# How Two Students Taught 52 Auditors to Build AI Agents

An episode of [All Things Internal Audit](https://invisiblenetwork.ai/podcast/1758983201), 15 Sep 2026, 26 min.

Audio: https://traffic.libsyn.com/secure/1984d534-f92f-4772-a722-6ac47400e67e/ATIA_-_UVU_Students_V4_MP3.mp3?dest-id=3677289

The Institute of Internal Auditors Presents: All Things Internal Audit In this episode, Alex Rusate talks with Ivan Diaz and Mohamed Maiga, two Utah Valley University students whose curiosity led them to build practical AI tools. They explain how AI agents go beyond chatbots to automate repetitive audit work, why understanding the intended output is essential for validating AI-generated results, and how organizations can begin adopting AI without sacrificing security or human judgment. • HOST: • Alex Rusate, CIA, CISA, CPA, AAIA, CERP, CRMA, CCSA Senior Vice President and Director of Internal Audit, Arrow Financial • GUESTS: • Ivan Diaz Accounting Student, Utah Valley University Mohamed Maiga Cybersecurity Graduate, Utah Valley University KEY POINTS: • Introduction: Students Teaching Auditors About AI Agents \[00:00:00–00:00:35\] • Ivan's Introduction to the Internal Audit Conference \[00:00:36–00:01:33\] • Finding an Audit Process to Automate \[00:01:34–00:02:44\] • Building an AI Agent Without Technical Experience \[00:02:45–00:04:13\] • Improving Results Through Iteration and Multiple AI Tools \[00:04:14–00:05:47\] • The Difference Between Chatbots and AI Agents \[00:05:48–00:07:05\] • Troubleshooting an Agent and Selecting the Right Model \[00:07:06–00:08:20\] • Validating AI-Generated Audit Work \[00:08:21–00:10:24\] • Designing Effective Instructions and Identifying Red Flags \[00:10:25–00:11:43\] • Protecting Confidential Information When Using AI \[00:11:44–00:13:12\] • Using Feedback to Strengthen an AI Agent \[00:13:13–00:14:54\] • Mohamed's Introduction to AI Agents \[00:14:55–00:16:38\] • Using an AI Agent to Navigate Purchasing Policies \[00:16:39–00:18:26\] • Automating Repetitive Work While Preserving Human Judgment \[00:18:27–00:19:16\] • Building an AI Agent for Cybersecurity Exam Preparation \[00:19:17–00:20:43\] • Reducing Hallucination Risk Through Sources and Validation \[00:20:44–00:22:26\] • Advice for Internal Audit Leaders Adopting AI \[00:22:27–00:24:24\] • Starting Small and Applying AI with Purpose \[00:24:25–00:25:17\] • Closing \[00:25:18–00:25:29\] IIA RELATED CONTENT: Interested in this topic? Visit the links below for more resources: • The NextGen Audit Mentor — Internal Auditor magazine (August Issue) • IIA Membership • Global Internal Audit Standards • Artificial Intelligence Auditing Framework • Knowledge Center: Artificial Intelligence Visit The IIA's website or YouTube channel for related topics and more. Follow All Things Internal Audit: Apple Podcasts Spotify Libsyn Deezer

## Transcript

[0:02] The Institute of Internal Auditors presents All Things Internal Audit Tech.
[0:07] What happens when 52 internal auditors sit down to learn how to build AI agents,
[0:12] and the experts at the front of the room are two college students who figured it
[0:15] out on the fly?
[0:16] I just screamed, "Yes. Yeah, I can do that," not knowing, and I had to go on
[0:21] YouTube for about 10 hours to learn about AI agents and how they work.
[0:25] Yeah, I had no idea what I was doing, I'll be honest.
[0:28] In terms of building the agent, I had no foundational understanding.
[0:31] Here's host Alex Rosati, starting with accounting student Ivan Diaz.
[0:37] So Ivan, can you help set the scene for us?
[0:39] You were in a room full of auditors at UVU.
[0:43] Can you give us an understanding of the event that you did, and how'd you end up in
[0:47] front of everyone presenting on this?
[0:50] So there's an annual conference for Utah State higher education for the internal
[0:56] auditors. And so what this event was is it gathered the internal audit departments
[1:00] from every higher education institution in the state.
[1:04] And my institution where I'm working at, they were the ones in charge of
[1:09] presenting, delivering recommendations or ideas on certain topics.
