All Things Internal Audit Tech: AI Education for Internal Auditors

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The Institute of Internal Auditors presents all things internal audit tech sponsored by KPMG and Workiva. In this episode, Charles King talks with Kelsey Murphy about what it takes to drive meaningful AI adoption in internal audit. They discussed the role of Education Trust.

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Leadership buy in in everyday experimentation, from training tips to real world wins, this episode is a guide for teams looking to integrate AI into audit workflows with strategy, not just buzzwords.

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Well, hello and welcome to the Institute of Internal Auditors. All things internal audit podcast. I'm Charles king. I'm the innovation and internal audit leader for KPMG. And I'm here today with Kelsey Murphy from Workiva. Kelsey, tell us a little bit about yourself.

00:00:53 Speaker 3

Yeah, awesome. Well, I know that you and I know each other well. And through many introductions. But for those listening, my name is Kelsey. My prior roles have been really in the realm of both auditor and auditee, and that's allowed.

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We'd have a unique perspective into the role that I sit in today, which is a.

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Solution consultant at.

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Workiva So what is that? I get to have conversations every day with internal audit teams and specifically those that are looking to make an investment in technology. So with these conversations, my goals are to really understand their challenges, their requirements, their needs and ultimately showcase.

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Well, our tech.

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Technology can get them to that kind of desired future state vision, whether it be their internal audit team or even connecting to those larger business initiatives. So my focus is very centered on and all around governance, risk and compliance. But given the evolution of our product and where AI is today, it's expanded to more and more conversations.

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Around AI. So now I truly get to be an educator on how and why I can be such a powerful tool and differentiator for internal audit teams.

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Yeah, I love that. Well, you know, Kelsey, one of the things we were talking about before was the idea that just giving people an AI tool is not enough. You really have to educate people on how to use AI. And even though it's a fair, you know, the learning curve for AI is not particularly steep.

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Sometimes you know, not focusing just on the business value, but focusing on the business outcomes that you want to get can be really, really important, right or not. Focusing on the on the technology alone, but focusing on the business outcomes can be really, really important. So can you talk a little bit about the role of training and education in using AI?

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In the internal audit practice.

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Yeah, absolutely. I mean, I think if we just start technology aside, the internal audit teams that I'm seeing make an impact already have this type of mindset where they're moving beyond right.

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Being the typical function known as being a cost center to truly being an advisor to the business. Now when we bring technology into that picture, the value of that technology brings, whether it's audit software, whether it's AI.

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It it doesn't just stop at the how am I going to use this? How is this going to impact my day-to-day? But why does this matter? So when you are focusing your education around AI just on technology but not connecting it to those larger strategic business benefits, you're stopping at the how and and not digging deeper into the why?

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And I I see this all the time when I'm giving product demonstrations. I'm working with teams that want to make an investment in audit software as I'm going throughout these demonstrations, it's key to not educate them. How is this going to improve their day-to-day? But why does this provide a benefit to them, their team and the greater organization?

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So all that to say, right, I think you're selling your team yourself short. If you're just focusing on the technology and not having that mindset shift to really think more strategically.

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Yeah, it's funny. I mean, people that know me well have heard me say this so many times, but I think there's a focus on people asking what are the use cases. Right. Tell me the places I can use AI and I. I feel like in a lot of ways it's it's the wrong question.

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You know, I always joke that it's like asking a Carpenter, what are the best use cases for a hammer, really? Just depends on what you're trying to build, and I would much rather audit leaders and auditors focus on what is the outcome that I'm trying to achieve. And there are many different outcomes that you can use AI.

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To achieve that may be being more insights.

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Educating auditors better. It may be improving your audit clients experience putting less of the burden on them to educate the auditors to gather documents to, you know, define processes or it could be pure efficiency plays. I'm trying to reduce cost or reduce.

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The race.

