Name: Re-Imagining the Future Finance Function with KPMG and Workday
Uploaded: 2026-09-29T11:16:12.683Z
Duration: 1 h 40 s
Description: Re-Imagining the Future Finance Function with KPMG and Workday

Transcript for "Re-Imagining the Future Finance Function with KPMG and Workday": Hello. Good morning, everybody, and welcome to today's webinar. We're just gonna give it about thirty seconds just to let, people join from perhaps, the last call that they were on. So we will kick off in about thirty seconds. Alright, everyone. Okay. So we're at one minute past the hour. We have a lot of really great content to share with you this morning. So I'm going to kick off, and I just wanna thank everybody for joining us this morning or this afternoon, dependent on where you are. But we are gonna be talking about imagining the future of finance function, and we're delighted to be joined today by the team here at QuickPMP. And, over the next hour, we're gonna talk essentially about how agents and agentic AI are really changing the shape of the finance function. We're not just going to look at some of the theory behind this, but also the day to day work of the close, the audit, and reporting, and look at some of what Workday is doing in this space, and some of what KPMG are doing to guide their clients. And before we get started, just one quick formality. Please note that some of what we'll show today does include some capabilities that are in development. So anything unreleased is subject to change, please make buying decisions on what is available today. And this is I'm just gonna kick us off, and then the, KPMG team will talk about the future operating model for finance and where they see some of those biggest opportunities with agents and agentic AI. And then I'm gonna show you where Workday is focused. We have John Paul here from our solution consultant and some controls. I'm I'm hoping that we have a few moments at the end for some questions, and we will be oh, apologies. I think my sound is gonna be shaky. I'm just gonna have to go off camera for a second. So, essentially, we will hope to answer some questions at the end, but, we will also address questions throughout the webinar itself. And this is just some housekeeping items. You will receive access to the recording after the session. Please submit questions through the q and a as we go. I'm gonna address them throughout the session, and you'll also be invited to complete a short survey after the webinar. So I just wanna introduce our speakers. I'm Marion Carr, product marketing director here at Workday for our financial management products. I'm joined by my colleague, Jean Paul Bell, who is a senior senior solution consultant who's gonna run our demos for us, and we're also delighted to be joined by Richmond and Nathan from the KPMG team. And they will introduce themselves in just a couple of minutes. But, you know, I just wanna set the context in terms of, you know, what we're gonna be speaking about today. So for years, you know, we've been using software that waited for us. We documented what we wanted, entered instructions, and moved forward step by step, and that is really changing. Increasingly, software can anticipate what's coming, advised before we ask and ask on our behalf with the right controls in place, and that's some of the shift that we're going to speak about today. Here at Workday we are evolving our software, so it doesn't wait for instructions, It anticipates what's coming, advises, and acts on your path, essentially working quietly in the background, engaging people on their teams, and completing work without needing that constant oversight. And this is what makes it possible. So our open AI platform, you know, underneath you've got one data model, one process framework, governed execution, data cloud, and AI APIs. And then on top, we're delivering agentic applications across HR, finance, and IT. And today, we're gonna drill into, some of the capabilities that we're building from a finance perspective. And it's really that foundation that lets agentic finance become a reality and not as a bolt on solution, but as a platform where all of this works together. And for finance specifically, this really helps to deliver three things, and we're gonna drill into these in the webinar. But firstly, you get to spend more time on growth, risk, and capital and not on the operational ground. Then finance gets an always on insight layer, so clear, defensible answers the moment that they're needed. And we're gonna show you our new decision intelligence platform that will enable you to achieve this. And third, autonomous controls can monitor every action and catch exceptions before they become problems alongside a continuous close. So we're gonna look at the audit agent, and we're gonna look at the close agent that Workday is building. So you see all of these come to life through a demo that John Paul is gonna walk us through, a little bit into the webinar. But before we jump into that, I wanted to bring the the KPMG team into the conversation. Nathan, Richmond, we're delighted to have you here. And there is a great opportunity for the finance function, and, you know, some of the technology advances, you know, that you're seeing with Workday and other vendors are fast driving faster change than we've ever seen before. I'm just really curious what you're seeing across clients and what trends you're starting to see evolve. And, you know, is there any notable differences across industries that you're seeing as well? Yeah. Thank you, Marion. So, look, welcome, everybody. I'm Nathan Patten, and I lead KPMG's FS financial transformation team. I guess to answer you your question there, so it's probably worth us taking a you know, we're gonna talk a lot this morning about AI and Workday, but it's probably worth us just stepping back and thinking around what the role of finance is. And a lot of the discussions that we're having with CFOs at the moment is how we start, changing the shape of finance and skill set of finance to meet those new demands that are coming through from technology. So you see here on the top left that, you know, the traditional view of finance where the majority of the effort was based on operational processing and a pyramid going up to sort of value adding activities at the top, but a much smaller proportion of the overall time of finance spent on those because of the need to do a lot of manual work around finance systems. And what we're seeing now is very much a shift towards flipping that pyramid on its head. And with AI and platforms such as Workday, it is now possible to do that, and we're seeing that the most of the day to day operational activity should be able to be automated, driven by agents, and that frees up the time of the finance professional to do much more value adding activity, become that strategic partner of a business, and drive the business forward. Then