Driving ERP Transformation | Innovation & AI Trends for Emerging and Mid-Market Organizations

All right, given about 15 seconds to get your answers in three responses, we're going to be closing the poll and moving on to our conversation to frame the discussion a bit, finance teams are juggling a lot right now. CFOs are expected to be strategic partners while still running accurate, efficient operations. And on top of that, AI inside ERP platforms especially Workday has accelerated. Quickly, we're going to start with Julie. How are CFOs thinking about the balance between innovation and risk management when adopting AI driven tools?

Julie Munn-Sims: No, I think that is a great question to ask, and it's definitely a balance because at the end of the day, they're asked to be a value proposition that is realized for anything that A CFO brings into the organization. So whether there's risk and scalability, but then also what's the overall value proposition of introducing AI in addition to other technology advancements is always topic of discussion and typically what drives a lot of the strategic momentum around technology. But thank you. I'm going to catch up a little bit with what are CFO challenges, what are CFOs thinking about? But not just in the context of just finance. How are CFOs conversing with their peers and other leaders in each organization and addressing key challenges? And when we talk to many in the finance and accounting area, it really comes down to four areas. What is the modern workforce?

What does my workforce look like today and how is that enabled? And believe it or not, organization agility is important and it's not always a human workforce. We are starting to see genic workforces enter the marketplace and the level of technology that enables new ways of working amongst our teams, both in the office and those from remote locations or in other geographies around the world. We also talk about operational efficiency and how do we enable growth from the office of the CFO to help drive operational value? How do we increase operational value with future ready processes? And so there's absolutely an opportunity to underpin technology to help gain additional value with your future ready processes. And then enterprise performance management, and this is probably the one area that a lot in the finance organization deal with day to day, which is around being able to have better, quicker access to data to make faster decisions, more informed decisions, really focusing more on proactive and strategic information and information sharing with your front office business partners or your external market in the street if you're a public organization. But it's really around data. How are you using your data? How are you managing your data and shifting to letting your data be an asset versus just a reporting capability? Then underpinning all of those is your technology investments. Where are we prioritizing our investment dollars to create enabling technologies that help our business partners, the front office as well as the fact function better and provide more value? So where are we investing in ERP digital data and of course artificial intelligence and what does that look like today?

And when you think through the role of the CFO in light of all the things that are changing in light of the fact that you have economic challenges, you have workforce challenges, you have technology issues, investment gaps if you will, and priorities, the role of the CFO has to shift. Many CFOs operate in what we call a guardianship mentality or a guardianship characteristic, and we're asking and watching those CFOs need to shift in an ever-changing uncertainty environment to a catalyst mentality. And what does that mean from a guardian perspective? Guardian CFOs or CFOs with the guardian trait tend to focus primarily around risk mitigation, safeguarding existing assets, protecting the status quo, if you will, with a heavy focus on ensuring compliance as well. They are protective and defensive in nature, prioritizing stability and minimizing potential losses for the organization, and they tend to favor established processes.

Norms, again, I mentioned status quo, they're cautious, a little more cautious around decision making and adherence to regulation is a priority, but some of the drawbacks to staying in a guardian mentality is really it leads to a very short-term transactional focus tends to stifle innovation as well as hinder growth opportunities because you're being overly risk averse typically in a guardian mentality. But if you shift that over into a catalyst behavior concept, catalyst, CFOs tend to focus more on driving growth, very much more open to innovation, not afraid to fail as much as more risk adverse guardian CFOs and catalyst CFOs are focused more on strategic change, not just change for the case, the sake of change, but really what's the strategic change that we need to put forth within the organization to continue to increase our value in the market? Catalyst CFOs are forward thinking, they seek opportunities and adapt to ever evolving landscape, and they really embrace calculated risk.

This is where, and this is where it's important from a technology perspective and an artificial intelligence perspective, catalyst CFOs encourage experimentation without an over fear of failure. They promote agility in being able to take ideation and see it through and test innovation concepts that will benefit the business. But some of the drawbacks of a catalyst mentality are obviously it requires a higher tolerance for risk. And depending on your organization, depending on market instability, that can be a little bit nerve wracking for some, but it also can lead to missteps and unforeseen challenges, things that happen when you're thinking about innovation and ideation. So in essence, a guardian mindset prioritizes stability and protection of the status quo. A catalyst mentality tends to prioritize growth and proactive change.