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From my audits, but once you have a really clear sense of what your goals are, then the AI use cases that make the most sense are much easier to come up with. And I think that's a much more sensible strategy than starting with well, what are all the things that AI can do? Give me a laundry list and then I'll go pick some random things.

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No, absolutely.

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And what we're always trying to convey and get across to those looking to make an investment.

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Is.

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I know sometimes you're focused on right, the day-to-day impact. AI is a hot topic. It's a hot buzzword, but when you're able to synthesize and get to the deeper impact, that's really when right you, your team is going to have really more of that impact to the organization.

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And I wonder one of the approaches to AI.

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Education.

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Is to have a CPE day or an AI workshop, and you know in my experience those are helpful. Those are absolutely helpful. But I think you have to be a little bit more intentional about having more ongoing on the job type training. And I wonder what, what have you seen?

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With your clients that works. And what would your recommendation be to audit teams that are looking to have a better approach to AI training?

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Yeah, absolutely. I mean, you know while those one off cadences.

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Write those the CPE workshops, or, you know, even facilitate Workiva days can be impactful. It's just like your continued education for whatever certification that you have, whether your CPA, CIA, it needs to be ongoing and also consistently right. If you think about the audit practice in general, right, it's continuing to evolve.

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So, but I think we really need to establish a tone at the top, get buy in from leadership that hey, we are going to tie these objectives for AI uses to these larger initiatives and go.

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And start to integrate this into your weekly day-to-day meetings. I think there's small ways that we can keep a eye relevant, keep your team informed and engaged. I know on our reoccurring cadences internally where I'm meeting with my my smaller subset of my team we're just sharing and we're expected to share. You know maybe a 5 minute segment of.

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How did somebody use AI? What value did it provide?

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Who did it impact? Or maybe what did you even learn? So I think there's small ways to initiate a kind of a continuous learning and adoption of AI. I know too. With larger organizations, there's the ability to also start to share some of the learnings, use cases, impact, cross functionally as well.

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Know internally and for other teams that I've been talking.

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Into, you know, maybe some organization organizations are now having formal AI ambassador teams or a I win slack channels where that's going to allow for you to communicate the wins, cross functionally and then again right, facilitating that tone at the top from a from an audit leader perspective to engage and participate is going to go miles.

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Yeah, I think I love your focus on tone at the top. I think if the leaders are using.

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I that makes a huge difference. It's so impactful. And I think when the leaders are not just using it, but they're asking people about it, when maybe it's when you know the first time they see an audit report, they're saying, did you check this with our AI? You know, reporting prompt, there are work paper review prompt, those kinds of things.

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Can drive real behavioral change and then you know you talked about this idea of having, you know, what what I might call a champions network or some, you know, power users or whatever you choose to call it. But you've got a few people that are.

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Maybe because of the role they have on your team, or they're just natural inclination and interest that they gravitate towards AI and they be the people that.

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Those are the people that that everyone else is going to go to, to ask questions about what's working, what's not working. Maybe they're having trouble with getting the AI to do what they wanted to do and and that sort of network of champions can really be helpful. And then, you know, you talked about this idea of having more ongoing, you know, cadences.

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Whether it's sharing success stories or having a slack channel or a teams.

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Channel 1 of the things we do at KPMG is we have an office hours and our office hours tend to focus on success stories that teams have had and sometimes failure stories as well. I tried to do this, I couldn't make it work and then we'll try to pull lessons out of that where.

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We can either talk about why that didn't work, or we can talk about what you can do to improve or or maybe get a better outcome from it. But I think all of that like just ongoing tone at the top champions Network office.

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Stores sharing success stories, like all of those things, play a huge role in not just educating people, but driving the behavioral changes that you want education to result in.

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Absolutely. And you kind of mentioned, you know, sharing the the successes, but also the failures, that's what's going to happen when you start to get hands on with AI.

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The actual applicability in using the tool is going to help that learning curve and you get over that initial hump exponentially. So you know, I've learned so much from others on what works well and what doesn't that similar vein that you highlighted.

