Name: Harnessing AI in FP&A: Strategies for Success
Uploaded: 2025-02-12T19:01:12.216Z
Duration: 45 min 28 s
Description: Harnessing AI in FP&A: Strategies for Success

Transcript for "Harnessing AI in FP&A: Strategies for Success": Hello. Good morning. Good afternoon, everyone. We'll give everyone, about another thirty seconds or so. I know we have a big attendance today. So, we'll start shortly. So thank you, and, we'll kick in, very shortly. Thanks. And just as we're waiting to get started, if you're out there, just let us know where you're coming from in the chat. We'd love to know where you're at today and your name. So feel free to put it out there. Mine's out there. Love that, Paul. Thank you. It's always fun to see how many different countries you get in these events. That's right. Exactly. Alright. So, let's get started. So, again, good morning and good afternoon to everyone. Welcome to our Workday looking forward, webinar titled, harnessing AI in FP and A and strategies for success. We're really, really looking forward to sharing some of our insights on a with AI with you as well as hearing from you about this exciting, topic, which is top on top of mind, for all of us, as we as we explore AI and hear all about it these days. So, my name is Greg Golpe. And before we get into that, a a Workday product statement, you might have seen this before, but, essentially, we likely will be talking about some forward looking content, regarding FP and A in terms of product capabilities. But, please, any investment decisions on Workday, please make them based on current capabilities. So my name is Greg Volpe. I am a product marketing director at, Workday. My primary focus here is AI and working with FP and A teams and how they can find value with it as they start incorporating all those great capabilities into their planning process. And I have the true honor of being joined today by one of the most compelling thought leaders on the latest trends for FP and A, and this the founder of the FP and A guy, Paul Barnhurst. And and we'll go through some of the details later on, but, Paul has a great website out there, the FP and A, and and that's and, febetweenpanda. Right? Dot com. Whereas he has had brought, podcast, newsletter. He's starting a great new free community, hub, which we'll talk about later as well. So welcome, Paul. Really, really, happy to have you here today. Thank you so much for, inviting me. I'm thrilled to be here and to talk about AI. I know it's a subject we're all trying to figure out, and so we're excited to share some of our thoughts today. And with that, we're gonna be covering some very interesting topics today with Paul, focusing on the impact and value of AI to for to FP and A teams, as well as a topic that we believe is forefront for many on on many people's minds, and that's how you successfully prepare for incorporating AI capabilities into your planning and forecasting process. What are the key things to think about? What are the first areas to prep for, etcetera? We'll also touch on some of the exciting upcoming AI innovation, and should you should have some time at the end to answer some of the questions submitted by you. So let's get started. But before we get into our content, we do wanna hear from you. So if you could take a minute to participate in the, survey question and you'll see a, survey link a poll link, excuse me, in the upper right hand corner. If you could maybe just participate on that, that would be great. Look forward to seeing your, feedback on this. What are you thinking, Greg? Do you have one you think will, most people Yeah. It's always interesting. We as we've been doing these webinars and things over the past year and a half, two years or so, we see people creeping up and starting to incorporate defining use cases and things. So year after year, we see that people moving from a to c, which is always great. So with that said, I guess, Tao, if we could disclose it out and see what some of the results are. There you go. So, Paul, your thoughts? Yeah. I'm not surprised. Right? You're seeing 75% research and explore more. I think everybody's still trying to figure out what exactly are those use cases. Everybody's using it. We've seen benefit. But, really, in FP and A, how do you integrate it into your processes? So the the results don't surprise me. I think that's where a lot of us are, and it's great to see some of those are active. Would like to see that a little higher. I would have thought maybe more sixty five seventy, but that's about where I expected. And you? Yeah. I yeah. Again, I I think it moves up all the time. So the good news is the great news is is people are interested in AI. Right? They're here attending the webinar. That's the great news. And I think I think as we'll kinda talk about later, Paul, is maybe you know, what we've heard from some of, the folks that we talk to, customers and things, is that so interested in AI, but a little bit hesitant on how to get started. Mhmm. So we'll talk about that as well. And before we move on, also just wanna mention couple, housekeeping things is this webinar will is recorded, and we'll be sending this as as well as the slides that we're presenting today to everyone, who has registered for the event. So, you know, things we go through, you can take a look at the