if you look here at the bottom left, what we're seeing is, you know, a change in the mix of the type of work that the finance is doing. Operationally, now, much more sort of globally aligned processes and global process owners, centers of excellence set up, all enabled by the new technology. And what you're seeing is not only that mix of, type of activity changing, but also the overall need for the size of finance to be smaller as well. And so you'd see a move towards a kite shape. And I think most organizations, this is a longer journey than now, so we're not seeing this happening immediately. But, certainly, the journey we're looking at here is a more kite shaped view of finance. And importantly, the skill set within finance will need to change as well. So, absolutely, you know, the core professional accounting expertise will need to persist and carry on, but that will also need to be added to with people in finance with data science backgrounds, business architects, people who can continuously improve processes around the technology that that is in place. So if we move on just briefly to to the next slide, I just want to talk what that then means for the CFO's remit. So very much the CFO will still need to do what it, you know, what is needed today around reporting control and everything else. What we see is that now expanding and technology enabling that that expansion. So the CFO will have a very key role around trust, and you could call it the tree chief trust officer under that sort of CFO remit. And that expands that trust into the AI operating model and ensuring all of the results that are coming out can be trusted and understood and explained externally. And the role of controllership will become much more around exception management and exception controllership rather than doing the day to day operations of finance that we see today. And then the right hand side, they're much more becoming the value, steward of the business, the chief value officer, the forward looking piece driving the business forward, moving up the value chain, and having the ability to get the information at a much more granular level more quickly into the business to help that decision support. So, Richmond, I think you just wanted then talk us through how that then goes into which bits of the, you know, process taxonomy we think most likely to be facilitated by by AI. Thank you, Nathan. So just a quick intro for myself. So I'm also a partner in our UK finance transformation practice. I'm currently leading a couple of Workday finance implementations, one at a bank and one at a higher education institution. Now we spent quite a bit of time at KPMG thinking about what a future finance organization could look like I'm thinking about which of the activities could then be automated. So let me just explain the slide that's on the screen. So each of the six pillars is a grouping of activities under typical finance functions. So from left to right, you'll see the regular execution activities. So these are your normal process operations. They could be in a shared service center, the activities for the financial controller, and then also FP and A. If you move further to the right, there are opportunities for AI enablement also in commercial finance, tax and treasury, investor relations, and even corporate development. But these may be activities where you may decide to have a human in the loop as well. So if we then move down into the boxes themselves, the activities in gray are the ones where we're seeing as KPMG the greatest opportunity in the short term for a Genpik AI automations. The activity in purple are more strategic and will probably come along later with more sophisticated development. The good news for existing Workday Financials customers and prospects is Workday are investing heavily in building integrated agents, which can be safely deployed and managed. I'll hand you back to Marion now to explain what Workday are starting to do around the call to report. Excellent. Thanks, Richmond. And apologies for the technical issues. I'm gonna stay off camera just so I don't, have a shaky, experience as well again. So Richmond highlighted, you know, a couple of capabilities here where they see great opportunity for automation, and I'm gonna focus in on some of what we're doing in the kind of left hand side of this. So particularly what we're doing in the controllership along into FP and a and then extending into corporate finance or commercial finance, we are also, you know, across the platform investing in areas like tax, you know, our planning cap capabilities in our planning platform, but in particular, we wanna focus in on kind of the controllership, and shared services and, into FP and A. And one of the processes that I wanna kind of drill into, that aligns really closely to this where a lot of this activity happens is record to report. So this is, you know, an area that's still quite manual in a lot of organizations despite, you know, a lot of, you know, transformation efforts focused on the finance function, and it's really where a lot of that pain is visible. You know, the close today, is still quite manual. We've got a lot of disconnected processes. We've got a lot of disconnected roles across different departments. We've still got, you know, people using spreadsheets, and using email to kinda chase statuses. And all of this has a really strong impact on the overall close process, brings in delays, risks, and a huge amount of time is spent managing the process rather than improving the outcome. And this is essentially one of the key processes that we're focused on here at Workday, looking to redesign. And this is where we see a huge opportunity for agents to really take on a lot of that manual work. So reconciliation, consolidation, financial reporting, agents can absorb more of that transactional load whilst accountants will own the outcome and really shifting towards what Nathan was speaking about in, the earlier part, of the presentation where, you know, finance is focused on the value outcome and partnering with the business because we can leverage agents to do a lot of that manual work in the background. And the real opportunity is to shorten the close, resolve intercompany activity earlier, reduce all of that spreadsheet based reconciliation, and, essentially, the balance of work changes. So instead instead of spending a lot of your time, you know, doing all of that manual work, matching data, checking data, you can focus on analysis, exceptions, and guidance and judgment leveraging the platform. And, essentially, the close doesn't just become faster. It really changes fundamentally. And our goal here is to achieve this through a number of different agentic capabilities and really to help finance teams to move away from automating individual tasks and instead orchestrating that overall close process. This is achieved in a couple of ways. Firstly, the