John Pitstick 0:00:10.4: 

Welcome, everybody. This is AI-driven workforce planning. We're going to talk about what Anaplan's doing to support workforce planning with AI. Then we're also going to talk a little bit about the most disrupted planning by the change of AI, which of course is the workforce. My name is John Pitstick. I've been with Anaplan for two-and-a-half years. It's my second tour with Anaplan. I was here before the IPO. I spent about five years at Workday in between, where I learned a lot about workforce planning and HR tech, HR systems, etc. I've been doing planning software for about 28 years - back to the '90s, actually. With me here is Annika, also from the team. 

Annika McGraw 0:00:53.0: 

Yes, hello everyone, I'm Annika McGraw. I am on the workforce applications team led by John. I get the pleasure of leading some of our development and architecture for workforce apps. We're really excited because we've been focused more recently on: how do we think about bringing the best of Anaplan intelligence and delivering that in an out-of-the-box, best practices solution, embedded with domain expertise with our subject matter experts? John's a great example. 

John Pitstick 0:01:27.4: 

We are yes, definitely from the apps team. Definitely product people. Only soft selling to you guys through this. Our agenda, I'll start us off with a little bit on what we're starting to learn and see in the market about whether it's the fear of AI or the excitement of AI and what that's going to do to the workforce. Talk a little bit about some learnings. Maybe some tips and tricks you guys can take back with you. Then we'll shift over to the actual platform and what we're doing. Annika will take us through that as well as a demo. Then we'll talk about our vision quickly and roadmap on this topic. We should have plenty of time for Q&A after that, so let's get going. Oh, I need a clicker don't I? Thank you. Workforce planning in the age of AI. Is anyone scared? It is kind of weird. If you let your mind go all the way down that path, what do we need? Do we need anything? Just AI building everything for us. I think we're a long ways off from that, but as a little… 

Annika McGraw 0:02:39.2: 

You have to aim it. 

John Pitstick 0:02:40.4: 

Which way, over here? 

Annika McGraw 0:02:41.2: 

There you go.  

John Pitstick 0:02:41.4: 

Here we go. This, to me, summed up the fear; this idea that the machines are taking over, and what does it mean for us and the jobs that we're doing? It's a nice little quote that does stoke that fear. Well, guess what? I'll stand a little bit further back. That quote's from 1835, almost 200 years ago. That was at the beginning of the Industrial Revolution, where the same sort of tooling and shift was happening back in those days. So it changed the way we work. That's going to happen - but we'll get through it; as humans, we always do. We can work together with technology. I'm going to talk through just some things that - maybe some new thought patterns and ways to think about this, again, that we're seeing and we're coming up with, and how you in your organizations can navigate some of these - this change and the challenge and the opportunity to do more and grow your business.  

John Pitstick 0:03:51.8: 

So we'll talk about some old concepts and how they need to be rethought, and then also maybe give you some guidance on ways you can apply this to planning. Beyond the bot is, everybody… If there are some workforce planners here, the Bs. Are there Seven Bs or Eight Bs now? Buy, build, borrow, bot, bounce - all of those. We've always just thrown bot out there as well; hey, you can take a position or a job and replace it with a bot. That's not really what's going to happen. There's no one individual job that AI can replace all of. It's going to augment it, it's going to change it. So the goal isn't to plan for where AI takes over; it's how we're using it. How we're going to adapt to making the human work we're doing more efficient and just overall more productive within the companies that we work for. It is going to redefine work. We are going to have to rethink skills. I like to think we're at skills 3.0, maybe at skills 4.0. We've always heard skills is the currency of workforce planning, it's so important. Most companies haven't really been able to adopt it. It's even more important now than ever before, getting to understand critical skills and the tasks that make up the jobs that your organization is made up of. So the workforce is going to change.  

