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Welcome back to Brain Chatter, where we listen past the daily noise and explore topics at the intersection of leadership, workplace culture, profit, and sustainability.
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I'm your host, Derek Brown.
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Today's topic is generative AI and workplace culture.
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The right choices provide an edge.
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We've seen a barrage of coverage and speculation in the media, traditional media and social media, about what impacts AI, or artificial intelligence, is supposed to have on business in America and worldwide.
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There has been so much coverage, interest, hype, and speculation, in fact, that it is difficult to comprehend what is realistic, legitimate, or even most important about AI.
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In our recent Brain Chatter podcast episode, we explored the basics of AI with Randy Sparkman.
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In this episode, we delve into a major dimension of AI.
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Today we have a national corporate expert to talk us through a critical piece of this, and that is how will it transform the work of both company employees and company leaders, and how will it impact company cultures?
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Particularly how company leaders should be making AI decisions that give them a competitive edge.
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This expert has been analyzing this very question and crafting strategy advice and applications around it for one of the cornerstone giants of corporate America, a very large consulting firm with over 75,000 employees.
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His name is Michael Mendola, and I am certain you are going to find what he has to say in this interview fascinating, practical, and enlightening.
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Michael, thank you for your valuable time and welcome to Brainchatter.
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Yeah, thank you so much for having me.
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It's great to be here.
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Well, let's start with a fundamental question.
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Will you clearly define artificial intelligence, specifically generative AI?
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Yeah, so artificial intelligence has been around for quite some time.
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Everyone uses some form of artificial intelligence in their daily lives.
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You're used to using it when Spotify makes recommendations for the next song to play, or your Apple Maps is trying to figure out what's the best route to take.
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Artificial intelligence is not new.
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It's really a way for computers to perform tasks that typically require human intelligence.
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Generative AI is a relatively newer subset of artificial intelligence that's really laser focused on creating net new content or data.
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It can generate text, it can generate images, it can create music, it can create any other type of content that you might not be thinking about off the top of your head.
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It can create using natural language.
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And so generative AI is really just that deep knowledge subset of the broader artificial intelligence umbrella.
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Will you talk a little bit about the intersection of this generative AI and the workplace over the coming months and years ahead?
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There's a lot that's coming with generative AI.
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And uh, you know, typically artificial intelligence has required computer software programmers and engineers who truly understand how to build these models.
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Um that's the that's the root of it.
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And artificial intelligence used to mean that you would train a model to do a specific task in a specific way.
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Generative AI is now enabling access to artificial intelligence in a way that's never really been seen before.
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So now people can in the workforce use generative AI right at their fingertips with natural language to create the things that they need to do.
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ChatGPT was released, which really was the first evolution of generative AI that we're seeing now.
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And right when it got released, I started playing with it to write my emails, um, draft agendas for me, use it as a thought partner, which is a concept I'll I'll elaborate on over the course of this interview.
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But generative AI in the workforce is transforming the way that people get work done.
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It's not replacing jobs, it's really enabling people to work differently, to work more efficiently, and to actually create a lot of value that wouldn't have been there otherwise.
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You've built out a knowledge base, a very large knowledge base that your consulting firm is sharing online through an online blog for clients, for prospective clients, and for anyone to access.
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This really puts you at the forefront of expertise sharing on this topic.
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And I'll link to the blog in the show notes so that anyone can go directly to all of this knowledge you've put together.
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Allow me to pull a quote from one of your posts.
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Quote, technological advances, surging investments, and the competition for talent are all working toward one thing.
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In 2024, artificial intelligence, AI, will start to fundamentally change how business gets done.
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It will impact how companies grow revenue, conduct everyday operations, engage customers and employees, build new business models, and more.
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End of quote.
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So, Michael, will you flesh out your point about how it ties into that third point, how artificial intelligence prioritizes the major issue of competition for talent in the modern workplace?
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Yeah, that's a that's an important point to hit on.
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It's one that's very close to what I'm getting to do day to day.
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Um I'm I'm getting to drive how we are upskilling 75,000 people at our workforce to understand how to use generative AI, how to become savvy and responsible users of generative AI.
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Um, and responsible is important too.
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But talent and people are desiring skills that they can use every day.
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They want to reimagine.
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People want to reimagine the way of the way that they work.
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They don't want to necessarily do things the way that they have done it the last year.
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People are looking for efficiencies, people are looking to use their brains and use the skills because technology is only going to evolve even more and more.
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And so using these tools, getting access to generative AI, learning about what is generative AI at its core, and also understanding what are the responsible practices to use when you're activating these technologies is critical.
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Um, what we've seen is that employee engagement has gone up.
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People are clamoring for more training, more education.
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And now that we've we've built a baseline at the company that I work for, a baseline understanding of what is generative AI, how do you use it responsibly?
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We're now giving all of our people access to our tools that they can use in their everyday work.
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People are looking for more opportunities to actually get more upskilling involvement.
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They're looking for other ways to engage, they're looking for how you can use generative AI to build imagery, how can you use it to write music beyond just the day-to-day tasks?
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People really believe that generative AI is a tool that they can use and actually take further than they originally thought.
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And I think once people open that door, it's hard to shut it.
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People are naturally curious.
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They want to know how they can use these tools to better their work life, but also they can use it in their personal life too, in a lot of different ways.
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And so to invest in the workforce, um, to educate people on how to use these tools, again, in a responsible manner, is so critical.
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I was recently speaking with a group of college students at my alma mater, and I asked the teacher, I said, what do you, what do you, what is your stance in using generative AI in the classroom?
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And uh he was one of the proponents of using it because why limit people's access to a tool that they could access outside of the classroom in a real work-life scenario?
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And so you really need to make sure that you're putting guardrails around people to ensure that they're using the tools the way that they're meant to be used and that they're verifying the outputs, but why restrict it?
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Um, they're going to get access to it one way or another.
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They're going to use these tools in their work, they're going to use it in the classroom.
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The goal is to really create the responsible use of these tools, making sure that they understand how to generate the outputs that they desire.
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That's what really is the most impactful thing that we can do from my experience in the workforce is education around these topics, not avoid, not avoiding them, because again, they're only going to continue to improve and grow.
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We've seen all the transformative change in just a year that's taken place.
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Um, it's only, you know, I can't even imagine a year from now, when we're listening back to this podcast, how much is going to be changed since then, too?
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Great and fascinating points.
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In one of your blog posts, you note that generative artificial intelligence will transform the work of leaders as much as employees.
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Will you expand on what you mean by this?
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Yeah, it's definitely interesting.
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A lot of times what we see is younger staff tend to gravitate towards new tools.
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And I think that that's just a natural evolution of society.
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People who are younger than me know about social media platforms and tools that I'm not aware of.
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And I probably know a lot about technology that my parents weren't aware of.
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And so it's a societal change and evolution that younger people entering the workforce will naturally gravitate towards wanting to use technology more frequently.
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Um, with generative AI, though, it's actually really burdensome to management to not understand the capabilities of generative AI.
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It only hurts them as managers and leaders and CEOs to not understand the impact that generative AI can have for their people and for their workforce.
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If you're not understanding how outputs are created, it's susceptible to risk and inaccuracy that you, as a manager, need to make sure your understanding is right and you're verifying it is indeed accurate and that the source data does make sense.
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If you're not understanding how this content is generated, but your younger staff members are creating it this way, um, there's this inherent risk.
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And so it is really important for leaders and managers at large and small organizations to know about the technology, know how it can be used, know how it should be used, and more importantly, know how to review the outputs for things like accuracy, for completeness, um, to make sure that they're not biased.
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That's what I think of when I envision leaders needing to understand.
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You don't need to know how to build a generative AI model, but you do need to make sure that you understand how to use it and how your people are using it, because the truth is, is they're likely going to get access to it one way or another.
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So it's best to be prepared.
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That makes sense.
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As the COVID pandemic ramped up and workplaces started really hitting fast forward on certain technological adoptions, such as using remote logins, Zoom, Microsoft Teams, et cetera.
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I personally know a number of people who had been nearing retirement who explained their decision to retire as basically, I'm not interested in learning all this new technology.
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I'm out.
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They represented jobs in the ones that I know in finance, heavy industry, and the federal government.
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I've even had a friend as recently as a few weeks ago say that their decision to take an early retirement buyout that will happen soon is a result of continued frustration at the continued clip of new technology adoption expected in what had been for very long a traditional role and predictable role.
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Do you think this fast-paced AI integration that is coming will have a similar impact even in a continued tightening of the overall labor pool?
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I don't think that we've seen technology quite like generative AI advance as quickly as it has.
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I think that people should be prepared and understand the fundamentals on how to use these tools because if you understand in theory how to prompt the technology, you're not just limiting yourself to understanding how to use a tool like ChatGPT, you're also enabling yourself to understand how to use more advanced tools like Microsoft Copilot that's embedded within Microsoft Slides and Microsoft Excel and Microsoft Word.
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If you understand how to use it responsibly, the core tenets of generative AI, and how to prompt the technology, those three key ingredients, which there's a little bit of a learning curve, but it's not that drastic because the interface is just using natural language.
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Just like you would instruct someone who is on your team to help you with a task, you'd want to give them a lot of detail.
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You'd want to be specific, provide examples of what you're looking for to make sure that they generate the output that you're desiring.
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It's no different in using generative AI.
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You really want to be thorough and precise and provide context to get the best output possible.
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And that's why I think that that skill gap, the the generations who are going into retirement, understanding those core tenants, not necessarily, again, understanding how to build a large language model, more how do you use it to get the most value?
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Um, isn't something that is all that steep of a learning curve.
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Once you get into it and get your hands on keyboard to practice a little bit, that skill gap can be closed relatively simply.
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Low uh threshold to get involved.
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And um there's so much content out there to learn so quickly.
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Um, a lot of great content out there.
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If anyone is thinking, man, I'm nearing the end of my journey, it's probably too late.
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I disagree.
