?

(31:43) Is it something that I would be expected to use as part of this job? (31:47) Like, I think it’s going to become that common that we kind of assume that everybody has one. (31:54) And if that hiring manager says, well, we just use Microsoft Copilot, I’m out, man.

(32:02) That’s not enough. (32:03) That’s not good enough. (32:04) And so I think it’s going to become ubiquitous.

(32:07) It’s that important.

Melissa

(32:09) Well, I love the Copilot example. (32:11) An organization I worked for, they, everybody got Copilot license, but five people were using it, right? (32:18) And nobody thought to ask who’s using it, who’s not using it.

(32:22) And so, you know, we laugh, but these are true stories. (32:27) These are true, true stories that are happening. (32:29) And, and so what mindset shift, kind of in closing, just to get, as we look to the future, what mindset shifts must executives and business owner make right now to stay ahead?

Hunter

(32:44) So using this Copilot thing as an example, I’ve seen this at a number of organizations. (32:51) Well, we gave everybody a licensed Copilot, but they hate it. (32:55) Nobody uses it.

(32:56) It doesn’t really get the job done. (33:00) And so the mind shift to me is you have to stop thinking about this as an expense and start thinking about it as an investment, right? (33:11) It’s not just, okay, well, we’ll just buy everybody a $20 a month license to this thing, but rather we’re investing in the future of this company by building out something that people will use, people will like to use, and we’ll supercharge our business and drive revenue or cut costs or improve the employee wellbeing, right?

(33:38) That is how we need to be thinking about it. (33:41) And I’ll add another one to it, which echoes what I said before. (33:47) Too many people are still in experimentation proof of concept mode.

(33:52) That’s canceled. (33:54) We’ve done that already. (33:55) That was last year and the year before.

(33:57) It’s now time to deploy into production across your organization. (34:02) That’s where we’re at on this adoption cycle. (34:05) So giddy up.

(34:07) Let’s make moves here.

Melissa

(34:10) Yeah, no, I agree. (34:12) It’s funny when you were opening talking about the law firm, I laughed because so many organizations, legal, finance, CPAs, absolutely not, never will we ever be using AI. (34:27) And I’ve bit my tongue several times in, you can lead a horse to water, but they got to really drink for themselves.

(34:40) And so as we close, anything that we didn’t touch on that you want to leave with our listeners before we close up?

Hunter

(34:48) I think we hit on it all, but I’ll just say it again. (34:53) Maybe this one for the naysayers, you got to lean in, please. (35:00) It’s for your own good.

(35:02) This is a big deal. (35:03) It’s really important. (35:05) And you need to lean in to this new technology or you’re going to get left behind.

(35:11) But there’s still time. (35:13) So make moves. (35:15) Yeah.

Melissa

(35:15) Well said Hunter. (35:17) Thank you so much for being here and sharing your knowledge with our listeners. (35:23) Please connect with Hunter and learn more about the good work he’s doing and check out Compass.

(35:29) That’s the Executive Connect podcast.