Anju Rao 0:00:13.0: 

Okay, so workforce planning in the age of AI, right. So, obviously AI is everywhere, right, and it's implemented in everything, and there's also a tremendous amount of pressure around efficiency, and AI efficiency, and it's starting to become this critical market pattern. CFOs want workforce efficiency from AI investments and from the workforce that are using AI. So the challenge is, how do we actually measure that productivity impact, right, because a lot of people don't know how to measure it. Also, there's no clear way to map AI tools and agents to the workforce in terms of the positions, the people, the tasks, or any outcomes that come out of that. So, typically, we're starting to hear things like, we need an efficiency target of eight per cent, ten per cent, fifteen per cent. The challenge is that that is very dangerous if you don't have the information around, okay, well, what are the tasks that are getting optimized, or how does that affect the people? We've all heard the edict of find me eight per cent efficiency in the workforce, and without that measurement infrastructure or alignment to who these people are and what they're doing, the end result becomes layoffs, right, when it could be and it should be something more thought through in terms of augmentation and the workforce. 

 

Anju Rao 0:01:40.9: 

This slide is interesting, right. So there's a great quote where it says, 'The machine is not a mere tool, it's a monster that devours the soul of the workman and turns him into a cog.' So you can see Ultron looking like AI bots, right, on a machine line. The interesting thing is that this quote came out in 1835 by Thomas Carlyle, who was a Scottish philosopher, and it actually happened during the time of the - towards the end of the Industrial Revolution, right. So we're starting to see something where artificial intelligence or agents or analysts don't need to be this heralding of the end of times, but more of a major tool shift, right, so similar to the Industrial Revolution. So we can start to figure out how our work will change and be augmented, but it needs to be done in a way that we're doing it hand in hand, right. It can't be, we built an agent, and now we're firing 15 people. How do we augment the workforce to use these tools effectively? I work for Anaplan that builds these tools, right. Applications, agents and everything, but we're going to talk about tools are just hammers, right. This is a nice analogy. Tools are just hammers, and we're talking about, instead of the hammer, what is the architecture of your house or your organization, and how we can actually support it. 

 

Anju Rao 0:03:12.5: 

So, when we think about workforce planning in this age of AI, there are five tips that we're going to go through. So, thinking beyond the bot, deconstructing the work that's happening within the organization, there's a skills velocity issue, and then we'll talk about planning for if, not when. Then, also, what I think is the most important part is planning with empathy, right. How do we actually do this and still come across as human beings? So let's talk about this concept of beyond the bot, right. If we think about workforce transformations, there's this concept of buy, build, borrow. The Bs of the workforce planning, right, and one of them has always been bot. In, I would say, the early days, but basically before agentic AI and agents and analysts, RPA was a big thing, right. So, how can we automate different tasks and just replace those menial tasks with automation? Unfortunately, now it's not that straightforward, it's not that simple, right. You don't create an entire capacity plan and then automate all these things with a bot or an RPA automation. You have to look at your organization where it's not to plan for a world with AI. It's the idea of you have a workforce that is now fundamentally defined by AI, right. We're all using Gemini, we're all using Claude Cowork, whatever in our daily lives. How can we support that in the workforce? 

 

Anju Rao: 0:04:46.5: 

So it's getting beyond the thought of just replacing someone with a bot, or a task with a bot. The big part of this is deconstructing the work that's happening in the organization, so planning with AI in which the workforce and the work has fundamentally changed. So, really, it's thinking about, what is the job role that we're looking at, and what are the functions, what are the skills? As you look at the funnel going down, you're going from functions to skills to these different tasks, right, and that's when you can start to see, that's where the analysis comes in. Where can I use AI, and where can I use agents to affect these tasks in a more optimal way? There are plenty of companies and organizations that are doing this, that are taking skills and job functions and breaking it down into different tasks. There are programs at MIT that help you figure this out, but the biggest thing about planning in this age is figuring out, what is this task bucketing, right, and then that's how you can start to learn how to define future jobs and future work and future skills. That's how we can, at the end of the day, start to adapt to the next AI agent that comes out. The other challenge and tip is around what we call the velocity problem, right. So the half-life of skills is getting drastically shorter because AI is completely changing the game for technology and building up skills. Two years ago, I wanted to learn Python to build something for Anaplan. I wanted to export a whole bunch of stuff and create a report and stuff like that. 

