Welcome And Guest Background

Tom DuFore 0:01

Welcome to the Multiply Your Success podcast, where each week we help growth-minded entrepreneurs and franchise leaders take the next step in their expansion journey. I'm your host, Tom Dufour, CEO of Big Sky Franchise Team. And as we open today, I'm wondering how you're using and implementing AI internally at your organization and how you're using it externally with your customers and others. Well, our guest today is Dr. Clark Cully, who shares with us some of his AI best practices and ethical considerations. Now, Dr. Cully is the technology division chair at Indiana Wesleyan University, overseeing their programs on artificial intelligence and other digital technologies. He recently co-founded the University Center of Applied Ethics and AI. Previously, Dr. Cully was the chief data officer for the Department of Defense as well as the military's top official for AI strategy. In addition to 14 years at the Pentagon leading nuclear, cyber innovation, and emerging technology initiatives, Dr. Cully served with the U.S. Senate Appropriations Committee, NATO headquarters in Afghanistan, the Department of Energy, and the National Academy of Sciences. Dr. Cully holds a PhD in high energy physics from the University of Michigan and is a graduate of Calvin University. You are going to love this interview. Clark is a phenomenal wealth of information and application. You're going to love this interview. I've had the great fortune of being able to spend time together with him and just listening and learning from him. So let's go ahead and jump right into it.

Clark Cully 1:38

Clark Cully.

Tom DuFore 1:50

Excellent. Well, we had a great opportunity to spend a week together studying abroad in Oxford. And while there, some of these topics came up. And so I'm excited to have you on the show to talk about this intersection of technology, AI, ethics, leadership, business, all of these things that it's all kind of converging. And I think what you're doing, you're at the center of all of these things coming together. So maybe get us started with a conversation about AI, some of its misconceptions, maybe with business, and just a general starting point for us. And we'll dig into it from there.

AI Hype Versus Real Risks

Clark Cully 2:31

Sure. I think a lot of the conversation around artificial intelligence is driven by the extreme forms of speculation about what might happen in the future over the longer term. And that distracts from needed conversation about what AI is actually enabling today and the kinds of moral and ethical decisions leaders need to make now. So while science fiction scenarios of the Terminator or Skynet or other doomsday scenarios might be what's in the media, there are a lot of more prosaic risks that business leaders should be considering. One of the most important concepts, we have to apply it at individual level, organizational level, and uh as a societal level, is looking at what AI applications and uses are actually like fit for purpose. When is using AI, which in many ways you could think of as a big crowdsourcing tool? Like when is the nature of the problem a good fit for that tool? And when is it not a good fit? And the most important skill is really distinguishing what are the kinds of problems that are well solved by the patterns that are in the data that this AI has been trained on? And when is it just guessing, just like any other clever person might be able to guess, but not really providing insight and being able to distinguish those two cases, kind of a structured match to an existing pattern or something that is speculation or eventually hallucination, really what's what's going on? And the more we can tell what makes a use case a good fit for AI, the better we can avoid a lot of the harms or prapfalls that leaders might make if they say, hey, AI helped me with hairy audacious goal X. It should clearly do well on hairy audacious goal y. And that's actually not really in its domain of competence. So getting to know the tool and where it's competent is an important part of using it wisely.

Tom DuFore 4:33

Very well said. And that's very helpful. And I'm sure the the follow-up question is only natural. Is okay, so how do we help figure this out? Are there things that leaders might be able to start doing or thinking about to help assess, as you described, these use cases, or for that business owner or that leader out there, just how they're using this in some of the day-to-day, how do they make sense of that?

