R1: 0:00:14.3: 

Hey business genius, you've got decisions to make. Not to mention changing consumer demand, hiring freezes and supply chain disruptions. Problem is one wrong decision and, boom, share price faceplants. That's where Anaplan Intelligence comes in. It's built different, with AI at the core, to process massive amounts of data with unlimited calculations and dimensions that uncover insights no human eye could see, and your finance team already sees a lot. 

 

R2: 0:00:41.4: 

We see a lot. 

 

R1: 0:00:43.2: 

Anaplan runs more scenarios than you can count, so you can decide in real time, with confidence. 

 

R3: 0:00:49.0: 

Predicting the future. 

 

R1: 0:00:50.2: 

If we were legally allowed to say that, which we're not. 

 

R4: 0:00:54.5: 

No. 

 

R1: 0:00:55.2: 

You still make the call, but now it's informed by AI that's trained on over a decade of planning best practices. So Bob here can deal with that pesky supply chain disruption like a boss. Might want to dial that confidence down a hair, Bob. Anaplan, right decisions, right now. 

 

Unknown Speaker 0:01:13.9: 
Please welcome to the stage, senior vice president and managing director of the Americas, Jack Carogal. 

 

Jack Carogal 0:01:25.0: 

All right. Good morning, everybody, welcome. Welcome to Anaplan Connect San Jose. Great to see you all. I should probably get the clicker. All right, well, I'm thrilled to be here with you all, my name is Jack Carogal, I lead the Americas for Anaplan and what a great group we have today. Over 200 organizations are in the room today. We represent key industries, like financial services, technology, both hardware tech, SaaS tech, pure play AI, CPG retail. Amazing group of people, from finance, marketing, HR, workforce planning, and some of the most well-recognized organizations in the world are in this room today. It's an amazing group of people, thank you all for making the effort, for those of you that travelled in, thank you for travelling in to be with us today. What I'm probably more excited about though is the amount of business value, collectively, that we have captured together through the use of Anaplan. Over 1000 use cases in this room specifically are live in the platform and, based on the business value that we've been able to validate with all of you, we calculated over $5 billion of cumulative business value. If you haven't engaged our amazing customer success team to work with you on business value validation, let's do it today. Stop myself or anybody from Anaplan in the hallway to make sure we're engaged with you to help you validate your own business value from the use of the platform. 

 

Jack Carogal 0:03:00.2: 

I want to take a quick moment to also thank our sponsors. We have some amazing partners in our ecosystem and they're all here today. They have booths outside. They all have great perspectives on how to deploy Anaplan from an industry standpoint, industry use cases, domain areas like finance, supply chain, workforce. A wealth of knowledge in our partner communities, so please stop by their booth, talk to them, hear a perspective. There's some amazing intellectual property that these organizations bring to bear for us. Wow, innovation is honestly the theme that you hear at every conference, and we're no different. We have invested over 500 million into our roadmap, specifically to build a foundation for AI, and we've released a lot of new technology in the last four months. So I think many of you were here yesterday and you were in the CoModeler sessions where we worked on building models from scratch or taking existing models and making augmentations, all through a prompt-based interface. [Unclear words 0:04:08.0], but for Anaplan. Amazing amount of productivity lift. We've got over 35 customers now on CoModeler and I'm super excited to see the success that they're driving in the market. Then the Custom Analysts. So obviously model builders need help through AI and that's CoModeler, but the analysts for the line of business functions, the ability for someone as a CFO to use a prompt-based interface to interact with Anaplan and get answers back is incredibly powerful. As we all know that's where the whole market is headed. 

 

Jack Carogal 0:04:41.5: 

So our Custom Analysts allow supply chain, HR, finance executives, to really interact with Anaplan through a prompt-based interface and get those results back in real time. So amazing opportunities for all of us to bring innovation back to our organizations, to help us drive the AI push forward. Of course, you heard it from us last year, you'll hear it again last year. Applications, right? These are the capabilities that allow us to take out of the box use cases and get to market more quickly. So if you're in the SaaS business, and whether you're a technology company, a manufacturing company, a retailer that has subscriptions, subscription revenue planning. The ability to understand revenue through the lines of consumption pricing or subscription pricing or a hybrid of both is an amazing new capability that we've released. If you're a CIO you're concerned about cloud spend, consumption-based software spend. So cloud spend management for CIOs, software spend management for CIOs. So a whole new buying center and a whole new set of constituents that you all, as COE leaders, can bring back to your organizations, to IT. Then, if you're in CPG, trade promotion management, if you're in retail, allocation and replenishment. So we continue to release more applications to help organizations at an industry level get to value more quickly through the applications and the AI solutions. 