detail. As well as in the upper right hand corner, you'll see a, a link to docs. It's between the chat and q and a links. There is a new ebook that we just recently published. Workday just recently published on getting started with the a AI and how f and a can, successfully prepare for it. We'll be touching on that some of that content today, but there's a lot of more detail in detail in the ebook. So please take a look at that if you can. And, Tal, I think we can go to the next slide. Okay. So let's talk about AI, the impact and value of AI on, FP and A real quickly. So first, embedded AI planning capabilities will drive an engaging consumer experience. It's all about achieving a personalized experience, and better experience for any user who's engaged with it. So think about our all of our interactions with Netflix or Facebook or Instagram or anything else, all that AI that is embedded in those types of applications. However, they're very personalized. They're very consumer friendly type of, interfaces, and that's really what AI for FP and A if FP and A strives to do. A software the big the big important thing is a software vendor who truly embeds AI Did we lose you first there, Greg? Looks like we've lost Greg for a moment. Planning purposes as an example, having the ability to surface data insights quickly by having a user simply ask questions or queries of the planning system is is something that any user will be able to do very comfortably, in a great, in a great and have a great experience. Increased automation. This is huge for FP and A teams. And there are lots of places where AI capabilities can assist FP and A in their work. Maybe it's automating report generation or automating variance explanations or identifying trends and patterns in large datasets to be used for more predictive forecasting. It's all about providing the ability to automate some of those repetitive manual tasks that just bog everyone down. Right? And provide more time for teams to focus on analytics, scenario analysis, business storytelling, and becoming more active advisors. But ultimately, it's truly it's really about augmenting decisions for f p and a. A a AI is not about replacing f p and a teams, but rather augmenting teams to allow for better and more timely decision making. Now, Paul, these last two points, automation and augmentation. Right? We believe, and we've talked about this, are extremely powerful value adds, of AI for f and a teams, but also provide some level of concern for folks, in terms of will AI replace people. So love to hear your thoughts on that, Paul, on these last two items here. At the end of the day, some roles will be displaced Oh. In AI. You know, not just talking finance, but just in general. And I think Paul has frozen. Tao, can you hear him? Or I can hear Hey, Thao. Thao says she can hear me. I'm gonna keep going. Okay. So, hopefully, everybody can hear me. What I was gonna say is the reality is AI will disrupt some jobs. It will disrupt industries. But for most of us, it is all about using it to be more efficient and effective. At the end of the day, AI is like other tools. Right? The computer displaced some people's jobs and created new jobs. The Internet did the same thing. Thanks. I'm frozen, but that's okay. As long as you can hear me, that's great. They they all have impacted us. But if you want to minimize the chances of you being directly impacted by AI, then you need to learn how to be more efficient and effective and view it as a productivity tool. That's kinda how I see it. I think for most of us, it's a human and AI proposition, not a human or AI. Greg? Alright. Looks like, Greg has frozen. We're having a kind of a few audio issues here. Give us just one moment. I do see a lot of, you know, comments coming in. Maybe we'll take just kind of, I'm gonna just go to the Okay, guys. Can you hear me or not? Okay, guys. Can you hear me or not? Can hear you. It's a little broke up just a touch, but go ahead and talk for a minute. I'll let you know if it breaks up again. Yeah. You're you're we're not hearing everything. You're spotty. AI for FP and A teams, you know, automation, augmentation of decision. Here's a real life story real life story of an organization, an international law firm, on how they realized value from incorporating AI into their planning, process, specifically utilizing predictive forecasting capabilities, which we'll talk a little bit later. Need a AI to fix this. I don't know. We're having some big time audio problems here. Paul, if you could just take over a little bit. I'll move to the next slide. Talk about a little bit about these things. I'll see if I can get my, I'll see if I can get the audio fixed on my end. Yep. We'll we'll let Greg go for a minute. But I I thought the survey was fascinating. It says, for those who have already put Gen I in place, 71% of CFOs say it has created value. And I've seen similar numbers other places. Right? 