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The Audit Podcast
Ep 260: How EY Is Helping Clients Implement AI w/ Paul Goodhew (EY)
00:38:47
This week on The Audit Podcast, our guest is Paul Goodhew, Partner and Global Assurance Innovation & Emerging Technology. In this episode, Paul discusses how EY is helping clients implement artificial intelligence and the importance of...
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- [00:00]
A
This is a technology that brings with it great power and fantastic capabilities. But there are risks around things like the hallucinations that come with artificial intelligence. And if people aren't understanding what some of those limitations are, it's a technology that increasingly we will be interacting with on a daily basis, personally and professionally. And we need to understand where we'll be interacting with artificial intelligence.
- [00:31]
B
Hello, everybody. Welcome to another episode of the Audit Podcast. I'm your host, Trent Russell. Today on the show, we have Paul Goodhue. Paul is a partner at ey. He's also the EY Global Assurance Innovation and Emerging Technology leader. And in this day of AI, kind of hard to imagine anyone that would be a better guest on the Audit podcast than someone like Paul. Some of the things we hit on is how Paul and EY are helping clients implement AI run through a couple of use cases also. And one of the questions that we get asked the most, and I've seen pop up more and more, especially on the assurance front, is how people are getting comfortable with controls that use AI, how to test those, where's the audit log, things of that nature. We also talk about AI agentic advances over the next seven to eight months. Kind of get Paul's prediction on where that's going to land. And then given the hundreds of thousands of people that are at ey, we want to understand how Paul and EY are getting all those people trained on AI. Here we go. So, Paul, what's in your either your Internet browsing history or like your Chat GPT, Claude copilot, whatever it is, tool of choice that you use, what's been going on in your world, both personally and professionally? Maybe an example of one of each.
- [01:49]
A
As you can imagine, I'm an avid user of the latest generative AI capabilities, whether it's ChatGPT in the personal context or EY, we've actually implemented our own private version, which is called EY Cube. I would say I found that these tools really almost particularly as they've got more and more sophisticated and more advanced over the last year or so, you can almost use them like a Swiss army knife, right? You could use them to do a whole variety of things, whether it's to perform research on a certain topic, if it's almost to discuss or bounce ideas off of the AI to noodle on something, if it's to create an interesting model or a scenario that you might want to explore. I've just found there are so many different types of use cases or ways of using the tool, so it definitely depends on personal preference. As someone who likes technology, history, sports, I found it to be a really fascinating research tool, one that helps me understand everything from a new technology concept all the way to researching historical sporting statistics. And so I would say in particular, to give you a good example, when I have a complex topic that I have to understand, maybe it's up a new emerging technolog technology, one I need to understand, unpack and then explain to others. I found that it's been like a really useful tool to actually go and understand, dive deep into technical topics, understand them, unpack them, create analogies, create ways of explaining a complex topic to a wide audience. So for me personally, whether it's outside of work or in the workplace, using our own private UIQ capability, I found that it's just been an awesome way of diving into different topics, particularly around the technology agenda, and then making them relatable, understandable to others.
- [03:48]
B
I like the analogy part. Certainly being able to use analogies helps people understand complex topics. So I haven't really thought about that to go like, hey, here's the thing that I need to understand, explain it to me. Great, can you make that analogous to a seven year old or something like that? I really like that one personally on my end. So I just did this shortly before the call. I'm putting in this very small scale misting, like water misting system and I was going through the notes on the one I'm going to buy and it said like in very small print, which was pretty frustrating. But it said note, you should use a filter on this because the minerals and everything that's in the water from your house is going to clog up the little bits that the mist comes out of. And I went, well, I don't really know anything about external water filtration system. So I took a picture of the likely misting system I'm going to buy. I just put it into chat and said, can you tell me what you.
- [04:55]
A
Know.
- [04:58]
B
Water filtration system, I should get on this, put it in a table, give me options, tell me why this one versus this one and give me links to each one of them. So it's literally, it's. I'm looking at it on my phone right now. I haven't had a chance to figure out which one I'm going to get yet because we're doing this episode right now, but nonetheless being able to take a picture of something, it recognize what the picture is and then be able to tell you, you know, whatever questions you have based off the picture. Another one that Someone mentioned this was a while ago, but I found it extremely helpful. Stuff around the house that breaks appliances and being able to say this is the make and model. Here's a picture of what's going on. Can you tell me how to troubleshoot this thing? Because I am horrible at that stuff and I definitely don't want to have to watch the 30 minute YouTube video where I only need to see the 60 second bit of it to fix the one part of mine. So now that our refrigerator broke yesterday and the door doesn't shut, I will be doing that at some point today also. But anyway.