Fit For Purpose And Data Hygiene

Clark Cully 4:58

Sure. Well, I would say one of the kind of misperceptions is that AI is magic and it's really just lots and lots of data. And so an important part of having a healthy organization, having a healthy climate ready to use the benefits of AI really starts with organizing and managing your data well, ensuring you have high-quality data for the things that you care about. Because you can't really run an agentic automation on something that's not well designed and structured. And the use of AI in some sense is a pinnacle on a foundation that you've built of healthy process flows that are that are digitized and managed, keeping and labeling data appropriately, ensuring that data is really of decision quality for the kinds of choices that you want to make as a leader using that information. And then having the modern IT infrastructure necessary to use these tools to their full capability. And then AI is kind of like the pinnacle of that. You can't sort of leave everything disorganized and cluttered and hire AI to basically rearrange your messy garage and clean things out for you. Really, the more you have done the work to have data hygiene in place, then the better prepared you are to really extract the exquisite and subtle signals in your business operations that will provide you a leg of compared to your competition.

Ethics That Customers Can Feel

Tom DuFore 6:26

One of the things you mentioned earlier is responsible AI use or kind of this ethical component to AI use. I mean, are there ethical ramifications we ought to be considering? I know I've seen this uh come up. I'm not super active on social media, but I see sometimes folks saying, hey, do we have to disclose that AI wrote this post or created this image or graphics and so on? So I'd love to just kind of jump in a little bit there.

Clark Cully 6:53

In many ways, some of the norms are still being developed. So this is kind of an emerging frontier capability, and different organizations are running with different levels of risk tolerance and perhaps aggressiveness in the integration of these tools. And even in the um, you know, the regulatory environment in terms of like intellectual property, you know, the courts are still kind of hashing through some of these implications. So there aren't easy answers. One of the things I would say from an ethical perspective is doing AI well often feels right when you're on the receiving end. And doing AI poorly kind of feels manipulative or transactional when you're on the receiving end. And so I think it's really important to consider the human and customer perspective or the employee perspective when you're deploying these tools, because it's not a it's not a yes-no. I mean, what one of the other ways people kind of get distracted is by it's good or it's bad, this kind of polarizing, bifurcated black-white response to every new thing. The how matters deeply. And so if you're gonna use, say, AI automations for customer service, there's one way where you just say, I'm gonna automate the whole thing because it's easy and saves money. I'm going to assume that AI can do most of what a person can do and that the difference is marginal. And then I'm gonna push that out at large scale and see how it goes. And you're kind of experimenting with your customers and seeing if if there are limitations, the the times that the AI doesn't get the right answer or can't really help with the complex problem. You know, how does that feel to be on the receiving end? And one of the things that AI makes possible is a lot of choices or optionality that provide curated and customized experiences. But instead, if you use AI as a giant funnel and you push everything into this big funnel and you make it the one size fits all solution, it actually makes things feel worse. So an example might be that if you give individuals the choice of whether they would prefer to start with a human or start with an automation, we we see often cases generational differences in what people's preferences are. And so using AI to add a new choice rather than remove choice or remove optionality, it can feel freeing and liberating. And similarly, when you deploy capabilities as tools to help employees be more productive, are you doing it in a way that creates new choices or are you creating new constraints? And I think kind of leaning into choice and empowerment and flexibility and trust is a better approach. And it both shows your regard for the person, but also your willingness to let them use their expertise and creativity to figure out what right looks like. And so I really think it's important that we trust people to have the courage to try new things and to be able to make appropriate mistakes that occur during learning. And we we do it in ways that take the feedback from what's working well and then adapt and scale it. I think this crawl, walk, run approach or innovate before we scale is better than just saying, hey, I'm going to push the big magic, I believe that automation can solve all the problems button. And then, you know, we've seen a number of big companies have to hire people back because they kind of rushed too quick for an easy answer. You kind of have to balance the desire for this AI easy button and kind of the messiness of developing new tools and processes in ways that protect your brand, protect quality, protect the relationship that the customer feels with your organization, and doesn't just treat them in a way that might come across as transactional or manipulative. It's like good advertising. Good advertising makes someone feel known. Bad advertising makes someone feel manipulated. And we want to use the you know, this really big hammer of AI in ways that feel empowering and not belittling. And really, it does take a good appreciation of the customer experience and the employee experience to do that with like authenticity and care and respect.