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Yeah. One of the ways that I got really into this and I think this is true for a lot of.

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AI users is you start using it in your personal life, right to do things on your own and you know whether that's simple things like travel planning, meal planning, you know, a lot of the the top, you know, top 510 use cases that people use AI for. And I think you learn a lot of lessons.

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When you're using it privately, that then when you get to work and you're using your enterprise.

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Instance of AI that's secure and safe. A lot of those lessons carry over how to write good prompts, how to provide good context, what AI is going to be good at, and what it's maybe not going to be as good at. You know, I wonder, has that been your experience? Did you start using it privately and then use it at work or did it go the other way?

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For you.

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It was actually the opposite way. Yeah, yeah. Yeah. So obviously big initiatives for us to kind of tackle that internally from an organizational level. But yeah, the the impact that can have on a a personal conduct, I'm using it every day both for work and personally it it's been so cool to see kind of the meal structures.

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We can plan the navigation routes. I can start to organize for maybe a trip that I have come.

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Going up, but for me it was actually the opposite cadence, but maybe that works a little bit better for me because you know, talking and working with internal audit kind of allows for me to be maybe a bit more structured and rigid in the prompt that I'm trying to derive that then translates well to maybe more personal fun use cases of AI.

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Yeah. Well, fair enough.

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I guess the learning goes in both directions, but I think bottom line, the more you the more you interact with it, the more you're going to learn about how to prompt, how to how to use it, how to think about the role that it's going to play in a.

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Other task you know, I do find that the initial learning curve is not very steep, right? Like you can set someone up with a generative AI tool like a like a GPT or a copilot or what have you. Jim and I. And getting them to get started is usually fairly straightforward.

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There is, I feel like an acceleration that happens when you get past that initial like ad hoc. I'm going to try some things to really being really competent a, you know, a power user, if you will, where you can really get a lot of value out of it that you know, education plays a role of that experience, plays a role in that, I wonder.

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Or like can you maybe talk through like what are some of the challenges you face when you go from being a a new AI user to a more advanced AI user? And what are some of the resources or tips and tricks that you found helpful at going from basic to advanced in generative AI?

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Yeah, absolutely. I think there's a, there's a few gaps or challenges that you.

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Space when you're starting to get comfortable with AI and one is right. Just how do I leverage this? You know what can I ask, how can I structure my prompt, but something that I found interesting was almost this challenge of trust and overcoming. Maybe skepticism of the results that I'm receiving. And so.

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Again, practice makes perfect to kind of understand the results and what derives a more accurate response, but.

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There are things that you need to be aware of, whether it's hallucinations, slight inaccuracies, misspellings of right, the output that you're receiving, and then I think about the role of internal audit. We have so much documentation, rationalization, justification to be doing. So the challenge that maybe.

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I've seen experienced myself is getting comfortable and being able to spot where maybe those inaccuracies lie.

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And then taking action to do my own independent research to compare contrast. Is this actually accurate so that I can have confidence and evidence to support and back up why I'm deciding to bring forward and output or response?

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What was your road to AI success?

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Honestly, a ton of through just experience.

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I've read through tons and tons of articles, whether it's from the IA or Workiva. Also releases some kind of best practices and tips and also through right making mistakes. There's the ability.

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To, you know, have a I kind of produce slides and some of the capacities that I use it and it looked great to me, maybe a little bit of the transcription of the the words were, you know, flipping the order. So when I took a a first pass, I was like, OK, great, I went to a call, presented this to a client.

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And.

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The words are spelled wrong, so right learning through failure it was something we kind of joke about and then opened up a a pathway to talk about AI in that capacity. But that's just something that kind of stuck with me. But again, through trials and tribulations is how I kind of uncovered areas to look for.

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I would be curious to your personal experience too, Charles.

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Yeah, it's interesting you say that I've learned a lot of lessons myself and I actually did take a few classes that I thought were helpful. But like you, I'm also an avid reader and podcast.