54% are citing better use of data and, you know, 48 higher order tasks. Now the question is how many CFOs have figured out how to put it in place? And that's what we'll talk about more here. But I think the key takeaway here is the initial results are really exciting, those who are using it. Like, if everybody could raise their hand, and I know you can't. You're all virtual. But I've asked everybody to raise their hand, how many have seen benefit in their personal life from AI? I bet everybody's hand would go up. And, eventually, that's where we're gonna get in finance. It's gonna take time Get out of here. For that. You wanna go to the next slide here? Let me just flip it. Alright. Sorry. I went to so here's some kind of trends and predictions that we're seeing. And I think one of the areas we're gonna see AI continue to impact the most, and not just generative AI, but machine learning as well, is a shift in planning. You know, data driven, exception based. I'm seeing you know, lots of tools finding ways to bring in that external data, to forecast for you, to bring in the statistical modeling and the machine learning to improve the process. We saw that example on the other slide of the law firm that made substantial improvements in its planning process. And so it's gonna get more and more automated, but you're still gonna need the human judgment. Scenario analysis. If I asked anyone that if a scenario analysis is ideal in in a spreadsheet, I'm gonna guess you're all gonna say no. Right? And if I ask how many are really doing complex scenario analysis, I'm gonna guess the answer is pretty low. But that's something that's gonna get easier with with AI. I get the opportunity to speak next month at AFP about scenario planning and generative AI. And I've I've done some playing with that, and it can definitely help us and it will get better with time. And then the third area, and I think this is one where everybody's a little concerned and there's some hesitation whenever you hear this because we're change resistant, is the skills evolution. Right? One, we're gonna need to get better at the storytelling, the business insights, the comprehension. And then the other area is if we start to learn some technical skills, a little bit of data science or a little bit of coding, not be a coder, there's opportunity to go even further with generative AI. So we're definitely seeing some changes. Welcome back, Greg. I did my best to cover, bro. You're the man. Thank you, and I apologize for I don't know what happened. We have some audio issues or something, but let's let's continue on. And thanks for filling in there, Paul. Okay. So we talked about the value of AI. So with that, there is still some hesitation to implement across organizations. And here are some challenges we hear from organizations as we speak with them, and these are based pretty much on any any type of AI technology that impacts the finance function of an organization. I'll have Paul comment on on a couple of them, when we finish up. But resistance to change. Right? This is probably an obvious one. Anytime new technology or process is implemented, not only for FP and A, but any group, there's always that resistance to change, especially with AI around, you know, is it gonna replace, you know, positions and roles and things like that? Reliance on legacy systems, You know, if you're still on spreadsheets or some lego legacy system that is difficult to integrate with AI capabilities, that's tough to do. One big one, which we'll talk about later as well as regulatory and ethical concerns. Right? A lot of folks are still concerned about data privacy, security, skill shortages. We'll talk about a little bit about this in the preparation of of of, being successful with AI. And then finally, concerns with the we're concerned with data quality. So, Paul, you know, comments on that, especially the last two, skill shortages and concerns with data quality. Yeah. So let me I would say one thing on three and then I'll cover the other two just real quick because I had a fascinating podcast interview that said one of the challenges on the regulatory and ethical is, right, none of the big four or the big accounting boards have come out and put guidance around audit and how you can use AI and what will pass an audit. That will come with time, but I think that's one area that slows people down because if you have it do an accrual and you don't have guidance on, hey. What do I need to support it? You're gonna have a real concern, especially as a public company. You know, on on number five, and then I'll come back to four last, concerns with data quality. We all have it. One of my favorite quotes that I kinda I saw that the play on another quote was, you know, everybody has data. Some data is useful. With AI, if your data is not useful, you need to fix your processes and your culture and then fix your data, because data is a symptom of a problem upstream. Data in and of itself is never the problem. So people say, hey. I need to fix data quality. Come fix my data. Ask yourself first, what processes do I need to change? What cultural changes do I need to make so that it actually sticks? And you're not just fixing your data every six months because you didn't take care of the reason why you have a lack of data quality. And then the skill shortage. I think this has been around for a while, you know, and AI is just accelerating it. CFOs have said in more than one study, even as far as I saw one in from 2018, the hardest role to fill is FP and A. I saw a study from Harvard that showed the level of communication and technical the combination of the two needed for corporate finance roles is as high as any job out there. And now we're