 

Anju Rao 0:06:23.8: 

It took me weeks because I was scouring YouTube. I learned how to program in Python in 30 days. Now I literally created a Claude work project, and I said, 'Build this thing.' Use Python and give me all the documentation and create a five-minute video to explain it. It was done in 20 minutes. The half-life is changing, right, and I did that on a Sunday because I was bored, which is sad, but at the end of the day, there are skills that are in our organizations that we may not need in three or four years, so that's the skills philosophy problem. We can't keep hiring every two years to fill skills and fill that gap; 1) it's not sustainable, it's too much drag on people, and 2) it's wildly expensive. Just the cost for recruiters and job reqs, time, money, everything. So we really have to start focusing on mobility, upskilling, reskilling in a more sustainable way than the constant external hire. I created this bot. I need to now hire someone. I need to [unclear words 0:07:28.7]. So we need to figure out, what are the skills in our organization, where is the gap and how do we reskill? Planning for 'if', right. So unpredictability is the new norm. Literally, I'm afraid to open my phone some days, right, because I have no idea what's going to happen, but now the 'what if' modeling in your applications needs to factor in things like price lifts. Especially with AI, token costs, usage, data center costs. 

 

Anju Rao 0:08:03.9: 

What if we don't get the efficiency out of agents or out of AI things that we initially planned for? What does that mean for our people? So, scenario planning is now around uncertainty rather than a more linear thing. It's changing to, I need to do it now, as opposed to, oh, next week is the plan, the following week, right? All those different aspects need to be factored into it, so now we're talking about multiple parallel scenarios where we're doing modeling and 'what if' planning, and the ability to pivot, right. So being open to that and also modeling alternative options. This AI agent that we've built is supposed to cover 70 per cent of my job work. Well, what happens if it doesn't, or if the adoption is lower, or people aren't upskilling? So, all of these things, we need to start really planning for if this doesn't happen, if this does happen. Going back to what I was saying at the beginning, I think that the most important part is planning with empathy, right. This is for roles in the organization that control the dollars but really trying to understand the human impact and the elements of what's happening. So, transparency is key, right. That people actually understand what's going on is a powerful thing, and we're starting to bake that into a lot of our applications on the OWP side. 

 

Anju Rao 0:09:34.5: 

People should know what's happening to the roles, why it's changing, and it allows you to connect the workforce to retention, adoption. The more that you can get ahead of, let's say, a RIF, a Reduction in Force, or a hiring freeze and start to communicate that out, the better the culture will be, the more transparency, and the better the employee morale will be. The worst thing, I think, that could happen is a large chunk of your organization wakes up at 6:00 am to an email that's almost out of the blue, and then the following week, a $30 million executive gets hired, right. The transparency is completely lost, but this is where planning with empathy, especially in this age, is critical. So, to wrap up this piece, and I'll pass over to [?Hilary] in a second, at the end of the day, for - not every person, but AI is not necessarily going to take your job, right, but the person in the room that is using AI efficiently and in the best way, might. So it's really about, how do you build a workforce in your organization that's flexible enough to handle a new Claude Opus 4.8 that comes out, right, or whatever. A Claude Cowork or Gemini or an OpenAI thing that comes out next year. How do we have the infrastructure to reskill or upskill and make that available to people? Every single one of us can save time using AI. I'm assuming a lot of you use AI, but every single one of us can. You've all seen that happen over the past ten minutes. So, every single visual that was on this PowerPoint was generated with Gemini, right, which took a total of ten minutes. 