[1:14] And one of the topics this year was AI.
[1:17] And prior to this, I wasn't on the list of presenters.
[1:22] I simply had created this agent beforehand. I expressed it to my managers.
[1:26] They saw value in it. And from there they just asked if I would simply present what
[1:30] I built out, if I could give a run through.
[1:32] And so that's really where this started.
[1:34] Can you give me some background as to how you came about this idea and how you
[1:39] actually executed on it?
[1:40] I built it out of curiosity, really.
[1:43] I think as a student, you hear a lot about AI this, AI that.
[1:47] I'm someone who does listen and read to a lot of what's going on in the business
[1:52] world, and I found out there was a need to adapt to this new technology.
[1:57] And so in my current position, I'm an internal audit intern at my university.
[2:01] And so, in that position, I see a lot of, obviously, the things going on around the
[2:06] university, and there was a process that works into our revenue work paper that
[2:11] takes departmental spending using purchase cards,
[2:16] travel. And we test that, obviously ensuring that what departments are purchasing
[2:21] is up to policy. And so I saw that, and the current process for
[2:27] me was a lot manual work, a lot of pulling from the accounting system,
[2:33] formatting Excel. And with what I knew, I kind of just dove into it.
[2:38] I opened the AI and said, "How can we automate this?" And from there I ran with it
[2:44] and built the agent.
[2:46] So it's really interesting what you said around you prompted the LLM
[2:51] to be able to help you to build the agent.
[2:54] It's not like you had to have a playbook that you knew right off the bat.
[2:58] Is that correct? Because a lot of people that you ask that haven't gone through
[3:02] this process before think it's too daunting because they don't know how to go about
[3:05] it. Can you get into a little bit of background into that?
[3:09] Yeah. I had no idea what I was doing, I'll be honest.
[3:11] Again, a little bit of background. I used the chat bots for school, get some ideas.
[3:17] But in terms of building the agent, I had no foundational understanding.
[3:21] So yeah, what I did is I explained to it what my issue was.
[3:25] I explained to it what the current process was.
[3:27] I even fed it some of the documents or the template that we use that helps
[3:33] process the whole transactions and whatnot.
[3:36] And so from there I was able to get an idea of what could be done.
[3:41] And again, like I mentioned, just continued iteration.
[3:44] I think it's very important to understand the process that you're trying to
[3:47] automate. And so because I had done the manual process multiple times now, I
[3:52] understood what the final output should've looked like.
[3:55] And I think that really helped the development of the agent.
[3:58] But again, I would say to people who are scared who don't know how to start, just
[4:02] ask the question. I think AI is smart enough to really understand.
[4:06] And just being able to get your feet wet, ask the question, and see what you can
[4:09] do. I think a lot of people will be mind blown at the results that they would get.
[4:14] So I've seen a lot of people that try to use a form of gen AI, and the first
[4:19] iteration doesn't come out what they want, and they just put their hands up and say
[4:23] it doesn't work. What would be your recommendation for that?
[4:27] Yeah. I don't want to sound like a broken record, but just keep iterating.
[4:30] I think once you really see a glimpse of what it can produce, I think you
[4:36] are open to a lot more opportunities.
[4:38] And so there were many times where I just wanted to give up.
[4:41] I would get frustrated because I knew what I wanted it to look like, but it wasn't
[4:46] giving me the output. And sometimes it's changing your whole framework.
[4:50] Sometimes it's using other AI tools, and that's what I dove into a little bit.
[4:54] I saw that the AI tool that I was using, Copilot, it wasn't doing exactly what I
[4:58] wanted to. And so I went to other AI LLMs.
[5:01] I went to Claude, I went to Gemini, and I changed my perspective, and I went to
[5:06] them. I got their feedback, which then I fed into Copilot.
[5:09] So I would say to use all the tools around you.
[5:11] That's great. Yeah. I've seen it so many times where one thing's broken, people
[5:16] give up. But if you start working through and saying, "This specific portion isn't
[5:20] working," it can be able to help you troubleshoot through that and fix it.
[5:24] So those iterations really help to be able to try to figure out the why.
[5:28] And the best thing is talk to it like it's a coworker or another student that
[5:32] you're working with. Because if you have a problem in real life, you have to work
[5:36] through those iterations and have that dialogue to be able to resolve it.
[5:39] So that really helps, and a lot of people don't.
[5:42] They treat it more like a calculator than it is a person, and treating it like a
[5:46] person really helps to get the best results.