- [05:51]
A
Well, I found that it's great point though, but I think that we're at a time, right, where everyone is deluged with information. Whether you need to make a purchasing decision, if you need to make a business decision, like there's just so much content out there that we could potentially consume. And so I think what people are looking, looking for is ways to get answers quickly. Right. Get to the right answers, but to get there quickly whether you're. And so both of the examples that we've given there, I think really kind of just point to point to the power of the tool as a way of getting to the right answer, but doing it in a way that helps you factoring in the fact that we're all very busy people these days.
- [06:35]
B
Hey, everybody, we're gonna take a quick break from our guests. And if you need to get analytics or AI actually working in your internal audit department, or if you already have some of it, you feel like you're not really getting exactly what you need out of it, you know there's more you're not getting that. Go to the show notes, look for the Green Skies analytics link. Click it on the website. There'll be other links that you can click that'll take you directly to a calendar to schedule time. It's literally three clicks to get the time scheduled to get it figured out. All right, back to the show. Absolutely.
- [07:07]
A
All right.
- [07:07]
B
If you could grab every auditor or CAE by the shoulders, grab them by the shoulders, shake them and say, just do this one thing, what would that be?
- [07:18]
A
Yeah, well, hopefully I don't have to shake them. But what I would say is, look, I think we're at a time where if you're not properly embracing the latest technology and looking to keep pace with the latest developments in technology at the moment, you're really at risk of falling behind the curve. Right. And I think that when I look at the pace of new technology, particularly AI, and if Reflect over the speed that things have moved in the last three years. I would really encourage people to be looking to try like one new AI tool a month, right? Even if you would just test one out, if it's a free tool that's been made in native L, if it's a new version of a large language model, if it's a new imaging tool, like just give it a go, right? Even if it's just one or two times, because that's the speed at which technology is advancing. I mean, honestly, I could not even imagine one year ago, like I would not have believed you if you told me that I would just be able to build a front end application in a few seconds using a generative AI product. So every time I found that I've used a new tool, a new AI tool over the last year or so, it's just opened my eyes to the art of the possible. And so I'm particularly impressed by some of those latest language models. Image video generation or interpretation, code development. Like the pace of change is really incredible and it creates some fantastic new opportunities. Also some risks that you need to familiarize yourself with. But I would just encourage anyone, any auditor or any accountant, like take the time to try out a new tool once a month because it'll just really focus, keeps, keep pace with the level of change there is at the moment.
- [09:15]
B
Mike put you on the spot here because that's definitely been said. I feel like for years to begin with it was, it was like, hey, you gotta know Excel if you wanna, if you wanna like thrive, grow, add value. Excel's where it's at right now. And then it was, you know, their bigger and better tools came out in the same context with analytics. And it was, you gotta know these tools, maybe you gotta know Python or something like that. And then it was maybe RPA or automation. You have to know this, this is the game changer, et cetera. And now it's AI. And I feel like there's some people who maybe have been around and seen those come and go and go. I mean, I didn't really learn much of that stuff and I'm still here. Why is it different? Do you feel like? Because I feel like it is very different. AI versus everything else. Why do you feel like it's different? And when we say you have got to learn how to do this stuff.
- [10:09]
A
I think it's for a couple of factors. Firstly, the pace is very, very different to the pace that we've seen before. If you imagine, I mean, these are tools that have gone from zero to hundreds of millions of users in a matter of months. That kind of adoption has never been seen before across any technology ecosystem. So the pace of change and the pace of adoption is just unprecedented. And if you want to retain a competitive advantage today in a business setting, you really need to be thinking about AI not just as table stakes, but actually the way to build a competitive advantage in your organization. And so for any, I would just encourage anyone to think about this as a technology was moving faster than any ever before for. But the second point I would point to is not just a technology that brings opportunities, but is also risks. Right. And if you're not understanding how these technologies operate by getting hands on with them, you might not really appreciate some of the challenges, some of the risks that exist around the use of AI. This is a technology that brings with it great power and fantastic capabilities. But there are risks around things like the hallucinations that come with artificial intelligence. And if people aren't understanding what some of those limitations are, it's a technology that increasingly we will be interacting with on a daily basis, personally and professionally. And we need to understand where we'll be interacting with artificial intelligence and how to actually interpret the outputs of AI. So I would just respond to your question there and I would say, look, the technology is moving incredibly fast, but you need to understand its potential and its limitations as well. And that's where I think it differentiates from some of the more, let's call them, linear automation tools that we might have seen in the past.