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Listener, but you know one of the things that I learned pretty early on is I have found for me that Gen. AI is a better editor than writer. And when I first started trying to use the tools, I would talk about what I wanted to write and I would ask it to produce.

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The content, like the meat of the content and I was rarely satisfied with the.

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But what I found was if I would do a brain dump of all the things that are swirling around in my head, and I have all these thoughts and all these ideas that maybe I haven't really organized yet, but I I'll just write almost stream of consciousness and then ask the AI to take that and put it into a more.

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Organized format that was very successful but.

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That was something that took.

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Some kind of trial and error, but you know I found today that the content that I think really matters still comes from my brain and my experience and my discussions with others and sometimes just shaping that into more meaningful content is where I use AI a little bit more. But I think we all have these lessons that we learn along the way that you know whether it's through formal education, through experience.

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On the job training or you know, like you said, podcasts, articles, et cetera, all of that experience will will yield, you know, a better AI.

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Well, one thing I wanted to add on that I I love that you kind of recognize that it's a better editor than a creator. I've also heard rather than kind of writing out your your streams of consciousness, you can actually utilize talk to text, have your phone out, walk around your room, just, you know, brain dump, truly brain dump. And then right, it's going to provide that really.

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Organized summary and and themes of you know how can you now take action. So I like. I thought that was an interesting use case that we've also seen and used internally as well.

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Yeah, absolutely. We we have that with our KPMG tool. We can talk to it, which is.

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And then in my personal use I use advanced voice mode on some of my AI tools and I can talk to it. And you know, I I joke with my friends only half joke. But you know, I used to listen to podcasts all the time in the car. And now if I want to learn about something or.

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I.

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Want to hear about something? I'll just have a conversation with my AI and and I learn. I learn so much.

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And I can sometimes it just helps me organize my thoughts. Sometimes I learn new things, but it can be a really, really powerful.

00:18:19

Cool.

00:18:20 Speaker 1

Hey, auto pros, we just wanted to give a shout out to our sponsors of this episode. Let's start with KPMG, recognized by source consulting as a top AI advice and implementation provider, KPMG offers internal auditors, Gen. AI services that include technology, strategy, use case development and AI governance.

00:18:40 Speaker 1

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00:18:48 Speaker 1

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00:19:08 Speaker 1

And empower teams in a secure audit ready AI powered collaborative platform. You can find the links to both of our sponsors in this episodes show notes.

00:19:21

So you know.

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AI is driving a lot of change in organizations, not just in the way that we individually act as auditors, but the way that auditors collaborate together to achieve the mission of internal audit. And so, you know, part of.

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Driving the adoption of AI, or really any technology, right, analytics, automation, you really have to have a culture of.

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Innovation. A culture of willingness to experiment, and we talked about tone at the top earlier, but I wonder, what are some other things that internal audit leaders and internal audit teams should do to drive a more innovative culture and make sure that people are comfortable using some of these?

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Tools.

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One thing that I think can make a big difference is whenever you're doing something for the first time, it's going to take additional budget. It might take additional time.

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And I think recognition on the part of leadership that this is going to take more time, it's not going to be faster, at least not the first time and probably not the second or third time too. It will take a little bit more time and a little bit more effort for the teams to understand how to use these tools, probably make a few mistakes.

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Have a few do overs if you will, and figure out what's the right way to embed this into your workflow. And I think being.

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Being, you know, really being open about that can be really, really helpful.

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I'll maybe add some color to where I've seen one organization have some great success. So they obviously.

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Has.

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You know, tone at the top. We've done a full.

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The day with them, where we had staff auditors as senior managers in the room and there was a segment where we focus solely on a I use cases impact what works well, how are other customers leveraging these abilities.

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So that was kind of the foundation, right? Very anchored and tied to right? Our conversation where AI education can kind of lead to these.