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Um, I think it's something that you could use personally after you know you end up retiring and whatnot, too.
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This the skills and the thought partner that you can use generative AI for applies much more broadly outside of a work environment.
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Well, the knowledge base that you have put together is an incredible amount of information that is consumable by people who are kind of looking for where do I start?
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What does this mean?
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What is your motivation for that knowledge base shared beyond just people inside your company?
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You're you're being generous with it to share it with everybody.
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Yeah, that there's definitely the aspect of hoping that people are engaged and hopefully it clicks.
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Um, generative AI, the word can be a little bit intimidating, and you might not want to dive in.
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And I think a lot of the thought leadership that we're releasing is geared towards making it digestible, making it easy to understand and with use cases and start and a place to start.
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Um, there's so much information out there that you could start with, and sometimes that's a quite overwhelming.
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With the thought leadership that we're curating, it's really geared towards oh, okay, this is what generative AI can be used for.
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That's great.
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Okay, oh, here's where I start as well.
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Using those two things hopefully does enable curious curiosity and continued evolution of learning and trying to get more involved.
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And honestly, it does provide permission to use the tools as well.
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Um, you know, I'm never gonna recommend just using one of the generative AI tools.
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There's so many potential options that exist.
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But it it almost gives folks permission to try.
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Because again, you're never gonna you can learn all about prompting generative AI models all you want.
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But until you actually get hands-on experience doing it, that's when it really truly clicks.
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Um, it's like riding a bike.
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You can read about how to ride a bike, but until you actually hop on and you start pedaling, that's when it really kicks into high gear.
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So um, the thought leadership that we're creating, just like you'd mentioned, definitely is meant to spark curiosity, but it is meant to also say, hey, here's where you can start, go and try these tools out.
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That makes a lot of sense and it's very generous of you.
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One app that I use personally that leans heavily into AI is a free app from National Geographic called iNaturalist.
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I'll link to it in the show notes also.
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And as you probably know, if you've ever used it, it it's for identifying all sorts of wild things.
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Once you've uploaded a photo, whether it's of a turtle or an oak tree leaf, it recommends to you what it thinks it is, but then it lets the community of users of the app come in and verify whether or not what you and artificial intelligence identified is accurate or not.
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How do you see the future of this human backup to artificial intelligence for various applications in the future?
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Do you think that will also always be a component, some sort of human verification, or will a knowledge base grow so much that eventually that component will not be needed as substantially?
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I am a believer that there's always going to be need, like a need for human intervention.
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I don't think that these tools are meant to be used without verification in some capacity.
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The truth is, is it's guessing what response to generate.
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It's just providing the best guess of the next word or the next content, the next image based off of the millions and millions and millions of data points that these large language models are trained off of.
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And so I'm of the thought that you should always trust but verify.
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Trust that the output is correct, but you need to verify it.
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Um human, humans still play a really critical role in the evolution of generative AI and AI in general.
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Um, we're creating the models, we're training these using training data.
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You want to make sure that these models, you know, that's where bias could come in if you're training it only using certain data points and excluding others.
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And so that's why I'm a big proponent of human intervention, especially in the review process as people use these tools.
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Um the rule, the rule of thumb is definitely trust but verify.
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Over the past year, as interest rates have spiked and we've witnessed other governmental policies and economic changes, we've seen the tech industry across the United States and beyond go through multiple layers of layoffs.
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The founder of one successful and growing tech startup that allows remote work from anywhere and which only has a few hundred employees recently told my Rotary Club that his tech company had over 70,000 job applications for only a handful of vacancies in 2023.
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Do you anticipate that the surge of workers with some level of tech background or interest will be helpful in the speed of AI adoption if they are recruited into non-tech companies, as those non-tech companies realize they maybe need AI integration?
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It's a great question.
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Um I had a leader of mine um explain the artificial intelligence boom like this.
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It's almost like math getting a calculator.
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Um it's like when the calculator was invented, you used it to speed uh to quickly do calculations and figure out the way to the formula and figure out the solution a lot quicker.
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The same could be said about generative AI.
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Um, if you have some baseline understanding of it, you definitely won't be replaced by the technology.
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It's really just a tool in your tool tool belt to help you, again, create new content or bounce new ideas off of the generative AI model to come to the best solution.
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Those that understand how to use the technology likely will see results in getting hired, maybe progressing throughout their careers.
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But those who don't embrace it and understand it and try to use it likely are at risk to not progress as quickly because you're only hurting yourself.
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Again, it's like doing math without the calculator sometimes.
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And even though it's definitely thorough and you should understand how to do the calculations, having that calculator is handy.
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And so I view it the same way when getting access to these generative AI tools.
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What advice would you give to a company leader, whether it's a small company or whether it's a very large company like the one you work with?
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We do expect for you to actively take on figuring out how to apply it to our company in a way that makes us more competitive.
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Yeah, I think it starts with education.
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It starts with opportunities for upskilling.
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And as a leader, I would encourage all leaders to take that initiative to invest in your people.
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People will find use cases and ways to apply the technology into their day-to-day jobs.
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Break down that barrier by again providing your people with dedicated time and dedicated training content that they can take and then use the technology in ways that they didn't think about before.
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Rather than prescribing them, you should use generative AI to do this, this, and this?
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Sure, that might be a good starting point, but I think it's better to paint a broader stroke, build the baseline understanding, and build savvy and responsible users and enable them to find ways to fill the gaps in their day-to-day work using generative AI tools.
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Let's turn our attention specifically to workplace cultures.
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What impact do you anticipate generative AI will have on workplace cultures?
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That's an interesting question, too.
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Again, I I don't know that folks understand quite quite as much.
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Now that I've had a lot of experience with it, the way that you can use generative AI as a thought partner.
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Um, for example.
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When I was preparing for this interview and this podcast, I use generative AI to figure out what are potential responses to some of the questions that I'm being asked.
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Like, what's another what's another consideration that I'm not thinking about?
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And the model was able to provide me with different points of view that would also be important to communicate to.
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And so I think that workplace culture also could benefit from having a thought partner in your back pocket.
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I think the culture of a company actually will only grow when more efficiency is being created in day-to-day work.
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You're no longer having to spend, you know, that extra 15 minutes trying to figure out how you want to write this email, how to make it more formal.
00:22:43.279 --> 00:22:49.119
You're not spending, you know, an extra two hours creating a draft of all of your PowerPoint slides.
00:22:49.359 --> 00:22:57.599
You're no longer having to write out, you know, take the meeting notes and transcribe them into a follow-up note that you could share with your team.
00:22:57.839 --> 00:23:06.799
Those are things that only will improve the workplace culture because now teams are able to focus more on the beneficial points of their day-to-day role.
00:23:06.960 --> 00:23:10.400
They're able to be more thought-provoking, less task-oriented.
00:23:10.559 --> 00:23:23.920
And I think that's where generative AI is going to play a huge boost in workplace culture, in creating new ideas, creating new points of view that weren't considered before that could be brought into the workforce as well.
00:23:24.079 --> 00:23:33.039
And it's just going to create a better overall experience for people and also create better outcomes for clients and customers.
00:23:33.839 --> 00:23:38.160
We've mentioned a couple of applications that use artificial intelligence.
00:23:38.319 --> 00:23:51.200
When I talked about iNaturalist, when you talked about Chat GPT, are there any other specific applications that you have found particularly interesting or helpful or fascinating that you've been using most?
00:23:51.839 --> 00:23:54.400
Yes, definitely a lot of them.
00:23:54.559 --> 00:23:58.880
And the ones that you mentioned definitely are great starting points, great places to go.
00:23:59.119 --> 00:24:07.200
Organizations like Microsoft and Google, what they're starting to do is integrate generative AI into the products that they have.
00:24:07.359 --> 00:24:12.240
And so one of the tools that I'm using now on a daily basis is called Microsoft Copilot.
00:24:12.559 --> 00:24:19.920
Microsoft Copilot is a generative AI tool built directly into each of the M365 applications.
00:24:20.160 --> 00:24:33.279
So when you're using Microsoft Word, PowerPoint, Excel, Microsoft Teams, and Microsoft Outlook for your emails, you're now able to use generative AI within each of those native applications.
00:24:33.440 --> 00:24:39.839
So if you need to create a new PowerPoint deck, you can use natural language using Microsoft Copilot.
00:24:40.000 --> 00:24:46.880
You can just type it in and ask it to generate three draft slides for your upcoming meeting on the benefits of generative AI.
00:24:47.119 --> 00:24:59.680
You can go to Microsoft Word and you can have it draft you talking points in a podcast style format that make it really easy to transcribe and it will generate all that net new content there.
00:24:59.839 --> 00:25:18.240
If you're analyzing your Excel spreadsheets and you want to identify and highlight variances that are above a certain threshold, you can simply use natural language now within Copilot in Microsoft Excel to highlight those records and find those variances for you rather than you having to do the formulas yourself.
00:25:18.880 --> 00:25:32.480
If you want to summarize that long email thread that you see, you know, the 16-thread email that you want to summarize, you can use Microsoft Copilot and Outlook to summarize that email thread and give you the key points.
00:25:33.119 --> 00:25:52.079
When you're using your meetings, you're in a Teams meeting, for example, you can have the transcription feature turned on and it will transcribe your meeting notes and output them for you in an easy-to-digest format that also has action items specifically laid out with the name of the responsible party who's assigned to that action item.
00:25:52.240 --> 00:25:56.240
These are the ways that generative AI are also being used.
00:25:56.400 --> 00:26:01.599
So, of course, there are tools like ChatGPT, there's Dolly that creates amazing imagery.
00:26:02.079 --> 00:26:04.880
There's so many amazing tools out there.
00:26:05.119 --> 00:26:14.559
But I think tools like Microsoft Copilot, and I'm sure Google will release something as well, are really meant to be hyper-efficient tools.
00:26:14.720 --> 00:26:30.000
They're meant to really make your job and the way that you deliver for your club, your customers and your clients way faster to market, way faster and more efficient, and providing different ideas that you might not have considered if you were building them out manually.