John Pitstick 0:05:15.0: 

Building on that point, one thing you need to do is start thinking about: how do we deconstruct the work? Sure, we can look at these jobs, we can look at the skills. We can then turn the skills into: what are the tasks that need to be performed? Which of those tasks can be automated, augmented by AI? Maybe there are tasks that go away. Maybe there are new tasks that get created. There are always going to be human-critical tasks and knowledge and governance over AI to support all this. Start thinking about future jobs and what they look like and how they look like, or what they would look like in this age of AI. Thank you.  

John Pitstick 0:05:56.8: 

The other thing: with technology moving so fast, this idea of, hey, skills can remain relevant for five, ten years is starting to change. It's starting to shrink down to a couple of years, so what does this mean? You're not going to be able to just continuously rehire every two years when these new skill requirements start coming up. You have to think differently: think about reskilling, think about mobility, think about fungibility within the organization, think about upskilling. What somebody is doing now is going to change significantly over the course of the next several years. So start thinking of your learning and development teams and how they can support that. It's just not going to be cost-effective to use as a process every two years, 'Hey, we've got to do a bunch of layoffs,' and then hire a bunch of new people with these new skills. Planning for if, so we all know what-if planning; it's what Anaplan is great at. This is good for us, good for Anaplan in this age, because there are a lot of different unknowns. There are a lot of different pathways, a lot of different scenarios.  

John Pitstick 0:07:07.4: 

The five-year plan, I almost think it's starting to become a little irrelevant, but you still need to do it. You still need to have those goals and ambitions. You're definitely going to work back towards two-to-three years. This is where big initiatives are taking place. Eighteen months, of course always the - in your stuff be ready to pivot and change, do continuous planning. The real big takeaway on this, though, is don't just plan when things are going to happen; plan for the different scenarios. What if regulations change and slow this down or speed it up? It's not so much when the thing is going to happen. It's what are the three, four possible things that are going to happen? Again, more about planning if. Model different adoption rates for this, for how you might roll out AI. It's probably not going to go exactly as you hope to initially. Backing up a point from before: again, when you're doing this planning, learning and helping your workforce evolve into this is going to pay off in the short term and the longer term over just doing layoffs and recycling the workforce.  

John Pitstick 0:08:16.3: 

Last one about the workforce - and this is really to the leaders out there in organizations - but transparency. We're heading into a stressful time. People are wondering about their jobs and what's - when are they replaced by robots, or when will AI… When will there be a CFO agent that can do everything that a CFO can? Probably never, but start to think about how we can engage the workforce. They understand, too. They know these tasks. They know it can be automated and they're also not going to get nervous if they're engaged through these processes. If the workforce is contributing to it, they feel part of this journey and change. It can be a tool for retention, keeping the best talent in these dynamic times.  

John Pitstick 0:09:04.3: 

Just some last takeaways: AI is not going to take your job, but a human who's a badass at AI, yes, they'll take your job. So we're all trying to learn, we're all trying to do that. Our job as planners is not necessarily to predict the future here especially as workforce planners. It's to be ready for what the unknown future is going to bring. A few things. Again, this is not going to solve all the problems and there are more things I don't know than do know about this - and this is all I do day to day. But start looking at your organization. What are the core functions? What are the core skills that are necessary? Identify which ones do have an opportunity for automation and start thinking about that, start planning for that. Also identify the ones that are always going to be human-centric and now are becoming even more valuable than they are in the past moving forward. That's going to matter; those domain experts, the people that can drive what AI can support, are going to be super-critical moving forward. 

John Pitstick 0:10:08.2: 

I often think we need to start thinking… We've talked about learning. We talk about investing in the workforce. It is going to pay off long term versus just modelling [?riffs 0:10:22.2] and then modelling aggressive hire plans. I'm going to come back to that point a few times, but think about that. Think about more investment in that and preparing your workforce to be upskilled. There's one last thing I'll say about that. Did anybody catch what I did as a product leader, trying to do a presentation, and how I used AI to augment what I did? Did anyone catch it? Every one of those images, I created with AI in seconds. I would've spent hours, maybe days trying, 'Oh well, let me put a square here and a circle there.' That was able to make my job easier and save me a ton of time just in doing this presentation to you guys. With that, I'll hand it to Annika. 