financial close agent. This is going to coordinate specialized capabilities across accruals, reconciliations, intercompany, and consolidation. So instead of monitoring every task manually, finance teams can focus on the exceptions that require judgment. And around that closed process then, the financial audit agent strengthens confidence by supporting continuous testing, error identification, remediation, and controls. And then the third part is decision intelligence. This helps to reduce those board ready and audit ready narratives that are fully traceable back to the source transaction. And the important point is that speed does not come at the expense of trust, and we're gonna show you that in a demo in a few minutes. Every agent actually needs to be explainable, governed, and tied back to the same current controlled information. And JP will walk us through a demo in just a couple of minutes, and you'll see some of that come to life. This is just a little bit more detail on the closed agent. It is, currently with early adopters, and, essentially, it will be released in phases. The first phase is that we're currently focused on our reconciliation and accruals. We have a number of customers at early adopters. If this is something that is of interest to you and you would like to be an early adopter, please do let me know via the survey at the end or simply put it into the chat, and I'm happy to reach out to you directly. And we can see if you can get involved in that early adopter program. But, essentially, it'll be a multi skilled agent, and it will, you know, really bring together task dependencies, exceptions, and status in one governed workflow. And we're seeing some really good early indicators from our early adopter program that we have running. The second agent that JP is gonna show us in a demo, and that I referenced in the slide previously is the financial audit agent. So this is comprised of two skills. The first, essentially, is that auto request packet so package compilation for your external audits. We're gonna see that. And the second is that always on auditability of your business processes, and tests running in the background that surface up issues, early on in the process. So we're gonna have a look now at a demo, and I'm gonna hand over to JP. I'm gonna stop sharing my screen, and JP will walk us through a demo of the closed agent as well as the audit agent. Cool. Thank you very much. Mary, right, let me just get my screen up. There we go. Okay. So I just wanna bring to life, some of the things that Marion's been, talking around for the last, few minutes and what they look like within Workday. So we're gonna start off with the close agent, have a look at the, questions about accruals. So we'll have a look at how that can work in an in, using the agents with reconciliations, how we can manage that kind of continuous auditing process with our, sort of test suite that is constantly running, constantly checking, and then also finish off with those, managing those audit requests and those audit packages. So I wanna start off. She's responsible for managing the period close. And on her landing page, we can see status. We can see our close readiness. We can see some items are complete. Some items are still in progress. And as ever within Workday, if she wants to know a little bit more detail about what is happening, she can drill in, to the breakdown of that and, again, drill down further to the individual tasks. And as ever with everything that's in Workday, if it's blue, she can drill in and get a little bit more detail, around that, a little bit more understanding as to what lies underneath that. Now on our briefing table, looking at our different close readiness is by work stream, we've got, Amber on our revenue across and deferred income. So we could start to go in and investigate that. Now the close agent is going to surface areas for you to look at, so there's some areas to focus on, and then there's some areas down here that require attention. And one of these is our revenue accrual. And, basically, the agent has gone through, given us an analysis saying that, essentially, we closed q three before our final trade volumes came through. What it has done to, remediate against this is prepared an accrual for us that we can review, so always got that human in the loop. And what next step do this. At any point, you can go down. You can have a JP JP, it's Marion. Sorry. Your network is a slightly bit shaky. Maybe best to go off. camera. Sorry for interrupting you. go off camera as well. Okay. Apologies for that. Hopefully, that'll run a little bit more smoothly now. So once I'm in here, I could look at the, reasoning behind this, so that audit, that control behind that. We can link it through to things like how your organization's rec rec policy. But, essentially, I can click into this, and it's gonna show me that journal that it's prepared. So I've got my journal header details, the type of journal, and the debits and credits behind that, and that bullet trailers to what it's done. I'm happy with that, so I'm going to approve the, revenue accrual. And if I come back to my, briefing screen, I can see that my close readiness has moved on to 85, percent now. The next element I wanna look at then is around the reconciliation side. So this is under mine today's focus area, and I can drill into this, have a look at the, reconciliations. Again, the agent is presenting me, with the tasks, the things that I need to do, that I need to review. It has already auto certified 16 accounts for me. We've classified those as low risk. Obviously, risk can be managed different, from an organization to organization, so you can, define what you class as risky. But I've got a couple of different areas here to review. And as before, I've got a list of everything, and I can always drill in, and have a look at what's occurred below that. So as before, it has prepared some commentary to let me know, what is happening. So it's saying the ledger balances have matched. We've got some flux analysis, but we know that that is related to that late invoicing that we've already accrued for. As before, we can drill in and see what it has done. So it's given us a breakdown of that, customer aging. We can go through all the different tabs so we can see the comments, so the details, that understanding that the agent has shown and why it has done what it has done, drill down into agent, reasoning. So, again, this could be linked back to specific policies, such as this one, and then the business process history. So the same business process framework that we use throughout Workday, the agents will also go through those guardrails. And if I'm happy with that, I can, approve that. So the next area that we're gonna look at was this kind of always on, financial test suite, this financial audit agent. If I move in as, Pedro so Pedro is an AP analyst, and he's