00:26:30.160 --> 00:26:36.799
So definitely a tool that I'm passionate about, just like ChatGPT, also Microsoft Copilot.
00:26:37.440 --> 00:26:38.079
Thank you.
00:26:38.240 --> 00:26:40.079
That's fascinating.
00:26:40.640 --> 00:26:51.599
Well, what about someone maybe who is not currently a business leader per se, and not a techie per se, but is an employee in an organization and they see the opportunity.
00:26:51.759 --> 00:26:59.599
They want to leverage learning about generative AI to improve the trajectory and speed of trajectory of their own career.
00:26:59.759 --> 00:27:01.200
What is your advice for them?
00:27:01.359 --> 00:27:12.000
How do they position themselves as, well, as Randy Sparkman said in our last podcast episode, the evangelist or the Sherpa for my company or my department?
00:27:12.640 --> 00:27:15.680
The most successful people are going to be curious people.
00:27:15.839 --> 00:27:22.000
We'll tend to probably outperform those who aren't really curious because there's a certain element of caring that goes on.
00:27:22.240 --> 00:27:35.200
And so my advice is if you are genuinely curious, really do that upskilling that's required to enable you to become a leader and be viewed as a go-to person for these types of tools.
00:27:35.359 --> 00:27:39.119
A quick example I am a certified public accountant by trade.
00:27:39.359 --> 00:27:43.359
I started in our tax practice at the consulting firm in which I work.
00:27:43.519 --> 00:27:50.079
And I navigated out of curiosity ways to make processes more efficient using technology tools.
00:27:50.480 --> 00:27:52.079
A lot of it was self-taught.
00:27:52.319 --> 00:28:00.079
Our company is amazing and did provide me plentiful amounts of opportunities to upskill and learn with dedicated time.
00:28:00.240 --> 00:28:07.440
And through that, I found my way into being fascinated once again by Generative AI last November when it came out.
00:28:07.599 --> 00:28:24.240
And out of that curiosity led me to learn about it, listen to podcasts, read the articles, look at thought leaders and technology art uh magazines and publications that I follow, and allow me to frame my point of view on how these tools could be used.
00:28:24.400 --> 00:28:27.519
And then I went and shared that insight with my leaders.
00:28:27.599 --> 00:28:44.799
Um, I showed them just how amazing a tool like ChatGPT is and how it can be used in a real life example for a project, obviously no client data, just internal content that I was using, but just showing how powerful that could be for the task that I was trying to complete.
00:28:45.039 --> 00:28:50.160
And I think that that really sparked this wave of, oh my gosh, that's possible.
00:28:50.319 --> 00:28:51.440
What else is possible?
00:28:51.680 --> 00:29:05.519
And so now here I am, a year later, helping to work on a team that's bringing generative AI education and opportunities to life at the company in which I work, and also for our clients to also take on that same role.
00:29:05.680 --> 00:29:12.079
It was all built on the foundation of being curious and open-minded and just taking the time to do the research.
00:29:12.240 --> 00:29:14.240
It wasn't ordered upon me.
00:29:14.400 --> 00:29:17.920
I wasn't assigned the opportunity to dive into this.
00:29:18.079 --> 00:29:20.160
It was just curiosity that led the way.
00:29:20.319 --> 00:29:28.559
That applies for so many different facets of life, but definitely the generative AI role if you're looking to promote that internally within your organization as well.
00:29:29.519 --> 00:29:31.279
Excellent advice.
00:29:31.920 --> 00:29:39.200
Not long ago, the co-founder of a news site, Axios, Mike Allen, spoke at my Rotary Club also.
00:29:39.279 --> 00:29:44.480
And in from that conversation, he he left everybody a copy of his book, Smart Brevity.
00:29:44.559 --> 00:29:50.880
And from that, I pulled a quote that one of the Axios writers, Scott Rosenberg, has.
00:29:51.119 --> 00:30:04.000
And here's the quote: The more our understanding of new technology is distorted by hype, the less thoughtfully we can apply it, and the more likely it is we will cause harm with it.
00:30:04.480 --> 00:30:08.480
He adds, today's AI promoters are trying to have it both ways.
00:30:08.640 --> 00:30:16.319
They insist that AI is crossing a profound boundary into untrodden territory with unfathomable risks.
00:30:16.880 --> 00:30:24.559
But they also define AI so broadly as to include almost any large-scale statistically driven computer program.
00:30:25.519 --> 00:30:45.039
As one personal example for me, I've noticed that some of the apps I have normally used or that I have used for quite some time, have suddenly changed their name to include AI, but nothing else has changed noticeably, such as the app TinyScan now switching their name to Tiny AI Scan.
00:30:45.359 --> 00:30:50.160
What are your takeaways from this, the the hype component?
00:30:50.640 --> 00:30:52.400
Yeah, that's interesting too.
00:30:52.480 --> 00:31:04.880
There's like anything, when uh I remember cryptocurrency was a hype cycle uh technology and and you know currency platform that really generated a lot of interest.
00:31:05.039 --> 00:31:08.000
And when it was hot, everyone was trying to transform.
00:31:08.160 --> 00:31:11.599
So definitely understand the AI hype cycle as well.
00:31:12.000 --> 00:31:18.240
The the true tools that help to get jobs done are gonna be the most useful.
00:31:18.400 --> 00:31:28.880
Um, but it really boils down, again, to education around um helping to demystify what these tools are, but also what they aren't.
00:31:29.119 --> 00:31:44.000
I work for amazing leaders, and they're very transparent and open about wanting us to understand the fundamentals of generative AI, but more importantly, too, just along with it, the responsible use of generative AI.
00:31:44.240 --> 00:31:48.720
So understanding what these tools are and are not is critical.
00:31:48.960 --> 00:32:03.759
So you can quickly diffuse and demystify a tool that has AI in the name, but truly isn't using it rather quickly when you're educated and have some foundational knowledge of the technology and what it's capable of.
00:32:03.920 --> 00:32:18.240
Um, you can quickly spot it and say, well, you know, they have AI in their name, but I don't know that they're really providing the value that they think they are when comparing it to a tool that truly is built off of a large language model.
00:32:18.480 --> 00:32:31.680
Um doesn't mean that these tools might not be useful, but yeah, definitely wish that there wasn't all this distortion out there in the market of tools that could potentially be thought of as AI capabilities, but you know, they're really not.
00:32:31.759 --> 00:32:41.759
So again, it it boils down to having amazing leaders who understand the importance of, you know, dedicated time to upskill and learn the foundations of these technologies.
00:32:42.079 --> 00:32:43.519
Well, let's talk more about you.
00:32:43.680 --> 00:32:53.200
So you're a certified public accountant, you are now taking on this major AI role for a large corporation.
00:32:53.440 --> 00:33:00.559
What are some of your future interests and how do you see this impacting the trajectory of your own career?
00:33:01.359 --> 00:33:04.960
It's um definitely been an amazing past year.
00:33:05.119 --> 00:33:16.000
Again, I was given an opportunity to be on a team of folks who are responsible for delivering learning content to all 75,000 people at the company that I worked for.
00:33:16.240 --> 00:33:21.680
And um, a lot of that has been a learning experience and it's been extremely beneficial.
00:33:21.759 --> 00:33:29.440
And again, I can't emphasize enough the leaders that I work for are all supportive and amazing individuals and professionals.
00:33:29.519 --> 00:33:32.720
Um, so the development there has been quite amazing.
00:33:32.960 --> 00:33:40.559
I think that again, learning these technologies improves your skill set and your marketability within your organization.
00:33:40.720 --> 00:33:54.640
You know, it only will help me continue to progress in my career, helping our firm better understand how to use these technologies, helping our clients understand what they're capable of doing as well.
00:33:54.880 --> 00:33:57.839
Um, I think we're at the tip of the iceberg still right now.
00:33:58.000 --> 00:34:05.440
Um, again, it's crazy to me to think that just a year ago ChatGPT was released and all this has happened since that point in time.
00:34:05.680 --> 00:34:13.280
So it's hard to say a year from now where we're gonna be when these technologies continue to get smarter and more sophisticated.
00:34:13.360 --> 00:34:28.320
Um, but I know at the core of it, my career will be rooted in helping our company become better and scaling generative AI solutions to make people's lives and work lives more efficient and easier and more productive.
00:34:28.480 --> 00:34:39.840
And then also helping our clients do that same thing, taking the lessons that we've learned and applying them to our clients' organizations to help them mimic similar patterns and create similar outputs that we've seen.
00:34:40.000 --> 00:34:43.519
That's probably the trajectory that I'm looking at now.
00:34:44.000 --> 00:34:49.440
Well, you also mentioned earlier that you're alma mater, and remind me, which school is that?
00:34:49.679 --> 00:34:54.960
Oh, the proud Miami of Ohio, Miami University, a school before Florida was a state.
00:34:56.239 --> 00:34:57.119
Very nice.
00:34:57.360 --> 00:35:02.800
You mentioned that you were asked to come back on campus and share some of your knowledge with students.
00:35:03.039 --> 00:35:19.119
What was some of your advice that you gave to those students to whom you spoke about the future of AI and how it's likely to impact their career and how to position that knowledge and information in interviews?
00:35:19.440 --> 00:35:24.320
I've since stepped away a little bit from using my CPA in a traditional sense.
00:35:24.400 --> 00:35:25.280
I still maintain it.
00:35:25.440 --> 00:35:28.960
I am a certified public accountant, and I care a lot about the accounting profession.
00:35:29.119 --> 00:35:36.880
So if I use that as an example, um, the accounting profession is typically a little bit later to the game with certain technology components.
00:35:36.960 --> 00:35:49.199
And so I'm a big proponent of the future of accounting needs to be driven off of technology and tools that can manipulate data and extract outputs more quickly.
00:35:49.440 --> 00:35:54.559
Um much of our profession is built around rules and regulations.