Annika McGraw 0:11:09.5: 

Thank you. Okay, so John just talked about: how is the way that we're thinking about workforce planning changing with the AI transformation? But I want to talk about now: how are we also changing the way we go about workforce planning? So on our team we develop workforce planning applications for Anaplan. Many of you might be familiar with our flagship application, which is Operational Workforce Planning. There are also two that are GA; today that's Contact Center Planning and Project Resource Planning. So I mentioned this earlier, but the focus on our team is: how do we take Anaplan intelligence capabilities that you've heard about this morning and bring them into the workforce planning domain in a way that is very valuable to our users? That's what we're going to talk about here and we're going to focus on these four different capabilities, kind of deep dive a little bit into each of them now specifically for workforce planning.  

Annika McGraw 0:12:17.2: 

The first of this is an Anaplan Forecaster, so Forecaster is a machine learning time series forecast. We have a - we run these forecasts against not just one algorithm but a whole catalogue of algorithms, including backtesting and things like explainability. But what's really important is: how do we think about this for a workforce? So that'd be forecasting attrition, for example, and understanding different trends that would be associated with our attrition forecasts. So that might be training spend or merit increases. Are these things positively correlated with our attrition forecasts? Can we make those forecasts more accurate over time to have a better understanding of our workforce plans? The other thing that I really want to call out here is the top bullet there on demand signals that drive workforce needs. This could be anything, but for example it could be contact center transaction volume. So the number of calls that a contact center gets.  

Annika McGraw 0:13:28.2: 

The screenshot here on the page is actually demand forecasting for a virtual banking branch, so not taking into consideration any actual physical branch locations. Do we have agents that our staff to handle all of those online contacts? How do that demand for those transactions or volumes of calls, texts, chatbots, how does that change based off things like statement and billing cycles or the location or the hours of our physical branch locations - or even bringing in external data? What is the impact of interest rates on the demand of our virtual branch? Again, as we get a more accurate forecast going forward, bringing in those related data, the goal is to ultimately have a very efficient understanding of our workforce needs. So we're not overstaffed, we're not understaffed; we know where to staff the locations, we know what skills they need, whether it's being able to speak French, etc. So that's Forecaster. 

Annika McGraw 0:14:43.8: 

The next thing I'm going to talk about is Optimizer, because this takes what we just talked about with Forecaster and brings it a step further. This is advanced linear programming and it's all about a set of constraints. Every business operates within their own unique set of constraints, whether it's based off production, capacity or budgets for example. So we might take something like demand. Maybe it's generated with an AI ML forecast via Anaplan Forecaster, and then understand: okay, given that demand forecast, given the various constraints and data of our business, how do we find the optimal way to staff these different bank branches, for example, in a way that optimizes our workforce spend - so minimizes spend - or in a way that maximizes our revenue, all while adhering to things like SLAs, customer satisfaction scores, internal transfer limits, whatever it may be? 

Annika McGraw 0:15:56.3: 

We also are starting to think about this in terms of breaking work down to a work activity. John talked about this a little bit earlier, so it's not just jobs. It's thinking about, what are the different activities in which we operate and what activities are best handled by our internal employees, our external workers - so vendors - and even across automated or intelligent systems as well? CoModeler, we're going to shift gears a little bit now because we've been talking about Forecaster and Optimizer, which is focused on our end user or workforce planner. But CoModeler is for the Anaplan COE. The reason I just want to bring it up right now is because of the value of a CoModeler with an out-of-the-box application like workforce planning - like our Operational Workforce Planning application. That comes pre-packaged with things like data ontologies, job frameworks, compensation frameworks, the understanding of a management org and a cost center and the relationship between those two things, as well as all of the semantics and domain expertise that we have embedded within our application.  