From Efficiency To New Business Models

Tom DuFore 11:24

That kind of leads to how you see businesses using AI today and in the near future. You've talked a little bit about this, and you said it very well in the beginning about some of the uh long-term thinking of, you know, robots taking over the world from pick your favorite sci-fi adventure or movie. How do you see even small businesses using AI today and into the near future?

Clark Cully 11:50

So most organizations by default tend to think of AI as helping me do what I currently do, but faster. And that's the move towards efficiency. And while that's an important first step, there are some additional steps. That includes not just refactoring a specific task, but refactoring kind of the entire workflow and then potentially in the longer run, reimagining your entire kind of business construct and how work gets coordinated and managed and these new orchestration levels that become possible. So if if you're just starting out, it efficiency is a great place to start. You know, AI is a way to take what used to be exquisite information and make it a more generally available commodity. So, you know, in its sense of intelligence and artificial intelligence, it's really making readily available lots of things that you would like to know. And the more you have clear, well, I mentioned quality data, but also clear metrics and stats and quantitative information about how your organization is doing, the more you can turn AI loose to understand kind of the subtle patterns and correlations and potential interventions you can make. Because we're all on this journey from first, let's let's just be able to describe what's going on in the organization and in the business environment. So description. Then we want to find the patterns that are that are in that information. So that's uh prediction. So being able to have a forecast. And you know, business intelligence is like a great place to use AI to collate more sources of information that you you don't have time to manage manually and say, hey, can you help me better understand shifts in the marketplace and see how things are trending and what what I can do? And so that's the third step is this moving from description to prediction to then having a prescription, having an intervention that you can make. Because I see this coming in the forecast, and I have a model for how my business operates, I know that I can make an intervention in time to better position myself for what's coming next. And so every business leader should be excited about the possibility of AI helping filter through all of the different relevant information, find the trends that might be important for their key business outcomes, and using AI tools to find that signal and make it readily available, because a lot of time the information's there. It's just not at one's fingertips. And in some sense, you know, AI is like a super search engine where it can help you collect and collate and curate information very, very quickly. But to so my other point about these later stages, don't stop there. In the long run, these tools can help you not just do your traditional process faster, it can also help you re-envision or restack that entire process. And again, it's it's often taking what used to be particular silos of exquisite expert information, and it's it's diffusing accessibility to that information across the workforce. So, you know, in previous rounds of automation with the computer, for example, a copy editing and typing tool used to be part of an organization's job, but that was a specialist position. But then, you know, uh word processors came along and then um office productivity tools on the computer. And that took what used to be a specialist skill and made it a readily available skill. So you now typeset in some sense your own Word documents. That used to be a specialist job. So there are other kinds of areas of specialized marketing knowledge, legal knowledge. I mentioned business intelligence, financial expertise, where AI can't replace all of those roles or functions, nor should it. But it can certainly be a down payment in having a more generalized literacy about the basics of those things available to anyone who can just ask a question in plain language. So again, kind of democratizing access to what used to be very specialized information. And being able to do that quickly and kind of asynchronously, you don't have to wait for your meeting with your chief legal officer to like start thinking about legal implications for a particular move or something like that. You can you can seed the ground or grease the skids kind of in in advance. And so that that again can help accelerate and streamline your operations. And then in the longer run, like AI will absolutely provide some disruptive levels of innovation that create new entrants into the market. And it really just takes a lot of imagination for how could the knowledge and expertise that makes this business operate be distributed in a different way? How could AI coordinate work that used to take all of us being together in one building and having meetings around an oak table? How could it make make it asynchronous, distributed, globalized? And I expect that that kind of disaggregation of work into new new units is going to be of interest to those in the franchising world.

AI Across Franchise Systems

Tom DuFore 17:13

With franchise systems, you have locations and different franchisees running separate locations regionally, nationally, maybe internationally. And how do you sort through this AI use with a local owner at a local level? Plus, you have the franchise or at the national or international level and trying to make that all work. So that's really interesting what you just described there.