 

Jack Carogal 0:06:10.6: 

So incredibly excited about all this new innovation that we've released and you'll hear more about this today. We've got different demo booths, we've got partners, to make sure you stop by and get a tour of the applications and the AI and, of course, you'll hear more about that in the keynotes today. Today's agenda, you're going to hear from one of, arguably, the most innovative companies on the planet and that's Nvidia, and their Anaplan journey. Then we'll do the deep dive on AI applications, and then you'll hear from another customer, another highly innovative customer, Synopsys. You'll hear from them and Deloitte about how they've enabled integrated planning and decision excellence on the platform. Look, as I mentioned, 200 organizations, 450 people here today. Make sure to build some new relationships, there are some amazing professionals that are here today. There'll be a networking lunch here in this room, so make sure you meet at least three to five new people, build some new relationships and we'll have a lot of different times for networking, but we have a big lunch planned here in this room. Okay, and with that I'm going to roll a video that shows the power of decision excellent and integrated planning. Thank you all. [Applause]. 

 

R5 0:07:38.8: 

I remember looking at the alert and just thinking, this isn't happening. Not tonight.  

 

R6 0:07:56.5: 

The math just didn't add up. We were looking at the total collapse of the quarter in a single night. 

 

R7 0:08:05.3: 

At 2:00 am you're not looking for a report, you're looking for a miracle. 

 

R5 0:08:21.2: 

We had promised them, to everyone, but 40 containers of our entire line were sitting at the bottom of the Pacific. 

 

R6 0:08:30.7: 

Nikki rang at 2:00 am and when your phone rings at that hour nothing good follows. My first question was, 'Which products?' When she said, 'Premium,' I knew we had a problem. 

 

R5 0:08:43.4:  

I remember looking at the alert from the Anaplan Supply Chain Analyst and thinking, this can't be real. 

 

R6 0:08:50.9: 

I told Nikki I didn't need a report in the morning, I needed to see the full impact right now. 

 

R5 0:08:57.2: 

I asked the analyst for an impact assessment and immediately got what I needed. If we prioritized our strategic accounts, we could still service nine of our most important customers, but the analyst also flagged our penalty exposure. Our three biggest contracts were fixed, no wiggle room. If we failed them we were staring at $900,000 in penalties alone. 

 

R6 0:09:22.9: 

Saving some customers is one thing, but a near $1 million penalty is a nonstarter. So my next question was, 'Well, what else can we do?' 

 

R5 0:09:31.7: 

Before I even finished typing, the analyst had already surfaced a recommendation. An alternative with an eight per cent lower margin. 

 

R6 0:09:40.9: 

A moment like this usually triggers a month of panic, of teams scrambling, leaders drowning in noise, but we had something that could actually see. The resolution saved us $5.1 million in revenue at 2:00 am. The bad news was I knew it was only a matter of time before our friends in finance would start asking questions.  

 

R7 0:10:05.4: 

I didn't get the 2:00 am call. I got an alert directly from the Anaplan Finance Analyst. Our cash forecast was bleeding red because of this supply shift. Honestly, my first thought was, why is it always a supply chain issue? My second thought was, what are our other options? We modelled the tradeoffs instantly. It gave us confidence to align ourselves with the supply chain decision, but we still had to balance our books at the corporate level. To protect the bottom line our AI analyst recommended we defer to a lower priority R&D project. The plan went back to green. 

 

R8 0:10:46.8: 

Here's the thing that people always seem to forget. Changes like this have a direct impact on our hiring plan. So prior to having Anaplan that would have meant a week of meetings and I wouldn't have let Raphael live that down. The moment Raphael's plan was updated, our workforce plan responded, no time wasted. The Anaplan Workforce Analyst identified the open requisitions, paused them and redirected the investment dollars in minutes, not weeks. 

 

R6 0:11:18.7: 

We averted crisis, saved millions in revenue, while the rest of the world was still asleep. Supply chain talked to finance, finance to HR. 

 

R8 0:11:27.4: 

We were all looking at the same reality, right as it was happening. A single source of truth, AI at our fingertips. 

 

R6 0:11:35.7: 

Usually teams are just trying to survive the chaos, but we were able to ask the what ifs and respond with confidence. 

 

EJ Tavella 0:11:59.7: 

Please welcome to the stage, general manager and executive vice president, EJ Tavella. 

 

EJ Tavella 0:12:17.6: 

We found it, all right. Thank you all, appreciate it, looking forward to talking. Look, what you just saw is a nightmare scenario. This is the kind of stuff that businesses dread every day, but guess what? With the power of applications, the unified data model, the ability to run real-time scenarios and leverage AI, these guys were able to make intelligent decisions quickly. This is truly the vision of integrated planning, right? This is why we're doing what we do. This is why we spend our time all day long working with customers and prospects like you to figure out how do we help you run your businesses more efficiently, right? Planning is not just forecasting, planning is being able to make intelligent business decisions faster. My name's EJ Tavella, as they just said, I am the executive vice president of applications. I've been here for about two-and-a-half years and I've been super proud to see the volume and the value that we've been delivering with applications, with our customers. Before I joined, I was a partner, so I spent about eight years implementing Anaplan with very large customers. We're going to hear from Nvidia, which is one of them. I think we're going to talk all about the platform today, initially. So let's talk about where we're investing, right? We've made massive investments in the platform. 