responsible for the supplier invoice processing. So in the old world, typically, when you've got an issue such as a duplicate invoice arising, it's very good at picking it up, but they're dealt with on a one by one, case by case basis. However, using the financial audit agent, the manager's built a test, to screen for these constantly, in the background. So it'll surface the issue for them and then help, Pedro act on them. So if I drill in as Pedro, we can see that an email has been sent out to him, saying there are various potential duplicate invoices that need reviewing and that they are high risk. You can click directly back into Workday. And if you note on the right, we've got our agent there. So you've got your agent chat. You can communicate with it in, plain text, plain plain English, and that has come through, give it in the context of the screen that we are looking at as well. So what I can do is, same as before, I can have a look at what the analysis is that the agent is presenting me with, what the actions are that the agent has taken. So in this case, it's put some invoices on hold, and then it's got some recommendations for me for what I can do. So I could click in on my express resolve, resolve those. I could view the agent reasoning. If I want to, equally, I can drill in and have a look at a bit more detail on an invoice by invoice basis. And, again, on the right, the agent chat, keeps up with the context of the screen that I'm looking at. So on first glance, this does look like it could potentially be a duplicate invoice. So we've got some very close comparisons, similar sorts of amounts, and that sort of thing. And if I scroll down, again, we've got some, sort of similar sorts of comparisons there as well. However, when we drill into it and have a look at the actual invoices themselves, we can see that certain fields have been highlighted by by the agent. And what it's saying is that, actually, although it's similar amounts and similar sorts of things, they've actually been sent to different locations, and they have also got different purchase orders. So, potentially, this is correct. So what it's saying is we should look at contacting the supplier. Again, it can link through to email and send an email out to that supplier, for us, and we can then await a response through from that supplier to decide what to do with those invoices. So, again, the agent is helping. The agent is assisting, you within your day to day tasks. The final area I wanted to look at then was on this audit agent, so getting this, AP test bundle. And what we can do here is it's when we've received a request from our auditors, to give them some, sample evidence, so, the invoices, payment support, all that sort of thing. And, essentially, you can type in in free text. So we've got our, AP cutoff. We can put in the specific invoices we want, and we can then send that request in. And first off, what it'll do is it'll go through, analyze, and understand, what we've asked it, and then it's going to give us back a list of the document types that we want to pull through. Again, you can go through with a checklist and check or uncheck the types of documents that you, that you want to to use. And then once you're happy with that, you can then go through, and click through this. My webinar button is hopefully in the, exact place I wanted to click, and I can say that that is correct. I want to confirm that request. It'll double check that with me. And what it will do is go away, review all those documents, pull the invoices through. Obviously, again, it's Workday, so any of these that are blue, I could drill into and review them myself. And, ultimately, I can then generate a package, which I can, provide out to the auditor of all that supporting documentation. So I can now go in and download, that package. So with that, I will stop sharing, and hand back to Marion. And apologies. I also seem to have the, sound issues as well. Hopefully, they weren't too bad. Okay. Thank you so much for that, JP. I hope you guys find that helpful, and it always see I think it's always easier to see something in demo format. There was a couple of questions around, like, availability, what you were seeing. This is a the closed agent in particular is a vision demo, of what we're building towards. So that first kind of interface that you've seen is Workday capability, as well as Sana technology that enables us to bring together all of the components of the closed process. You also saw the financial audit agent that the PPC request, that is GA at the moment. And then the second component that you saw within the audit agent, which is the testing, that is, just about to go to early adopters, in '26 or two kind of plus. So that will be going, more broadly to early adopters, very, very soon. So I've seen a couple of questions in the chat as well on audit the auditability of AI processes, and if KPMG have any thoughts on that. We do have a section after the section, on capabilities, just talking about governance and controls. So we'll try and address them there. The KPMG team will take a look at the questions, and if not, then we can, take some of those specific questions live as well. So I wanna drill into the second part, that we wanted to show you. So if you remember that slide that Richmond showed, which had, some of those capabilities, and, the first one that we looked at was the controllership, and the second one then is around, you know, data. How do you partner with the business leveraging tools to AgenTek tools to enable reporting analysis much faster? So I am essentially gonna look at what we're doing to support faster insights across the platform, and that's using, our decision decision intelligence platform. I just wanna give a quick introduction to this capability and some of the use cases, and then JP is just gonna do a, a short demo of this. But just to kinda ground the conversation, you know, Workday's had an exceptional data story from the very beginning. You know, we have one platform for your HR and your finance data. And then over time, we have innovated to expand that story in many different ways. And and what's really incredibly, I suppose, exciting about this moment that we're in right now is we're tapping into that foundation in ways that will deliver immense value to customers in the AI era. So levering agentic capabilities with that deterministic, data layer that fits underneath. And our story really is of compounding value. So we have our, you know, unified data core, our HR and finance data, consistent definition, security, and dimensionality right across the business, storing and maintaining deep context behind every transaction. And then we have accounting center, that extends this unified model by bringing in rich operational data into the platform, transforming it, and then, bringing it into the GL. A new capability such as CPM, so our cost and profitability