Clark Cully 17:34

Yeah, the the hope is that we'll learn new ways of working. I mean, a lot of organizations are structured around how information flows and how trust is delegated. And as this tool allows people in some sense to operate higher up the value chain or to think at a um uh a higher level of sophistication, I think that will disrupt our kind of organizational constructs. And and you know, I always remind people that like organizations and policies are technologies. They are part of the ways in which we organize our environments in order to get things done. And therefore, to use AI to its fullest capability, you don't just say, where can I plug AI into my business? You say, how does the technology layer and the process layer and the people layer, how do they all flex in a coordinated manner to enable new things? And so one of the other reminders for many leaders is do not look at this as a technology problem. Look at this as a culture problem, as an organizational development problem, as a broader kind of innovation paradigm. And that really requires that people, processes, and tools move in orchestration, not in an individual silo.

Cognitive Surrender And Human Oversight

Tom DuFore 18:55

You know, it reminds me, as you're describing that, of an example that came up recently with one of our team members. There was a client he was working with, and the client had produced uh, you know, 100, 200 pages worth of content that he had sent over for review by our team member. And so he schedules an appointment to review it with the client, and he's going through it. And it was obvious that he had used some AI to produce this. And so he's going through all of these pages and asking him, okay, well, you produce this. What does this mean? What do you mean here? And eventually, partway through the conversation, the client says back, well, actually, I don't even know what this acronym means. I don't know what this means. I don't know what that means. You know, I didn't make this. And so all of a sudden, so it was like this pretty looking thing, but at the end, it really wasn't useful because he didn't even really understand what he was doing anyway. So it makes me think about what you're sharing and saying, well, is that really valuable or useful to the client that produced that? Probably not.

Clark Cully 19:59

Well, there's there's a related term often comes up in the education context of cognitive surrender, where you use AI to make things so complex that you don't really provide appropriate oversight and you don't feel adequate to necessarily provide oversight. So you can use AI to write hundreds of pages of text and then feel that you're accountable for that work product, but you don't necessarily have the bandwidth or the capacity to really carefully vet that text. And that that also is the case where AI gives a very eloquent and compelling sounding argument for whatever you've you've asked it to do. And you're not really able to critique it strategically because there's just too much detail, too many words, too many aggregated pieces of information that make maybe you're not familiar with. And so that's why just backing up and saying, like, am I trying to just own the outcome, or am I trying to really own the broader process and the competence that it requires to create that kind of outcome? Now, you know, in in some fields like software development, there are some companies that aspire to create code that no human has to look at. So they will ship code that is not touched with direct human oversight as you know, part of their competitive strategy. I would claim that most virtuous business leaders would want to reinvest in the human capital required to sustain their organization and its competence going forward in a way that's assisted by technology. But in some sense, if if AI can generate the entire work product and you're not trying to touch it, then like what do I need you for? Like, why why don't I just mediate with AI directly and just ask it the same prompt you ask? Like, you know, why do I need your corporation to um be an intermediary in this thing? This process. And that's what we don't want to do is uh kind of externalize our own value add by just surrendering analysis entirely to AI. Now, what that looks like is something that you kind of have to feel your way through. You have to kind of work through complex tasks in modest chunks and figure out like what is the right amount of touch point, what is the right amount of vetting, what is the right amount of subject matter input into this process to feel that like I authentically own the work product and I have put my own or our organization's competence into this work product. It's there's not an easy answer. It's not just just do this, but that's that's why I think your corporate values matter, because what right looks like is going to depend on what you believe you owe yourself, you owe your employees, and you owe your customers. And the higher bar you set at those levels, then I think the more you'll still want to be involved in the integral work that it takes to be successful. And learning the to sharpen your competencies as a provider will be the seed corn that lets you harvest new ideas down the road. If we just get rid of all the entry-level jobs and use AI to do a lot of our thinking for us, we're going to maybe be faster in the sprint, but I think we're going to be slower in the marathon. And I think part of what we have to remind leaders amid all the AI hype is that building value and creating a company that lasts is a marathon. And yes, people will outpace you in the sprint, but but that's okay. That's to be expected. They're choosing to manage risk by front loading everything. And that that works great until there's a disruption. And then it's the companies that have a little more depth and resilience that are going to be able to endure a disruption or endure a pivot or a change in market conditions. And that includes relying on your human capital that you've continued to invest in. So I'm I'm still a big believer that people with machines are better than just trying to go faster with machines alone in in most of the kinds of service industries that we represent.