 

EJ Tavella 0:13:32.8: 

Jack mentioned a $500 million investment by our investor, 18 months ago, and that month is going to great use, right? We're putting that into how do we drive massive volume? So first off, we've got accelerated customer adoption, right? We've got two million models in production today, now it's growing by about 50,000 models a month, right? So that acceleration of how our customers are actually using Anaplan to run their business is wildly fast. The number of integrations that we have, we all know, all of you that have been through any kind of implementation, be it Anaplan or others, the data is king. We have three-plus million integrations happening every single day. That's like 40 per second, right? This is an insane amount of data passing into and out of Anaplan to make better decisions across the overall business. We've deployed 6000 deployments over the platform, and we continue to see an increase in that volume and that rate. The availability rate of 99.6 is absolutely spot on target for where we want to be as far as industry standards. We've also deployed six new regions. So what does this all mean in practical terms? Look, when disasters happen, and they do, talk about the war in Iran over the last few weeks, look, there's been huge disruption in the data center space. We've had customers that were impacted massively. We were able to get them basically completely reconnected within 24 hours, move all of their data, move all their backups, get them to a [unclear word 0:15:03.0] setup that allows them to run their businesses. 

 

EJ Tavella 0:15:05.7: 

So this kind of infrastructure that put in place that allows us to translate across different data centers, and run them globally across wherever you are, from a business perspective, allows our customers to truly scale at a global level. So let's talk about the platform and what's coming and the momentum. We've got month-over-month growth with Polaris of 30 per cent. This is huge. Polaris has been talked about and been used by customers now for about three or four years, but we're really seeing massive acceleration. All of the applications are powered by Polaris, right? This is allowing us to go from millions of cells of planning to quintillion cells in planning, right? So these are massive models that provide huge global scale. We want our customers to be able to plan all the way down to the minute, the day, the hour, whatever level they need to, and that's the baseline foundational functions that are letting that happen. Data Orchestrator has been a massive investment for us. We heard you guys as customers, you need to be able to get data into and out of Anaplan faster. You need to have a place other than a custom-built data hub to be able to manage that information. So we invented Advanced Data Orchestrator, and this has been wildly successful. This is also integrated into all of our applications and this is something that's evolving over time, we're going to talk about it more as we get into the details today. 

 

EJ Tavella 0:16:24.1: 

Applications, we're going to spend about 15 minutes at the end of this on, but I will say we've gone from five applications about three years ago to 28 applications now. We've deployed 300 applications last year, so the adoption of applications has been huge and it's been adopted across not just new customers. Frankly, when I joined the premise was, great, hey, we've got a lot of new adopters, they're going to want to use applications. They want to have out of the box best practices to start their business. What we didn't realize was a lot of our existing customers wanted to leverage applications to get into new lines of business. They wanted to up-level their business. So more than 50 per cent of the applications that we've been deployed have been to existing customers, not just net new customers. So that's been super exciting. Then innovation around AI has been massive. So as you guys can imagine, everybody's talking about it, we are investing big, big dollars in this and really bringing this all together to make sure we can use intelligence at all the different layers to help you make better decisions. So with that, let's talk about the platform. So our overall stack, if we think about it, right, is really bringing together those three things that we just talked about. So the analytics layer at the bottom, which is the Polaris, the forecasting engines, and the ADO that allows us to bring massive sets of data in. The applications, that now provide you with functions that are at a domain level, very specific, best practices. At the top, the agentic layer that changes the game in how you interact with that data. 

 

EJ Tavella 0:17:55.6: 

So let's drill into these just a little bit. So if we start and we look at the core, right? At the foundational level we've got a calculation engine that is, bar none, the strongest in the industry. It allows us to manage planning across all the different domains. We are truly the only planner out there that can plan supply chain, financial planning, workforce planning, IT challenges within planning, sales planning, performance management and, frankly, there's a huge, long tail of other use cases that customers have come up with to manage planning across Anaplan. So this foundation at the bottom with Polaris, along with our other analytics engines, things like Forecaster, which is a reinvent of PlanIQ. It's faster, it's easier to use and it's got better results. We've got Optimizer, which is tried and true and we're building that into applications to make it easier for customers to adopt it and understand it, to make it truly deliver value. Then we've recently acquired Syrup. Some of you may have heard of that, that's part of our massive investment into retail, which is a huge, growing space for us. This forecasting engine allows us to now forecast down to skews, store, daily level detail, bring in external data, look at imagery, etc. That will be not only the foundation for retail planning, so it will drive allocation and replenishment planning, but it will also now feed into assortment planning, hindsighting for MFP, pricing and markdown planning. Shortly after that, it will be feeding into other industries, as well. So there's huge opportunity to use this within CPG, as well as other industries in the future. 