00:35:54.639 --> 00:35:57.280
And so there's a framework there to build off of.
00:35:57.440 --> 00:36:04.480
You can build these rules into models like generative AI models to create the outputs that are desired.
00:36:04.719 --> 00:36:07.679
I think that students understand that too.
00:36:07.840 --> 00:36:25.760
Um, no longer is it quite as important to memorize every line of the book when you now have a tool that can also help you get those insights and allow you to focus on providing more consultive advice rather than just providing an output that you're used to providing.
00:36:26.000 --> 00:36:40.559
So I think students, um, my encouragement when I go to campus has been understand these technologies because now is that time that's so fundamental when you're still building up your education base when you're in school.
00:36:40.719 --> 00:36:52.239
Take that time because when you enter the workforce, um, you know, it's a little bit of a struggle to get up to speed with all the coworkers who have been there for you know many more years than you have if you're just starting off fresh.
00:36:52.400 --> 00:37:05.920
And so my advice is always jump in and get some baseline understanding of how these technologies can impact the way that you do your schoolwork so that you're prepared for entering the workforce ahead of your peers.
00:37:06.079 --> 00:37:11.599
Um, get that, get that knowledge and understanding and also the hands-on skills that you can.
00:37:11.920 --> 00:37:19.360
Um, what's amazing about today, even when I was in school too, like YouTube education was really beneficial.
00:37:19.519 --> 00:37:23.360
There's there's videos that can teach you how to do just about anything.
00:37:23.440 --> 00:37:25.599
Um, I was sanding a table today.
00:37:25.760 --> 00:37:26.719
I didn't know how to do that.
00:37:26.800 --> 00:37:30.079
I looked at YouTube and it taught me how to do that in three minutes.
00:37:30.320 --> 00:37:38.719
Um I just think that there's so much power in understanding where to go for sources of information and applying them.
00:37:38.960 --> 00:37:51.679
And uh if you're a student and you're listening, I would encourage you, highly encourage you to go look at the fundamentals of generative AI, the responsible use of generative AI trainings that are all available online.
00:37:52.079 --> 00:37:58.239
Um, view our thought leadership as well, and understand how it could be applied in a business setting.
00:37:58.320 --> 00:38:15.599
So that way, when you are in an interview, when you are talking to someone who hopefully also understands the power and the transformative nature of generative AI, you can bring that up in that interview as well and explain how you've used it on a school project to create a brand new output that wouldn't have otherwise been created.
00:38:15.760 --> 00:38:25.679
Um, if you can explain ways that you're using these tools and make it click for the person that's interviewing you, that's really going to give you an upper hand when entering into that job market.
00:38:25.920 --> 00:38:38.880
People are, I'm interviewing candidates quite often, and one of the things I look for is practical use cases of knowing that you've applied a tool or you've been in a situation that would make me comfortable to put you on the project and use the tool in the same way.
00:38:39.119 --> 00:38:43.199
So just some advice for those who might be entering the workforce.
00:38:43.679 --> 00:38:45.119
Excellent advice.
00:38:45.440 --> 00:38:52.320
Well, one thing that you've mentioned or touched on a couple of different points in the interview is the ethical components.
00:38:52.400 --> 00:38:54.480
And let's talk about that for just a moment.
00:38:54.800 --> 00:39:04.800
One of the reasons that Google gave for not releasing their version of AI Sooner, now called BARD, was the ethical component that they were working around.
00:39:05.599 --> 00:39:14.960
Can you say more about how you see the ethical component and what a business leader really needs to be aware of as it relates to ethics and AI?
00:39:15.360 --> 00:39:18.960
Yeah, ethics play a big role, no matter what tool you're using.
00:39:19.119 --> 00:39:24.159
Um, being ethical, acting responsibly is always really important.
00:39:24.320 --> 00:39:32.320
And so um AI models are prone to bias because they are still created by humans.
00:39:32.480 --> 00:39:41.119
Humans have input into the models to determine the data sources that are used to create the outputs that you and I might use the model for.
00:39:41.599 --> 00:39:44.480
Um, the model itself isn't inherently biased.
00:39:44.559 --> 00:39:55.280
Um, there are ways to make a model biased if you only upload, you know, one side of the fact pattern and not the other side intentionally, because that's your point of view.
00:39:55.519 --> 00:39:58.480
Well, then that model is prone to being a bias model.
00:39:58.639 --> 00:40:06.480
And um, you know, that output might not be as accurate and as complete as it's made out to be.
00:40:06.800 --> 00:40:10.800
And so ethical use of these tools is critical.
00:40:11.039 --> 00:40:22.000
Um you have to be understanding that the model may seem like it understands what it's saying and understands all the concepts that you're providing it.
00:40:22.239 --> 00:40:26.639
But the truth is, it's just trained on so much data.
00:40:26.800 --> 00:40:31.679
Um, it's like talking to someone that's done everything in every way in every situation.
00:40:32.000 --> 00:40:37.280
And that's why we're just so prone to trusting it, just saying, well, how could that be wrong?
00:40:37.519 --> 00:40:44.960
Well, the truth is, is it's still just guessing off of that data that it's trained on what the best next word should be in the output.
00:40:45.039 --> 00:40:47.599
It doesn't actually know what it's producing.
00:40:47.760 --> 00:41:01.440
And so that's why it's so critical to thoroughly review the outputs of the models that you're provided, making sure that it's up to your brand standards so it doesn't uh jeopardize and harm your organization's perception within the marketplace.
00:41:01.599 --> 00:41:08.880
You want to make sure that these outputs are truly representative of the organization that you work for and that they're up to the same standards.
00:41:09.039 --> 00:41:15.119
The way that I can ensure that it's being ethically used is truly to verify that my outputs are indeed correct.
00:41:15.360 --> 00:41:23.440
Maybe even providing it examples when I'm interacting with a generative AI model to make sure that it isn't providing me a biased response.
00:41:23.599 --> 00:41:27.599
If a generative AI model doesn't know the answer, it can lie to you.
00:41:27.760 --> 00:41:29.760
It will just provide an answer for you.
00:41:29.840 --> 00:41:39.280
And so it could be even as something as simple as instructing the model when you're using it to, hey, if you don't know the answer, just specify that you do not know.
00:41:39.440 --> 00:41:40.639
Don't make it up.
00:41:40.719 --> 00:41:48.559
And so that's how you can use these models ethically to make sure that they're not just going to provide you non-factual information that really isn't useful.
00:41:48.880 --> 00:41:50.000
Truly fascinating.
00:41:50.159 --> 00:41:51.039
Thank you for sharing that.
00:41:51.360 --> 00:42:05.280
As we wrap up our interview, what have I not asked you that I should have asked you, or what would you add that's a key piece of information for somebody who's a business leader and really trying to wrap their mind around AI?
00:42:05.840 --> 00:42:16.159
Since I'm not necessarily in a leadership role at an organization, I have this different perspective to offer to leaders who are who might be listening.
00:42:16.320 --> 00:42:21.039
And that is to really encourage your people to take that step.
00:42:21.280 --> 00:42:25.920
That step of encouragement means the world to your staff.
00:42:26.079 --> 00:42:41.599
You know, to give them the go-ahead to openly use these tools in a responsible way goes such a long way into getting curiosity built up and getting these tools into the hands of people to produce the outputs that make things more efficient.
00:42:41.760 --> 00:42:43.360
Um I can't emphasize that enough.
00:42:43.519 --> 00:42:53.280
As someone who has tremendous leadership, who's supportive of that, has really enabled me in my career to explore the different use cases rather than just acting alone.
00:42:53.440 --> 00:43:04.639
Um, given permission to do it really helps me to think outside of the box, think a little bit more curiously with all technology, not even just artificial intelligence or generative AI.
00:43:04.719 --> 00:43:06.320
It'll be the next thing too.
00:43:06.480 --> 00:43:13.760
Knowing that there's leadership support for me to go and execute and try some of these things out is really beneficial for me.
00:43:13.840 --> 00:43:15.360
It's been beneficial in my career.
00:43:15.519 --> 00:43:26.960
And I think you'd find benefit as well for the people on your teams to also find value, give them the permission to act and to learn and to use these tools in their day-to-day roles.
00:43:27.280 --> 00:43:28.239
Thank you very much.
00:43:28.400 --> 00:43:39.039
And thank you for your vast array of valuable and practical insights and just in general for sharing your valuable time with the listeners of this podcast and with me.
00:43:39.199 --> 00:43:40.000
Thank you very much.
00:43:40.239 --> 00:43:41.840
Thank you guys for having me.
00:43:42.159 --> 00:43:42.880
Absolutely.
00:43:43.039 --> 00:43:54.480
And I will link to the resources mentioned, including this incredible knowledge base that you've built out on artificial intelligence, in the show notes at brainchatterpodcast.com.
00:43:54.639 --> 00:43:59.760
And with your permission, I'll also include a link to your LinkedIn in case someone wants to connect with you.
00:44:00.000 --> 00:44:00.639
That sounds great.
00:44:00.719 --> 00:44:02.239
Yeah, please add me on LinkedIn.
00:44:02.400 --> 00:44:03.519
We'd be happy to connect.
00:44:03.679 --> 00:44:05.039
Definitely open a chat.
00:44:05.119 --> 00:44:08.639
And uh again, just thank you for listening and the time.
00:44:08.960 --> 00:44:12.480
Brainchatter is a production of Ken Chapman and Associates Incorporated.
00:44:12.639 --> 00:44:33.119
We assist clients across North America and beyond with leadership development, corporate culture transformation initiatives, behavior-based professional development for health and safety issues, individual and team assessments, conflict and performance management, succession planning, and team building, making a difference in organizations and individuals' lives for over 40 years.
00:44:33.440 --> 00:44:45.360
Please, if you don't mind, leave a review or ranking for us wherever you listen to podcasts because the artificial intelligence will then in turn recommend us to additional new listeners.