 

Anju Rao 0:11:29.7: 

A year-and-a-half ago, if I had to create all those images, go to marketing, get the branding template, pass it through, do this, get it photoshopped, it would have taken me weeks, and I probably wouldn't have done it, right. Not even taking into account finding the images on OneDrive. So this is how we can literally upskill and use AI to augment what we're already doing, so we can get to the more interesting, better and more honestly fonder, interesting things that we're doing at work. So, the three steps I think we want to start thinking about is - and this is the call to action, right. So, what are the top five tasks in your org that are most vulnerable to automation? Not skills, but tasks. That breakdown. Then find the skills that your current staff has that AI can't mimic, right. AI is great at producing PowerPoint presentations. It can't give a presentation. Then start to allocate budget for that. It can't always be on the employee, right. So, adaptability training, and it's not just staff reduction. I'm going to pass to Hilary who is going to talk through our AI offerings and do a demo. 

 

Hilary 0:12:44.3: 

Awesome. Thank you, Anju. So I'm going to talk about AI in the context of Anaplan, which we call Anaplan Intelligence. How convenient that Anaplan starts with an A. So we're making massive investments in AI, and that's really to bolster our workforce planning application so that you as a customer would be able to plan more effectively and more intelligently. So, whether you're trying to predict the future shape of your workforce with Forecaster, optimize your existing resources with Optimizer, customize the operational workforce planning application with CoModeler, or really surface insights and take action with Workforce Analyst. I'll double-click into each of these different products that we're bringing to market and talk about how they can support your workforce planning process. So we'll kick off with Forecaster. Forecaster is our AI ML-driven linear forecasting capability. It comes with ten out-of-the-box algorithms, and what's really unique about Forecaster is it's not just leveraging your historical data in order to extrapolate what it thinks is going to happen in the future, but we're able to embed related data as well. So, in the context of workforce planning, the job market can be very unpredictable, and there are risks, especially if you have attrition that was unexpected. 

 

Hilary 0:14:15.1: 

So, thinking about departments that really move the needle for you, maybe you weren't expecting a number of engineers to leave, which impacts things like your road map and when you can release products, or even the sales side of the organization, which is directly going to impact your ability to bring in revenue. With Forecaster, you'll be able to forecast out your attrition and then layer in related data, whether that's macroeconomic factors that you're getting from a third party or whether that's your personal data. Not your personal data, but your organization's data, like trainings, are managers leaving, what are the different trends that are actually impacting attrition? A lot of the innovation with Forecaster that we're doing is shifting from black box to glass box, so embedding a lot more explainability, so that as you're layering in that related data, you're able to see what is actually moving the needle on my forecast, what are the indicators that are causing - that have a strong correlation to attrition. Then are those things that you can actually go back and remedy as an organization? The next product I'll talk about is going to be Optimizer. Optimizer is a really powerful, linear algorithm that allows organizations to solve super-complex business problems with just the click of a button. 

 

Hilary 0:15:41.0: 

So, the way that Optimizer works is you're able to define an objective, and then you can layer on various constraints. So, say for example, you have a number of strategic initiatives that you're trying to execute on for the year, you may want to be leveraging Optimizer to look at your existing workforce and understand how you can optimally deploy your resources within the organization against those initiatives in order to be successful, essentially. Part of the constraints that you could layer on could be cost, for example. It could be layering in the concept of having a digital workforce that's actually informing how you're going to execute on your strategic initiatives. CoModeler is just how it sounds. It's really intended to model build with your model builders. So, the people that would be interacting with CoModeler are your model builders, your administrators, your Anaplan CoE, and what it can support doing is 1) building new use cases, but 2) updating, appending, extending your existing Anaplan implementation, or if you're deploying an application, helping you to customize it, so it's super-bespoke to your specific business planning processes. Then, finally, we come to the Workforce Analyst, and this is where we're going to focus the demonstration today, so I won't go into too much detail because you're going to get to see it all live. So, the Workforce Planning Analyst is really designed for your end users, to give your end users a seamless experience as they're in the Anaplan platform. 

 

Hilary 0:17:24.2: 

Everybody has their LOM of choice, and they love to interact with - Anju mentioned Gemini. It could be ChatGPT. LOM of choice, but really, Analyst is intended so that your workforce planners can come into the application, ask natural language questions, and surface insights instantaneously without having to dig through dashboards or find the data. A lot of end users of Anaplan aren't super-analytical. They just want to get in, get the information they need, take action on that information, and move about there then. In the context of workforce planning, there are a number of personas that are actually interacting with the operational workforce planning application. So, depending on your persona, you may be taking different actions and needing different insights, and so, as a Workforce Analyst, it's able to support all of these different personas, whether you're the HR and talent acquisition team who's working on candidate pipelines, if you're finance and you're trying to understand budget versus forecast and get line of sight there. If you're a business leader and you want to be able to look across your organization in terms of, who are the resources we have on board, what skills do they have, and where do I need to potentially invest in additional skills, or if you're a workforce planner doing really strategic planning and looking to the future and liaising with all the other personas. 