Joshua Greenberg: Yeah, so the shift you described from mainly protecting the business to help moving it forward really sets up, I think what our next question is, which is when you're advising CFOs who are thinking about modernizing their finance function, where do you encourage them to start? What should they be solving for? First,

Julie Munn-Sims: It's really about what do your internal customers need? It's really about being that better business partner. And a lot of times that ends up being around data, which is obviously enabled by technology and different maturity levels of data management with organization, but it's underpinned by technology. And so when you think about what are the capabilities that enable finance to really deliver that value, these are the ways that we sort of think about how finance can create capabilities with their business partners. Thinking about strategy and innovation, what does a dynamic finance organization look like? What's our service delivery model? How are we organized as a finance function? Looking at process excellence, how can we further automate processes either through automation of an ERP or replacing manual processes with genic capabilities as well? AI agents, if you will, thinking about digital finance, really looking at your data intelligence, accounting automation across the board, gen ai, of course, robotics process automation falls into that as well, depending on the transactional nature of the business versus the strategic nature.

But where can we enable a reporting strategy that really incorporates digital innovation into the landscape? Obviously, ERP evolution, this is really, it's not always about re-implementing an ERP, but it's about modernizing your ERP, looking at your ERP every few years and thinking, what am I not using to the fullest capability or what additional connected applications are out there that can help me fill the gaps of my ERP without having to go down a full-fledged implementation project? Again, those are never fun and often can be expensive, but also very tiring on the organization. So how can we evolve our ERP solution to meet our needs for scale and growth? Enterprise performance management is probably one of the biggest areas where a finance function can partner with the business to deliver value, putting together a strategic reporting, or pardon me, a reporting strategy that really meets the needs of business decision making, not just historical look back of what we did last month or last quarter or last year, but really what are the types of data and decision-making processes that will help our front end business, the customer facing part of our business deliver value?

And sometimes that means going outside of our four walls for data, for information, leveraging things like AI to bring in exterior pieces of information, external pieces of information to help us provide better insights to our business partners. And then ultimately looking at finance as a service. How do I service the organization as a finance function? Do we have a shared service center? Do we have a global business capability for some of our multinational clients that we have? Do we have capacity support services that we need to incorporate to further enhance the finance function and allow us to work faster, if you will?

And then thinking about core finance insights, how does information help the business evolve at an enterprise wide level? Looking at your business strategy, building new business value and thinking about strategic planning. Finance has a front row seat at those decisions, at that conversation, if you will, to really provide insights and information to help the business create a very forward thinking strategy, moving them into the next level of value creation, digital innovation, really helping and working side by side with your CIO or your VP of it, if you will, to get the most from your technology. How can we leverage digital dashboards, digital layers of information capability to enable our business partners to work faster, to have a more sound and stable technology infrastructure? And then looking at your organizational model and ever evolving that model. A lot of times companies get our clients of ours get stagnant in a standard organization model.

That's how they've been for the last 20, 30, 40 years. But really transforming your operating model to fit your strategy and your growth plan to fit your need. And that can include workforce assessments. What does your workforce look like today? What is the service delivery model, not just to finance, but your other internal back office functions? What do those look like and what does your future state design look like to help you meet your strategy goals? And then obviously around deal advisory, post merger integration, as your business grows, as you look at potential target acquisitions or mergers, what does that look like in terms of identifying the most efficient and effective deals for your organization?

Joshua Greenberg: Julie, you also hit on how wide the finance role has become strategy, operations, technology, all of it is what you were talking through, but even with that big picture, teams still deal with everyday bottlenecks that slow them down. How do you encourage CFOs to solve for challenges that they face on the day-to-day level?

Julie Munn-Sims: I always encourage CFOs to do a self-diagnostic or to do an assessment of their organization. Some people shy away from that because they view it as a report card on themselves, but I look at it as what do our people, our process and our technologies doing today that could be hindering us from meeting our strategic goals? And so really looking at how things are done, why things are done that way, just because someone's creating a reporting package that nobody really looks at today, but it's been done for the last 10 years, for example. Why asking ourselves, does that drive value for the organization? And so really allowing people to look at their organization. And to do that, we leverage what's called a target operating model. When you think about the various layers in an organization, this is how you can examine how you operate as either a finance function or an enterprise in general.