management solution, will transform data in new ways, and you'd be able to reveal that true cost of serve for every part of the business. We're also launching a new tool or a new platform called Workday Data Cloud that enable customers to bring make zero copy connections to their data in their data warehouse, connecting this governed data to external tools and agents without losing lineage and control. And then lastly, this kind of layer underneath, the agent governed. So the financial audit agent tests transactions and supports remediation to ensure the integrity of that data. And then on top of all of these, we have Workday decision intelligence. This adds that agentic BI layer on top of all of the capabilities that are built within the Workday platform. It delivers deeper insight in ways and at speeds that were just really impossible, you know, even a year ago. So the story is not that Workday has more data. It is that the data is unified, granular, governed, and tested, and increasingly agent ready. And then there's a couple of different use cases that we are looking at. You can see here, decision intelligence, the, availability. So for planning, we launched it through general availability in July of this year. We're currently in early access, in for financials, and then we're moving into early access for workforce data. So these are, like, kind of multiple cape or multiple use cases for decision intelligence right across crossword data. And then one thing you know, these domains become part of one governed analysis platform. And then one area then that we wanna drill into, particularly for this webinar, is some of the use cases for decision intelligence in finance. And I'm just gonna jump over to this slide, in the interest of time. But, essentially, decision intelligence lets finance investigate questions at any level of detail. It can identify the drivers of material changes, explain movements in balance sheet accounts, and analyze performance across dimensions, such as company, product, customer, or cost center. It can also support cash flow analysis and trace a balance from the general ledger through journals, through to the underlying operational transactions. And And I suppose the key point is that analysis is grounded in governed workday data, not detached from the system of record, which just makes that analysis, much more accurate and also really important from audibility, and I see that question coming up a lot, in the, q and a. So with that, I'm gonna hand back to JP. He's gonna do a really short demo just kinda showing you some of the use cases of decision intelligence in action. So I'm gonna stop sharing again, and JP is gonna jump back on and show us decision intelligence. Thanks, Marion. Right. Let me just share my, screen. Okay. Good stuff. So, this is, running live in my, demo environment at the moment. So I'm going to go in and have a look, and show you some of the things we can do with decision intelligence. So what this is doing is it's allowing you to ask questions in natural language. So it's giving you that much more conversational approach, which I think everyone's starting to get used to today with things like, you know, ChatGPT, Gemini, Claude, etcetera, whichever your, AI of choice is. So I'm just gonna put in a a a prompt now. So give me total March revenue by legal entity, and then put it in a pie chart and kick that off. So what that's gonna do is pull together all the data. So, again, it's respecting all my security, all the kind of built in, details we've got there. And while that is running, as it takes a minute or so to run-in the background, is I'm gonna have a look at some of the the, projects that I've already, set up, already ran within, within Workday, within the decision intelligence area. So one of the most powerful ones is this corporate, consolidation packs. So this corporate consolidation board. And if I click on here, it's gonna give me the visibility of this, of this sort of board pack that I've pulled together. If I scroll back up to the top, it's quite a meaty, prompt. You can see I've just prompted this all using, natural language, and it's gone away, pulled this together for me. And then I've gone in and asked it to also create a a PowerPoint as well related to this. But, essentially, it's giving you, a highlight. So I've asked for the NAB SEC summary at the start. And it's not just giving you the visualizations. Once again, the AI can, take some analysis of what is happening. So talking about our largest positive drivers being the revenue over performance and then, headwinds being around the sort of OpEx side of things. So, again, it's doing some of that job for you. It's presenting some of that information through to you in advance. It's also then pulled through things like a a consolidate p and l. So, again, you know, consolidation or real time in Workday, but it's pulled this together as part of its board pack. I then asked it to give me a bridge analysis. So what was the differences? What were where my budget, deviate from my actuals? So what was that made up of? A breakdown then of different segmented performances. Again, Workday, very powerful from a dimensional perspective by division, by revenue category, etcetera. Also then asked it to give me a year to date outlook. And what's important is I did also say if you don't know, or if you haven't got enough data to extrapolate, then flag that. So it's giving me that that data limitation warning there, and then ultimately finished off with some, working capital, and balance sheet data. But as well as presenting that data in a a a board pack and giving us the numbers, again, the AI has done some analysis for us. So it's given us some risks. It's given us some opportunities, and then it's brought forward some suggested actions. And this is much more like what people are, I guess, used to using nowadays with, say, the the kind of, day to day, chat tools that we that seem to be ever present, chat GBT, etcetera. You can also present this directly from within Workday as well if you wanted to do that. But we've had a look at that. So let's go oh, sorry. Let's go back and have a look at how we've got on running that, prompt for me in the background. So run that prompt for me in the background in real time. So, shows you the work and shows you the time, and it's presented for me in graphical format, that revenue by legal entity, and giving me a little bit summary down a little bit of a summary down there as well. You can also ask it to suggest additional areas. So it's that natural language interaction giving you that more conversational approach to reporting as opposed to going away and then to build something. And it's that that what we're increasingly used to in our day to day life when it comes to using AI tools. So with that, I'm gonna stop sharing and hand over to, Richmond now. Thanks, Thank. you, JP. I'm just gonna, bring up