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More impactful moments and improvements and buy in from the entire internal audit team, so kind of going forward from that education piece that we had with that company for our Workiva Day.

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I was reached out by this audit supervisor and she kind of highlighted how she used our internal AI tool to facilitate and expedite their risk assessment.

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Process so their risk assessment process was more interviews, survey style workshops where they kind of interviewed 30 or so key stakeholders. They received responses through transcripts or even written summaries. So it was as simple as just, you know, taking what they learned, summarizing finding trends in this data.

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To identify top risks, what's mentioned the most? The least that then saved them over 100 hours. Their risk assessment process. Now that's over 2 weeks of work, depending on how long of work weeks y'all are working. But that's super impactful, but not it didn't just stop there to the reduction in time.

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But they were then able with that free time to scale and tackle other projects, like incorporating AI into their fraud risk assessment and maybe revamping their overall audit universe. So this was coming from, you know, you know, a leader in the space but sharing those wins and examples again with your team.

00:22:58 Speaker 3

Right.

00:22:58 Speaker 3

Others to actually see the quantifiable impact, I think we'll gain excitement and momentum for the rest of the team to get buy in.

00:23:06 Speaker 2

Yeah, I love that you mentioned quantifiable. I'd love to talk a little bit about how how you're seeing teams measure the impact of AI, what you know, whether that's at a, a personal level like in terms of goals or or more at a departmental level in terms of strategic key performance indicators.

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For internal audit, what are some ways that audit leaders can think about measuring the impact of this?

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Technology.

00:23:33 Speaker 3

Yeah. So from a quantitative perspective, I think it's very easy for us to see, you know, from an audit cycle perspective, where can we focus on maybe one or two areas and we can start to see a time reduction time reduction can sometimes translate into cost savings that right that return on an investment that you're seeing.

00:23:54 Speaker 3

But I also like to highlight and illustrate not just what you can quantify from a reduction lens, but also what can be gained. So I know to right maybe the goal and objectives for the organization is to have.

00:24:08 Speaker 3

Have you know improve audit quality? Additional audit coverage that shift from right core internal audit socks work to maybe more strategic activities. So I'm seeing it really all across the board, but I would caution and encourage you all to think creatively on the impact that it ultimately.

00:24:29 Speaker 3

Valves.

00:24:30 Speaker 3

Data tells a great story, but there's also a lot of qualitative aspects that can be gained with AI usage and enhancement to your team.

00:24:38 Speaker 2

Yeah, yeah, I love the idea of of not just thinking about AI as a race to the bottom in terms of, you know, fewer hours, shorter audits, but really thinking about it as a race to the top right. Better audits and higher quality.

00:24:53 Speaker 2

Reports and some sometimes things.

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Like quality can be hard to measure.

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But some of the ways.

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That internal audit teams can think about this is looking at your Q AIP.

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Outputs right? You have an internal quality review that you do every year and.

00:25:09 Speaker 2

You know some.

00:25:10 Speaker 2

Subset of audits gets reviewed and and you'll you know different people do it different ways, but they may have a score. They may have a set of themes or or observations that they're, you know, whoever's doing the internal quality review reads back.

00:25:25 Speaker 2

To the team and hopefully in the world of AI, you'll see some of the maybe historically persistent quality issues we'll start to go a.

00:25:35 Speaker 2

Today, hopefully you'll start to see fewer thematic issues where you're getting you're not seeing the same issues from audit to audit. I think that can.

00:25:45 Speaker 2

Be really helpful.

00:25:46 Speaker 2

But there's other things that you can look at too, like mini internal audit teams do a post audit survey, and if you're giving a higher quality.

00:25:55 Speaker 2

Audit you should in theory see improvements in survey scores in places like the experience of being audited, the level of knowledge of the auditor, the amount of time or effort that the audit clients had to spend.