Tom DuFore 24:20

Very well said there, Clark. How can someone get in touch with you, learn more about what you're doing?

Clark Cully 24:26

You can reach me via email at Clark.cully, C-U-L-L-Y at IND WE S for Indiana Wesleyan dot edu. We're also creating a website and some additional resources for our Center for Applied Ethics and AI. So if you search for that term, you should be able to find our website and materials, podcasts, AI ethics frameworks, responsible AI best practices, et cetera, that we're compiling. Yeah, I'd love to talk to anybody who's who's interested in learning more about this topic. It really does take a network because we're all kind of experimenting in our own domains, trying to figure out what successful patterns can we share across businesses, across industry sectors. And we can't generate this knowledge in academia in a vacuum. We have to really be good listeners and figure out how to collate what works best and to make that part of a discussion. So one of the things we want to do is continue to facilitate discussions between industry groups, academic thought leaders, organizational and community organizers who are all trying to, you know, navigate this AI era with wisdom and prudence and thoughtfulness. And certainly we we enjoy hearing what's working well and what questions people have. So I'd be happy to reach out to anybody.

Leadership Lessons And Personal Success

Tom DuFore 25:50

Well, Clark, this is a great time of the show. We make a transition and we ask every guest the same four questions before they go. And the first question we ask is have you had a miss or two on your journey and something you learned from it?

Clark Cully 26:02

I'm a parent, so I I I always can can think of things in uh parenting or or child rearing that that I would have loved to have done better. But I've shifted industry careers for a few times. And and you I originally started out as a physicist doing basic research. I spent most of my career at the Pentagon doing technology policy and leading as a federal employee. And this is kind of my my third season in academia. And each time you strike out somewhere new, and and all of those required moving to new locations. So moving from Chicago to DC and from DC here to Indiana, you you feel the loss of the things that you say no to. And one of the things I've had to remind myself, a lot of my military and government work has been around strategy development is strategy is not about strategy like life is not about all the things that you desire to say yes to. It's about the willingness to say no to certain things, to close certain doors, to reject certain possibilities. And that always kind of feels like you leave a little part of your heart or your imagination behind. So some of my even even some of my dreams at night still pertain to like my life and perspective in government service and working with the military. But it's also kind of a leap of faith and a leap of courage to do the new thing in a new place and kind of reinvent oneself in a new context. So yeah, for me, I just say like leaving federal service was was difficult. I s I still think about the blessings of that season, and I miss the friends and context and career opportunities that came with kind of being at the heart of big government decision making, including, you know, important ethical issues in government's consideration of AI and its use in the military.

Tom DuFore 27:57

Let's look on the other side. Let's talk about a make or two, some highlights.