Looking at the organization layer, what does that look like today? Looking at your processes and activities, the very task level, how are people operating? Are there manual processes? Are there a lot of exception based processes that are requiring angst in the system, if you will? And bogging down how your functions are working. Looking at your culture and your people today, is there a growth opportunity? Is there complacency in your organization? Do we have the right culture and mindset to match the growth as an organization that we're trying to accomplish? Your data and insights, again, I won't repeat, but kind of going back to what does your strategic reporting solution look like and then how are you enabling that digitally either with basic technology like an ERP or through digital dashboards and digital layers that allow you to really be an innovative organization? And probably last but probably most importantly, is your governance structure. How are you, what is the framework in your organization tied? Looking at controls, looking at policies, looking at how you are conducting and improving your business operations today. That predicates everything that we talked about. And so EisnerAmper leverages a target operating model to help customers assess how they are identify gaps that are going to enable organizations to progress, to improve, to where they want to be, to provide more value for their business partners, but then also meet their strategic goals and objectives.

Joshua Greenberg: Thank you, Julie. That was a great breakdown of how CFOs can use that target operating model in their business. So we're on our next CPE question, and it's just more talking about the people who are on the call today. How ready is your organization to adopt AI and finance? Are you actively adopting AI today exploring AI use cases but haven't implemented yet? We're in early awareness and education stages, or we see potential but haven't been prioritizing it. And we'll give just around a minute, but if we start getting a lot of responses, I'll tell you when we're wrapping up,

Am going to keep it open for about 10 more seconds and then we'll move on to David, our Chief AI officer at Eisner Emperor. 

Am going to be closing the poll right now.

Thank you, Julia again, and thanks for responding to that CPE question. It's always interesting to see where teams are in their AI journey. And that leads perfectly to our next topic because AI readiness isn't just about the tools, it's the mindset governance and taking a thoughtful approach. So with that, I want to bring in David, our chief AI officer at Eisner, er David, you lead our firm's AI strategy and how we evaluate and roll out these capabilities. Can you walk us through how AI is shaping Eisner ER's technology decisions and overall approach?

David Frigeri: Yes, and thank you Joshua, and thank you everyone for investing the time with us today. I think we want to start at a higher level with our strategy. And our strategy is fairly simple. You can encapsulate it in the context that we want our colleagues spending more time with our clients directly, less time behind a keyboard and more time helping our clients solve their toughest problems. And really what that implies on a go forward basis is really AI becoming the service, AI becoming our service delivery layer for really everything that we're doing. But in order to do that, there's really four main areas that we really need to excel at. First is value realization, and this is the intersection with finance. And I'd say one of my most trusted partners within EisnerAmper is the entire finance department. I'll spend some more time on that. But there are also very important areas such as build versus buy.

We have an array of different areas that we think that AI can have a positive impact. So to know where's the right place to use a workday, and just knowing that they have that area of the organization covered versus where do we want to build differentiation for our service lines and for our business development folks in terms of how we compete and deliver out in the marketplace. The third area is governance, data governance, and AI governance. We're a professional services firm. We're a next generation accounting firm, but we still operate at the nth decimal decimal point, right? Trust is in everything that we do, just not the accuracy, but being good stewards of our client's data. And then finally that fourth pillar is around enablement and adoption and ensuring that our colleagues have the right skill sets that they need to be successful and get the greatest return overall from these investments that we're making in ai.

Joshua Greenberg: So great segue there. So that really shapes how we think about AI across the firm. Building on that, how do we actually decide which ideas are moving forward? What does the evaluation and rollout process look like when we're considering a new AI capability?

David Frigeri: Yeah, thanks Joshua. And I want to answer this in really direct context of the relationship with our chief financial officer and the entire team. And it's important I think, to do that because this relationship spans the entire life cycle for our clients. And so if you think about when we first got started with EisnerAmper, one of the first conversations I had was with our CFO around what's the one or two levers that you would pull that you think could have a real positive impact on our economics overall? And that wound up being a really key kind of north star for us in terms of how do we go about prioritizing what use cases we invest and act on now which ones do we share with a partner to take on? But having those metrics aligned upfront, super important because it ensures that you have that kind of executive level alignment.