being asked to do even more communication, learn more new skills. So it's really difficult to find really good FP and A. Yeah. That's great. And and and and, as we talked about before, Paul, before, before we got on was, the data quality data quality issue is big. Right? And I know you have a lot of passion about that. So with that said, we, again, would like to hear from you folks on another survey. How ready is your data for AI? And, again, you can participate in the poll. There's a link on the upper right hand corner, and we'll just wait thirty seconds to get those results in. What do you what do you think, Paul, on this? What do you think, we'll be skewing towards on this? I would say, obviously, a and b. I think c will my guess is 20% c, maybe 30% b, and 45, 50 percent a, something like that. Alright. Let's open that up, Tao, see what's going on. Yeah. Well ready to go was lower than I expected. I got the right first one right. You did. But it feels like 90% of us are still struggling with data even if we're getting there. Yeah. Okay. Cool. Thank you for your participation. So, Tal, let's move on. And we'll address this, data data preparation, in the sec section here. So so we've talked about, the real key value that FP and A's can realize from AI as well as some of the perceived adoption challenges. So let's now discuss how f and a teams can successfully prepare and get started with incorporating AI into their planning processes, right, and get beyond these challenges and realize that value. So I think most people will agree, Paul, that adopting AI depends on thoughtful preparation. And so at Workday, as you can see, we've come up with some practical preparations, steps and considerations that we believe will allow FP and A teams and organizations to successfully get ready for planning with AI. As you can see here, we've broken them up into logical categories that should provide a productive AI journey, and will pay dividends. However, these preparation steps are flexible and can be performed concurrently if you want and will also all depend on what your current status is today with with using AI. So let's dive into some of these. We won't get into all the details, but, highlight some of the key areas. And I will remind you again that in the upper right hand corner, there is a link for documents or docs that you can link to Workday's recently published, getting started with AI and the value to FP and A for some more details, on this. Plug for that. I've I've seen it. I saw the draft of it, and I highly recommend downloading it. I think there's some really good practical stuff in there. Yeah. And some tips and tricks too. So good stuff. Alright. So the first step, we're gonna walk through these real quickly. The first step is, which absolutely cannot be overlooked, is to define your AI requirements. And and this might seem to be an obvious step, but it really needs to be emphasized. FP and A teams need to understand their ultimate goals with AI. Very important is to ensure your AI goals and requirements at the FP and A level are aligned with not only your leadership or CFO's f AI expectations, but also with the overall organization. So and then and then at that point, you'll be able to more clearly, prioritize or or you'll be we at that point, be able to understand what these requirements specific requirements are and then implement, AI capabilities that should should impact the organization favorably. So for example, maybe all stakeholders need better, more reliable revenue forecast, due to recent volatility, in your market. So, hence, AI driven predictive forecasting capabilities might be a top priority. On the flip side, it's also important to understand those, you know, higher risk activities that your team believes might might not be ready yet for AI. So, Paul, your your comments on this step? Yeah. You know, so as you talk about the technical and team capabilities, I think that's something we should address, you know, all the time. One of the things I like to think of is you have people, process, and technology. Even AI, whatever the technology is, it's an enabler. If you're implementing technology just to solve your solution, just to solve things, you're doing it wrong. It's your team. It's your people. It's your processes to solve things. And then once you bring in that right technology to free people up for higher level thinking, which is what AI will help do, that's when the magic happens in my mind. It's not from just implementing the tool. It's the, higher level thinking that it can allow. Yeah. Very good. The you know, I think it's also important to emphasize to evaluate the technical skills of your team team as well, which might which, you know, might or might not play into what you're gonna be incorporating initially, with AI. But lots of things to think about absolutely in this first step. So another important step, is to determine the best approach deploying AI capabilities that fits your team, your organization, and your culture. So overall, though, we recommend a human centric approach. And what does this mean? And this this goes back to what we were talking about before in terms of AI will augment, human skills and not replace them. So when introducing AI, it's very important to make it clear to your team that their expertise remains