your slide, Richmond. No problem. Okay. Let me know. Can you see it okay? I can. Yeah. Okay. I just wanna. say thanks to JP. It was great to see decision intelligence in action, dealing with some real life use cases. So if we just go back to reimagining the finance organization. So what we've now done is annotated the activities on this slide with the blue circles for the activities you've seen as part of the demos today. So you can already see with what's generally available and what's in early adopter, there's quite a lot of coverage now. So if you are an existing Workday Financials client, then, there's no reason why you can't start looking at these tools in your sandbox environment using the Workday console to look at the flex credit usage and see if actually you can start your your journey to piloting some of these agents. Now the agents are only part of the story, and to deploy them safely will require a redesign of your interim processes with careful consideration of the controls, not just in finance, but your IT controls as well. So, Nathan, given you spent quite a lot of years helping clients with controls and the new emerging KPMG trusted AI framework, Could you share your thoughts on how clients can mitigate the risks as they move into implementation? Yeah. Absolutely, Richmond. So, look, as Richmond said, I spend a lot of time, focused on controls, control issues, and control improvements in in various organizations. And a lot of the discussions that that we're having now is around how control frameworks will need to adapt to, I guess, be relevant and appropriate in the new world or where AI and agentic AI has a has a huge part to play in the day to day, running and operational running of of finance. And what we've got is, you know, a broader trusted AI framework that we've developed at KPMG, and this is just as applicable enterprise wide across the organization. And then what we're seeing is clients are taking that view of what that should look like and then thinking through in each of those key areas what that means for finance and what might need to change in the controls framework to make it applicable going forward. You'll see on here 10 areas. I won't go go into all of those now. I think the key thing to point out is a lot of this is are things that you would have anyway and be thinking about. But there are two or three, I think, new areas that need to be, really thought through and considered and make sure that the controls that are in place going forward are appropriate against those. That is around fairness, to make sure that models that are used and being used across the finance architecture are not biased. Transparency. So really clear that, the organization understands what is actually happening and what each of the agents is doing and how each of those agents are operating and controlled. And then the final bit is then explainability. Can the end result that is being produced be fully explainable, taken through the audit process, and be checked all the way back through. And that's really important to consider start embedding. And what we're seeing is that the I think that the the companies and groups are being most successful in actually driving through AI change that is sustainable and scalable are the ones that are thinking through the controls aspect of this, how to be controlled, what kind of framework changes need to happen upfront as part of that initial design phase for for finance. Think we move on to the next slide. I think Richmond so if we can move just. gonna cover off basically, I was just gonna go. through some of the controls, and, Nathan, yeah. that Workday has in place. I. know there was a couple of questions, you know, that were in the chat before we kinda jump into what Workday is doing. There was a couple of questions around navigating data privacy and compliance challenges, and any, you know, the auditability of agents. Any advice you're kinda giving to your clients at this time? There was a question. Do auditing standards need to catch up with the practical applications of AI in the finance ledger? I don't know if you've got any thoughts on that. I'm sorry to put you on the spot. I don't think necessarily all listing standards need to change, but, certainly, you know, auditors need to think through how they are, you know, tackling the new world. And I think AI, you know, as I'm sure everybody's aware, is, you know, new tools for audit as well that, you know, increases the ability to go much further and test far more that than has been there in the past. I think in terms of those auditability, I think when you're setting up an agent and developing an agent, you need to, at the same time, think around whether there's a control agent that sits alongside that mitigating any risks that may come over that agent or what the control will be that mitigates that. So it's a it's a thorough, well thought through, linked into risk and control matrices as we've always done around the finance piece. But just recognizing that there's some new and different risks within the ecosystem that need to be properly thought through and assessed and properly controlled, you know, in a variety of different ways as you would do normally in, you know, currently in the in the finance operating model. Absolutely. Yeah. And I think that we you know, from a Workday perspective, obviously, the audit agent is with, its GA now, and we've seen you know, that's saving you time. But I think the next phase of delivery for that, which is the, you know, testing of your processes and testing of all of the work that your agents have done is will be super important as we come into this kind of new agentic landscape working alongside your existing processes. So, yeah, I I completely agree. Okay. I'm gonna move on a little bit. I'm kinda coming back to this slide. This is more of a built type version. I kinda showed a simplified version of this earlier. But, essentially, this is AI's, platform fork, and I just wanted to before we jump in, I I've seen a question in the chat asking, you know, are these Workday built agents, or are they agents built by, partners? So the agents that you've seen today are agents built by Workday. So that layer in the middle there, Workday built team of agents. So they're the ones that we showed you today. But this is how our, you know, overall architecture really works. And in an AI for and somebody also asked, you know, around, data and context and that unified data model. And for Workday, that's, like, critically important because, you know, it means that the data that your agent and capabilities sit on are operating within the same data processes, approvals, and governance that run, you know, some of, the most important work, across your enterprise. So that foundation becomes super important, and agents can really be only trusted if they understand the rules of the business, who can approve what, and the