 

EJ Tavella 0:19:24.3: 

So massive investment in the platform. We also have a last, but not least, open architecture. So we also appreciate that especially here in the Silicon Valley, lots of you have your own analytics teams, you built your own analytics that you want to be able to leverage. Great, we want to let you plug those into the applications, plug those into the platform and take advantage of those. Layered on top of that is ADO. ADO is what it sounds like, Data Orchestrator, right? We want to make it easier for you to get data into and out of the platform, at massive scale, right? So this allows us to now not only bring in external data, but also bring in other types of data, as well, in the future. So transactional type data, other feeds from other data sources that you'll be able to use for intelligence across that overall process. We also have now native APIs out of the box. So you can connect directly with tools like SAP, Snowflake, Databricks, Salesforce.com. This is really the foundation, when we talked about having AI connected, we've got to have all that data available and understand how that data is connected. So that takes us to the next step, which is applications. Applications are bringing together this data with a unified data model, planning flows, and best-in-class functional solutions. Spend just a second on that, what do we mean by unified data model? This is a place where we allow you to bring in data once and share that data across all of the applications, right? So if you brought in your [unclear word 0:20:52.7] data, if you brought in your historical data, now all of a sudden can feed that directly into the next application, I may have to add some additional details. I can also translate historical sales from a sales plan to a shipment plan to a revenue recognition plan, right? 

 

EJ Tavella 0:21:06.8: 

So this is huge as far as being able to accelerate the delivery of applications over time and simplify that challenge of loading in data into the overall models. The planning flows are really the extension of that. Now, how do I systematically have the connection between different planning processes, right? How do I go from setting up my demand plan and connecting that back to my sales forecast, right? So how do I translate a sales forecast into a shipment forecast, how do I connect the impact of trade promotion planning into the overall demand signal? I want to be able to understand if I've got excess inventory, which opportunities do I have within trade promotion planning to use to drive the consumption of that demand? How do I connect that all back into finance and decision making that we talked about earlier in the video? With workforce planning, what's that going to mean to the impact on the workforce planning? So these planning flows, we have 30 planning flows that we're rolling out and these will allow you to connect those things seamlessly across your end-to-end business. That's going to continue to scale as we go. So on top of that, we'll talk about the applications and the functionality in the second session that I have today.  

 

EJ Tavella 0:22:13.2: 

So the third piece is what everybody is talking about, right? AI, AI, AI. I love that commercial. Let's start where we are today, we have really two pieces, right? So CoModeler is really a tool for your master Anaplanners, right? This allows master Anaplanners and analysts the ability to accelerate their building, accelerate their time to value and get more agile when it comes to delivery. I was at dinner last night and I was talking to a customer that's been around for five or six years within Anaplan. They said to me, 'Hey, look, one of the number one reasons why we're so excited about Anaplan is the ability to be agile,' right? 'We can very, very quickly add in new features and new functions. We can very quickly add in scenarios. CoModeler just makes that faster.' There's a ton that's coming with CoModeler, that's going to expand the ability to connect and do upgrades, the ability to connect Polaris, etc. The second piece is Analyst, and Jack talked about this a little bit. This is an always-on analyst that lets you access data very easily, at your fingertips. No matter what your role is, as long as you have permission to see that information, you can ask your phone, you can talk to the application and it will tell you information about your business, right? Where is my business today? What products do I have available? How do I get a change across the overall model? This is evolving, right? So what's next? Next is this agentic platform. So the agentic is platform is really just getting started. 

 

EJ Tavella 0:23:39.0: 

We're now building an agentic framework that creates autonomous agents across the whole platform, and it will all be powered by a tool we call Agent Studio, right? Then in the future we're going to bringing in detailed, specific line of business functionality that will allow us to go from tell me information about my business but really depicting why actions happen. Why is the plan the way that it is? Why are those the biggest exceptions? Take action on those anomalies. So if there's an issue, there's an anomaly, maybe the inventory isn't set up correctly because the lead time assumptions are incorrect. Great, I can identify those anomalies, I can use the actions in the agents to actually fix the data and drive decisions. Then I can get into where I'm driving proactive recommendations, where the agents are constantly monitoring the business, identifying issues and sending your CFO notes in the middle of the night. So that could happen. I'll tell you right now, nobody else in the market, none of our competitors are shipping AI at the velocity that we're delivering today. There is nobody else innovating at the scale, at the enterprise grade level, that we're delivering today. We are super excited about all of you as customers, to help us give us feedback and be our early adopters of AI, because we truly believe this is going to change how you run your businesses as we go forwards. With that, talking about AI, I'd love to welcome Jonathan Goldsmith on stage to tell us a little bit about his journey. [Applause]. Welcome. 