- [12:16]
B
Got it. So not even necessarily the use of, but in addition to the use of, it's if you're in audit, you're in compliance, risk management. Understanding the risks associated with this, I think is an excellent point. Okay, well how are you all, how are you helping clients implement AI? Any client use cases that you're able to share would be fantastic as well.
- [12:39]
A
Yeah, absolutely. Well, look, as you can imagine, whilst a huge part of our business at EY is delivering audits, we also have teams across our network that provide services to clients, to non audit clients where we're actually helping them implement artificial intelligence or consider the impacts of AI. So we've got part of our business which is helping our clients think about the implications of AI. And then across our audit business, we're also having to pay particular attention to how our clients implement artificial intelligence because increasingly we're having to think about how are we going to address the risks of AI in an audit context. And so from that kind of broad perspective that we get as an organization, there are a number of use cases that we're increasingly seeing. So I think look initially what we have seen, at least in the first wave over the last two, three years at the beginning, clients were first looking to implement AI either within their core business or typically within a customer service type function, right? So like some classic examples you might have seen would have been AI helping to handle customer service calls. Or another good one would have been AI being used to help make decisions on decisioning for whether or not to issue a loan or financing to a customer. Those were some of the very first use cases that really became obvious and on our radar. Now what we're starting to see in the Last sort of 18 months or so is organizations have realized that they need to establish an AI strategy. They need to look at how they're investing in artificial intelligence, not just in isolated parts of their business, but across the entire enterprise. And what that's meant is that we're now seeing functions like the finance function actually starting to also embrace the use of artificial intelligence. So there are some cases that we're seeing, such as a use of generative AI to help search and summarize accounting content. That's something that we'll see from the various different finance and accounting teams. There's AI being used to automate reconciliations at the very far end, at the advanced end of the spectrum, we are seeing some technology clients and some technology companies even thinking about how they might be able to use generative AI or AI agents to even do things like help automate drafting of financial statements. Now that's really cutting edge R and D that's happening. But what I think it points to is we're seeing a shift from where AI was only implemented in, let's say, the core parts of the business. Now across the entire organization, down at the function level. And what that's having to mean for our own teams and ui, particularly our audit teams, is to start thinking about what those risks might be from that use of AI, particularly AI that might be embedded into the, into our clients financial reporting processes and where AI could even trigger a risk of a material misstatement. And that's where we're actually having to think about how do we equip our professionals with the right guidance and the right skill set to address our risk. And so we've actually gone as far as actually implementing what we call an AI assurance framework, that's guidance that's been issued to all of our professionals across our assurance practice globally to really help them take the Right. Steps in asking questions to their clients about how they're using AI, but also navigating what to do if you actually have a client that might be implementing AI within their financial reporting processes, whether they're building it themselves or they're buying it from a third party. So that's increasingly developing. And so we're having to think about that from an audit perspective. Outside of the audit, we're also thinking and looking at how we can develop new services that can also help our clients. And walking with our clients, particularly non audit clients, as they're thinking about the robustness of the underlying data they need to have in order to fuel AI, how they can think about being compliant with AI related regulations, how they need to relook at their own governance over their use of artificial intelligence enabled technology. So we're actually walking with many of our clients as they go on this AI journey now and start to implement the sort of use cases we've just been talking about.
- [17:32]
B
I think one of the hottest questions I've seen in the past four to six months speaks to something that you were talking about. So you're talking about using AI and agents to draft financial statements or to do some level of control, like it's involved somewhere in the execution of a control by management. And so now it's the question that I've seen the most around that is how should we, as internal audit, get comfortable with control owners using AI to execute on control? So even something like a forecast as an example. Well, I just took the data, put it into the AI tool and prompted it to, you know, give me the forecast. Here's the forecast. Well, that's not very auditable in some senses. You could review the Python code that it's using to generate that, of course, but I don't think a lot of people are super equipped to that. Although you could use AI to do some of that review for you. But overall, and so you mentioned this AI assurance framework, you definitely didn't have to go into a ton of detail on that. But that's the thing that I feel like most internal audit departments are struggling with right now is this control is using AI to some degree. We don't know how to get comfortable with it and we don't know what to do with it. And similarly, the other side of that is these control orders want to use AI, we definitely want to encourage them to use it, but we can't like give them the the pass and say, yes, you can do this, because we don't know how it's going to be, we don't know how to audit it as internal audit or we don't know what the external auditors, how they're going to think about it. So any, any kind of overall maybe high level guidance mindset perspective to think about. And how can internal audit get comfortable with controls that use AI?