Clark Cully 28:02

I think the career skill that I have been blessed with that has served me in so many different realms. So I've mentioned these different pivots and how to do that. Well, is most of the work that I've ever done has been driven by the willingness and curiosity to learn a new thing and try to figure out what's what's relevant, what's kind of the key turning point, what's kind of the crux of the issue, and then filter that into a recommendation for a leader to take action. And that's often involved building coalitions. So on the one hand, you know, my my misses have sometimes come from I'm not all the way a scientist, I'm not all the way kind of a corporate leader. I'm kind of in between the technologists and in between the executives. And that means that I don't fit fully well in either of those spaces, but that means I do incredibly well at translating between the two. And so much of good strategy, especially in the space of technology where I work, is about envisioning the art of possibility from the technologists and translating it into the pragmatic implications and, you know, so what? Why should I care? What what's this going to make possible that executives care about? And so I have been able to be a bridge builder and I've really enjoyed a long career kind of as an emerging technology strategist, in part because I'm I'm a bit of uh afoot in both both worlds between the scientist and technologist side and the kind of corporate side. And so I've enjoyed that and you know that that translation that that helping one's constituency get excited about the possibility and translate it into terms that resonate with other constituencies. That's also what makes me love education. I mean, education is the same, the same kind of distinction of trying to walk a mile or at least half a mile in audience's shoes so that you can take the idea that you're excited about, but translate it into terms that your audience was excited about. And so I really enjoy AI because in a in a topic that there's a lot of confusion, quite a bit of pessimism and uncertainty, helping people have clarity, have confidence, have courage, like that that excites me. And so this has been a fruitful time and a fruitful season, and I've enjoyed uh you know thousands of students and and many different public and intellectual professionals in this field that I get to engage with and and ideally help inspire.

Tom DuFore 30:46

The name of the show is multiply your success. Have you used a multiplier to grow yourself personally, professionally, or organizations you've run?

Clark Cully 30:54

Sure. I'll I'll offer two. AI drives everybody to kind of quantitative, very left-brained ways of seeing the world. And if we let, you know, intelligence be a proxy for all the human things that we care about, we're we're going to be missing something. And so two tools that I have found helpful. One is design thinking or human-centered design. That tool set really involves a lot of like embodied practices where you're using sticky notes and you're interacting with people and you're visualizing things and you're taking a lot of kind of implicit analytical thinking and you're getting into a little more of a an emotive and expressive mode of viewing things. That kind of perspective is really helpful in rounding out what might be some of the excesses of thinking a little too mathematically in kind of the AI age. And similarly, the use of strength finders as a tool to better understand your tendencies in a work environment and the different ways that you can better communicate and connect with those who might have different strengths and abilities. And I've used that to really help me understand when what might seem at first like a disagreement is actually kind of a reframing of like what we mean by good. You know, I've I've had coworkers that when we say, like, oh, what's the right thing to do? You know, person A is thinking about, well, what's the cost-effective thing to do? And per person B might be thinking about the most accurate answer, and person C might be thinking about what's the most compassionate answer. And strength finders is another tool like design thinking to help take some of that analysis and make it explicit, make it interactive, and really bring some of our kind of internal thinking out into the world where we can talk about it and make sure that uh we we have kind of a shared vision for what right looks like. So both of those have really been good multipliers for me.

Tom DuFore 32:55

And the final question, Clark, we ask every guest is what does success mean to you?

Clark Cully 33:00

I I'm gonna think back to Gab. Success for me is always going to be attention for kind of whole person balance. So working in my profession, working as a as a husband and father, working in my my church and as a community leader and kind of other extracurricular things. If you don't feel some pressure across those different dimensions, you're not really probably balancing things because you're always wanting to put your whole heart into everything, but you're also wanting to understand your limits. And, you know, you can you can flex as you you go through different seasons, but there's always a healthy tension. And I think, you know, life is always easier if you can just throw yourself fully into one thing with with with no constraints and and be kind of unbounded. And I have just found that part of understanding my own makeup is success never feels like it in the moment. It's something that you can kind of evaluate only in hindsight when when you look at what has been the fruit of trying to maintain a healthy balance when maybe in the moment it still felt like a lot of tension or competing commitments or or other kind of sacrifices that you might had to make for personal commitments or or other kinds of things. And so I try to keep that big, big picture in mind and try to be patient and looking for success kind of in hindsight and again by like what has been the fruit of the season rather than how did I feel in the moment. And Gallup, you know, reminded me that the highest question that correlated with employee satisfaction from their various polling surveys was like, did I feel that I contributed something unique and that if I didn't show up for work, like I would be missed? And part of part of my being at peace with kind of an eclectic and changing career has been the ability to do something new and different and to be comfortable kind of being being active in some of the gaps that maybe aren't well known, are new and kind of untested, maybe expectations are unclear. And I've I've really been blessed to feel that I have been able to step into certain gaps that have been left open and make a unique contribution, again, kind of using my diverse perspective of different audiences, where I'm not all the way in kind of one traditional domain, but but bridging across domains and helping people across that bridge or helping their kind of conception and understanding across that bridge. So I've I've really felt that by doing something that's unique and aligned to my gifting, that I've been able to serve a meaningful purpose and that that's my definition of success.