 

Hilary 0:18:48.0: 

So, Analyst is really designed to support any of the personas that come into the Anaplan platform and can answer questions on those various business planning processes. The last thing I'll call out before I jump into the demo is that the security access of the Analyst is going to mirror the security access of the users within the Anaplan platform. So, in short, a user that doesn't have access to data would not be able to ask a question to the Analyst and receive information that they don't have actually permission to receive. I think that's a pretty important one. Then, yes, we'll jump into the demo. Perfect, thank you. 

 

Anju Rao 0:19:32.5: 

Sure.  

 

Hilary 0:19:34.4: 

Okay, so on this particular dashboard, I am a workforce planner, and I'm working on an AI initiative. This is a great connection point with the finance organization because likely your finance team has set strategic initiatives at the beginning of the year, and we need to be able to execute against those. One of the really critical ways that we're going to do that is ensuring that we have the right resources in place to do this. So, here I can see some KPIs at the top of the screen, things like my planned positions, my open positions. Really, any data that's important to me, the operational workforce planning application can be configured to surface in that user interface. I'm going to go ahead and open up my Workforce Analyst, so it's going to be these purple sparkles on the right-hand side here. This is where I'm going to have preceded questions, and so you as a customer would be able to decide, what questions do I want to be surfacing to my end users based on the common questions I know that they're asking? Here I'm going to ask, what are the biggest hiring bottlenecks for my AI initiative? I'm really concerned about executing against this specific initiative, so I want to make sure that I'm tracking against our hiring plan. So, here I can see the biggest bottlenecks are a number of open positions that have been open for a significant amount of time. 

 

Hilary 0:20:56.1: 

So you can see the top two. Hopefully, you guys can see that. You can see we have a software engineer over 209 days we haven't been able to fill. We also have a software engineer over 100 days we haven't been able to fill, so that's going to be an area that we want to drill into. I also have the flexibility with Analyst to visualize this as a chart. A lot of our users are really visual learners, so we wanted to make it quick and easy and efficient for people to understand the information in a way that they like. Additionally, if you're a more detail-oriented user, you can always check the sources for the response that you're getting back from the Analyst, so you can drill in, you can get access to the model, the module. Any of the context that's relevant to the response that Analyst provided to you. Analyst will also suggest additional dashboards to navigate to, based on the context of your question. So, what you can see here is that Analyst is suggesting for me to jump into - we're not going to relaunch Chrome right now. It's prompting me to jump into hiring insights, so I can get more information on these specific positions. So, clicking into that dashboard, I'm now in my hiring insights dashboard, and I'm able to see all of those positions that were stalled out or open for too long. Again, I have relevant KPIs at the top of the screen. 

 

Hilary 0:22:22.8: 

Now if I look at the two longest positions, they're both located in Boston, so it may be that we're not having - we don't have a very strong candidate pipeline in Boston. We're not able to actually find the right person to fit these particular roles within that market. So, within Anaplan, we would be able to update that location on the fly and shift that resource into say San Francisco where we know that there's a lot of engineers. We could also update the compensation package if we wanted a more aggressive one in place. Ultimately, this is going to recalculate for me in real time that fully loaded cost associated. Now what I can do is start liaising with my talent acquisition team, so I'm going to go ahead and kick off a workflow where I can shoot a notification to my recruiter and ask them to please prioritize hiring this role. I can customize the text, I can give them any additional information. As I send this off, my recruiter is going to get a notification in three ways. They're going to get an email notification with a link to this dashboard, they'll get an in-app notification, meaning as they open Anaplan, a notification will pop up, and then if you are a Slack customer, they'll also receive a Slack notification. 