- [19:25]
A
Yeah, no, it's a great question. I think a couple of points there. So number one, there's definitely a maturity curve that organizations are on at the moment. So I think these are the sort of questions that people are starting to think about now, recognizing that actually a lot of these use cases might not be in production yet, they might be in the R and D phase, but we know of this adoption of artificial intelligence is coming, right? And so it's the right time to be asking these types of questions. What I would point to, particularly if I refer back to our AI assurance framework, just to offer in a perspective from more of the external audit side. When an external auditor has to consider the risks associated with AI implemented by a client, they have to think about a couple of things and it really starts with governance. Has an organization really thought about the relevant governance that needs to be in place over an organization's use of artificial intelligence? And many organizations are having to go back and actually think about have they got that right governance framework in place? Is there documented governance in place? Is there an oversight committee actually reviewing AI model changes and performance? Are roles and responsibilities across an organization clearly assigned? Do they have accountability defined and is there evidence of accountability? Do they have some sort of real time monitoring system in place to monitor an AI system's behavior and certainly react quickly as well if the data or the performance starts to drift as well. So that's just at the governance level, then we have to think about it down at the risk level, the control level as well. But ultimately it starts at the top, where organizations need to make sure they've got the right governance in place. And those are some of the first questions that our teams will be asking to our clients when they're looking to understand what type of AI might you be implementing? Is it something you built? Is it something that's been embedded into one of your systems from one of your vendors? Is it a third party? Is it some sort of third party application and how are you using it? So it really starts at that first level of looking to understand where AI is being used, how it's being used, and then looking at the governance, it's on top of that before then diving deeper into performance of the artificial intelligence itself and actually even looking at the controls in place that actually help manage and govern the use of the AI itself. So that's more of a perspective from an external audit side. But those are some of the key questions that our teams are having to think about at the moment.
- [22:08]
B
And you mentioned governance. So we have, I don't even know what we're going to call it, a companion show to the audit podcast that we started, I don't know, maybe two months ago called IA on AI. So internal audit on AI. So it's strictly, here's the news from the past week, relative AI. Here's the way that you should think about it. Could think about it from the internal audit or risk perspective, just to try to really educate people on what's going on with this. And the overarching theme of pretty much every episode has been AI governance. Like everything, every issue, every risk. And it seems pretty obvious, right? Is around AI governance, but the clients that we work with, still there's some. I go, what's your AI governance policy look like? Like, can you send it to me for review? Give me some context? And they go, I mean, we kind of have one, but it's like one page and it just says, don't put this data in the tool. Or it's, we're working on that right now, or we have something in line for next year, something like that. And I just go, obviously, I don't know every given client's priorities, but that one should be kind of towards the top. And I'll say it again, for those that maybe only listen to the audit podcast, where we have the guests like yourself, Paul, and maybe don't listen to the AI one that we do. Also, if you're an internal audit and you don't have an advisory, if you don't have. Or you haven't put, you haven't already done an advisory or an audit around AI governance, again, I can't really think of anything that's going to be too much higher on the priority list than getting that knocked out. What do you think?
- [23:48]
A
No, I agree. Look, and I think it's also reflective of the pace of technology. Change has gone incredibly fast. Organizations are looking to keep pace with that change. And so I think it is to be appreciated and understood that organizations are currently revisiting their governance over technology, are thinking about how do we make sure that we've got visibility over technology usage across our organization. Where is it being used? Is it AI? Many organizations are having to even create their own definition of artificial intelligence. There are many definitions out there, but to make sure they're even clear on what is AI and how can they actually identify where it's being used before then looking to inventorize AI usage across their business and understand, okay, how is this technology being designed, tested, built, operated and upgraded? Because particularly with different types of AI that could be underpinning those products, organizations need to make sure they have that level of governance and visibility in place so that they can ultimately manage and use this technology aligned with what we would think of as responsible AI principles.