00:44:45.519 --> 00:44:46.639
Thank you for your time.
00:00:01.439 --> 00:00:10.800
Welcome back to Brain Chatter, where we listen past the daily noise and explore topics at the intersection of leadership, workplace culture, profit, and sustainability.
00:00:10.960 --> 00:00:12.720
I'm your host, Derek Brown.
00:00:12.880 --> 00:00:16.239
Today's topic is generative AI and workplace culture.
00:00:16.559 --> 00:00:18.879
The right choices provide an edge.
00:00:19.120 --> 00:00:32.000
We've seen a barrage of coverage and speculation in the media, traditional media and social media, about what impacts AI, or artificial intelligence, is supposed to have on business in America and worldwide.
00:00:32.240 --> 00:00:43.759
There has been so much coverage, interest, hype, and speculation, in fact, that it is difficult to comprehend what is realistic, legitimate, or even most important about AI.
00:00:44.000 --> 00:00:50.320
In our recent Brain Chatter podcast episode, we explored the basics of AI with Randy Sparkman.
00:00:50.479 --> 00:00:54.799
In this episode, we delve into a major dimension of AI.
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Today we have a national corporate expert to talk us through a critical piece of this, and that is how will it transform the work of both company employees and company leaders, and how will it impact company cultures?
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Particularly how company leaders should be making AI decisions that give them a competitive edge.
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This expert has been analyzing this very question and crafting strategy advice and applications around it for one of the cornerstone giants of corporate America, a very large consulting firm with over 75,000 employees.
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His name is Michael Mendola, and I am certain you are going to find what he has to say in this interview fascinating, practical, and enlightening.
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Michael, thank you for your valuable time and welcome to Brainchatter.
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Yeah, thank you so much for having me.
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It's great to be here.
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Well, let's start with a fundamental question.
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Will you clearly define artificial intelligence, specifically generative AI?
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Yeah, so artificial intelligence has been around for quite some time.
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Everyone uses some form of artificial intelligence in their daily lives.
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You're used to using it when Spotify makes recommendations for the next song to play, or your Apple Maps is trying to figure out what's the best route to take.
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Artificial intelligence is not new.
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It's really a way for computers to perform tasks that typically require human intelligence.
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Generative AI is a relatively newer subset of artificial intelligence that's really laser focused on creating net new content or data.
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It can generate text, it can generate images, it can create music, it can create any other type of content that you might not be thinking about off the top of your head.
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It can create using natural language.
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And so generative AI is really just that deep knowledge subset of the broader artificial intelligence umbrella.
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Will you talk a little bit about the intersection of this generative AI and the workplace over the coming months and years ahead?
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There's a lot that's coming with generative AI.
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And uh, you know, typically artificial intelligence has required computer software programmers and engineers who truly understand how to build these models.
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Um that's the that's the root of it.
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And artificial intelligence used to mean that you would train a model to do a specific task in a specific way.
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Generative AI is now enabling access to artificial intelligence in a way that's never really been seen before.
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So now people can in the workforce use generative AI right at their fingertips with natural language to create the things that they need to do.
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ChatGPT was released, which really was the first evolution of generative AI that we're seeing now.
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And right when it got released, I started playing with it to write my emails, um, draft agendas for me, use it as a thought partner, which is a concept I'll I'll elaborate on over the course of this interview.
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But generative AI in the workforce is transforming the way that people get work done.
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It's not replacing jobs, it's really enabling people to work differently, to work more efficiently, and to actually create a lot of value that wouldn't have been there otherwise.
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You've built out a knowledge base, a very large knowledge base that your consulting firm is sharing online through an online blog for clients, for prospective clients, and for anyone to access.
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This really puts you at the forefront of expertise sharing on this topic.
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And I'll link to the blog in the show notes so that anyone can go directly to all of this knowledge you've put together.
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Allow me to pull a quote from one of your posts.
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Quote, technological advances, surging investments, and the competition for talent are all working toward one thing.
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In 2024, artificial intelligence, AI, will start to fundamentally change how business gets done.
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It will impact how companies grow revenue, conduct everyday operations, engage customers and employees, build new business models, and more.
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End of quote.
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So, Michael, will you flesh out your point about how it ties into that third point, how artificial intelligence prioritizes the major issue of competition for talent in the modern workplace?
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Yeah, that's a that's an important point to hit on.
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It's one that's very close to what I'm getting to do day to day.
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Um I'm I'm getting to drive how we are upskilling 75,000 people at our workforce to understand how to use generative AI, how to become savvy and responsible users of generative AI.
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Um, and responsible is important too.
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But talent and people are desiring skills that they can use every day.
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They want to reimagine.
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People want to reimagine the way of the way that they work.
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They don't want to necessarily do things the way that they have done it the last year.
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People are looking for efficiencies, people are looking to use their brains and use the skills because technology is only going to evolve even more and more.
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And so using these tools, getting access to generative AI, learning about what is generative AI at its core, and also understanding what are the responsible practices to use when you're activating these technologies is critical.
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Um, what we've seen is that employee engagement has gone up.
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People are clamoring for more training, more education.
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And now that we've we've built a baseline at the company that I work for, a baseline understanding of what is generative AI, how do you use it responsibly?
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We're now giving all of our people access to our tools that they can use in their everyday work.
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People are looking for more opportunities to actually get more upskilling involvement.
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They're looking for other ways to engage, they're looking for how you can use generative AI to build imagery, how can you use it to write music beyond just the day-to-day tasks?
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People really believe that generative AI is a tool that they can use and actually take further than they originally thought.
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And I think once people open that door, it's hard to shut it.
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People are naturally curious.
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They want to know how they can use these tools to better their work life, but also they can use it in their personal life too, in a lot of different ways.
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And so to invest in the workforce, um, to educate people on how to use these tools, again, in a responsible manner, is so critical.
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I was recently speaking with a group of college students at my alma mater, and I asked the teacher, I said, what do you, what do you, what is your stance in using generative AI in the classroom?
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And uh he was one of the proponents of using it because why limit people's access to a tool that they could access outside of the classroom in a real work-life scenario?
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And so you really need to make sure that you're putting guardrails around people to ensure that they're using the tools the way that they're meant to be used and that they're verifying the outputs, but why restrict it?
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Um, they're going to get access to it one way or another.
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They're going to use these tools in their work, they're going to use it in the classroom.
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The goal is to really create the responsible use of these tools, making sure that they understand how to generate the outputs that they desire.
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That's what really is the most impactful thing that we can do from my experience in the workforce is education around these topics, not avoid, not avoiding them, because again, they're only going to continue to improve and grow.
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We've seen all the transformative change in just a year that's taken place.
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Um, it's only, you know, I can't even imagine a year from now, when we're listening back to this podcast, how much is going to be changed since then, too?
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Great and fascinating points.
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In one of your blog posts, you note that generative artificial intelligence will transform the work of leaders as much as employees.
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Will you expand on what you mean by this?
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Yeah, it's definitely interesting.
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A lot of times what we see is younger staff tend to gravitate towards new tools.
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And I think that that's just a natural evolution of society.
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People who are younger than me know about social media platforms and tools that I'm not aware of.
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And I probably know a lot about technology that my parents weren't aware of.
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And so it's a societal change and evolution that younger people entering the workforce will naturally gravitate towards wanting to use technology more frequently.
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Um, with generative AI, though, it's actually really burdensome to management to not understand the capabilities of generative AI.
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It only hurts them as managers and leaders and CEOs to not understand the impact that generative AI can have for their people and for their workforce.
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If you're not understanding how outputs are created, it's susceptible to risk and inaccuracy that you, as a manager, need to make sure your understanding is right and you're verifying it is indeed accurate and that the source data does make sense.
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If you're not understanding how this content is generated, but your younger staff members are creating it this way, um, there's this inherent risk.
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And so it is really important for leaders and managers at large and small organizations to know about the technology, know how it can be used, know how it should be used, and more importantly, know how to review the outputs for things like accuracy, for completeness, um, to make sure that they're not biased.
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That's what I think of when I envision leaders needing to understand.
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You don't need to know how to build a generative AI model, but you do need to make sure that you understand how to use it and how your people are using it, because the truth is, is they're likely going to get access to it one way or another.
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So it's best to be prepared.
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That makes sense.
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As the COVID pandemic ramped up and workplaces started really hitting fast forward on certain technological adoptions, such as using remote logins, Zoom, Microsoft Teams, et cetera.
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I personally know a number of people who had been nearing retirement who explained their decision to retire as basically, I'm not interested in learning all this new technology.
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I'm out.
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They represented jobs in the ones that I know in finance, heavy industry, and the federal government.
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I've even had a friend as recently as a few weeks ago say that their decision to take an early retirement buyout that will happen soon is a result of continued frustration at the continued clip of new technology adoption expected in what had been for very long a traditional role and predictable role.
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Do you think this fast-paced AI integration that is coming will have a similar impact even in a continued tightening of the overall labor pool?
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I don't think that we've seen technology quite like generative AI advance as quickly as it has.
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I think that people should be prepared and understand the fundamentals on how to use these tools because if you understand in theory how to prompt the technology, you're not just limiting yourself to understanding how to use a tool like ChatGPT, you're also enabling yourself to understand how to use more advanced tools like Microsoft Copilot that's embedded within Microsoft Slides and Microsoft Excel and Microsoft Word.
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If you understand how to use it responsibly, the core tenets of generative AI, and how to prompt the technology, those three key ingredients, which there's a little bit of a learning curve, but it's not that drastic because the interface is just using natural language.
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Just like you would instruct someone who is on your team to help you with a task, you'd want to give them a lot of detail.
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You'd want to be specific, provide examples of what you're looking for to make sure that they generate the output that you're desiring.
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It's no different in using generative AI.
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You really want to be thorough and precise and provide context to get the best output possible.
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And that's why I think that that skill gap, the the generations who are going into retirement, understanding those core tenants, not necessarily, again, understanding how to build a large language model, more how do you use it to get the most value?