 

Hilary 0:23:41.8: 

So, for this first vignette, I was really focused on my hiring plan. How do I get [unclear words 0:23:46.8] to support this strategic initiative? So now let's shift gears. We want to look at our existing workforce and start thinking through, are there other ways that we can fill these gaps? Are there other ways we can execute against this initiative? So I'm going to jump into my org-based scenario modeling, and this is where I can start scenario modeling against my existing organization in order to figure out how I can execute on this goal. We still have our AI initiative at the top left of the screen, and that's because we want to keep that top of mind as we're executing on this plan. I also have a number of red flags, unfortunately. I can see my budget, I can see I'm over budget from a forecast perspective. I have a bunch of late open positions, and I have a very low skills attainment against that strategic initiative, meaning that the current team I have doesn't have all of the skills that we require to effectively execute on this initiative. So, again, I'm going to open my Analyst and I'm going to ask, are there any skills with significant gaps on my team? So I'm trying to drill into that skills attainment and really understand why that's so low and what are those gaps? 

 

Hilary 0:25:11.5: 

So, immediately, the Analyst is going to give me a stack ranked list of the top ten skills by lowest attainment, and we can immediately see that it's prompt engineering, closely followed by prototyping, as well as deep learning. So this is giving me visibility into, okay, we have a low skills attainment, but what exactly are those skills and how can I take action to remedy that? So the next question I want to ask is, how long would it take to train people to do these activities? So, maybe we saw it's taking a while to hire our people on board, so we want to start thinking internally and figuring out, can we cross-train people? Immediately, the Analyst is going to tell me, okay, it typically takes two months. Great. Everything comes with a cost, so let's find out what it costs because we want to be able to measure that overall cost against our hiring plan today. Here, what we can see is that it's going to cost about $180,000 to train my resources on prompt engineering. I also have visibility on the left-hand side into those to be hired within my software engineering department, so I can see the cost of each of those heads is about $350,000. So that gives me a data point in terms of, okay, there is a cost associated with training. It is less than hiring a person. There is a third scenario that I want to assess, and that's going to be looking at people on other teams and understanding, are there internal candidates that we could potentially bring on to this initiative to help support it? 

 

Hilary 0:26:51.7: 

So here, I'm going to ask, are there any good internal candidates with a strong skills match? So this is going to index our existing workforce and come back with an answer to let us know where we see strong skill matches for this initiative. So we've got Emily and we've got Jason. Emily with very strong skills, 71 per cent, and Jason with strong skills as well, almost 60 per cent. Next, I'm going to go ahead and search for these people. I want to figure out where they are and get some additional information around them. So, in my org chart, I can simply search for Emily's name, and immediately then I can pull up the profile, I can see the skills match as well as any skill details. So, if I wanted to get more data around Emily and what she knows, I can go ahead and drill into all those details. Now, one thing I'll call out here is that this hierarchy chart - this has embedded versions. So, if you look at the top here, there's a 'what if' scenario toggle. Within hierarchy charts, we can always layer in as many versions as you want. This way, you can shift resources around, reorganize your organization, and then do compare and contrasts, so that you can ultimately figure out what is the right shape of my organization and then you can push it back to your actual hierarchy. So this is really powerful because it allows you to do scenario modeling within an organizational structure. 

 

Hilary 0:28:21.8: 

So we'll go ahead. We are going to actually shift Jason and Emily into my software engineering organization to do some scenario modeling. What's cool here too is we now have this drag and drop capability, so I can just simply pick up Jason and Emily and shift them over. Again, I'm working in a version, so this isn't impacting any of my actual hierarchies. I'm just doing a sandbox here. So now I have Emily and Jason within that software engineering organization, and maybe I want to deprecate those other software engineering roles. We no longer need those two new hires if we're able to shift those other resources. So, now we can see the output of this scenario model. We can see that our forecast has come in line with our budget. We still have some open positions, that's fine, but our overall skills attainment has greatly increased by bringing Emily and Jason on to this initiative. So, this was an example of how a workforce planner can come into the Anaplan platform, immediately have visibility into not only, who are we hiring and when are we hoping they come on board, but who's on board, what is their skills mix, and all of these different factors are really allowing us to make an informed decision in terms of how we can execute on our strategic initiative that we're chartered with. Anju, I'll hand it back to you. 