data that they can access, where separation of duties applies, and essentially how actions are governed. So at the the bottom layer there, you have that foundation, that unified data governance, our business processes, our security and compliance. I kinda highlighted this, a little bit earlier on. Then you have our core applications all sitting on the one platform. And then on top of that, you have the Workday team of agents that we are building across finance, HR, procurement, and legal, for example. And then we to extend that further, we have DataClient and Workday Build. So DataClient makes Workday data trusted, governed, and agent ready, and then Workday Build lets partners and customers to build their own agents, apps, and orchestration that essentially work within Workday's context at Garvils. And then we have the agent system of record, and this is what I'm gonna drill into in just a second because I've seen a few questions in the chat around how do you how do you manage agents? How do you manage flex credits? How do you report on how agents are operating, within Workday? So they were some of the questions that I've seen come up. And all of this kinda ties back to the agent system of records. And then on top of that, we have that experience layer, which brings it all together. So, essentially, agents meeting users where they work, whether that be in Gmail and Slack. We are also using Sana, as many of you might have heard, as that kind of single interface within Workday to all of the agent and capabilities. So I'm gonna drill into the agent system of record for a second, and, essentially, think of this as that governance layer for your digital your digital workforce. So just as your GL provides an audible record of your financial activity, the agent system of record provides visibility into every agent registered, whether that be third party agents, partner agents, customer built agents, sign up, you know, agents, or Workday agents. All of these are managed if they access Workday data, they're managed in the agent system of record, and this gives visibility into every registered agent, its identity, its permissions, its accessible data, and its actions. And, essentially, what this is doing is really creating a consistent governance model across agents built by Workday, our partners, and our customers. And another key point to note is that it's a native part of the Workday platform, which means your agents have real time access to the data that they need based on the rules that you have configured. That's an important point to make, And then they get the outcomes that you're essentially working towards. This is just a kind of drill down then into the, you know, agent security framework within the agent system of record. So we hold agents to the same security principles as human workers. Every agent receives a unique identity, no shared accounts within the system, no anonymous bots. It has to authenticate that identity and explicitly authorize to access the resource this set of needs. Every action is logged, and that creates a clear, provable audit trail. And I've seen a lot of questions about auditability of agents. All of this is managed in that agent system of record. And because employees and agents are managed kinda in the same place, agent performance is understood then in the context of that blended workforce. So I'm hoping that gives, some context, around how we're thinking, about security and governance of agents. But I wanna bring Richmond on for a second. How do you see organizations actually scale this in practice? Thank you, Marion. So, obviously, from our work with clients, we recognize that every organization is at a different stage in their journey. Some clients are just starting to think about it and lay out their strategy. Others have already started deploying bespoke agents they built in, for example, Azure or Gemini technologies. So what we've done is developed a road map. It can be applied to any AI technology, not just Workday, which sets out the eight steps organizations can follow to start scaling agentic solutions safely. So starting out with a clear vision, and you need alignment across stakeholders, including technology and risk as well and being clear on the goals, not just picking quick wins. Then identifying the areas where you can get the most benefit. So it might be looking at time to close in finance. It might be looking at cost out where there's significant human workforce or improving reliability of processing. And in making those decisions, you need to consider the level of process change that will be required as you start introducing agents. Next, it's really important to start the business and culture change work early. So when you're ready to implement the process changes and deploy the agent, leadership and teams are ready to adopt. You need to think about your AI architecture to avoid rework later. So, for example, if it's within Workday, thinking about how you use the agent system of record, what standards you will establish to make sure that you can write them, roll out agents in a consistent manner. Decide which agents you will use from your ERP platform, so what you'll take from Workday and what you might want to build yourself because there might be reusable components, for example, around data quality where you may want to use them broader than just in finance, and you may decide to develop those in bespoke technology for your organization, and then you can still plug them in and manage them in the agent system of record. Building on from what Nathan covered, clearly AI governance and controls is is fundamental. And there is actually significant work to do here, so you should start this early before you get too far in deploying agents. The other part that can be overlooked is really thinking through what are the changes to your operating model. So when you start deploying your digital workforce, your processes and your workflows are gonna change significantly. And in to enable the human and digital workforce to operate seamlessly together, you're gonna have to decide where you still want to have a human in the loop, to give you that extra judgment or control. And then finally, you still need to performance, manage, and measure your digital workforce. So this is around deciding what KPIs and metrics you put over the top of your agents and how do you review those through governance to make sure that the agents are performing as expected. So, clearly, there's a lot to consider. I hope the information we've shared today will give you some insights on how to take the first steps, and Marion, I'll hand back to you to conclude the session. Thank you. Thank you so much, Richmond, and thank you to the KPMG team. I was sorry. I'm I'm frantically just trying to keep up with the q and a that's come in. But this is just a, you know, a quick kind of key takeaway, in terms of what you've seen. I think