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Um, isn't something that is all that steep of a learning curve.
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Once you get into it and get your hands on keyboard to practice a little bit, that skill gap can be closed relatively simply.
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Low uh threshold to get involved.
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And um there's so much content out there to learn so quickly.
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Um, a lot of great content out there.
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If anyone is thinking, man, I'm nearing the end of my journey, it's probably too late.
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I disagree.
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Um, I think it's something that you could use personally after you know you end up retiring and whatnot, too.
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This the skills and the thought partner that you can use generative AI for applies much more broadly outside of a work environment.
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Well, the knowledge base that you have put together is an incredible amount of information that is consumable by people who are kind of looking for where do I start?
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What does this mean?
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What is your motivation for that knowledge base shared beyond just people inside your company?
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You're you're being generous with it to share it with everybody.
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Yeah, that there's definitely the aspect of hoping that people are engaged and hopefully it clicks.
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Um, generative AI, the word can be a little bit intimidating, and you might not want to dive in.
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And I think a lot of the thought leadership that we're releasing is geared towards making it digestible, making it easy to understand and with use cases and start and a place to start.
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Um, there's so much information out there that you could start with, and sometimes that's a quite overwhelming.
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With the thought leadership that we're curating, it's really geared towards oh, okay, this is what generative AI can be used for.
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That's great.
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Okay, oh, here's where I start as well.
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Using those two things hopefully does enable curious curiosity and continued evolution of learning and trying to get more involved.
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And honestly, it does provide permission to use the tools as well.
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Um, you know, I'm never gonna recommend just using one of the generative AI tools.
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There's so many potential options that exist.
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But it it almost gives folks permission to try.
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Because again, you're never gonna you can learn all about prompting generative AI models all you want.
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But until you actually get hands-on experience doing it, that's when it really truly clicks.
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Um, it's like riding a bike.
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You can read about how to ride a bike, but until you actually hop on and you start pedaling, that's when it really kicks into high gear.
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So um, the thought leadership that we're creating, just like you'd mentioned, definitely is meant to spark curiosity, but it is meant to also say, hey, here's where you can start, go and try these tools out.
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That makes a lot of sense and it's very generous of you.
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One app that I use personally that leans heavily into AI is a free app from National Geographic called iNaturalist.
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I'll link to it in the show notes also.
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And as you probably know, if you've ever used it, it it's for identifying all sorts of wild things.
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Once you've uploaded a photo, whether it's of a turtle or an oak tree leaf, it recommends to you what it thinks it is, but then it lets the community of users of the app come in and verify whether or not what you and artificial intelligence identified is accurate or not.
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How do you see the future of this human backup to artificial intelligence for various applications in the future?
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Do you think that will also always be a component, some sort of human verification, or will a knowledge base grow so much that eventually that component will not be needed as substantially?
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I am a believer that there's always going to be need, like a need for human intervention.
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I don't think that these tools are meant to be used without verification in some capacity.
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The truth is, is it's guessing what response to generate.
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It's just providing the best guess of the next word or the next content, the next image based off of the millions and millions and millions of data points that these large language models are trained off of.
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And so I'm of the thought that you should always trust but verify.
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Trust that the output is correct, but you need to verify it.
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Um human, humans still play a really critical role in the evolution of generative AI and AI in general.
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Um, we're creating the models, we're training these using training data.
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You want to make sure that these models, you know, that's where bias could come in if you're training it only using certain data points and excluding others.
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And so that's why I'm a big proponent of human intervention, especially in the review process as people use these tools.
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Um the rule, the rule of thumb is definitely trust but verify.
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Over the past year, as interest rates have spiked and we've witnessed other governmental policies and economic changes, we've seen the tech industry across the United States and beyond go through multiple layers of layoffs.
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The founder of one successful and growing tech startup that allows remote work from anywhere and which only has a few hundred employees recently told my Rotary Club that his tech company had over 70,000 job applications for only a handful of vacancies in 2023.
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Do you anticipate that the surge of workers with some level of tech background or interest will be helpful in the speed of AI adoption if they are recruited into non-tech companies, as those non-tech companies realize they maybe need AI integration?
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It's a great question.
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Um I had a leader of mine um explain the artificial intelligence boom like this.
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It's almost like math getting a calculator.
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Um it's like when the calculator was invented, you used it to speed uh to quickly do calculations and figure out the way to the formula and figure out the solution a lot quicker.
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The same could be said about generative AI.
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Um, if you have some baseline understanding of it, you definitely won't be replaced by the technology.
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It's really just a tool in your tool tool belt to help you, again, create new content or bounce new ideas off of the generative AI model to come to the best solution.
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Those that understand how to use the technology likely will see results in getting hired, maybe progressing throughout their careers.
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But those who don't embrace it and understand it and try to use it likely are at risk to not progress as quickly because you're only hurting yourself.
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Again, it's like doing math without the calculator sometimes.
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And even though it's definitely thorough and you should understand how to do the calculations, having that calculator is handy.
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And so I view it the same way when getting access to these generative AI tools.
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What advice would you give to a company leader, whether it's a small company or whether it's a very large company like the one you work with?
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We do expect for you to actively take on figuring out how to apply it to our company in a way that makes us more competitive.
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Yeah, I think it starts with education.
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It starts with opportunities for upskilling.
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And as a leader, I would encourage all leaders to take that initiative to invest in your people.
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People will find use cases and ways to apply the technology into their day-to-day jobs.
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Break down that barrier by again providing your people with dedicated time and dedicated training content that they can take and then use the technology in ways that they didn't think about before.
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Rather than prescribing them, you should use generative AI to do this, this, and this?
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Sure, that might be a good starting point, but I think it's better to paint a broader stroke, build the baseline understanding, and build savvy and responsible users and enable them to find ways to fill the gaps in their day-to-day work using generative AI tools.
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Let's turn our attention specifically to workplace cultures.
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What impact do you anticipate generative AI will have on workplace cultures?
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That's an interesting question, too.
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Again, I I don't know that folks understand quite quite as much.
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Now that I've had a lot of experience with it, the way that you can use generative AI as a thought partner.
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Um, for example.
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When I was preparing for this interview and this podcast, I use generative AI to figure out what are potential responses to some of the questions that I'm being asked.
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Like, what's another what's another consideration that I'm not thinking about?
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And the model was able to provide me with different points of view that would also be important to communicate to.
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And so I think that workplace culture also could benefit from having a thought partner in your back pocket.
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I think the culture of a company actually will only grow when more efficiency is being created in day-to-day work.
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You're no longer having to spend, you know, that extra 15 minutes trying to figure out how you want to write this email, how to make it more formal.
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You're not spending, you know, an extra two hours creating a draft of all of your PowerPoint slides.
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You're no longer having to write out, you know, take the meeting notes and transcribe them into a follow-up note that you could share with your team.
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Those are things that only will improve the workplace culture because now teams are able to focus more on the beneficial points of their day-to-day role.
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They're able to be more thought-provoking, less task-oriented.
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And I think that's where generative AI is going to play a huge boost in workplace culture, in creating new ideas, creating new points of view that weren't considered before that could be brought into the workforce as well.
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And it's just going to create a better overall experience for people and also create better outcomes for clients and customers.
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We've mentioned a couple of applications that use artificial intelligence.
00:23:38.319 --> 00:23:51.200
When I talked about iNaturalist, when you talked about Chat GPT, are there any other specific applications that you have found particularly interesting or helpful or fascinating that you've been using most?
00:23:51.839 --> 00:23:54.400
Yes, definitely a lot of them.
00:23:54.559 --> 00:23:58.880
And the ones that you mentioned definitely are great starting points, great places to go.
00:23:59.119 --> 00:24:07.200
Organizations like Microsoft and Google, what they're starting to do is integrate generative AI into the products that they have.
00:24:07.359 --> 00:24:12.240
And so one of the tools that I'm using now on a daily basis is called Microsoft Copilot.
00:24:12.559 --> 00:24:19.920
Microsoft Copilot is a generative AI tool built directly into each of the M365 applications.
00:24:20.160 --> 00:24:33.279
So when you're using Microsoft Word, PowerPoint, Excel, Microsoft Teams, and Microsoft Outlook for your emails, you're now able to use generative AI within each of those native applications.
00:24:33.440 --> 00:24:39.839
So if you need to create a new PowerPoint deck, you can use natural language using Microsoft Copilot.
00:24:40.000 --> 00:24:46.880
You can just type it in and ask it to generate three draft slides for your upcoming meeting on the benefits of generative AI.
00:24:47.119 --> 00:24:59.680
You can go to Microsoft Word and you can have it draft you talking points in a podcast style format that make it really easy to transcribe and it will generate all that net new content there.
00:24:59.839 --> 00:25:18.240
If you're analyzing your Excel spreadsheets and you want to identify and highlight variances that are above a certain threshold, you can simply use natural language now within Copilot in Microsoft Excel to highlight those records and find those variances for you rather than you having to do the formulas yourself.
00:25:18.880 --> 00:25:32.480
If you want to summarize that long email thread that you see, you know, the 16-thread email that you want to summarize, you can use Microsoft Copilot and Outlook to summarize that email thread and give you the key points.
00:25:33.119 --> 00:25:52.079
When you're using your meetings, you're in a Teams meeting, for example, you can have the transcription feature turned on and it will transcribe your meeting notes and output them for you in an easy-to-digest format that also has action items specifically laid out with the name of the responsible party who's assigned to that action item.
00:25:52.240 --> 00:25:56.240
These are the ways that generative AI are also being used.
00:25:56.400 --> 00:26:01.599
So, of course, there are tools like ChatGPT, there's Dolly that creates amazing imagery.
00:26:02.079 --> 00:26:04.880
There's so many amazing tools out there.
00:26:05.119 --> 00:26:14.559
But I think tools like Microsoft Copilot, and I'm sure Google will release something as well, are really meant to be hyper-efficient tools.