 

Anju Rao 0:29:51.0: 

Amazing. That was awesome, thank you. Let's see what's coming up next, right. So, if we think about our road map over the next year - and my own safe harbor statement, any of this could change, but this is what we have right now for the road map perspective, right. So, this half of the year, CoModeler is GA, generally available. In addition, Workforce Analyst that Hilary demonstrated is available within the operational workforce planning application as of now. Coming up in the second half of the year, enabling Optimizer for our applications in addition to native integrations with Forecaster, Workforce Analyst for our project resource planning application. So, how do you plan people with skills aligning to different projects? Also, Analyst for contact center, and then two potential new applications that are coming out, basically like a general workforce demand application, and then strategic workforce planning. Then beyond is the Workforce Analyst for both of those two new applications, augmented org design, and then an autonomous workforce agent, right. Something that can surface highlights and insights to you on a scheduled basis without interaction. So that's what we have planned, and we have a few minutes for Q&A if anyone has any questions, comments. Yes. 

 

Unknown Speaker 0:31:24.1: 

Hold on one second. 

 

Anju Rao 0:31:24.8: 

Oh, okay. 

 

Unknown Speaker 0:31:25.4: 

Just running with the mic. 

 

Anju Rao 0:31:26.9: 

Oh, there's two. Okay. 

 

Unknown Speaker 0:31:28.7: 

Who was first, sorry? 

 

Anju Rao 0:31:30.2: 

Up here at the front. 

 

Unknown Speaker 0:31:30.9: 

My bad, sorry. 

 

Audience 0:31:32.3: 

It's okay. 

 

Unknown Speaker 0:31:32.6: 

There you go. 

 

Audience 0:31:33.5: 

So, from a maintenance administrator perspective, we're here representing HR and workforce planning pieces of it. Would this typically be the overall administrator for Anaplan at our company administering this and just partnering with us, or typically, do you see customers have - not customers, but the HR department, HRIS, have a role in Anaplan as well? What do you see with your clients? 

 

Anju Rao 0:32:06.1: 

So, I was one of the original architects on OWP, and we built OWP for HR in mind, right. A lot of the model or application maintenance and drivers, let's say, we built so that it's all surfaced on the application side. Ninety five per cent of the model maintenance, you don't have to go into the back-end model to do. There are some things that you have to do, right. The idea that we wanted, especially with applications, is for it to be business owned, right. It sits on the Anaplan platform, right, so it has all of those features, and IT will have some part in it, but this is really specifically for OWP. We built it with finance, HR and talent in mind, so there are talent settings, recruiter capacity. Who is a recruiter, and what management orgs are they tied to? That can be managed by either talent or HR. There are specific HR settings around approval workflows. When I'm planning for an associate, does it need CEO sign-off? No, but if I'm planning for an SVP, yes, that needs to be a track to the [?CF 0:33:17.0]. So, things like that are HR administrations and then there's finance pieces, like rates and guidance, things like that. So there's a shared role, and we have customers that HR is fully owning it, and they will contact finance and say, 'What are the rates we need to load?' We have some where it's a shared responsibility, but we have the ability to have different settings based on the role. 

 

Audience 0:33:42.6: 

Okay, thank you. 

 

Anju Rao 0:33:43.7: 

Yes, sure. 

 

Audience 0:33:50.0: 

Hi, I'm [?Jonny 0:33:50.9], I'm from HubSpot. We use Anaplan. We do not use the workforce planning application, but we have our own workforce models. My question is, the Workforce Analyst that you showed us, is it available as a standalone tool that we can use, or is it integrated with the workforce planning application? 

 

Anju Rao 0:34:11.1: 

So, what we showed is integrated with OWP, but [?Kurst 0:34:14.0], do you - I don't know the answer to this, so this is probably a good question for you. 