Tom DuFore 35:50

Wonderful. Well, Clark, as we bring this to a close, is there anything you're hoping to share or get across that you haven't had a chance to yet?

Clark Cully 35:57

I would just say if I were to leave leaders with like one idea to chew on and to maybe like take as homework for their organization in the context of getting used to AI, it's how do you use AI in ways that still feel authentic? And I kind of talked through that customer service example, but there are a lot of other ways where AI done in the right balance feels like it's still caring and compassionate and empowering, that it still respects the dignity of employees and and customers. And that's that's kind of a tenuous balance. Too much AI and it starts to feel you know coercive, too little AI, maybe it feels maybe too too slow or or kind of a legacy paradigm. And so really trying to understand how to get the integration of new ideas and information that AI makes possible, but putting it in a form factor that still feels considered and caring. And again, you know, giving people the option to help personalize things in a way that makes sense for them might might be a good balance. I mean, uh, I would love to see AI not drive people towards monolithic solutions, but to say that each employee and each customer has a toolkit that they can find the right balance for what's best for them and that they can interact with their employer or with the company or with the product or service in a way that meets them at their point of need. And I would just encourage everybody to like figure out what that what that looks like and share those insights with me. I'd love to hear more.

Key Takeaways And Closing

Tom DuFore 37:41

Clark, thank you so much for a fantastic interview. And let's go ahead and jump into today's three key takeaways. So, takeaway number one is when he talked about at the very early part of the conversation, and he said that there's an extreme discussion and force going on about what might happen in the very long term with AI. I like how he said it distracts away from what AI is enabling today and the moral and ethical decisions that we need to make today. I thought that was a great point. So let's stop talking about doomsday and terminator type scenarios and focus in on what's the now. And takeaway number two is when he talked about AI and ethics. And this was a quote from what he said. I found this phenomenal. He said, Doing AI well often feels right when you are on the receiving end, and doing AI poorly feels manipulative when you're on the receiving end. So I thought that's a great, great way to think of this. And he said another quote in you apply this in another setting. He said, good advertising makes someone feel known, and bad advertising makes someone feel manipulated, and essentially applying that same concept to AI. Takeaway number three is when he said, do not look at AI as a technology problem, view it as a cultural problem. So not a technology problem. AI is a cultural problem, and view it through that lens. And now it's time for today's win-win. So today's win-win comes from when Dr. Collie was talking about how you use AI in ways that still feel authentic. And he said, How does it still feel like you are respecting your team or respecting your customers? Is it showing them respect or disrespect? And it gets back to this idea are you giving people options to personalize or make it their own, or are you taking away options? Again, this gets back to that quote that he said of doing AI well often feels right when you are on the receiving end. And doing AI poorly feels manipulative when you are on the receiving end. So I think that's great perspective for all of us as leaders to keep in mind as this AI adventure that we are all on continues down its path, and we are all trying to sort it out and make sense of it in the best way possible. And so that's the episode today, folks. Please make sure you subscribe to the podcast and give us a review. And remember, if you or anyone you know might be ready to franchise their business or take their franchise company to the next level, please connect with us at BigSkyFranchise Team.com where you can schedule your free, no obligation consultation. Thanks for tuning in, and we look forward to having you back next week.