00:26:14.720 --> 00:26:30.000
They're meant to really make your job and the way that you deliver for your club, your customers and your clients way faster to market, way faster and more efficient, and providing different ideas that you might not have considered if you were building them out manually.
00:26:30.160 --> 00:26:36.799
So definitely a tool that I'm passionate about, just like ChatGPT, also Microsoft Copilot.
00:26:37.440 --> 00:26:38.079
Thank you.
00:26:38.240 --> 00:26:40.079
That's fascinating.
00:26:40.640 --> 00:26:51.599
Well, what about someone maybe who is not currently a business leader per se, and not a techie per se, but is an employee in an organization and they see the opportunity.
00:26:51.759 --> 00:26:59.599
They want to leverage learning about generative AI to improve the trajectory and speed of trajectory of their own career.
00:26:59.759 --> 00:27:01.200
What is your advice for them?
00:27:01.359 --> 00:27:12.000
How do they position themselves as, well, as Randy Sparkman said in our last podcast episode, the evangelist or the Sherpa for my company or my department?
00:27:12.640 --> 00:27:15.680
The most successful people are going to be curious people.
00:27:15.839 --> 00:27:22.000
We'll tend to probably outperform those who aren't really curious because there's a certain element of caring that goes on.
00:27:22.240 --> 00:27:35.200
And so my advice is if you are genuinely curious, really do that upskilling that's required to enable you to become a leader and be viewed as a go-to person for these types of tools.
00:27:35.359 --> 00:27:39.119
A quick example I am a certified public accountant by trade.
00:27:39.359 --> 00:27:43.359
I started in our tax practice at the consulting firm in which I work.
00:27:43.519 --> 00:27:50.079
And I navigated out of curiosity ways to make processes more efficient using technology tools.
00:27:50.480 --> 00:27:52.079
A lot of it was self-taught.
00:27:52.319 --> 00:28:00.079
Our company is amazing and did provide me plentiful amounts of opportunities to upskill and learn with dedicated time.
00:28:00.240 --> 00:28:07.440
And through that, I found my way into being fascinated once again by Generative AI last November when it came out.
00:28:07.599 --> 00:28:24.240
And out of that curiosity led me to learn about it, listen to podcasts, read the articles, look at thought leaders and technology art uh magazines and publications that I follow, and allow me to frame my point of view on how these tools could be used.
00:28:24.400 --> 00:28:27.519
And then I went and shared that insight with my leaders.
00:28:27.599 --> 00:28:44.799
Um, I showed them just how amazing a tool like ChatGPT is and how it can be used in a real life example for a project, obviously no client data, just internal content that I was using, but just showing how powerful that could be for the task that I was trying to complete.
00:28:45.039 --> 00:28:50.160
And I think that that really sparked this wave of, oh my gosh, that's possible.
00:28:50.319 --> 00:28:51.440
What else is possible?
00:28:51.680 --> 00:29:05.519
And so now here I am, a year later, helping to work on a team that's bringing generative AI education and opportunities to life at the company in which I work, and also for our clients to also take on that same role.
00:29:05.680 --> 00:29:12.079
It was all built on the foundation of being curious and open-minded and just taking the time to do the research.
00:29:12.240 --> 00:29:14.240
It wasn't ordered upon me.
00:29:14.400 --> 00:29:17.920
I wasn't assigned the opportunity to dive into this.
00:29:18.079 --> 00:29:20.160
It was just curiosity that led the way.
00:29:20.319 --> 00:29:28.559
That applies for so many different facets of life, but definitely the generative AI role if you're looking to promote that internally within your organization as well.
00:29:29.519 --> 00:29:31.279
Excellent advice.
00:29:31.920 --> 00:29:39.200
Not long ago, the co-founder of a news site, Axios, Mike Allen, spoke at my Rotary Club also.
00:29:39.279 --> 00:29:44.480
And in from that conversation, he he left everybody a copy of his book, Smart Brevity.
00:29:44.559 --> 00:29:50.880
And from that, I pulled a quote that one of the Axios writers, Scott Rosenberg, has.
00:29:51.119 --> 00:30:04.000
And here's the quote: The more our understanding of new technology is distorted by hype, the less thoughtfully we can apply it, and the more likely it is we will cause harm with it.
00:30:04.480 --> 00:30:08.480
He adds, today's AI promoters are trying to have it both ways.
00:30:08.640 --> 00:30:16.319
They insist that AI is crossing a profound boundary into untrodden territory with unfathomable risks.
00:30:16.880 --> 00:30:24.559
But they also define AI so broadly as to include almost any large-scale statistically driven computer program.
00:30:25.519 --> 00:30:45.039
As one personal example for me, I've noticed that some of the apps I have normally used or that I have used for quite some time, have suddenly changed their name to include AI, but nothing else has changed noticeably, such as the app TinyScan now switching their name to Tiny AI Scan.
00:30:45.359 --> 00:30:50.160
What are your takeaways from this, the the hype component?
00:30:50.640 --> 00:30:52.400
Yeah, that's interesting too.
00:30:52.480 --> 00:31:04.880
There's like anything, when uh I remember cryptocurrency was a hype cycle uh technology and and you know currency platform that really generated a lot of interest.
00:31:05.039 --> 00:31:08.000
And when it was hot, everyone was trying to transform.
00:31:08.160 --> 00:31:11.599
So definitely understand the AI hype cycle as well.
00:31:12.000 --> 00:31:18.240
The the true tools that help to get jobs done are gonna be the most useful.
00:31:18.400 --> 00:31:28.880
Um, but it really boils down, again, to education around um helping to demystify what these tools are, but also what they aren't.
00:31:29.119 --> 00:31:44.000
I work for amazing leaders, and they're very transparent and open about wanting us to understand the fundamentals of generative AI, but more importantly, too, just along with it, the responsible use of generative AI.
00:31:44.240 --> 00:31:48.720
So understanding what these tools are and are not is critical.
00:31:48.960 --> 00:32:03.759
So you can quickly diffuse and demystify a tool that has AI in the name, but truly isn't using it rather quickly when you're educated and have some foundational knowledge of the technology and what it's capable of.
00:32:03.920 --> 00:32:18.240
Um, you can quickly spot it and say, well, you know, they have AI in their name, but I don't know that they're really providing the value that they think they are when comparing it to a tool that truly is built off of a large language model.
00:32:18.480 --> 00:32:31.680
Um doesn't mean that these tools might not be useful, but yeah, definitely wish that there wasn't all this distortion out there in the market of tools that could potentially be thought of as AI capabilities, but you know, they're really not.
00:32:31.759 --> 00:32:41.759
So again, it it boils down to having amazing leaders who understand the importance of, you know, dedicated time to upskill and learn the foundations of these technologies.
00:32:42.079 --> 00:32:43.519
Well, let's talk more about you.
00:32:43.680 --> 00:32:53.200
So you're a certified public accountant, you are now taking on this major AI role for a large corporation.
00:32:53.440 --> 00:33:00.559
What are some of your future interests and how do you see this impacting the trajectory of your own career?
00:33:01.359 --> 00:33:04.960
It's um definitely been an amazing past year.
00:33:05.119 --> 00:33:16.000
Again, I was given an opportunity to be on a team of folks who are responsible for delivering learning content to all 75,000 people at the company that I worked for.
00:33:16.240 --> 00:33:21.680
And um, a lot of that has been a learning experience and it's been extremely beneficial.
00:33:21.759 --> 00:33:29.440
And again, I can't emphasize enough the leaders that I work for are all supportive and amazing individuals and professionals.
00:33:29.519 --> 00:33:32.720
Um, so the development there has been quite amazing.
00:33:32.960 --> 00:33:40.559
I think that again, learning these technologies improves your skill set and your marketability within your organization.
00:33:40.720 --> 00:33:54.640
You know, it only will help me continue to progress in my career, helping our firm better understand how to use these technologies, helping our clients understand what they're capable of doing as well.
00:33:54.880 --> 00:33:57.839
Um, I think we're at the tip of the iceberg still right now.
00:33:58.000 --> 00:34:05.440
Um, again, it's crazy to me to think that just a year ago ChatGPT was released and all this has happened since that point in time.
00:34:05.680 --> 00:34:13.280
So it's hard to say a year from now where we're gonna be when these technologies continue to get smarter and more sophisticated.
00:34:13.360 --> 00:34:28.320
Um, but I know at the core of it, my career will be rooted in helping our company become better and scaling generative AI solutions to make people's lives and work lives more efficient and easier and more productive.
00:34:28.480 --> 00:34:39.840
And then also helping our clients do that same thing, taking the lessons that we've learned and applying them to our clients' organizations to help them mimic similar patterns and create similar outputs that we've seen.
00:34:40.000 --> 00:34:43.519
That's probably the trajectory that I'm looking at now.
00:34:44.000 --> 00:34:49.440
Well, you also mentioned earlier that you're alma mater, and remind me, which school is that?
00:34:49.679 --> 00:34:54.960
Oh, the proud Miami of Ohio, Miami University, a school before Florida was a state.
00:34:56.239 --> 00:34:57.119
Very nice.
00:34:57.360 --> 00:35:02.800
You mentioned that you were asked to come back on campus and share some of your knowledge with students.
00:35:03.039 --> 00:35:19.119
What was some of your advice that you gave to those students to whom you spoke about the future of AI and how it's likely to impact their career and how to position that knowledge and information in interviews?
00:35:19.440 --> 00:35:24.320
I've since stepped away a little bit from using my CPA in a traditional sense.
00:35:24.400 --> 00:35:25.280
I still maintain it.
00:35:25.440 --> 00:35:28.960
I am a certified public accountant, and I care a lot about the accounting profession.
00:35:29.119 --> 00:35:36.880
So if I use that as an example, um, the accounting profession is typically a little bit later to the game with certain technology components.
00:35:36.960 --> 00:35:49.199
And so I'm a big proponent of the future of accounting needs to be driven off of technology and tools that can manipulate data and extract outputs more quickly.