And really it starts with your head of AI and CFO and then bringing everyone else along in the C-suite. But other areas, the CFO and finance wants to know, Hey, how can AI help us potentially surface new opportunities within our existing clients? How can AI help us find new solutions for client problems that someone may not be aware of that EisnerAmper can help more, maybe boring things, but very exciting for some folks, which are things around billing. How can AI help us ensure that we're billing every minute of every engagement letter that we are entitled to, and then you really get into delivery. How can we use AI to not only maximize the client's experience, but also maximize our engagement margin and velocity? And then ultimately we're moving in a direction where our clients will be interacting with our AI more and more directly as part of how we evolve our relationship with our clients. And so across each of these different areas, for folks in the call right now in finance, you can recognize how important these intersections between the AI team and finance really are. But ultimately what we're looking to do is stay aligned or anchor to those north star metrics and then really applying our prioritization scheme and calculations against those. Okay, let's keep moving.

Joshua Greenberg: Alright, so looking ahead, what areas do you see as biggest opportunities for AI adoption within EisnerAmper?

David Frigeri: Great. Yeah, so this is probably the most exciting part for me and I think where EisnerAmper is headed, and we really see three key areas or advancements that everyone should, I think should really be cognizant of. First is the rapid advancements that we are seeing in the foundational models. And these foundational models are becoming more and more sophisticated, which is allowing us to give it tougher and tougher client problems within the same kind of experience. So the rapid evolution of the models keep a really close eye on that because it, it's happening basically every couple of months right now. The second area to keep a close look on is what we refer to as computer use. Computer use is this idea where an AI agent is able to actually navigate your software as a service tools. Imagine being able to do data entry through an intelligent agent, an agent that understands of an errors thrown, how does it actually correct it, allowing you huge opportunities to reallocate that human capital or that time.

So computer uses is a second topic. The third topic is the advancements in the context of the models now residing in our core business applications. So for those of you who are like a Microsoft 365 client and agent mode in Excel, total game changer. It reminds me the first time I wrote in a Waymo and watching formulas get written, watching a three year financial model get built across all three statements in I 10 or 15 minutes. It's amazing. And for those organizations that just love their spreadsheets paying really close attention to this, I think that you're going to see huge productivity gains. And Eiser Amper has certain service lines that are really wedded. And what this does, it brings the AI to the workplace, it brings it to the user so the user doesn't have to go into another interface to figure out the ai. It's all right there in their home system, which really drives up your adoption and value realization because it's so accessible to your user. So those three areas, rapid advancements in the large language models themselves, computer use, and really having the AI resident in your core applications are three areas that I'm particularly excited about. Joshua,

Joshua Greenberg: Yeah, thank you for that and a great overview of where we're heading. I think we started, we started with Julie who's talking about just from the CFO perspective, but actually learning from you how EisnerAmper is incorporating AI into its various different service lines and what we do in the back office as well is extremely helpful to frame our conversation here before we get to Workday. But before we get to Workday, I do want to have our next CPE question, which is, what is your top priority for ERP investment for the people on the call? Is it increasing automation and reducing your manual effort? Strengthening data quality controls and compliance, enhancing forecasting and planning capabilities. And lastly, integrating systems for a more unified technology stack. It's probably all of the above, but which one is probably your top priority? We give it 15 more seconds when the majority of people have submitted, we'll move on.

All right. I'm going to be closing the poll and moving on to our next part of the conversation. Thank you for everyone for responding to that poll. It's always helpful to see how organizations are thinking about their ERP priorities. Before we bring in our next speaker, I want to pause for just a moment for a product statement slide. Some of the features and capabilities we'll discuss in the workday section may reference future roadmap items. As always, any decisions you make should be based on functionality that's available in Workday today. Dan Wesley's Workday's chief technology officer for E-R-P-E-P-M and planning, and he's been at the center of how Workday is designing and delivering AI across their platform. Dan, to kick things off, can you walk us through how Workday's philosophy for embedding AI into financials and ERP processes?