 

Unknown Speaker 0:34:18.2: 

[Signal breaks up 0:34:18.2] that you will be again creating on your own tool, work with any of these models that you might have, but I think, with workforce, there is the security aspect of it, which is you might be rolling it out to your HR business partners, to [?tell not but 0:34:35.5] everybody else. So there is a deeper level of security that you really have to be aware of, so you're not opening it up to everybody. From that perspective, [unclear words 0:34:44.7] to create your own analyst for your custom model, so those ones that you built yourself. 

 

Hilary 0:34:51.8: 

I'll just repeat, in case people couldn't hear it. 

 

Anju Rao 0:34:53.6: 

Oh, yes, that's a good idea. 

 

Hilary 0:34:55.0: 

Yes, in short, the Workforce Analyst that you saw today comes out of the box with the workforce planning application preconfigured by Anju and the Anaplan team. Alternatively, if you already have an existing application with Anaplan and you don't have an analyst, you're able to build a custom analyst to support that. So you would be able to have a custom analyst that works on your specific workforce planning model and can answer questions that are related to your model. 

 

Audience 0:35:30.9: 

Thanks. Hi, [?Thea Matthews] from Salesforce team. So we're currently using Anaplan and will be integrating some of the apps in this coming year. I actually have two UX-related questions, specifically about the org hierarchy functionality, the first of which, is there a way to view hierarchy side by side, scenario-wise, or like a layer? So you talked a little bit about comparing scenarios. Is that a page selector thing, or is it something that you can view on one screen? 

 

Hilary 0:36:01.4: 

Oh, I can go. 

 

Anju Rao 0:36:02.3: 

Yes, go for it. 

 

Hilary 0:36:03.3: 

You can go. 

 

Anju Rao 0:36:03.8: 

We can both go. You go first. 

 

Hilary 0:36:05.0: 

The way I would recommend doing that is publishing the same chart twice, and then just putting them side by side on the dashboard and then using the selectors to visualize them side by side. 

 

Anju Rao 0:36:17.3: 

Yes, and to add on to that, the org chart drag and drop design is a new feature that's only available in OWP. It's going to be rolling out, but the difference is that the org chart functionality right now, outside of OWP is list based, so you have to tag it to a list and then you can do that. This is actually based on module data, so that's how we're able to support - not slowly changing dimensions, but your organization over time. So, not only can you dimension it by scenario, but you can also do it by time, so you can see what your positions look like, January, February, March, and how it moves over time. 

 

Audience 0:36:53.5: 

Okay, so that might lead into my second question. So, very similar to cell history, I was curious about if there's functionality on the current UX with the org chart that allows you to see the history of parent changes as you're picking up people and dropping them off. 

 

Anju Rao 0:37:05.0: 

Yes, there is. Right now, that availability where you can dimension it by time or whatever dimensions you want is only available in OWP. I don't know when it's going to be rolled out globally. Globally meaning everywhere. I'm hoping this year, but don't hold me to it. 

 

Audience 0:37:23.1: 

Okay, thanks. 

 

Anju Rao 0:37:23.8: 

Yes, sure. 

 

Hilary 0:37:25.5: 

Yes, and that's probably one of my favorite features of that hierarchy chart, is the effective dating. 

 

Audience 0:37:34.6: 

Hey, everybody. 

 

Anju Rao 0:37:35.3: 

Hi. 

 

Hilary 0:37:35.7: 

Hey. 

 

Audience 0:37:36.4: 

How are you doing? 

 

Hilary 0:37:37.2: 

Good. 

 

Anju Rao 0:37:37.6: 

Good. How are you? 

 

Audience 0:37:38.4: 

So [unclear words 0:37:39.0] a gap, how much, I think about OWP. You've got all the analytical pieces. How much room are you going to have to inject organizational context around things like functional roles and other kinds of things where you're going to be making some other kinds of decisions? So it's not even necessarily just skills and it's not just - it's not skills, it's not title. It's in between, but it matters in terms of the restructuring you're doing. One of the magical parts about AI is that you can start injecting some other operational items that are going to make a big difference for you, especially when you start getting in the planning. Have you guys gone anywhere with that kind of thing now, knowing what that means when you inject context into those kinds of planning discussions? 