00:26:12 Speaker 2

In performing performing their part of the audit, all of those things are maybe not direct attributes of quality, but they're good proxies and I think easily, easily measurable and some other things you might think about are like adoption stats. If you have the ability to see how often.

00:26:31 Speaker 2

Your people are using AI and maybe even I mean, depending on the size of your team, it may be relevant to think about are your staff auditors using it more often than your management, or are your management not using it at all?

00:26:47 Speaker 2

Where are you seeing adoption or the IT auditors using it more than the general business auditors or or something like that, but there may be stats you can look at both from an outcome standpoint and from an activity standpoint to get a more.

00:27:00 Speaker 2

Holistic view of the impact that AI is having in transforming the way that you deliver audits.

00:27:08 Speaker 2

Kelsey, as we look to the future, we are at a really interesting time right now in the profession. AI is reshaping the way internal audit teams operate.

00:27:20 Speaker 2

And I know at Workiva you have been putting together new features and functionality that have over the past year have been released. And I know there's several, several new things in the pipeline as you think about the future of the profession, you think about the technology that's going to enable that future. Tell me a little bit about.

00:27:40 Speaker 2

Your vision about where Kiba's vision, for how the work of audit is going to change over the next few years.

00:27:50 Speaker 3

Absolutely. I think the work of internal audit is going to get just that much more exciting.

00:27:55 Speaker 3

It's going to transition to right us doing less tedious manual activities and time with right. The deeper analysis allowing us to nerd out in those specific areas where right we can maybe tackle those additional product projects those.

00:28:10 Speaker 3

Advisory and consulting consultative impacts that actually make a difference from an operational lens.

00:28:17 Speaker 3

How we're seeing that transcribe into right, how we're leveraging AI within Workiva today?

00:28:25 Speaker 3

Is it is the magnitude of ways right? We are leading our products to now be AI driven rather than right having you know the standard chat box and prompt, although that still provides great value. Add an insight to be added, but it's just a bit more integrated within the workflow.

00:28:45 Speaker 3

So let's say that you're performing a gap analysis or rationalization on your risk and control matrix.

00:28:51 Speaker 3

Cities within moments, it's going to suggest maybe some AI prompts of hey, I'm noticing that XYZ control does actually not have any risk mapped to it or has key documentation attached to this risk and control detail and roll up and maybe not the other. So having that more integrated workflow where it's going to start.

00:29:13 Speaker 3

Suggesting action to be taken place, of course with write that layer of professional expertise, expertise and.

00:29:23 Speaker 3

And additionally, our goal with technology is to make your lives easier, reduce a lot of the efforts that take the most time in your day-to-day. So every conversation I get on customers are asking, OK, well, can I create a flow chart for me? Well, can I do my testing?

00:29:43 Speaker 3

And that's really where we are getting towards. Well, it's not going to be direct solely 100% reliance on the API function. We're going to be a compliment. So that right, it's going to allow you to have more confidence in your conclusion.

00:29:56 Speaker 3

Actions create those conclusions more efficiently and effectively, and then allow you to expand your work to write the value, add that you all are here to provide to your organizations.

00:30:08 Speaker 2

Right. You know, I don't think any auditor got into auditing because they like ticking and tying working like everyone. I know that that really loves this profession that we're all in. They like the problem solving. They like the root cause analysis. They like the the interaction.

00:30:26 Speaker 2

With all the different parts of the business and feeding that curiosity, that understanding how things work and why things work and maybe why things aren't working and some ways to solve that, the auditors that I think are really passionate about audit, they like those questions and usually the red pencil work if you will.

00:30:47 Speaker 2

Of going back and forth and saying, does this number agree to that number, like the classic staring compare type work that auditors do?

00:30:55 Speaker 2

If we can.

00:30:56 Speaker 2

Get a I or or any technology, whether it's analytics or or some kind of automation.

00:31:00 Speaker 2

Tool to do.

00:31:01 Speaker 2

That.

00:31:02 Speaker 2

I don't think anyone's.