[5:48] Some of the audience might be varying who's listening, and some of them might only
[5:53] have the chat bot LLM experience versus any kind of agent experience.
[5:59] Can you talk to that a little bit about the difference with that, and why you chose
[6:03] an agent to be able to help out this group of internal auditors versus the
[6:07] conventional chat bot function?
[6:09] So the chat bot, like a lot of us have used, it's more so I ask a question, it
[6:14] gives me a result. I ask how many states there are. It will return 50, right?
[6:19] So it's a simple back and forth, whereas an agent is more of an augmentation
[6:24] aspect. And so you are working alongside AI. You are telling it what it should do.
[6:28] It then produces output. You then reinvent its output.
[6:32] And so it's a little different, and it works differently.
[6:36] Agents are tasked, and they're meant for manual things that can then be
[6:41] automated, right? So the process I did, me feeding that into a chat bot won't
[6:46] produce an output, right? It can't. That's not how it's created.
[6:49] However, with an agent, because I prompted it in a certain way and gave it
[6:53] instructions, it was able to produce an output that I can then use.
[6:57] So I would simply explain that the chat bot is more of a Google search almost,
[7:02] whereas an agent is more of a coworker or partner.
[7:06] How about areas where you had speed bumps, lessons learned that you could be able
[7:10] to share with the audience so they don't encounter the same kind of problems?
[7:14] The biggest thing, and this is what I actually shared when I did present this to
[7:18] those 52 auditors, was in Copilot, you can change the model.
[7:24] And so ordinary model as a default version is to keep it on the auto version.
[7:30] And the description for that quite literally is will think how long as I want to,
[7:34] right? And so for more complex tasks, I would definitely change it to a thinking
[7:40] model, something that can actually comprehend what you are doing.
[7:44] And once I changed it to a better model, I found out my results were more
[7:48] consistent and were actually better.
[7:51] And for those who are starting to build out, again, back to the initial question,
[7:57] just keep iterating with AI. I would say when you run into an issue, ask the model
[8:02] why it's running into that issue, ask what the obstacle is, and ask for the way
[8:07] to reiterate and fix the issue. I think that for me was the biggest help
[8:13] during this process was to simply ask the AI why it was making that issue.
[8:17] It would tell me, and from there we would find a way to change the instructions.
[8:21] As a follow-up to that, any good coworker still needs to have their work reviewed
[8:25] and everything. What recommendations would you have if you're building out an
[8:29] agent, not only on the front-end design, but on an ongoing monitoring perspective
[8:33] to make sure the outcomes that you're getting are what you're intending from a
[8:37] design perspective?
[8:39] This is something I discuss a lot with my managers and the director of internal
[8:44] audit, because I think that's the most important thing.
[8:47] And for us who create recommendations based on the output that we are giving,
[8:53] it's very important that we automate our work and we are able to validate it as
[8:58] well. And so the thing I did personally was I built a verification tab
[9:04] within the output, and so I told the AI to validate its own work and to make sure
[9:08] that it's including all the values, all the transactions from what I'm pulling.
[9:13] And so yeah, just be able to, again, going back to your earlier point, communicate
[9:18] with AI that it's important that you want to validate this and create a way that
[9:22] you find more efficient for your company and for the whole process to automate.
[9:27] To create a way to validate the work.
[9:31] I've seen a lot from a front-end design perspective, validating, like you said,
[9:35] reconciling back to original reports, having a use case example where you know the
[9:41] items that should pop. If you had five that should pop, did five actually pop?
[9:46] And doing those use case testings.
[9:48] And then on a go-forward basis, the human in the loop, or I know some people have
[9:53] coined it the auditor in the loop in internal auditing, making sure you have that
[9:57] ongoing monitoring to make sure has something shifted where it might've worked when
[10:02] you initially designed it, but now it doesn't work anymore.
[10:06] Yeah. And it's always good to validate it with the core data, the main data.
[10:11] And that's what I do. If I find a transaction that I think should be looked at, I
[10:15] say, "All right. Let's make sure this was in the initial output from the accounting
[10:19] system." So that's another way you can go back and forth from what the AI produces
[10:23] to the data you actually know is correct.
[10:26] Yeah. As you're going through, it's definitely trying to build out the
[10:29] comprehensiveness of what is a red flag, what would constitute something that
[10:35] should be triggering is a big part of the design there.