Dan Wesley: I think it's a careful balance for us, Joshua, and thank you for having us today. It's a very careful balance because we want to give you an industrial strength ERP platform. We want to make it ed. We want to make it resilient and reliable and trustworthy at the data layer as well. But our customers are asking us to enrich it in ways through argentic, through AI iss, and really give us solid foundation, but look at all the new things, the new technologies that are available to us. So how do we get these things into the platform and help our customers? So we kind of have to balance both sides of that equation. But what I wanted to do before we really got into some of the features and functions and where we're moving us directionally, I think it's important for the audience to note when we talk about ai, there's really two different flavors of AI in common terms in what we're seeing in the marketplace and what's developed in the marketplace today.

I want to separate those two things. It kind of leverages the things that Julie talked about and the things that David talked about as well. Generative ai, you think of that as chat GPT, you think of that as the ability to ask AI a question and for it to deliver a document or an answer is declarative in nature. So if you ask the question, it must give you an answer. It's a great thing for speed and et cetera. As David said, building formulas in that framework allows you to be very efficient. The open-ended caveat to that, is it better? It has to give you the answer, is the data accurate? Is the data trustworthy? It is foundationally dependent on the accuracy and the viability of the data that you're asking the question again. So it must give you an answer. And if that answer is wrong, the answer is wrong because the data is corrupt.

So you've got to be careful from a generative declarative nature, Chad gt, ask a question, write me a formula, answer what my projections would be, because the heavy dependence here is on data. The agen side of the discussion is a digital persona or digital worker who makes problem deterministic outcome testing part of the way it reflects back to you and answers questions on your behalf. So one gives you an answer. The other is testing those answers either based on summited as watch and co-pilot it against or being able to test outcomes. And under that same tested data quantum be able to answer those questions as a digital persona would be. So I think it's important to note digital conversational falls within that term. RPA falls within that terms. It can help you be efficient, but highly dependent on data, an agent effectively having the intelligence to walk through those outcomes and determine what those outcomes will be. It's important to note that before we start our discussion because we're going to be applying these two frameworks or methodologies or technologies in different ways within our platform.

I think now in reference to what Julie had talked about, the difference between a guardian and a catalyst CFO, from what we see from our customers today, and I think we're from a financials perspective, we're north of about 3,500 customers all using the same workday. We've been told that it is not an option anymore. It will be part of the office of the CFO from today and into the future. And the facts that are represented in this slide that we see the retirement of CPAs and accountants, that it's harder to find those people. The experience that is in the financial office today is retiring and moving on to that final stage of life and experience. And so it's going to be harder and harder to fill and find these roles. So that's one thing. There is an impending staffing shortage that we see in our customers reflect and we see from an HR perspective, well, we look at AI as helping.

How does it help an organization if your top talent is moving on? We see it in really three different frameworks. How do we help our customers and the software that we deliver be more productive? Is that using conversational AI to ask simple questions? The applications of your financial systems, is it looking for errors? Is it effectively going over repetitive tasks? When I submit expenses, why is Dan Wesley submitting an expense for Home Depot on the corporate credit card, things like that. Or why was there a $10,000 meal here or et cetera, or an invoice. Why wasn't this invoice paid within the 30 day requirements that we have? Or why have the penalties been applied accordingly? So from an AI perspective, we see three different swim links, a helping be more productive through RPA through conversational or through anomaly detections. Where we see a tremendous advantage to most organizations is regulated organizations as finance is audit and compliance.

So using a combination of ai, so declarative or problem ag agentic solutions to be able to help you from an audit perspective to see where internally things may need to improve processes are flagged as deficient, or where your auditors need to go in and prepare for the external audit or to pass your internal audits before the external audit. So really helping you accelerate the way that you look internally to meet audit risk requirements. And then as importantly, how do we help you take the rich financial data that you have and give you time to analyze it and really be able to change the way you look at profitability revenue or shift your organization in a way to give you more time to analyze and run a better financial business. So these are the three swim lanes that we see that our customers tell us and where we're investing our technology from an AI and gentech perspective.