- [25:08]
B
And I think, I mean, I can sympathize for anyone that's listening, go like, you know, it's a tough situation to be in because this, the pace like you've talked about is unprecedented. And so it's, how do we keep up with the pace and use the latest and greatest while also trying to keep our governance model around it up to date? Also it's definitely, there's, it's definitely not easy. So I can sympathize with that, that it is very difficult. And even the folks that go, we don't even have a tool yet and we are like begging to get this thing, but they don't want to move on it until we're ready. So there's a lot of complications. And I'll ask, kind of based on your experience, is this unprecedented in the sense of the governance? I know governance can't keep up with tech in general, but in the AI case it's definitely unprecedented.
- [26:00]
A
I think it's quite difficult to measure the pace of change around governance. I think that it would be fair to say that this is a technology that is challenging organizations to rethink their governance, probably at a pace never before experienced. And then there's part of the fact, just because if we rewind three years ago, generative AI was only just being launched when we saw the first releases of ChatGPT and other similar large language models. If you fast forward today, you've now got organizations looking to implement artificial intelligence enabled agents and multiple agents working together in some forms of multi agent frameworks. That's an enormous shift forward in how people are thinking about the application of this technology and it brings about different risks and it will require a more robust governance response for organizations to make sure they have the right risk management framework in place over their use of artificial intelligence. So certainly I think it's fair to say that this is a technology moving at a pace which is challenging organizations on those questions, probably at a pace that we've not seen certainly in recent times.
- [27:30]
B
You mentioned Agents a couple of times. Where do you feel like the AI agentic advances are within the next, call it seven, nine months.
- [27:42]
A
Yeah. So maybe if I were to just speak about how we're thinking about it from an audit delivery perspective. So over the last three years we've invested to implement AI at global scale within our audit business. At ey, we have a global audit technology platform and we've actually looked to implement artificial intelligence into that platform to help our audit teams deliver high quality audits. Some of the early use cases that we used were what we would call traditional machine learning type capabilities or point like AI solutions. So one of the first capabilities was AI that helped recommend risks to our professionals. Risks that might be comparable to risks on other similar peer ordered engagements where you as an audit professional would have an AI recommending to you. Hey, here's a risk that you might want to consider that happened on a similar client to the one that you're auditing. Very simple types of use cases. Over the last year or so we started to introduce generative AI, including actually our own EYQ assurance knowledge product in our platform that helps our audit teams search and summarize audit and accounting content. It helps them get to the right answers and get there quicker a little bit like we spoke about earlier, in fact, in some of our own personal and professional use of gen AI, where we go next is taking this step forward with agenda and that's where we're really excited about the potential of what comes and what we'll be introducing in the coming months, which is where you actually have multiple AI agents interacting with one another. I always analogize it to almost like virtual chatbots talking to one another in a virtual chat room on behalf of the end user. Right. It's a little bit like that. Right. That's a crude analogy, but I'll call it out as an example or what it really means is actually we have the potential to use agentic AI where agents can perform multiple tasks or multiple activities to help deliver an audit. They can do so with a good degree or a high degree of autonomy. There always needs to be a good level of. There needs to be an appropriate level of human supervision and review of that artificial intelligence. But ultimately those agents can work to support our audit professionals in performing tasks and activities and will do so directly in our audit platform. So we're really excited about the potential of those agents. They can do things like simple administrative tasks to audit specific activities and it will create a real transformation in the way that our people are using technology and AI to deliver audits in the future.
- [30:48]
B
I like the, the chatbot analogy. The way I've, I've been talking about agents and the analogy is an agent is imagine it's just a person on your staff. So if you have a, an internal audit, you have a data analyst that's an expert, you have a controls testing expert. And we'll kind of make this one up because most people don't have this unless you're on a huge team. But with agents you can, is like a report writing expert. And so you have an agent that does the analysis, takes those results and sends them to the controls expert who gives their opinion, maybe drafts their report. Here's mitigated controls, here's the findings based on the analysis that the data analyst did. And then that agent can kick it over to the report writing agent who's brilliant report writer and can take those results and then put them into the format of the report, can use the right tone, all that kind of good stuff. So you can think about agents as individual people doing tasks expertly or even tasks within like an audit process that you may not have a dedicated person to that, but now you can with an agent. And so that's how I've been thinking about it and explaining it to folks.