 

Anju Rao 0:38:28.0: 

Yes, so my understanding of your question is, how can we add more attributes to what we're doing from a - let's say an org modeling change or Reduction in Force, things like that. So, out of the box with OWP, if we think about the two, I would say, main 'what if' scenario modeling pieces we have, which is hiring freeze planning and then Reduction in Force modeling, it's customizable by all the different lists that you have in the model, right. So, let's say if you want to do it by hiring freeze, we want to look at region [?GO 0:39:00.0], time, management org. 

 

Hilary 0:39:02.6: 

Performance. 

 

Anju Rao 0:39:04.0: 

Yes, things like that. Performance, job level, function. We can start to extend it to include those, so that we can do different types of rules, include and exclude, that you can customize on a customer. On the Reduction in Force model, or Reduction in Force 'what if' modeling, that gets fundamentally a little more personal, right. There are two things. One is you can start to layer in things like work location, remote, hybrid, etc. Performance, right. Tenure. Personal demographic data like gender, things like that. Not as a driver of Reduction in Force, but when you plan out the Reduction in Force, we have a management report that says, 'Okay, here's everything.' You layer in severance. What's my time to recoup my severance cost payout? This is where you can start to use Analyst to say, 'I've modeled out this RIF scenario, are there any metrics that may be skewed based on personal data or demographic data?' Then it will come back, because we've done this on other demos and customers, and say, 'Okay, this scenario has a skew by 30 per cent in this gender,' or in this location, or by this race ethnicity, or we're skewing more towards baby boomers versus millennials. So, all of those demographic data, we have the framework to bring it in. It's up to the customer, honestly, whether or not they want to provide that data. 

 

Audience 0:40:31.2: 

Yes, and just for context, I'm thinking about the Analyst portion that you were just showing, right, and now I'm talking about, in the Analyst portion, you've got the planning side of OWP, and this is the insertion of context at the point of analysis where you're starting to talk about things where it's not just RIF and it's not just this. 

 

Anju Rao 0:40:52.6: 

It's in the context, yes. 

 

Audience 0:40:52.6: 

It's literally you're shifting the whole organization into a bunch of new roles. There are some RIFs, there are restructures, right, and when you get into that, functional roles suddenly matter because they present a very specific operational risk that you can cost and codify down in, across the workforce. This is just what I'm thinking about, and I'm just watching Analyst and wondering how much of that kind of stuff. So you've got all the planning stuff in the model itself, and then you've got Analyst coming in afterwards, and it's that specific place of insertion where you insert that kind of context that can suddenly make a difference in how you're thinking about these large-scale shifts. Now that we have the ability to throw all this unstructured stuff in that describes the messy part of the organization that's not really codified, it's [?tribal 0:41:41.6], then you can start wrapping it into the structural nature of the planning that you're doing in OWP. So that's where I'm coming from when I'm saying that. 

 

Anju Rao 0:41:50.6: 

Yes, I think that makes sense. I think that's getting the unstructured into the structured. That's probably later this year, but I think that's where we want to go, right. Okay, now Analyst has told me this, but what about all this messy stuff that we all know is happening. 

 

Audience 0:42:09.0: 

Yes, I'm just going to add one more thing to that. A lot of organizations that we talk to are going through a job re-architecture. So they've got 10,000 people, 11,000 job codes, and how do you bring that down to 300 job codes, standardize, and then go down to the skills level? Most organizations, they're starting to capture skills, but it's for limited segments of their population. It's parallel exercises that you're going through to really get there, yes. 

 

Audience 0:42:40.1: 

[Unclear words/inaudible 0:42:40.1]. You're going to have to start acting on all these parameters and really, we're talking about everything, everywhere, all at once, and so the fact that we [unclear words 0:43:05.8]. 

 

Anju Rao 0:43:13.7: 

Exactly. 

 

Audience 0:43:14.2: 

[Unclear words/inaudible 0:43:14.2]. 

 

Anju Rao 0:43:22.7: 

Yes, absolutely.  

 

Unknown Speaker 0:43:25.3: 

Great questions, everybody. Unfortunately, we've run out of time. We have to wrap this up to give people time to get to the next session. So, really excellent. Thank you, both. Really great session. Thank you for your questions. 

 

[Clapping]