Annika McGraw 0:17:20.8: 

You take that and then you take an AI-assisted model builder agent; it can understand all of those semantics in that ontology. On day one you have CoModeler understanding all of these pieces of domain expertise, as well as things like model building best practices - DISCO, for example, for those of you who have gone through Anaplan model building 101. Then putting that together, the way we think about this with apps, is being able to do an extension on top of our application. We deliver a lot out of the box, but if there's a specific need based off an industry or a nuance for any of our customers, they can then extend an application. CoModeler is a great way to increase the model builder COE productivity in the process.  

Annika McGraw 0:18:22.5: 

Okay, the last thing we're going to talk about is Workforce Analyst, and we're going to jump into a demo in just a second of Workforce Analyst. Analyst is a conversational AI assistant that is embedded into every UX page, every part of the user experience in our Operational Workforce Planning application. What you're seeing listed there is just a number of the different key capabilities or processes that Workforce Analyst is designed to support. But it really just aligns with the processes that our application is designed to support, and have been embedded with that same domain expertise that we work on and bring to our apps. Just to speak a little bit about that, what you're seeing here are some of the primary user groups that interact with Operational Workforce Planning. We have a saying that all roads lead to OWP, because we think of it as a central connection point to a business doing anything. So if that's restructuring, if that's building a new team, going into a new region or territory - any sort of business change that requires adoption and adaptation - it usually starts with the business.  

Annika McGraw 0:19:52.6: 

A business leader saying, 'Hey, I need to grow my team by 20 per cent.' Then HR; your HR BP needs to determine the who, what, when, where, how. Finance needs to know, what are the impacts to our budgets, and then talent acquisition needs to get into the picture, start priming their pipelines, begin the recruiting process to grow the business. So again, central connection point, and these are just some examples of the different ways all of these unique users interact with the application and interact with our Workforce Analyst. This alignment between these four different user groups is so critical because it's the challenge at a lot of organizations. It's slow - and that's what makes it hard to adapt to different strategic priorities or changes. That's what OWP solves. Now we're going to move into a bit of a demo of it.  

Annika McGraw 0:20:57.9: 

So we're going to start off here. We are looking at a landing page for that of an org owner. So this would be a business leader seeing a number of just metrics, understanding where are we at compared to our budget? What does our org look like from a demographic perspective, from a regional perspective, etc.? In this specific example we are going to focus on the same theme of adapting to the age of AI. So in our business we're building out a new team, a new applied intelligence team, so the business is shifting. We need to bring these new skills in - and we're building a new team to do so. Now, as we build a new team, we need to understand: where are we at in the hiring? We've got a huge dependency on our recruiters. We have 26 open positions in our organization, but what does that mean to our most critical business priority that we're responsible for? We're going to ask our Workforce Analyst to help us figure that out - and by the way, here is the Workforce Analyst. So this little purple button embedded on every single page pulls up the Workforce Analyst, and then we can start conversationally interacting with the Analyst here. 

Annika McGraw 0:22:31.2: 

I'm going to just pull it up a little bit larger here so we can see. What did I ask? I asked, what are the biggest hiring bottlenecks for my AI initiative? The key point here is that I didn't ask - I didn't reference a specific line item or module or speak in Anaplan Model Builder language. I asked it in my plain business language as a people leader, for example. Analyst understands that and it's able to return where we have the key software engineering roles that have been open far longer than we typically expect. We also can have Analyst generate a visual as well for us. So things like 209 days, 134 days, taking that long to hire roles for our top initiative is a serious problem - something that's not exactly represented on our dashboard in the first place. Analyst is great at taking those numbers and then drilling down and finding what matters for us so we don't have to scan through those data and get on a call, whatever it may be.  

Annika McGraw 0:23:48.2: 

Now, before we move on, I just want to show that every interaction we have with Analyst today is going to be able to be traced back. Here, we're looking at the data analysis, thinking about can we trust this information that Analyst is giving us? It drills back down to: what model did this information come from? How is this calculated? What dimensions and formulas were driving these results so that I know that I can trust in the data that I'm receiving on the page and I'm using for a planning decision. This is important for us because we know that it's hard to trust AI, when AI feels like a black box. So having this traceability, visibility into how the AI is functioning is really key so that we can move forward and take action on the problems.  