indispensable. Right? As we know, FP and A professionals excel in areas such as strategic decision making, analytics, and storytelling, and tasks that require intuition, judgment, and context that AI simply can't replicate. So really important to emphasize this to your team, along the way, which might be obvious. And a great tip is to hold workshops, or q and a sessions to address any of the their concerns and emphasize how the roles of f and a will evolve because of AI. Paul, thoughts on this or, you know, any quick thoughts? I think phase in is huge. It's like it's just like when you implement some like, say, you're implementing Workday adaptive planning or whatever. You're implementing, you know, your ERP or you don't try to do it all at once. You're like, okay. Let's get the actuals working. Let's let's make sure the reporting and the consolidation and then the planning. It's gonna be similar with AI. Start small. Get little wins. Don't think it's gonna transform your business from day one. It's not. We're still figuring out different use cases, and I've seen some q and a people asking about use cases. We'll get to that during q and a, but I I love that, learn and grow, because it's a it's a process and it's a journey. Yeah. We we talked, to several customers at our at our last user conference in terms of how they were deploying. They were just starting their journey, and they were talking about definitely phasing it in, and seeing how it works, play with it. You don't necessarily have to go live with with your capabilities, but test test them. And then get constant feedback from the FP and A team, leadership, and other key planning stakeholders. And then that kind of encourages everyone to kinda be champions in a way and know that they're involved with implementing this new, these new tech, capabilities. Okay. So this next step is incredibly important and interesting. And, Paul, I'll say a few words and then love to hear what you have to, talk about because because I know you're passionate about this area as well. And that's thinking differently about adjusting and applying FP and A skills in this new age of AI. So we believe to really thrive in an AI driven future, f and a teams need to go move beyond traditional number crunching activities. Right? But you don't need to become data scientists. You can read some of the recommendations here, but I wanna highlight something before turning it over to Paul, for his insights. So we've heard from many, FP and A teams that we work with that are just starting the journey with AI, who have told us that one of the most critical things to ensure success is to have their teams think differently and be curious about new AI capabilities. They've told us that to resist and be skeptical, about a is risky and will just leave them, you know, trailing behind other forward looking organizations. So, Paul, your thoughts. I mean, what do you what do you think about in terms of getting, the skill set ready for FP and A? You know, what type of skills do you think are needed? Love to hear your thoughts. The one thing, you know, I I think you addressed is people have to be willing to experiment. I interview, you know, people all over the globe, and one of the questions I ask is, what's the number one technical and soft skill needed for FP and A? Or what's the number one thing people can do to be a better business partner? Again, again, one of the things I hear is curiosity. We need to be curious and always wanna be learning. We need to apply that to AI. If we're not being curious about AI, we're being left behind. If we're not figuring out the learning. You know? And so when you're doing exercises, especially if you have access into your company, try asking AI. You're not gonna get the % answer. It's not gonna be completely right, but it may get you 80% of the way there. So that's the first thing I see. The second on skills is you'll continue to focus on those soft skills, but also think of, is there things I could learn? Is there technical skills that would help me take AI to a new level? Because the people who I think are most efficient with AI that I've seen are the people that understand something deeply, and AI can do the work for them and take it higher and they can validate it. Right? If I ask AI to spit out 10 pages of code and I have no idea what code even looks like, pretty hard to validate it. Not saying we need to be coders, but I think just developing those skills that allow us to be more efficient with AI. Yeah. And I think and I think, you know, as we kinda mentioned before that the solution vendors, software vendors that embed AI in their architecture, you know, will facilitate better user experience and also, you know, limit the limit FP and A from becoming data scientists, right, to your to your point. Right? Just be curious about it. Yeah. You know, I think that you made the important point about, you know, play around with asking questions because that's gonna be part of all planning capabilities. Right? Kind of assistance to, explore new data insights and and really just practicing asking the right questions. Right? So you get the right answers and the data that you need. Definitely learning how to ask questions. It it'd be conversational with it. Don't accept the first answer. Keep working. And sometimes it will be frustrating