 

Jonathan Goldsmith 0:25:11.3: 

Thanks, EJ. 

 

EJ Tavella 0:25:12.2: 

Good to see you. 

 

Jonathan Goldsmith 0:25:12.9: 

Yes, nice to see you too. 

 

EJ Tavella 0:25:14.6: 

Jonathan, you've been a great friend and customer for Anaplan for a while. Tell us a little bit about yourself. 

 

Jonathan Goldsmith 0:25:23.2: 

Yes, so I really got started in Anaplan about ten years ago. I was working in consulting, we had a small little boutique consulting company, had a little bit of software. We did a bunch of supply chain projects, mixing supply chain and finance and how to approach really risky, cool problems. Travelled all over the place. It was going great. Then we had this big bid for a big customer that was going to keep our business going for a couple of years, and we competed against this dinky little software company, happened to be Anaplan. We lost miserably and, of course, can't beat them, had a look into joining. So did a whole bunch of research, figured out what made them different and interesting, and actually joining Anaplan, working there for a little while, then you came and started having your consulting company implementing Anaplan. I wanted to see more, instead of working for the company, how it was going to be implemented, how that was really going to look, staying on for a little while longer. So came over, started doing implementation work at Nvidia, really, from the beginning. Then moved over and joined Nvidia full time, happened to time that pretty well, so that part was nice, and have really been there ever since. So yes, it's been a good journey. 

 

EJ Tavella 0:26:37.9: 

Running the COE now. 

 

Jonathan Goldsmith 0:26:38.7: 

Yes, now. 

 

EJ Tavella 0:26:39.9: 

No pressure. 

 

Jonathan Goldsmith 0:26:39.9: 

No, no pressure, yes. 

 

EJ Tavella 0:26:42.1: 

So you guys have seen amazing growth. I think since you've been there you've gone from $10 billion in revenue to $180 billion in revenue. That's multiplication, 1800 per cent, I think, growth, kind of crazy. Probably because you joined, at least partially. 

 

Jonathan Goldsmith 0:26:57.5: 

Sure, yes. 

 

EJ Tavella 0:26:58.1: 

Probably because of Anaplan, but the timing was definitely good. Tell us about what's changed and what it's been like with that and how Anaplan's helped them scale that business. 

 

Jonathan Goldsmith 0:27:08.4: 

Yes, it's been a crazy ride. One of the big things about Nvidia is the constant need for trying to get to perfection. I think [?Kay 0:27:18.4] is going to talk about this in a session later. You'll see her talk about the speed of light, the Nvidia idea that we're not just going to try to go faster, but go as fast as we possibly can and really try to aim for the best that you can be. Anaplan's really been supportive of that. So it's all about moving as fast as we can. So I think Anaplan's been there supporting us. Not just getting something done, but being able to get things done quickly and being extremely flexible and responsive to everything. I think the best thing for us is we get the right people, we get the right data in the room. In a couple of weeks we're able to turn around almost any of these, frankly, sometimes ridiculous requests that our business gives us, but we have to respond. There's no choice, you've got to make the business move forward and done a good job with that. 

 

EJ Tavella 0:28:05.1: 

I love it, and, as you mentioned, I think [unclear word 0:28:06.7] speaking this afternoon at two o'clock, so there's a deeper dive even into Nvidia's use cases. So you guys should go join that. We'll remind you at the end. You mentioned the complexity. So your business has evolved a lot, you've gone from being a chip manufacturer to chips and then boards and then systems. As everybody knows, you guys are truly what's helping to drive the data center craze and the AI craze. Tell us how those complexities in the business, and other things, have impacted how you've thought about planning. 

 

Jonathan Goldsmith 0:28:37.3: 

Yes, it's really just adding more complexity, more scale to everything to we do, right? So we've moved from mainly focusing on gaming consumer market, more and more towards data centers and all these crazy things. There's been a lot more world events happening lately, having to move things around, more tariffs, more rules about what we're allowed to ship and what we're allowed to do. All of that causes us to have more complexity in our planning process. One of our good examples of this, our customer allocation process. So we, on Anaplan, run how we're - who we're going to ship all of our constrained products to. Of course, this started as a fairly simple thing back in 2022 when we first talked about it. Where we've - in the tens of products shipping to tens of customers and trying to make decisions about who gets what, when. Of course, just that level of complexity has gone up a lot. Now we're not just figuring out the customer, but exactly where that customer needs the product, which adds an order of magnitude. So we end up with now tens or hundreds of thousands of lines where you might want to ship things to and thousands of products that are constrained. Making it just infinitely more complex and Anaplan has helped us get there. 