Annika McGraw 0:24:52.5: 

Let's do this. We were given this information by Analyst about our bottlenecks in hiring. We're also recommended another planning page to go to for more information. Analyst understands what we're asking about and is able to recommend other areas in the app for us to go to next. On this page - I'll just pop this up quick, hopefully it is a little clearer - we can see all of these different open roles specific to this new applied intelligence team that we're opening up. Here are all those roles that have been open for a very, very long time and we need to do something about. At this point we can say maybe we want to play with a location hiring strategy and see if we have better luck if we move these roles to New York instead of Hartford. Maybe you want to offer more competitive compensation packages to get these roles in the external market. Maybe we want to notify our recruitment teams directly so that they can come into the tool and start reprioritizing their hiring plans as well.  

Annika McGraw 0:26:09.7: 

We're going to shift. I've got one other page to show you. We're going to shift from acting as a business leader building a team to a workforce planner focused on the strategic objective. More so the organization as a whole. This is really exciting because I get to share the new interactive org chart feature that we have in OWP, so I get really excited about it. You don't have to be as excited as I am, but I'm always happy to share it with others, okay? We're looking at an org chart. We're focused, still, on our strategic initiative here to build an applied intelligence team. I just want to point out that as we look at this chart, we can start thinking: this functionality enables us to do things like visualize what the org looked like six months ago, six months from now, six years from now versus today. We can also look at different scenarios. So what does the org look like if we go through a restructuring, or what does the org look like in some sort of rapid growth scenario? Visualize that and have associated metrics. Whether that is span of control, organizational depth, any sort of cost metrics. Embed this into our strategic decision-making for the business.  

Annika McGraw 0:27:44.6: 

So here, back to building up our new applied intelligence team, this is that new team in the context of the organization. Here we can actually see where we have some open roles, open software engineering roles in the context of the org chart. Now, we've got a number of issues to address as it relates to this project. Number one is: we are over budget. Number two: we've got all these open positions, we know that, but what really matters is not necessarily how many empty seats we have. It's more, do we have the capability, the critical capabilities we need, to grow our applied intelligence business unit? Here we're seeing that we have about 45 per cent skills attainment. What does that mean, though? It's not enough information for us to really do anything about it. Again, I'm using Workforce Analyst to help us uncover: hey, we see this 45 per cent but where do we really have gaps? Are there any gaps that we really need to be concerned about?  

Annika McGraw 0:29:13.2: 

We want Workforce Analyst to be able to actually diagnose the problem for us and be prescriptive so that we can do something further. What we learn… We asked: are there any skills that have significant gaps on the team? That 45 per cent is an overall number; what's actually going on beneath it? We learn things like prompt engineering, it's only a three per cent drastically dragging down that overall number to get to forty-five per cent. We've got a few other ones here that we could work on, so now we have an actual diagnosis of what's going on and we can be tactical in how we move forward. We're going to try out three things here. The first one is simple. We now know we have a list of the skills that we need to go out and bring into our organization. We've got these nine open roles. Let's prioritize this experience. Let's prioritize prompt engineering. Let's prioritize deep learning and algorithm design when we go out into the market and try to source candidates, okay? 

Annika McGraw 0:30:32.7: 

But as a workforce planner, what can we do today to adapt? What levers do we have, we can start pulling and get this back on track? We're going to assess two different levers. The first one is, let's look at this, our existing team, our existing applied intelligence team. John mentioned this earlier; a lot of organizations get stuck in this hire-first mentality. But we can think about training our existing team as well and so we're going to use Analyst to bring in the information we need to make a data-driven decision about this. Let's say if we train in - train up our team on these skills that we desperately need, we may not need to hire one of these open software engineering roles. But we have to make the data-driven decision, as I said. The first question I asked was: how long would it take to train for prompt engineering? We learn it's about two months. We're going to also ask: how much would it cost? Then we can truly make this decision.  