because it'll never quite get what you want, but it gets better better with time. Alright. Let's return and let's revisit data. Right? So data quality preparation for data, and what, we always hear is AI is only as good as the data it analyzes. So, Paul, I'm gonna turn this over to you initially because I know you're extremely passionate about this. Thoughts? Yeah. Yeah. I mean, at the end of the day, we're never gonna get to a % data. You know, no data is clean. We want it to be useful. I know everybody would like a %, but the cost doesn't make sense. The key is, do we have good processes? Do we have good data? AI could do a great job of analyzing data today. I mean, take an example if you're a public company and you are private, but you wanna look at all your competitors. Go run it through AI and have it start the benchmarking against them. It's great. So if it has good information, not perfect information, it can do work. But the better the data, the less you're cleaning it manually, the more you can rely on those insights and the more comfortable you can be. Yeah. And I think I think this kind of the tip that you see there on the screen too kinda relates to what you just said is that start early. Right? Start early. Use it so you don't there's no delays. And the other thing that I'll I'll mention, the other point I just wanna highlight here is that, with predictive forecasting, especially with market leading solution vendors, you'll be able to not only incorporate internal data like historical actuals, but also external data. So also start looking at external data that that you could use as an organization that would better be applied to get you better, scenarios, based on your industry and and things like that. We'll talk about a a a customer use case coming up, where they did just that. So, the final, preparation, step that we'll talk about today and, again, reiterate, e go to the ebook and you'll see a lot more details on this. So is the essential software vendor approach. So who do you want to partner with on your AI planning journey? Right? Your goals, your readiness, your strategies, everything we've been discussing, these are essential to pin down. But if you don't have the right solution vendor to help you execute, you're likely gonna move one step forward and two steps back. So here's a checklist of some things your organization should think about when partnering with a market leading, solution provider, especially with the lens of AI. Essentially, you know, if you boil it down, the key things are to look for is dynamic innovation around AI and a responsible AI approach from a market leading, you know, vendor, and this will accelerate and and ensure your success. So proven innovation, track record, you know, find out how long they've been in the AI business and and being innovative around AI. Data privacy and security. This is huge. Right? So prioritize a responsible a vendor needs to prioritize a responsible AI governance framework. Check their website. Check their white papers on what their policies and procedures procedures are. Obviously, market leading a AI capabilities ensure they fit what you need to do. Ease of use. Right? This goes back to the personalized, experience, consumable experience. AI should be embedded in the underlying architecture of a vendor, and thus allow it to be more transparent and ease of use for any user, FP and A or other planning stake stakeholders like like business users. It has to be cons cons scalable and configurable configurable to meet your growing needs in any size organization, and the vendor has to offer some certification and training and extensibility, meaning that, you know, do some research. Make sure that the that the solution vendor has an extensive ecosystem of partners in it, preferably a marketplace where, they have partners that are extending their AI capabilities and providing other solutions for you that might be, you know, uniquely to your, unique to your industry. So just do your research. From our end, from Workday's perspective, we've seen many of our own customers successfully employ a you know, deploy AI capabilities for their plans and forecast, and this is as a result of Workday's responsible and innovative development approach, approach to, AI. Alright. Just a couple customer stories to, reiterate some of the things and bring this to life is, so one of the real compelling, AI capabilities available today is predictive forecasting, which is the ability to leverage historical data either internal or external along with machine learning algorithms to quickly generate forecast that are reliable and based on key data patterns and trends. His basically, historical data trends and patterns. And these types of forecasts form a very reliable baseline, which can then be further modified based on expertise of the FP and A teams. Right? And that's that's where your expertise comes in. So this story, Team Car Care, who is the largest largest franchisee of Jiffy Lube Shops with approximately 500 outlets across The US is using weather data, of all things, with predictive forecasting capabilities to forecast the number of customers expected across the regional locations. This then has a direct impact on their revenue, and also planning for staff in those locations, which then has a direct impact on