 

EJ Tavella 0:29:45.4: 

Yes. I can only imagine you have constrained shipments every week. I think this is one of the things you were talking about earlier from this is a weekly process that was manual, to now you can run almost near real-time scenarios multiple times a day, if you need to, to get to the answers you're looking for. 

 

Jonathan Goldsmith 0:30:00.2: 

Yes, now you have to be able to respond faster, right? Something changes, you need to be able to run that scenario that says, hey, here's the new result that we're going to go plan to. 

 

EJ Tavella 0:30:10.1: 

Yes, absolutely. Love it. So let's talk tech for a minute. You guys are an early adopter. I know you guys rolled out Polaris a while back, initially, and are continuing to roll out across your end-to-end models. Talk about the use cases that you guys have found the biggest impact on and why you guys have decided to migrate everything other. 

 

Jonathan Goldsmith 0:30:29.8: 

Yes, we started off Polaris early, because we had one model in particular doing our component liability model, where we try to figure out across every single product, when we move orders around, how much liability are we going to incur. 

 

EJ Tavella 0:30:44.9: 

This is every product through the entire bill of material? 

 

Jonathan Goldsmith 0:30:47.0: 

Yes. Which is a lot. There's a lot of components, a lot of products, a lot of things happening. We just really required Polaris for that model, which is too big, we couldn't do it any other way. Because we needed it all integrated to give one answer. Since then we're now starting working with you guys' CS team, as well as other partners, to try to make that move over to Polaris. Now it's a little bit more around the performance, a little less just around can we do it, it's like, hey, there's the performance and simplicity of moving your models to Polaris, and you can do things you just couldn't do before. Especially combined with ADO, so that we can not just make these big, cool models, that are simpler, but then get that data in and out fast, so that we can meet all the needs of business that we have. 

 

EJ Tavella 0:31:32.3: 

Yes, love it. We're seeing a ton of adoption now in that space and I think you hit on the spot, which is just it's not always necessarily about needing to manage the size of the model, but it's also the performance and their stability to then scale that out across the end-to-end business. 

 

Jonathan Goldsmith 0:31:46.4: 

Absolutely. 

 

EJ Tavella 0:31:47.3: 

So I know you guys are not using applications today. That's okay, but we've talked a lot about them. As you guys look at your roadmap into the future, tell us a little bit about your feelings around applications and how that might help you across your end-to-end process and vision of where you guys are going. 

 

Jonathan Goldsmith 0:32:02.5: 

I think it's most interesting to us in the brand-new areas, especially things that we haven't yet touched with Anaplan, which still is a lot for Nvidia. There's still places that we can go use those applications. I think, for us, applications are interesting from two perspectives. One is going faster, right? You can get that initial true production run happening a lot faster with applications. Then the other part is just getting those upgrades over time, making sure it's not just, hey, here's an app, and then we're going to then put it on my team to support it for the next five years. You guys will still be there, still making upgrades, putting us on that latest version of all the new features that are coming out all the time, which is getting a little hard to keep up with. 

 

EJ Tavella 0:32:47.1: 

Yes, the new features and the new technology. So no more upgrades to Polaris, because you've got applications. 

 

Jonathan Goldsmith 0:32:51.6: 

Exactly. 

 

EJ Tavella 0:32:53.1: 

Yes, perfect. Well, look, I really - oops, went back there. I really appreciate your time, and thank you for joining us on stage. We're excited to see what you guys are going to do in the near future. I'm not sure if you're going to go from $180 billion to $1 trillion in the next five years, but we'll see. Keep it up. 

 

Jonathan Goldsmith 0:33:11.3: 

We'll see. 

 

EJ Tavella 0:33:12.3: 

We appreciate you as a customer. Thank you. 

 

Jonathan Goldsmith 0:33:14.0: 

Thanks EJ. [Applause]. 

 

EJ Tavella 0:33:16.8: 

Good job. Okay, well, love hearing from customers. I think probably one of my favorite parts of the job is being out with customers and hearing about the solutions that they're trying to solve. What their problems are, how they've used Anaplan and the creative ways they're doing it. That's really been what's fed our applications. So we're going to dig into applications for a few minutes now and talk about what's here today and what's coming. I will say, as we looked at the applications business in detail and really jumped in about three years ago, the first thing we did was we did this analysis of our 3500 customers, 10,000 plus deployments, to understand what are customers building, what are they using Anaplan for, what are the key, primary use cases? So that we could really focus on solutions that we know they want to solve every single day. That list is very, very long, but that's what drove our prioritization of where the applications came from. As we delivered those applications and built those out, we also worked really closely with key customers to define what good looks like. We worked with institutions and we worked with partners to also do that, as well. So when we think about an application, really what is it? It's built-in business expertise. We truly believe that Anaplan can do anything, but you shouldn't have to figure it out from scratch, right? So now when you walk in the door and you want to do demand planning, you've got a demand planning application that's already got all the score metrics built into it. It's got all the workflow built into it, all the exception dashboards, it's already built on Polaris, ADO, etc. 