Annika McGraw 0:31:41.0: 

So it might take two months to hire a software engineer as well - hard to really make a decision there - but when a software engineer is about 300,000 of an annual fully-loaded cost versus what we learn now is a 180,000 one-time training cost for the team, it's a no-brainer. Let's build this in house. Let's invest in our existing people versus being dependent on the external labor market and thinking about things the old way and that hire-first mentality. Let's investigate one other lever that we can think about pulling as it relates to getting these critical skills deployed on our most important priority at the company. We're going to start looking beyond just the applied intelligence team now. So I'm going to start thinking about: are there any individuals elsewhere in the organization that could be good candidates for our currently open software engineering roles on our most important project?  

Annika McGraw 0:32:54.8: 

Now, while Analyst is doing this, I want to show you exactly what I'm asking the Analyst to do. I'm going to pop open Norman here, who's on the applied intelligence team, because he is a shining example of the sort of skills profile we're looking for on the team. He's an expert in things like deep learning, artificial intelligence, anomaly detection. What we're asking the Analyst to do is take this profile of what we're looking for, compare it with all of the rest of the resources in our org and see if there are any that have a skills profile that would be a good match for this team. Let's go back to Analyst. It's identified - it's hard to see - but two individuals; there's Emily and Jason here that seem to be good candidates for the currently open roles. But let's investigate this information ourselves. I'm going to find Emily and Jason within the org chart itself. Here's Emily. We'll just pop open her skills profile here, so she is a pretty strong match. Then Jason is over here and he is also a pretty strong match, has a number of the skills that we're really looking for. Maybe a couple that we would have to be training him up on.  

Annika McGraw 0:34:28.7: 

Another key point here is, you'll see some yellow text that is very hard to read; I don't know why I put that in yellow. But it says available for staffing, and this speaks a little bit to some concepts that we really think about in our Project Resource Planning app. We're thinking about software engineers who might be working on a certain product line, but then they become available. They have skills and talents that could be used elsewhere in the organization. But it takes planning and putting all of this together to be utilizing those resources, those skills in the right way. So in this example, we find some internal resources becoming available soon that are perfect to bring over to our applied intelligence team and train them up a bit. Let's see what that actually looks like. What I'm going to do is interact with the org chart itself, select Jason and Emily and see what it looks like if I actually bring them over into Norman's team where we have these open roles.  

Annika McGraw 0:35:43.8: 

As I do that, you'll see that skills attainment number jump up by about 30 per cent, so that's because we're able to deploy critical resources to our biggest priority today, not having to wait on the external market. Not having to wait on hiring process or productivity ramps. Then we can also think about removing some of these open positions that we no longer need because we've filled these roles internally. So I'm going to remove some of these open software engineering roles. As a result, my open positions will decrease and my forecast will decrease and get me back within budget range. The key point of the story here is that it's a win-win-win because we're taking control of our costs. We're minimizing our spend on recruiting and external hires. We are getting internal resources deployed towards our project so they can start on day one. Number three is that we're investing - this is most important, in my opinion - we're investing in our own resources, which is going to grow our team and increase things like retention.  

Annika McGraw 0:37:08.9: 

So again this is an example of the Operational Workforce Planning app, some of our latest innovations with interactive or modelling that you just got a taste of today and Workforce Analyst, how it's embedded within our planning process to drill down into information, understand and sift through data that typically are very difficult for the human to do, to look through all those data, even hoping that you might find something meaningful. This is how we're thinking about it today. This is what we're currently working on and innovating with. John is going to just wrap up the conversation with understanding our roadmap and what's coming up next. 

John Pitstick 0:37:57.5: 

Thank you. There's actually a fourth win in there. Our team in workforce planning and how we develop, we have a lot of themes and we think about the front end. One of my favorite themes is everything we build is going to be anything but grids. So the fourth win: all of that was achieved without doing data entry in a grid. Yes. Maybe you guys aren't as passionate about that as we are [over speaking 0:38:20.8]. Yes, we like to do stuff not in grids whenever possible. Keep it interesting, keep it visual. Okay, this is a roadmap - at least for now. AI might influence this, just like it influences everything else. It probably will at some point. We're in, what is it, March? Yes, we're still in March, so the first half of this year, CoModeler just GA… By the way, we would prefer you use all the org modelling stuff within our applications, but I believe a GA announcement just went out for that capability native in the platform as well for custom use. So look out for that. It would go under the name data-driven hierarchies, and obviously the org chart drag and drop.  