- [31:57]
A
Yeah, I would certainly agree that the, the important point to highlight here is this is about equipping audit teams with knowledge, with experience, in some cases with specialist experience and specialist knowledge. The important piece for me is it's augmenting the team. Right. It's not replacing it. Right. And that's the key, the key message. This is really about helping people get to the right answers and the right outcomes quicker and doing so in a more effective way rather than just as an automation tool. Right. I think that's almost reductive. Right. To the conversation and I think it oversimplifies how we think about the technology. For me, it's a booster. Right. AI can help you do a first version, or it can give you some recommendations, or it can provide some options, or it can identify some potential changes. It doesn't replace what a human does, but it augments and boosts it. And when you have AI connected to the right data, integrated into your workflows and integrated into your platform, that's where you can bring your collective knowledge of your organization together and really use that to bring that to the fingertips of your professionals and to your teams. And that's where we're seeing and anticipating to see the biggest impact from this technology.
- [33:27]
B
Here at EYE was curious about this, because the other theme on the IA on AI show that we do is so there's governance is one of them. And then right underneath that is training, which go hand in hand. And having close to just north of maybe on any given day, 400,000 employees at EY and having to equip them and train them to be able to audit AI, implement AI, use AI, talk to clients about AI and AI risks. I mean, this is. That's a lot that has to go into that. And I'm former ey, we did a lot of training before AI, and so I can't imagine what it takes to get that many people trained up. But for the, you know, the. And again, this is more specific to internal audit, but we can think broader. How are you guys going about training 400,000 people on AI?
- [34:27]
A
Great question. And I think that the topic of training and upskilling our people is something that we'll continue to revisit year on year. Certainly what we have done recently is make sure we've made training available for all of our EY people just on the foundations of artificial intelligence, so that everyone gets up to a certain level of understanding about what is technology, how does the technology work, what are the different types of artificial intelligence, what are the risks associated with AI. That's training that we've made available for all of those people across our entire organization. Now, when I think about the 140,000 people that work in our assurance business, we've gone a little bit further. Not only have we made available foundational training for those professionals on AI and specialist training on specific AI types of topics that they might be interested in, we've done a couple of other things. We've implemented acceptable usage policies, how our people should actually use artificial intelligence appropriately and responsibly in the course of delivering our assurance services. That's been something that's been very important, particularly when you think about the fact that we're operating in a global regulated business. We have to make sure that our people have the right policies and guidance for how to use the technology in the right way, including that level of supervision and review that still needs to come in with our people providing their own judgment when using the outputs of artificial intelligence. But, but going even further beyond that, we've also now looked to make training available for our people around how to address the risks of AI when they encounter it at their client side.
- [36:18]
B
Paul, thanks for coming on the show. I think there was a lot of, I mean, there's some tactical information that you gave which is Always, always appreciated. As well as some high level conceptual parts you included and then kind of a peek under the hood of what EY is doing, which is amazing as well. So a lot of good stuff. Really appreciate the kind of the level of detail you went in on some of these with that said, if there's anything else you want to leave the audience with, I'm going to hand the microphone over to you and let you close us out.
- [36:48]
A
No thanks, Trent. It's been great to join you today. Look, I would say there's a final thought or reflection. There's a lot of discussion about this topic of responsible AI and we've, we've touched on some of those themes today. What I would flag is, is, is for all of us, I mean, that starts with us as, as the human end users. This is a technology that is particularly unique in the intersect at the intersection of, of a human and the machine. Right. And I would just flag that. It's really about understanding how the AI operates, how AI works and what it can do, what are its limitations, what are its weaknesses, but also what are the opportunities it brings. I'm personally optimistic this is a technology that's going to be a real force for good. But what I would encourage everyone is really to make sure that you're experimenting with AI, using it, testing it, understanding how it works, because being able to use it responsibly is on all of us. So I think for that reason it's vital for people who are upskilling themselves on AI and how to use it in a responsible context.
- [38:09]
B
Hey everyone, thank you very much for listening to this episode of the Audit podcast. Whatever platform you're listening on right now, I'm sure there's a subscribe button somewhere, so please hit the subscribe subscribe button there. If you're listening through itunes or Spotify, feel free to go give us that five star rating. It only took me about 16 seconds to give myself a five star review and it really helps to get future guests to come on the show. So we'd really appreciate that. Lastly, be sure to check out the show notes and follow us on all our social media channels, on Instagram, on LinkedIn and on TikTok. Also, if interested, please sign up for our weekly newsletter from the Audit Podcast. Thank you all. Have a great one.