[10:38] Because you can say for X, Y, and Z, flag these as regular activity,
[10:44] but what about A, B, and C that might also be flagged?
[10:47] So it's super helpful as you're building that out to have a comprehensive list.
[10:52] And me personally, I'll try to come up with a list together on my own, but then
[10:57] I'll work with Claude or ChatGPT to be able to say, "Here's what I have.
[11:02] Am I missing anything here? Are there risk factors I haven't considered?" And
[11:06] really stress test it through that and a couple different channels before
[11:11] finalizing that list. So that really helps to be able to ensure that you're getting
[11:15] a good end product, because sometimes it's only as good as the instructions as
[11:19] provided from a prompt perspective.
[11:21] And I think that goes back to really understanding what the output should be.
[11:26] I think a lot of people have a misconception of AI will do it for me.
[11:29] But if I don't know what the output should be, then how can I validate the work?
[11:33] And so by understanding what the red flags are and what would cause an issue, I
[11:39] think that in and of itself will increase the effectiveness of the AI agent.
[11:44] I use the enterprise version of Copilot, so obviously that is given out by the
[11:49] organization, and all the data that I feed it is protected.
[11:54] To those who have their own and that isn't protected with the enterprise, obviously
[11:59] be careful. Obviously, I think the most important is talk to those who may be more
[12:04] experienced, whether it be a manager, whether it be IT.
[12:07] Understand the environment, understand that there are ways that giving AI can leak
[12:12] confidential information, and obviously there are security risks.
[12:15] But personally, I did use my local to help me with this agent I was building.
[12:19] I never fed it the actual data. But what I did feed it was my questions.
[12:24] What I did feed it was the instructions I gave it, because the instructions don't
[12:27] lay out any confidential data. It's just telling it what the agent should do.
[12:31] And so I think it was a good way for me to, again, just get a better understanding
[12:36] and more ideas of how I can better my instructions.
[12:39] So yeah, I would just say communicate with those around you.
[12:42] Understand what's confidential, what's not.
[12:45] And for the tools you do have available, definitely use them.
[12:49] But I'd definitely be careful with what you're feeding it.
[12:52] And obviously never want to feed AI stuff that is confidential to the company.
[12:57] So one of the recommendations, always look at acceptable use policies within your
[13:01] organization and talk to your information security team before you put anything in
[13:05] there. Even if you think it's okay, just always check to make sure that that aligns
[13:10] up with the organization's risk appetite.
[13:13] So any lessons learned that you guys had as far as from the start of this project
[13:18] to the end? Where did you grow and learn and understand better from the needs of
[13:24] internal auditors as well as the technical onuses of creating an agent?
[13:30] Getting various perspectives I think I learned a lot from.
[13:34] When I initiated the project, I only saw it from my eyes and from the person
[13:40] doing the manual work. But when I got the perspective from my manager, for example,
[13:44] I was able to alter the agent in ways that he saw to be more effective.
[13:50] And so that was implementing a validation sheet inside the Excel file that it was
[13:55] given. And so I think the best thing I learned from start to finish was just being
[14:01] able to change perspective and being able to kind of see the vulnerabilities.
[14:06] Because I think when you talk to someone who's more experienced, they can give you
[14:10] a situation that you may have not thought of.
[14:13] And so I would say from this agent to the next agent, I definitely saw in that
[14:17] aspect a little bit. I realized that there was a lot of interest in the topic.
[14:23] I found that a lot of people were trying to get into it, and were asking questions
[14:28] similar to what we're answering now, and I enjoyed that.
[14:32] I really do think that a lot of people are starting to realize that there is a need
[14:36] to adapt to this new technology, and I believe people are finding ways in their own
[14:41] work to automate. And so although I haven't been able to communicate with any of
[14:45] the auditors since the presentation, I really felt like throughout the presentation
[14:49] they were engaged, and they were really eager to be able to apply what they were
[14:53] learning into their own situation.
[14:55] While Ivan was solving work paper headaches as an audit intern, recent
[14:59] cybersecurity graduate Mohamed Maiga took the stage from a totally different angle.
[15:04] Here's Mohamed.
[15:05] So Mohamed, appreciate the opportunity to be able to connect.
[15:08] Ivan walked us through some components of what his element of the project was.
[15:13] But could you be able to outline what you did on your side for the project?
[15:17] I kind of stepped into that project a bit abruptly.
[15:22] It was very much out of my own curiosity.