00:35:49.440 --> 00:35:54.559
Um much of our profession is built around rules and regulations.
00:35:54.639 --> 00:35:57.280
And so there's a framework there to build off of.
00:35:57.440 --> 00:36:04.480
You can build these rules into models like generative AI models to create the outputs that are desired.
00:36:04.719 --> 00:36:07.679
I think that students understand that too.
00:36:07.840 --> 00:36:25.760
Um, no longer is it quite as important to memorize every line of the book when you now have a tool that can also help you get those insights and allow you to focus on providing more consultive advice rather than just providing an output that you're used to providing.
00:36:26.000 --> 00:36:40.559
So I think students, um, my encouragement when I go to campus has been understand these technologies because now is that time that's so fundamental when you're still building up your education base when you're in school.
00:36:40.719 --> 00:36:52.239
Take that time because when you enter the workforce, um, you know, it's a little bit of a struggle to get up to speed with all the coworkers who have been there for you know many more years than you have if you're just starting off fresh.
00:36:52.400 --> 00:37:05.920
And so my advice is always jump in and get some baseline understanding of how these technologies can impact the way that you do your schoolwork so that you're prepared for entering the workforce ahead of your peers.
00:37:06.079 --> 00:37:11.599
Um, get that, get that knowledge and understanding and also the hands-on skills that you can.
00:37:11.920 --> 00:37:19.360
Um, what's amazing about today, even when I was in school too, like YouTube education was really beneficial.
00:37:19.519 --> 00:37:23.360
There's there's videos that can teach you how to do just about anything.
00:37:23.440 --> 00:37:25.599
Um, I was sanding a table today.
00:37:25.760 --> 00:37:26.719
I didn't know how to do that.
00:37:26.800 --> 00:37:30.079
I looked at YouTube and it taught me how to do that in three minutes.
00:37:30.320 --> 00:37:38.719
Um I just think that there's so much power in understanding where to go for sources of information and applying them.
00:37:38.960 --> 00:37:51.679
And uh if you're a student and you're listening, I would encourage you, highly encourage you to go look at the fundamentals of generative AI, the responsible use of generative AI trainings that are all available online.
00:37:52.079 --> 00:37:58.239
Um, view our thought leadership as well, and understand how it could be applied in a business setting.
00:37:58.320 --> 00:38:15.599
So that way, when you are in an interview, when you are talking to someone who hopefully also understands the power and the transformative nature of generative AI, you can bring that up in that interview as well and explain how you've used it on a school project to create a brand new output that wouldn't have otherwise been created.
00:38:15.760 --> 00:38:25.679
Um, if you can explain ways that you're using these tools and make it click for the person that's interviewing you, that's really going to give you an upper hand when entering into that job market.
00:38:25.920 --> 00:38:38.880
People are, I'm interviewing candidates quite often, and one of the things I look for is practical use cases of knowing that you've applied a tool or you've been in a situation that would make me comfortable to put you on the project and use the tool in the same way.
00:38:39.119 --> 00:38:43.199
So just some advice for those who might be entering the workforce.
00:38:43.679 --> 00:38:45.119
Excellent advice.
00:38:45.440 --> 00:38:52.320
Well, one thing that you've mentioned or touched on a couple of different points in the interview is the ethical components.
00:38:52.400 --> 00:38:54.480
And let's talk about that for just a moment.
00:38:54.800 --> 00:39:04.800
One of the reasons that Google gave for not releasing their version of AI Sooner, now called BARD, was the ethical component that they were working around.
00:39:05.599 --> 00:39:14.960
Can you say more about how you see the ethical component and what a business leader really needs to be aware of as it relates to ethics and AI?
00:39:15.360 --> 00:39:18.960
Yeah, ethics play a big role, no matter what tool you're using.
00:39:19.119 --> 00:39:24.159
Um, being ethical, acting responsibly is always really important.
00:39:24.320 --> 00:39:32.320
And so um AI models are prone to bias because they are still created by humans.
00:39:32.480 --> 00:39:41.119
Humans have input into the models to determine the data sources that are used to create the outputs that you and I might use the model for.
00:39:41.599 --> 00:39:44.480
Um, the model itself isn't inherently biased.
00:39:44.559 --> 00:39:55.280
Um, there are ways to make a model biased if you only upload, you know, one side of the fact pattern and not the other side intentionally, because that's your point of view.
00:39:55.519 --> 00:39:58.480
Well, then that model is prone to being a bias model.
00:39:58.639 --> 00:40:06.480
And um, you know, that output might not be as accurate and as complete as it's made out to be.
00:40:06.800 --> 00:40:10.800
And so ethical use of these tools is critical.
00:40:11.039 --> 00:40:22.000
Um you have to be understanding that the model may seem like it understands what it's saying and understands all the concepts that you're providing it.
00:40:22.239 --> 00:40:26.639
But the truth is, it's just trained on so much data.
00:40:26.800 --> 00:40:31.679
Um, it's like talking to someone that's done everything in every way in every situation.
00:40:32.000 --> 00:40:37.280
And that's why we're just so prone to trusting it, just saying, well, how could that be wrong?
00:40:37.519 --> 00:40:44.960
Well, the truth is, is it's still just guessing off of that data that it's trained on what the best next word should be in the output.
00:40:45.039 --> 00:40:47.599
It doesn't actually know what it's producing.
00:40:47.760 --> 00:41:01.440
And so that's why it's so critical to thoroughly review the outputs of the models that you're provided, making sure that it's up to your brand standards so it doesn't uh jeopardize and harm your organization's perception within the marketplace.
00:41:01.599 --> 00:41:08.880
You want to make sure that these outputs are truly representative of the organization that you work for and that they're up to the same standards.
00:41:09.039 --> 00:41:15.119
The way that I can ensure that it's being ethically used is truly to verify that my outputs are indeed correct.
00:41:15.360 --> 00:41:23.440
Maybe even providing it examples when I'm interacting with a generative AI model to make sure that it isn't providing me a biased response.
00:41:23.599 --> 00:41:27.599
If a generative AI model doesn't know the answer, it can lie to you.
00:41:27.760 --> 00:41:29.760
It will just provide an answer for you.
00:41:29.840 --> 00:41:39.280
And so it could be even as something as simple as instructing the model when you're using it to, hey, if you don't know the answer, just specify that you do not know.
00:41:39.440 --> 00:41:40.639
Don't make it up.
00:41:40.719 --> 00:41:48.559
And so that's how you can use these models ethically to make sure that they're not just going to provide you non-factual information that really isn't useful.
00:41:48.880 --> 00:41:50.000
Truly fascinating.
00:41:50.159 --> 00:41:51.039
Thank you for sharing that.
00:41:51.360 --> 00:42:05.280
As we wrap up our interview, what have I not asked you that I should have asked you, or what would you add that's a key piece of information for somebody who's a business leader and really trying to wrap their mind around AI?
00:42:05.840 --> 00:42:16.159
Since I'm not necessarily in a leadership role at an organization, I have this different perspective to offer to leaders who are who might be listening.
00:42:16.320 --> 00:42:21.039
And that is to really encourage your people to take that step.
00:42:21.280 --> 00:42:25.920
That step of encouragement means the world to your staff.
00:42:26.079 --> 00:42:41.599
You know, to give them the go-ahead to openly use these tools in a responsible way goes such a long way into getting curiosity built up and getting these tools into the hands of people to produce the outputs that make things more efficient.
00:42:41.760 --> 00:42:43.360
Um I can't emphasize that enough.
00:42:43.519 --> 00:42:53.280
As someone who has tremendous leadership, who's supportive of that, has really enabled me in my career to explore the different use cases rather than just acting alone.
00:42:53.440 --> 00:43:04.639
Um, given permission to do it really helps me to think outside of the box, think a little bit more curiously with all technology, not even just artificial intelligence or generative AI.
00:43:04.719 --> 00:43:06.320
It'll be the next thing too.
00:43:06.480 --> 00:43:13.760
Knowing that there's leadership support for me to go and execute and try some of these things out is really beneficial for me.
00:43:13.840 --> 00:43:15.360
It's been beneficial in my career.
00:43:15.519 --> 00:43:26.960
And I think you'd find benefit as well for the people on your teams to also find value, give them the permission to act and to learn and to use these tools in their day-to-day roles.
00:43:27.280 --> 00:43:28.239
Thank you very much.
00:43:28.400 --> 00:43:39.039
And thank you for your vast array of valuable and practical insights and just in general for sharing your valuable time with the listeners of this podcast and with me.
00:43:39.199 --> 00:43:40.000
Thank you very much.
00:43:40.239 --> 00:43:41.840
Thank you guys for having me.
00:43:42.159 --> 00:43:42.880
Absolutely.
00:43:43.039 --> 00:43:54.480
And I will link to the resources mentioned, including this incredible knowledge base that you've built out on artificial intelligence, in the show notes at brainchatterpodcast.com.
00:43:54.639 --> 00:43:59.760
And with your permission, I'll also include a link to your LinkedIn in case someone wants to connect with you.
00:44:00.000 --> 00:44:00.639
That sounds great.
00:44:00.719 --> 00:44:02.239
Yeah, please add me on LinkedIn.
00:44:02.400 --> 00:44:03.519
We'd be happy to connect.
00:44:03.679 --> 00:44:05.039
Definitely open a chat.
00:44:05.119 --> 00:44:08.639
And uh again, just thank you for listening and the time.
00:44:08.960 --> 00:44:12.480
Brainchatter is a production of Ken Chapman and Associates Incorporated.
00:44:12.639 --> 00:44:33.119
We assist clients across North America and beyond with leadership development, corporate culture transformation initiatives, behavior-based professional development for health and safety issues, individual and team assessments, conflict and performance management, succession planning, and team building, making a difference in organizations and individuals' lives for over 40 years.
00:44:33.440 --> 00:44:45.360
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00:44:45.519 --> 00:44:46.639
Thank you for your time.