00:31:03 Speaker 2

Going.

00:31:03 Speaker 2

To miss it, I think the work that we what we really want is we want to go have that stakeholder interaction. We want to see improvement in processes, we want to leave, you know, leave a function a little better than we found it. I think that's that's really, really important. If this helps us do that.

00:31:22 Speaker 2

You know, maybe that means we get to do more audits every year or we get to do bro.

00:31:25 Speaker 2

Other audits or or we have, you know, a lot more confidence in our conclusions because of the tools that we have. I think that's really going to make make our profession better and and, you know, for me at least, that gives me a lot more job satisfaction at the end of the day. Then, you know, some of the manual work that that AI is.

00:31:46 Speaker 2

They may be able to do now and in the future. Kelsey, any kind of, as we wrap up here any any last.

00:31:53 Speaker 2

Thoughts or recommendations for teams that are thinking about using AI? Maybe in workiva? Or maybe you know, maybe outside of Workiva if they've got a a copilot or a white label. You know, Jim and I are TPT type solution. What what would you suggest people use AI for or?

00:32:12 Speaker 2

Do with their AI that maybe they're not doing now.

00:32:16 Speaker 3

Yeah, I would say, right, if you are not using AI today.

00:32:20 Speaker 3

You need to start right after you listen to this podcast, right? If you are not taking the actions to get educated, familiar with.

00:32:30 Speaker 3

Right, just simple AI abilities. You're going to fall behind because this is an ever changing landscape. It's moving fast, but again, there are so many larger aspects to AI that technology is providing, but it's OK to start small in right basic day-to-day productivity.

00:32:50 Speaker 3

Points think about maybe one to two things that you can do in your day-to-day that I can help you with and start to build from. There is really how I've seen myself. Others really start to gain that confidence and reach to those a I use cases that are a bit more tied and strategic to those larger business initiatives.

00:33:11 Speaker 2

Yeah, that's great. And and I'll add my two cents in here. I think. I think getting started is absolutely critical like the ostrich strategy is not going to work. You can't just put your head in the sand and hope it's going to go away. It's it's.

00:33:26 Speaker 2

But I also think with any new technology, whether it's e-mail or texting or cell phones or social media, we establish norms over time that are not ever going to be written in a governance document or a policy document. And we established those norms through.

00:33:45 Speaker 2

Talking about what's working and what's not and what we like about this technology and what we don't like about this technology and the role that we want AI to play in our working life and and no one can dictate those norms, right? Me, me, you, the IA. No one's going to come along and say.

00:34:03 Speaker 2

Here's here's what's acceptable and what not acceptable. That's going to be something that we.

00:34:09 Speaker 2

Socially, negotiate right professionally, negotiate and and I.

00:34:15 Speaker 2

Would encourage us all to.

00:34:17 Speaker 2

Really talk about how we use AI at work and you know what we like about it? What we don't like about it, where we think it's should be used, where we think it shouldn't be. Uh.

00:34:29 Speaker 2

Then we're probably all going to disagree for years, and we may never all agree. But I think what we're going to find is we'll start to.

00:34:35 Speaker 2

Converge on what is?

00:34:38 Speaker 2

Let's say socially acceptable at work in terms of how to use a I and I and I think that's a really important conversation to have, because norms will develop. It's just a matter of how consciously we shape those norms.

00:34:52 Speaker 2

Where we allow ourselves to be shaped.

00:34:54 Speaker 2

By those norms.

00:34:55 Speaker 3

Couldn't agree more.

00:34:56 Speaker 2

Kelsey, it's been a pleasure to talk with you today on all things internal audit. I really appreciate you.

00:35:02 Speaker 2

Sharing all of your great insights and thoughts about AI and AI education, we get to do it again soon. So for those of you that are going to amplify this year, we'll we'll see you there. Otherwise, keep auditing, keep learning, keep using AI and we'll see you soon.

00:35:20 Speaker 1

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