[15:25] I met the senior director of the College of Applied AI Institute at one event where
[15:31] students were essentially presenting about how AI can be applied in the classroom,
[15:36] what bothers them about AI, and that's how I got to meet the senior director, Tyler
[15:41] Small. And at the end of that meeting, he asked me a question, "Can you build an
[15:45] agent?" And at the time, I had no idea what an agent was.
[15:50] I've built it in the past not knowing that I built an agent before.
[15:55] So with my very much excited, willing
[16:00] to learn, college student heart, I just screamed, "Yes.
[16:04] Yeah, I can do that," not knowing. And I had to go on YouTube for about 10 hours to
[16:09] learn about AI agents and how they work.
[16:12] So for the specific example of the 52 internal auditors from the USHE,
[16:19] Tyler Small, the director of the AI Institute, reached out to me and he said, "Hey,
[16:23] Mohamed, do you want to present at this event?" I was like, "Yes," again with my
[16:29] college student curiosity. He gave me this project that he had presented in the
[16:34] past, so I was just basically using his work to teach other people.
[16:39] Here's how it works. People had problems at UVU with pro cards, the procurement
[16:45] cards that people use to make purchases on campus.
[16:48] And the reason is there's a lot of rules.
[16:51] Like the document probably has 99 pages, and people are doing hundreds of purchases
[16:57] every day, right? So for every single one of those purchases, you have to ask your
[17:02] question, is this right? Does this align with our policies?
[17:06] Am I going to get in trouble? Am I going to get deducted points for this purchase?
[17:10] So what Tyler Small did to tackle that problem, he created an agent that
[17:16] essentially has all the knowledge of the different policies that Utah Valley
[17:19] University has to be able to explain to people whether or not their purchases are
[17:25] legit in just one simple chat instead of reading through 100 pages, having to do
[17:30] Control + F and looking keywords. Maybe they're not even there.
[17:34] So that's the project that I presented during that presentation, and essentially
[17:40] how to build an agent from scratch, meaning from the first step to the last step.
[17:46] And I focused on telling them about the importance of AI, what it
[17:52] can do for us, and what it cannot do and definitely should not do, which is the
[17:56] reviewing part. Which is where we need the expertise of those internal
[18:02] auditors, right? You have to be able to tell the difference between AI slop and
[18:07] where it's doing well in your industry.
[18:10] But I quoted President Tuminez, president at the time of Utah Valley
[18:15] University, who usually says, "We are human beings, not human doings." And I think
[18:20] with AI, we're getting the ability to spend a lot more time on
[18:26] being instead of doing. Because to be honest, in every single job that is on a
[18:31] computer, we've created a lot of Meaningless meaningful tasks, meaning
[18:37] the output is very important.
[18:40] Huge thing about being an internal auditor is having that level of professional
[18:44] curiosity to be able to be open to learn, because there's no expectation that
[18:49] someone's going to start as an internal auditor and know every aspect of the
[18:52] business, doing the repetitive tasks that you've already built a test of design
[18:57] review over, and then you've got to do 24 more samples of it.
[19:01] Have an agent be able to do that while you're focusing on higher-risk items that
[19:06] add more value to the organization.
[19:08] Perfectly summed.
[19:09] During your presentation to these internal auditors, did you go through any kind of
[19:13] specific examples, a case study? Can you expand on that a little bit?
[19:17] I also had some personal examples in my own learning process.
[19:23] I just recently took the Security Plus exam, which is a very important professional
[19:28] certification in the cybersecurity space.
[19:30] See, I had a lot of the knowledge about it already through my courses and stuff,
[19:35] but I had a hard time coming to pick an exam day due to graduation happening
[19:41] and so many things just scrambling together at the same time.
[19:45] So I was like, how can I make my learning process faster with AI,
[19:50] right? So I had a lot of course materials from previous classes that I took.
[19:55] There was YouTube content from Professor Messer.
[20:00] He is a quite renowned professor in the cybersecurity space.
[20:03] I had all those information combined in one simple agent that would create me a
[20:08] website where I would be able to learn all the content without going outside of
[20:14] that context, right? So what agents allow us to do is take the knowledge that you
[20:20] already have, put it inside of a knowledge base that is just focused on your own,
[20:25] and then use that to learn or to teach or to perform tasks.
[20:29] In this case, it helped me to learn.
[20:31] And in just within five hours of studying that course, I was able to pass the exam.