 

EJ Tavella 0:34:53.8: 

You can truly spend your time now as customers thinking about and customizing and configuring the pieces that are really unique for your business. So instead of having to do it all from scratch, maybe tweak 20-25 per cent of the overall process to make the things that are really unique about your business shine and accelerate that time to value. Then connect it to the next process. These are also AI driven. So we're spending a ton of time integrating our analytics into the applications. This comes in the form of tools like Syrup, Forecaster and Optimizer, at their core, allowing you to make better decisions. So we don't want these applications just to be business process drivers, but actually make recommendations and drive decisions, right? On top of that, we're tying in Analyst and using CoModeler to help with building out those extensions. Analyst to help you answer questions, etc. Then scalability for us is massive, right? So we fully expect and we are selling these applications to some of the largest customers in the world. Last year, of our ten largest deals, nine of them were applications customers, and these are global Fortune 50 sized companies. $30-$40 billion type organizations. So we've built these applications to truly scale. The great thing about them is they work awesome for small companies, as well. So the good news is we can get into a small company that's growing really quickly, maybe they're a couple of $100 million and they want to grow to $1 billion quickly. Great. If I'm a multi-billion-dollar company, I still can use the same application and I can scale it out. 

 

EJ Tavella 0:36:21.2: 

So let's bring this to life. I want you to see it in action, right? We're looking at inventory now, across the process, this is within the Inventory Planning application, and I want to understand what products I have. I can search it and see it here or I can go use Analyst. So I can just ask Analyst a question. 'Hey, what products do I have [unclear words 0:36:40.9] at this distribution center.' It's going to go search the data, it's going to find that information, it's going to tell us what it's doing, so we have very clear understanding of where it's searching, what it's looking for, where it's getting the information from. It's going to come back and it's going to give me results instantly. I can do this from my phone app or I can do it directly from the application. I can also generate charts, dynamically, from the application, as well. So those charts are up. I can forward those on, share those with other colleagues, etc., as part of the process. Now, if I want to take an action on this, I can go directly from here into one of the models that's set up, and I can understand how to now trigger activities across the end-to-end business. So going from the inventory visibility page to understanding what are the recommendations? I can see that the system has now recommended some transfers, I can review those, I know based on what I just saw that was there, and I can action this to take this process forward and drive the results that I'm looking for from the inventory perspective. 

 

EJ Tavella 0:37:40.1: 

As you saw, applications are really well thought through, we understand the process, there's details around each of the pages and we spend a lot of time thinking about how the user experience is going to be, right? So as a user, what are they looking for? What's your role? How do you need to run your process? How do you do it in an exception-based way, so that you can minimize the amount of time you have to spend in Anaplan. We love you there, you can be there all the time, but we don't necessarily want to make you spend time looking for things. So building applications to make that efficient is key. So here's our portfolio. When it came into the year this is what we started with, we talked about our focus initially and we focused on really the four domains, which were financial planning, supply chain planning, workforce planning and territory quota planning/revenue performance management, right? These are foundational applications, we call these the core applications. This is what we sold 300 of last year, right? On top of that, we've been rolling out new applications and I want to talk about what those are today. So within finance and IT, we've launched five new applications. Subscription revenue planning, project cost planning, consensus margin planning, software spend optimization and profitability analysis. These are all tools that connect into our baseline integrated financial planning solutions. They're all connected, and it allows you to extend out what you're doing within the financial planning space. So let's look at this, right?  

 

EJ Tavella 0:39:03.6: 

For profitability analysis, we have the ability now to understand what's truly driving profit. Pricing with confidence and reduce the overall costs in your business. When you talk about supply chain I get excited. I did come in, I am a supply chain guy at heart, but we've got a lot of stuff in supply chain. So we focused on a couple of different areas. We focused on things that were connected to supply chain, trade promotion planning, this is a number one ask across our consumer businesses. Anybody that's doing trade, trade promotion planning connected into demand planning is critical. Also we're getting into now procurement planning. So looking at spend analysis and forecasting. This is actually applicable across all industries, where we need to understand what is your short, medium and long-term spend look like? What are the critical components or raw materials you need to buy? How is that going to impact your overall budget? So it's procurement planning, it ties directly into financial planning, and back down. Then you'll see the light purple here, retail planning is a wildly fast-growing industry for us. We also find that retailers truly understand the impact of integrated business planning, right? So they truly are looking for the ability to make decisions across their business from financial planning, merch planning, assortment planning, allocation and replenishment planning. So we've launched specific applications in retail today. We will be launching more industry-specific applications in the future, so don't worry, they're coming. 