John Pitstick 0:39:08.8: 

That was another big one that we pushed for. Does anyone remember when that first was on the roadmap? Have you been with Anaplan a long time? It's like 12 years that the org chart was on the Anaplan roadmap. Happy to see that. A lot of kudos to Adam Their, for those that noticed. His arrival at Anaplan unleashed a stalled roadmap for Anaplan for a long time. Super-happy about that. Workforce Analyst, we're working on it right now; you saw it. We might be a little bit of a laggard compared to some of the other teams with that. We're dealing with some pretty sensitive data so we can't make any mistakes. Workforce Analyst for OWP will be available, though, before the midpoint of this year. Optimizer, you've seen it more in the custom sense. It's not built into our application yet, but it will be. For those that follow the technical stuff that really has to do with Optimizer support for Polaris, which all of our apps are Polaris-based applications. Forecaster works today with our out-of-the-box apps, but we want to do some extra packaging, incorporate some pre-built stuff with that. That'll come in the second half of the year, as will adoption of Workforce Analyst for our new apps, Project Resource Planning and the Contact Center Planning application. 

John Pitstick 0:40:26.7: 

Then before the end of the year, we will have two new applications. Strategic Workforce Planning is - has to happen. That one is, I like to say, carved in stone for us. It's really important right now. Everything we've just said is really backing that up in terms of thinking about the future, so we will deliver that. The last one you're seeing on there says general workforce demand. Earlier, if you were paying attention to the roadmap, they were talking about merit promotion cycle as a process. We might do a packaged application for that. Show of hands. This one, this general workforce demand, this is leaning into this idea like it's all going to be about the skills, the activities that the business is doing to get to staffing plans. The capacity planning that needs to be done. Contact Center is a form of capacity planning, but it's pretty specific to the call center concept. Show of hands: would you be more interested in getting an out-of-the-box foundational demand workforce demand application, or something to once a year run the merit promotion cycle process a little more cleanly? Workforce demand? Merit promotion? Oh, interesting, a couple. You must not have fantastical HCM solutions - which usually does do that process - but all right, that's good. For those that didn't see: the vote did go about two to one to the demand app. Hey, you're influencing things on the fly with our strategy right now.  

John Pitstick 0:42:01.2: 

Then beyond that, again with those apps we'll get them out, get them right and then layer in the Workforce Analyst partner for each of those. Then some of the weirder, wonderful things we're thinking about are, okay, when can we start bringing AI into that org design experience where it's even more suggestive, it understands the strategic objectives you're trying to do and it actually starts proposing alternate structures in the future based on those constraints and strategic objectives that you have? What I'm really excited about is, I just made it up this morning - I'm kidding - but what if these workforce agents were autonomous? Like, you didn't have to ask it questions; it was just working all the time. All of a sudden after seven days, it spotted a trend. It was sprinkled throughout the organization. It wasn't in one organization but it's a trend evolving. I don't know, it's like people with a certain skill just start attritting all over the organization. Someone's not going to see that when looking at one organization, but AI can find those things. It can find what humans might take a little longer.  

John Pitstick 0:43:13.2: 

So that's the autonomous workforce agent; always on, always working. You wake up in the morning and it's got some news and advice for you. That's where we're going eventually after this year. I think that's it. That's AI and workforce as we're seeing it. Come to the next Connect if you can. It is the age of AI, things are changing fast. Some of that might change that quickly. No, I'm joking. For this year we're pretty set in stone on what we want to do, but yes, we'll take any questions if anyone has anything right now. 