Joshua Greenberg: Thanks, Dan. The breakdown of generative versus Gentech AI and why AI is now a real advantage for CFOs and productivity compliance and insight to action was great. Building on that, I'd love to bring it down to what customers are actually connecting with which AI capabilities are resonating with customers right now.

Dan Wesley: So I think there's a couple different ways that we see this and our customers hold us to the highest standards. So as one CIO and CFO told me, they're like, Dan, we want all these cool things in your platform and we don't want to build it and we don't want to code it. We want it in there or we want it ready for you. So if you look at the accounting practice for our customers today, traditionally it's managed from a transaction management perspective, closing the books, I have to consolidate my journal lines, I reconcile the things are out of whack or the areas that need improvement, I report and I move forward. What our customers are experiencing today is how we've already implemented AI within the system, through the ability to allow you to adjust how your transactions are oriented, how your chart of accounts looks, and then the secondary fact and our most used AI is in within all Workday is our journal anomaly detector. So our journal anomaly detector looks at your journal entries and says, you can't close your books until you look at these top five, top 10. So we waited average and determined which ledger items you need to look at in order to clean up the close your books. That is our most important. AI is embedded, it's in production today.

Auto reconcilement within Workday, within the systems that we own is also an AI is that helps you point in the right place to see things that are out of balance, out of alignment or have not been reconciled in the proper way. The AI iss around reporting, what if I change my budget by 1%? What if I change my headcount by 10%? Being able to use AI within the way that you look and analyze your data effectively gives our customers more time to start moving to the right. So all we're trying to do as a technology organization or platform or organization is do the efficient things that Julie talked about RPA and make your process better. But at the end of the day, give our finance team a better chance and a better opportunity to be analyze. And that's really one of our core foundations for where we're using AI and where it's in the system itself.

Where we're seeing this move beyond in 2026 and forward is the use of agents, again, agents, a digital persona who can make determinations, test outcomes, and make recommendations to close your books to complete bank reconciliations or to be prepared for an audit in that way or to go through contracts and say, we'll build a contract standard and be able to determine a standard agreement to our customers. So from our perspective, it's taking AI now leveraging agents to help you again, automate tasks, make deterministic problem, determine outcomes, and give you more time to analyze your systems.

So Workday has been in the AI business, it's been part of our platform for the last 15 years, so this is not new to us. We are fortunate in Workday that we don't ever have to delete data. It's one of the benefits of our platform. We don't have to delete data, we can keep history alongside current. We can do trending actuals or futurism from an EPM perspective, from the same system. So we're fortunate from the standpoint that we don't have to collect data, warehousing data into our platform. What we have delivered to our customers has been journal insights. That's part of our system today. Customer payment matching being another example of that, accounts receivable and customer overpayments through doing RPA and being able to look and close your DSO through both agents, internal agents and AI automation, AI matching as well, our expense management system, being able to manage exceptions, contract management, being able to scan contracts for validity to certain standards to pull that back to AR and see who's not paying, which payments have been not received on time. So AI has been part of work and delivered in our platform for the last 15 years in the last five years. The AI is, as I'll call them, are what are represented on the screen today and in our platform today.

Joshua Greenberg: Thanks, Anne. Seeing what's already feasible in Workday really brings it to life. I've seen the new account reconciliation capabilities in action and it's a game changer for the clients that I've worked with. The same goes for the expanded expense features, the journal insights, these are real tangible wins. So building on that momentum, what's next, and I think you've touched on it a little bit,

Dan Wesley: The rise of the agents, the digital persona who can do work. We see the accounting journey transformed by the use of ai and more importantly, the use of agents to help accelerate repetitive processes to see errors before they occur, to manage the auditing process in a way that gives you more time back, manages the mundane document, evidential tasks that have to go in place and really be able to apply agents in a way to shrink and shorten the regulatory requirements and the standard financial close requirements and projection requirements. And to give you more time to use finance to transform your businesses. This is by tucking in agents where it makes sense, expanding our use of AI and being able to really shorten and make more efficient the processes that are in place.