costs. So really compelling story there. Here are some other really interesting customer use cases where AI driven predictive forecasting has been used to drive more accurate forecasts. As you can see, these customers across different industries have been using unique data to to drive their forecast. For example, a lead a leading financial insurance company is you utilizing policy data to drive the revenue forecast, and a multinational telecommunications company, for example, is forecasting subscriber growth using historical data. So great things to think about in your own organization of how you could leverage predictive forecasting and then also what type of unique datasets, including external datasets, you could use to better, drive the forecast, make it more reliable. But again, just to reemphasize, even things like predictive forecast forecaster are really there to create an initial baseline of a forecast, which is reliable and accurate. And then, really, it's up to the FP and A team and with all of their expertise to adjust it, make it, you know, make it relevant to their industry, to their business scenario. And so that's where AI and and and FP and A teams can really live together and really provide some extremely reliable, accurate forecasts. Agree. It's exciting, the forecasting side. Absolutely. Yeah. Alright. And so with that said, lots of things coming up that are currently available today in the market for FP and A teams as well as things on on the horizon. Here's a quick snapshot of those, capabilities. And as you can see, a lot of exciting capabilities coming up for f and a teams. And one of the more profound areas is around conversational AI or sometimes referred to as conversational planning. And these that's the capability that enables users like business managers to surface data and insights quickly by simply typing in a question or a query within their planning application. And this even goes further by having the planning application recommend, recommend, other, things that a user might do, other actions that they might take, as well as providing AI generated narrative on metrics and KPIs and data variance and so forth. So basically allowing any user to have a a conversation with their plan or forecast data within a a very streamlined user experience. So lots of things coming up, that's just gonna be incredible and so much value add to FP and A teams and organizations. Okay. So before we can get into some q and a here, just a final recap, and I'll I'll love to hear your, your thoughts as well, Paul, on this. So but just as a final recap, I mean, three takeaways. Hope and, hopefully, you get some nuggets from this webinar today. But first, obviously, acceleration of AI. You know, there's so much r and d investment going on with with market leading solution vendors on AI. We'll be hearing a lot more of that, obviously, over the next year, coming years. Preparation is key. We talked about that. Defining requirements, you know, realigning skill sets, prepping data, etcetera etcetera will ensure your access. And, again, I will, you know, reiterate the e ebook under the document link in the upper right hand corner. And then AI innovation. I mean, innovation is is at the core of market leading vendors like Workday, and you'll be hearing so much more on future road map on these on these capabilities. So, Paul, any of your quick thoughts, takeaways here before we turn it over for some q and a? Yeah. I mean, I think you covered it well. We got a lot of q and a. So I think we maybe we just jump into that for our last few minutes here. Seeing a lot of lot of good questions coming. Alright. So let's let's go forward here. So if you do have, questions, please drop them into the q and a link in the upper right hand corner. And, Paul, anything that, you'd like to touch on initially? And, again, we won't we won't get to all of these based on time, but, hopefully, we can follow-up after the webinar and send you some some, some answers and our feedback for that. Yeah. So I'd love to get your thoughts on this one. I think this one's interesting. How do you see prescriptive forecasting developing in the FP and A world with kinda with AI? In terms of prescriptive, in terms of Really, prescriptive, the whole idea is what should we be doing next? Not just predicting what's coming, but helping us make smarter decisions, I guess, in the way Yeah. So that those capabilities are actually gonna be coming. A lot of that that that's gonna be coming probably over the next year in terms of actually built in prescriptive, forecasting and, you know, some of the explanations of of things and and some options to make some decisions. But I'd say in terms of what's available now with predictive forecasting, it's all about, you know, assure assuming, you know, ensuring that your your drivers are correct, your data is correct, the type of data you're using. And a lot of lot of there's a lot of, actually built in capabilities actually within Workday that once you once you generate a predictive forecast, lots of confidence metrics in terms of is it accurate, is it not. But then also those that prescriptive, forecasting will be coming in the future as well. Yeah. So we got a couple questions about Workday adaptive that I think are interesting. One asked, hey. Can we see an example of AI