Audience 0:43:49.0: 

I have a general question, so related to skills: are you… First of all, I feel like I should disclose; I'm not an Anaplan customer. This may be an uneducated question, but are you considering just whether or not the skill is there or not? Or is Anaplan also factoring in actual versus target proficiency and the gap sizing in your analysis? 

John Pitstick 0:44:19.0: 

So in terms of confirmation of purported skill or - yes, I think… So the short answer is: no, Anaplan doesn't automate that for you. Just a process of understanding skills in your workforce. It's easy to do the demand side of skills. You just: what are these jobs and what are the critical skills? That's pretty straightforward. You can see how, by understanding high performers - we didn't really make that point - but obviously… Who was the example, the first one? 

Annika McGraw 0:44:50.3: 

Yes: Norman. 

John Pitstick 0:44:51.4: 

Yes, Norman. Let's call him a - he's a one or a five based on your performance rating, but obviously that can give you a blueprint. I don't have a silver bullet for how do you… Again even being at Workday, they even struggled with that sort of process because people can promote their own skills. You can see that just like on LinkedIn. Actually validating it is a more difficult thing that has to involve the management chain above them. But yes, usually a high performer is going to be a good sign of they've got those right ones. There are some unknowns just in terms of, again, if skills are the currency of workforce planning, a lot of processes are going to have to change in organizations. There will be focus on that and validation. We'll try to help with it. I think we might look to some other vendors to partner with, though, for some of the existing workforce validation. 

Audience 0:45:50.1: 

That's interesting, thank you. 

Audience 0:45:56.4: 

Yes, I'm curious. I think this is really great for strategic workforce planning, but I'm curious if the workforce planning AI assist actually is good at anomaly detection, if you will, on actual errors in the underlying data. That's where a lot of my time is spent with workforce planning - obviously because I'm in finance - but I don't manage the HRIS or the ATS system. I think I'm more interested in, frankly, how Anaplan is leveraging AI in error detection within underlying datasets to inform something like this. I'm wondering if you guys had that already built in, or is that something on the roadmap, or what? 

Annika McGraw 0:46:37.4: 

Yes, there is anomaly detection. AI-driven anomaly detection is on the roadmap. I don't personally have enough information to give you a great answer right now or put it on my slides. But we know that it's coming soon. We're keen on figuring out, basically, exactly what you said so as soon as we… The same way that we've taken all these other Anaplan intelligence capabilities and figured it out, okay, great, how do we solve problems for our workforce planning customers? We will do as soon as we get access to the anomaly detection AI capabilities. 

John Pitstick 0:47:20.2: 

Yes, just to add to that. One of the reasons we built OWP and there were all those different stakeholders in that earlier slide - and you saw finance and HR - so you know there are two different systems. A lot of times, these systems don't talk to each other, so just built into OWP - forget about AI - is that there are different attributes on the workforce like cost center, like their management org. There should be most of the time consistency in those kind of relationships. So we could manually help identify those things now in reporting or like, 'Hey, these people all seem to be in the wrong cost center based on everybody else in this particular organization.' AI then supporting that, yes, that'll probably come. That's a good use case. We'll think about that one as well. 

Annika McGraw 0:48:12.4: 

Absolutely. 

Audience 0:48:14.3: 

A question about CoModeler for workforce planning: will it be trained or equipped to build out fringe calculations specific to different geographical regions? For example, if you have employees across different states or countries and there are applicable fringe benefits or pension plans, for example. 

Annika McGraw 0:48:31.4: 

Yes, we would think of that as a great use case to use CoModeler to help extend the application. So typically, the way that we see customers doing this, though, is having… CoModeler is not meant to replace the COE team. So you have your COE skills, you have your architects there. CoModeler can make them much faster. CoModeler can also help them understand models and structures and ontologies. To your point, that's a great example of what we would see as a potential extension to the OWP app because it's not like a core standard process that we would necessarily deliver out of the box, but would nicely fit into the overall process as well. Thanks, everyone. 

John Pitstick 0:49:21.1: 

Thank you.