And we're fortunate to be able to show you one of those things. This is from an early adopter perspective. So as we develop these new features and functions, our customers are testing them as part of the engineering teams and the product management extension teams. So the most exciting accounting agent that we have had, and we get the most enthusiasm from our customer, is the financial test suite. So we've built a set of agents that can reconcile the auditing requirements, internal, external, and help accelerate find errors and point things out and give you suggestions as to how to meet the auto requirements that you have in place. This is now soon to be the number one AI is within. Workday is the financial test suite, and we have a video to share with you

Video: Introducing the Workday financial test suite and financial audit agent, the solution that elevates your financial assurance by strengthening your controls data integrity and confidence. From the overview dashboard, you get a clear view of your financial operations with key insights that need immediate attention. Right away. It has flagged four high priority risks, including a duplicate invoice that could have led to a costly overpayment. But upon further review, the agent has already taken action stopping the duplicate payment before it even went out acting on the agent's recommendation. I'll send a quick email to the supplier to verify the invoice. The agent doesn't just stop problems, it helps us prevent them. It's also recommended additional tests we can enable to ensure this issue never happens again. Here in the test marketplace, we can find test developed by Workday along with those recommended by top audit firms like KPMG and technology partner kinos.

I'm going to immediately enable the recommended high risk supplier verification test. This test will continuously monitor our master data going forward, ensuring its accuracy and completeness at all times. The agent isn't just running proactive tests. It also acts as our financial audit agent automatically preparing a PBC response by assembling sample data directly from auditor requests. So when a request from my auditors comes in, I simply navigate to the audit request work area. From here, I can engage the agent in a conversation to initiate a new request. I'll enter the details, an AP cutoff sample, and I want the agent to include all the related documentation, invoices, payments, delivery dates and contracts. The agent immediately gets to work, collecting the data and quickly surfacing an initial sample selection. It looks correct, so we'll move to the next step. The agent automatically pulls in the company year and reporting period and assigns a preparer and reviewer. We can see it's already selected, the right company based on my description. Excellent. We complete our review and with one click, the agent assembles the complete data sample request. And just like that, it's done. This comprehensive solution goes beyond simple error, spotting it proactively, prevents future issues, provides continuous assurance and dramatically simplifies your audit process, giving you complete confidence in your financial data.

Dan Wesley: I think the best way to answer that is a customer testimonial of a large SI here in Chicago who saw in their external audit a 50% reduction in time spent with the auditors giving the auditors that test suite and that audit, that audit agent, they were able to reduce 50% of the audit request and get them the information that they needed that much faster and save their time fulfilling those documents as well. And we flash by Joshua, I'm sorry, I moved a little too slow there. This is the use of AI as you do work, and I just want to highlight this as well. The Workday assistant is our new self-service agent that will be asked, that'll be part of every step that you work within Workday. It is a help source, it is a document source. Wherever you are within the financial suite of products or the HCM seed of products, the Workday assistant will always be at your side as an AI conversational assistant to be able to give recommendations, point you to things or highlight what you're seeing on that screen. And thank you, Joshua. So that's AI in action in the platform itself. And this is the example from the video itself, the test agent being able to test where things are working, not working in places to look, and then the financial audit data. Are you ready for your internal or external audits?

And in the brief time that I have left, we know we are a system of record, but you have many systems of records in your organizations. So being able to do account reconciliation, bank reconciliation, or be able to give you time to analyze futures is really independent on having a technology that could pull all of these things together. And a solution that Workday offers that is in production today is the accounting center. So the accounting center allows us to plug agents on top of a lean ledger as one example to be able to pull data from other sources into one accounting description or one accounting data dictionary. And then on top of this, as Joshua pointed out, having bank reconciliation account reconciliation at a macro level in the agentic deterministic outcomes to test what's working, what's not working or where to look sits upon this accounting center that allows you to span non Workday systems in that way.

And I think if you look at giving you more time finding efficiencies using AI and agents in the right way, account reconciliation and end-to-end workflow, allowing you to see what's working, and I'll use a macros instead of the detail behind it, but a way to dashboard and be able to showcase what is ready, what isn't. Can I close my books? Can I close my books for the year? Is this data in alignment for what I need to make decisions against? And we see AI and the ag agentic aspect of the AI technology, just part of the accounting and financial assistance in the future. One customer, he asked me not to name himself using AI and agents has truly moved to a zero day budgeting scenario. Now he's a private company. He doesn't have any public responsibility from an SEC or anything like that, and he has some advantages that most customers don't have, but he has used the technologies within Workday, the technologies within AI and some of our early adopter AgTech technologies that he's actually able today to operate his billion dollar business on zero day budgeting, which that's been, we haven't seen that for a while.