capabilities? Is there a video out there, maybe a link that we can send out to everybody to say, hey. Yeah. You can see what we have today and what's coming. I know we talked to it a little bit, but we've had several questions around that. So I'll let you answer. Absolute yeah. Absolutely. So if you if you do go to workday.com and navigate into the planning, area, so under products, I think you can go to planning. There is a innovation page, out there on the website which will talk about all the current innovation, AI, predictive forecasting, Workday assistant, as well as a lot of the other innovation that's coming out as well. And that will be updated, and there's some videos attached to those. You can take a look at those and share them with your your folks back at the back at the office. Yep. And so I've had a few questions. We had one kind of pre revenue, others saying, hey. We're really small. We don't have much in the way of IT or budget. For those really small companies, any advice on kinda getting started with AI and how they should think about it? Yeah. Well, I think I mean, honestly, for even small companies, I think it's the same preparation steps that we talked about, you know, defining requirements, your approach, make sure it's a humanistic approach, realigning skill sets, choosing the right software vendor. And one of the if you remember, one of this one of the key things in in choosing the software vendors is to ensure that whatever, partner you're going with, make sure the solution is scalable. Configurable and scalable, meaning that as you grow from this from a small or smaller company to a large one, it will kinda meet that your growth requirements. But I think it's, Paul, I think it's all the same preparation, steps. But I will add one thing is that when you are looking at a solution vendor, ensure that those cape the capabilities that are provided by the vendor do not force you to become a data scientist. Right? Because smaller companies have limited FP and A teams, and they don't have time to go out and start learning code and, you know, and and lend you know, having a resource dedicated to something like that. Make sure that they are ease of use. They're understandable. They're in in they're very intuitive. And I think any small organization can successfully, implement and deploy AI regardless of the size and, again, just following the preparation, steps that we we talked about. Yep. Great. You know, I've had we've had several people ask, can you give an ex specific examples, particularly around generative AI, you know, kind of that work case of where you're seeing it help the customer. And I'll share one I've seen is I've seen cases where it can help start writing your variance commentary because it has all the data in the database and can give you the basics. Do you still need to add some stuff? Yes. That's where I talk about it's human and AI, not just AI. It's not always gonna not necessarily gonna know the now what and so what, especially just from database numbers. But it can get you started and maybe get you 50% of the way there and save you four hours every month. Yeah. Absolutely. Workday's actually has a capability intelligence variance analysis coming up this year, and that's exactly what it's doing, Paul. It it's creating that baseline of commentary, variance commentary, as well as providing, insight into the into the, material variances. Right? Displaying that as material variances so then you can dig deeper into into that. And that I mean, just creating that baseline, not it saves so much time. Right? If any of you have done and I'm sure most of you have preparing variance analysis. And as Paul mentioned, you know, preparing the the commentary on that, it just takes a ton of time. Right? And you're never sure if it's accurate and so forth. So that's that's one example, I think, great example, Paul, on on on the generative AI, capabilities and use cases that FP and A should be able to take advantage of. Yeah. I'm looking through here and trying to find maybe one more question. I know we're just about out of time or two, and there there's a lot. Yeah. There's a lot. Why don't we why don't we call it I think we have about ten seconds left. So first of all, let me thank you, Paul, for, being here today and all the great insights. It was so much valuable insights that you provided today. And, and please look at his his website, the FPNA ffpandaguide.com, and you'll find a newsletter, a free community to join, as well as, a podcast. So I know, Paul, you have a a a lot of podcasts that are ongoing that Mhmm. You have touched on such such, you know, relevant topics for the FP and A team. So just check out that website. You'll you'll see so much so many, great assets there. So thank you, Paul. Really appreciate you, you joining us today, and, hopefully, you can come back for lots of other webinars with us. Great. Thanks, Greg. Love being here. Love getting to kinda chat with everybody and the activity we saw on the q and a and the questions. Yeah. I know we're all Yeah. Yeah. Yeah. So and, Brings says, most importantly, thank you for all of you for joining today, taking time out of your day. Your feedback was incredible. Hopefully, you can walk away with a few nuggets, from today's and and good luck on your AI journey. Thanks so much. Thank you.