[20:36] So that is just one of the many use cases where I use AI on a daily basis to learn
[20:42] and to perform tasks.
[20:44] When you were working on this project, what were the different validation
[20:48] procedures, what you guys did for this project?
[20:50] So we told the AI in our prompt to give us where he got that information from.
[20:56] So it wasn't like just rambling up. Okay, yeah, you can do that purchase for sure.
[21:01] Yes, you can do that purchase on page 97, article dot, dot, dot.
[21:07] Okay, that's where I found the information.
[21:09] You click right there, you see the information. Okay, it's validated.
[21:12] It's in the document. It didn't make that up. So hallucination is a big concern.
[21:17] So I think that's where the expertise is needed as well.
[21:20] Sometimes AI can make up information, but if you have someone to review the
[21:26] output and validate it, that is where it's going to give you the right outcome,
[21:32] right?
[21:33] So if you give a general loose prompt, an open-ended prompt, the risk of
[21:38] hallucination is much higher. If you upload specific policies and procedures and
[21:44] criteria and give very specific prompts to it, the hallucination risk gets
[21:48] mitigated significantly. So really, really important if you're working off of
[21:54] criteria as an auditor, that you have on-hand policies, procedures, a risk and
[21:59] controls matrix, make sure you're uploading that as long as it's a secure way.
[22:04] You mentioned having enterprise license of Copilot or something like that.
[22:08] If you're confident that the information that you're uploading is being secured
[22:12] through that measure, upload that information because it gets a much, much better
[22:16] result from there. It's one of those you get as much as you put in type things.
[22:22] AI is definitely that case there.
[22:23] Exactly. Your input is your biggest output, yeah.
[22:27] So if you were a director of internal audit that's listening to this and you
[22:32] haven't rolled out AI agents within your internal audit function, what would be the
[22:37] biggest takeaway coming out of this for you?
[22:40] If you're a director of an internal auditing team or any kind of leader really,
[22:46] have a meeting with your people and ask them, where are you spending the most of
[22:49] your time, right? If those times are being spent on repetitive
[22:55] tasks that can be automated by AI. Now, the problem is people want to jump too much
[23:01] to conclusions. People want to implement AI too fast.
[23:05] I think you can start with something narrow, something very easy, something as
[23:09] simple as replying to emails in my tone, right?
[23:13] You train the AI with your own writing style, with your own voice, and
[23:19] you feed it
[23:22] emails that would take you tons of time to reply to.
[23:26] I think a lot of people spend a lot of time on those tasks as we go because there's
[23:32] so much communication in companies now.
[23:34] And something as simple as that, you see the result and you're like, okay, this can
[23:39] do that, then where else can we apply it? And that's where it expands.
[23:43] You don't have to do something big from the beginning.
[23:46] And two, make sure that you are not inputting any kind of personal identifiable
[23:52] information, any kind of social security.
[23:55] Whether the AI is being trained by the data or not, that is just not something
[23:59] advised from a security perspective.
[24:01] So start small, incremental steps towards a bigger goal and talk to
[24:07] your employees. Maybe they're already using AI in a shape that is not being
[24:11] controlled. So you want to avoid that, right?
[24:15] So if you as a leader don't take the decision that's going to enable your team to
[24:20] be more efficient, then you're doing everyone a big disservice.
[24:25] You see most of failed AI implementations at a company is because they try to go
[24:31] from zero to 100 immediately. They try to do a full-scale rollout.
[24:37] It's really important, like you said, to be able to start with those small wins, do
[24:41] a pilot program, small incremental buildup, so people build the awareness and
[24:46] understanding of how to use AI first, and then be able to do practical applications
[24:52] of the areas that take up a lot of their time.
[24:55] You don't want to do AI just for the sake of doing AI.
[24:57] You want to be able to take off that time, free up your space, and use it to become
[25:01] a force multiplier.
[25:03] Yeah. I'd even go as far as saying, don't apply AI everywhere.
[25:07] You don't need it everywhere.
[25:08] Well, thank you so much, Mohammed.
[25:10] Really appreciate the time to be able to talk, and very exciting work that you're
[25:13] doing.
[25:14] Hey, Alex, it's been a pleasure. Thank you for having me.
[25:18] If you like this podcast, please subscribe and rate us.
[25:21] You can subscribe wherever you get your podcasts.
[25:24] You can also catch other episodes on YouTube or at the iia.org.
[25:28] That's T-H-E-I-I-A.O-R-G.

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