Joshua Greenberg: Here. No, that sounds pretty exciting. That sounds pretty exciting. And thanks, Dan. That's a great look of where Workday is headed and how these capabilities are going to continue to evolve. Before we go into questions from the audience, and you can start typing 'em in as well, we're going to pause for just our last CPE question. So that is which ERP capability would benefit your organization, streamlined financial close and reconciliation, improved reporting analytics and dashboarding procurement and spend management automation end-to-end AP AR process improvements. All right. I am going to give this last CPE question about 15 more seconds and then we'll close it and move on to questions. If you've already answered the question, feel free to type in a question to any member from this panel, Julie, David or Dan or myself. All right. Make sure you submit your response to this CPE question. Definitely want to make sure that you get your credit hour of CPE. All right. Just given that there is four minutes left in this hour that we scheduled, let's go look for some questions. I think everyone's going to join us back on screen here, but I believe we have a couple questions. So Dan first shared a lot of exciting AI capabilities. How can CFOs feel confident relying on these AI driven capabilities inside critical financial processes?

Dan Wesley: I think David touched on it as well. You have to have an AI governance policy. You have to be able to look at it from a policy perspective. It all is first relying on data. Is my data accurate? Isn't in one place that I can make accurate decisions against? Is it quality tested? Do I have a data harmonization strategy? It's the foundation of any AI that you have. So do you have a handle on your data and how consolidated is that data dictionary that sits above it? Secondly, at least what we're doing here at Workday is encourage your employees to try it. David, you've made such a great example of letting the Excel copilot be able to build macros and formulas and such. Maybe you wouldn't close your books or run your FP a organization yet on that, but try it. Don't be afraid of it. If the data's good, the data's clean. If there's a governance policy around it, try it.

Build a copilot at Excel spreadsheet. Ask, what if I changed my budget by 2%? And I would also, whether your Workday customer or not, put pressure on your vendors to include this in the platform, not force you to buy additional seats, or more importantly, I have to build an IT staff that is building clawed expertise and things like that. Keep us responsible as vendors to enrich the platform in these ways and make it part of the platform itself and not force another burden and responsibility, so data play with it, and then hold to us as vendors accountable for enriching the platform in that way.

Joshua Greenberg: That's a good answer there. Julie, there's a question for you. As CFOs evaluate different ERPs and all the different AI capabilities that are being marketed with them, what should they be looking for to make smart informed decisions?

Julie Munn-Sims: I think the first is agility and flexibility in the solution. 10, 15 years ago, ERP platforms were very rigid, very prohibitive in terms of really allowing customizations and not just customization, sorry, adaption of processes and capabilities. And so looking for an ERP system that's going to allow you to scale, allow you flexibility as your business model changes, but then also from an AI perspective, someone that's been there and done that, Dan talked a lot about ai, pardon me, within Workday in leveraging the genic workforce, having an ERP platform that's already incorporating AI versus a plan to do I think is important because when you're designing your ERP, your future state ERP solution, you should already be thinking about ai, not just have it on the list as something down the road. And so really thinking about ERP with the flexibility and the innovation already a part of the design solution

Joshua Greenberg: All, David, I'm just going to kind of transition that question to you just because I don't see many questions flowing in, but definitely type 'em. I know we're right at coming up the time, but David, if you don't

David Frigeri: Just put a bow on. I think both responses so far is that I encourage the CFOs to be proactive. You need to define what is safe for you, what is responsible for you, define your own controls, and then have your partners demonstrate to you that they meet your controls. I think a lot of times companies get themselves in a bad place because you're reacting to what the vendor or partner is telling you that what they're doing. But ultimately, you have to set the bar and have the vendor or partner meet it.

Joshua Greenberg: Awesome. All right, Astrid, I'm going to bring you back into the conversation as it looks like we are wrapped up. So I thank all my panelists for answering my questions today. It's been very much appreciated and I hope everyone has a great rest of the day. Astrid, I'll let you close it out.

^{Transcribed by Rev.com AI}