based on what the k p KPMG team shared, redesign don't just automate. So rich Richmond and Nathan showed, you know, that finance function is not today's team with AI built on. It's really a genuine redesign of the operating model, where high volume rules based work becomes AI delivered, and your people then focus on judgment, exceptions, and accountability. Second, really focusing on the areas where that pain is greatest. So we looked at record to report. We looked at, you know, the close process. I know that's a big bone of contention for a lot of organizations, and that's why we as an organization are focused on investing in this space. Reporting as well, building reports, accessing data within reports, understanding information within reports can be really challenging for organizations, and that is another area that we are investing in. But it's really, you know, making sure that you're leveraging the tools and the relationship with Workday and with KPMG to, you know, essentially identify where some of those pain points are in your organization, get involved with early adopter programs, and then, you know, scale with governance. So speed, you know, only matters if the numbers stay trusted. And it's if you want to learn more, there's lots of information on the community, on the agent system of record, and our responsible AI program, that, you know, lets you scale agents with full visibility, you know, and human accountability. And I'm hoping some of what we covered and some of what JP showed you in terms of the agent reasoning and it's, you know, kind of dipping into your, underlying processes and policies to ensure that it follows the right steps. But before I kind of close-up, Richmond, Nathan, I'd love to kind of bring you on and, you know, is there anything to add? I I see some questions from customers, you know, who are you or prospects who are on the call. They're using multiple ARPs like 40 plus. How do they manage that within this new agentic landscape? I don't know if either of you wanna take that one, and then anything else that you wanna add based on what we discussed today. And what would you advice would you give to the audience in terms of where to start? Maybe, Richmond, do you feel wanna something we haven't? spoken much about on this call is the importance of having good data lineage and clean and complete data because your agents will be the same as your current workforce. Right? It'll only be as good as the quality of data. So I think a no regrets activity is to get a good grip on your data lineage, your data controls, data quality, then identifying the use cases and identifying where to start first. Clearly, if you've got lots of different disparate ERPs, you've gotta make some decisions around, do you use what's in the platform, or do you build bespoke agents that could sit across multiple ERP platforms? And I think for each organization, that decision is gonna be different. And certainly, you know, we've we've been given that quite a lot of thought and happy to help clients in thinking about that journey. Nick, nothing to add on your side? Yeah. I mean, I think I think for me, if you I I guess in terms of what what I see around, you know, the market at the moment is, you know, there's there's probably quite a lot of, you know, what you might deem as sort of AI theater and a lot of AI, activity, lots of busyness, around around the piece. And I think now, I think given just all the developments that happened and how quick everything is developing, I think it's a great time to step back, think about that end to end strategy, how that links into the broader organization's AI strategy, really setting out and focusing in, as as Richmond said, on those highest value use cases and building out from there and doing it in a safe, controlled way that is, you know, thinking strategically from from the start. And I think if you do that, then I think, I think that'll set you up along with the data foundation to to having, you know, a solution for the future that, you know, will continue to be scalable and ability to adopt the technology as it continues, you know, to develop at pace. Nice. Thank you. There was one other question in the chat, from Aaron, and I presume it's to the KPMG team. Do you provide a guide for business to review their systems to ensure they meet the minimum functionality that the AI needs to work across API, CRM, ERP, HR, and bespoke systems? I don't know if you wanna take that one or how you're currently working with clients in that space. I'm sorry. I didn't quite understand the the the question. Sorry. Before. Do you provide a guide for businesses to review their system architecture to ensure they meet the minimum kind of functionality that AI needs to work just across all. systems within the ecosystem? So we've got lots of, I guess, guides and approaches and everything else that, you know, we'd be very happy, you know, if people want to reach out and contact us to to discuss with them. I think with all of these things, you know, guides and, you know, Iran is good. You know, you know, they they can be generic and less than nothing. But what's really important is, you know, taking those guides and applying it specifically, you know, to a particular business finance function or, you know, solution or problem that needs to be, you know, addressed. I think, you know, with all of this, I don't think, you know, the the generic approach is is is necessarily the the the right one. So I think, yes, we have guides across lots of different things. I'm very happy to to discuss that, you know, in more detail with, you know, our various industry specific things. And just to add on that, I guess, one of the advantages of the Workday platform and the agents that are coming is the business processes are clearly defined. You can output the process from Workday to see the steps. You can then identify how much time is being spent in each step, and that can help really inform your prioritization of your use cases. So making use of what's available in the Workday ecosystem is a good place to start for those customers who aren't on the Workday journey yet, then I'm sure there are, you know, kind of similar tools and approaches available, and we'd be, yeah, happy to help. Super. Thank you both, and thank you for your time. I think we've got to most of the questions in the chat. Sorry. I was trying to keep up with them, as we went on. I've seen a lot of questions around being joining an EA program. If, you, if you identified it, I'm gonna I will have a look, after the webinar, and I will follow-up with you directly. Any questions that I didn't get to, I will look to see if I can follow-up directly, with the, people that we didn't and some of them we did, address live there. But thank you all for your time, and thank you so much to the KPMG team. I hope you enjoyed today's webinar, and we look forward to seeing you again soon.