 

EJ Tavella 0:40:25.4: 

For here, allocations planning has now launched and assortment planning is now launched. These also take advantage of the Syrup investment, so they have the ability to drive true intelligence into the allocation and replenishment process. Making sure you've got the right product, at the right time, at the right store. These are driving literally hundreds of millions of dollars of value for our customers. They're able to optimize where their inventory is, which means they both sell more and they have less stockouts, right? They don't have excess. So it's a combination of all those pieces that drive huge value. So let's talk a little bit about it. Spend analysis is one. Again, this is targeted towards the procurement team and it connects into inventory planning and supply planning, but it also connects into financial planning. It really helps you to optimize and understand what your spend will be, what's your performance for your suppliers are. Dynamically drive decisions around procurement. The second one is assortment planning and you'll see, again, user experience is very, very sexy. We're bringing in the ability for you to be able to manage and understand, what is your assortment? How do I optimize my assortment over time? How many shirts should I have in this assortment? Should I have five or should I have fifteen? Manage the key inputs in that, that are going to drive decisions for the business. The assortment is what's driving the overall retail business and actually we're seeing some applicability for this also in a lot of CPG customers or other customers that have assortment challenges, as well. 

 

EJ Tavella 0:41:49.0: 

Okay, let's get into workforce planning. Workforce planning is a super fast-growing line of business for us. It's a perfect connected planning solution and it is an area where there's a ton of value, right? There's not a lot of tools out there that let you plan your people, right? Understand the cost and impact of people, understand how do you dynamically shift those resources, either within the sales organization or across the end-to-end business? So now we've expanded on top of our operational workforce planning with tools like Project Resource Planning. If you've got lots of projects, how do I make sure I've got a balance of resources to deliver those projects? If we cancel projects or add projects, what does that mean to our staffing? As well as call center planning. So let's look at what that looks like. This is call center planning. This is an interesting one, it's a combination of workforce planning and capacity planning. So capacity planning across the overall business, based on how it's growing, how do I manage my capacity across the end-to-end business and how do I model that to make sure I've got the right insights around what the staff needs to fulfil that demand? Then the last area, clearly not the last, is for sales and marketing, we call it Revenue Growth Management. We've got a suite of tools here now for this. A suite of tools to allow us to do everything from territory quota planning to sales forecasting, to go-to-market capacity planning, to segmentation and scoring around the models. 

 

EJ Tavella 0:43:06.2: 

We've now recently added in sales forecasting. So I'm taking all that information and I'm generating my sales forecast, I can connect that into demand planning and other tools, to be able to make more intelligent decisions and truly have a unified decision-making cycle. Understanding the impact of the pipeline on the business, being confident about your forecast calls and making sure you're hitting your revenue plans. So there's a lot here, right? There's a lot of applications, they're all available to see. Now, all of these things we've talked about, if you guys walk around the room today, you go to the events, there's sections for each of these functions. So there's a line of business meeting sessions, multiple, for each of these. Retail, supply chain, workforce planning, sales planning, finance, etc. Go to those sessions, you'll be able to see these applications in action. If you were here yesterday you probably saw our rockstar solutions team demoing these applications for you. [Applause]. They did a great job. I heard tons of good feedback. So they're there and if you want individualized sessions, there's people around that can do that for you too. So jump in and look at them, I think you'll be wowed when you actually see them in action. 

 

EJ Tavella 0:44:09.6: 

So we're not stopping there. I'll end it with this. This is our current roadmap, and it is still growing, but we are expanding out now across these same lines of businesses and we're getting into more industry-specific functions, as well. We're doing specialty lending, we're doing purchase order management, we're doing strategic workforce planning. We're getting into capital expense planning. We're doing things like incentive comp within the sales organization and we're building out additional functionality around trade spend and other functions, as well. So we're very excited about this, again, we're working with partners to help us expand this out faster over time. I will also say our customers are being engaged in these processes. So if you're already an application customer, you may already be working with us, you may be involved in some of our future-looking roadmaps. If you're a prospect, let's talk about it. We really want feedback on our customers on where we should be investing, what's going to drive the most impact. Both expanding our current application portfolio, so adding new features and functions to existing applications, as well as driving net new applications that will drive business value. So with that, thank you very much. [Applause]. 

 

Unknown Speaker 0:45:17.7: 

Please welcome to the stage, senior vice president of global solution consulting, Joe Horsey. 

 

Joe Horsey 0:45:28.0: 

We're in it, we're in stage three of this Broadway play, right? It's a long keynote, so thank you for hanging in there with us. So I'm Joe Horsey, I lead our global solution consulting organization. Thus the very random clap from the corner over here when EJ shouted out my team. They're an amazing team, if you haven't got a chance to talk to them they're out in the hallways doing a lot of the sessions that a lot of you attended yesterday. So really exciting. So let's jump into this and I'm assuming - okay, wow, we're ahead of schedule, this is great. So my session is around AI. We left it for the last part of the section, to keep you here for the whole three hours. Listen, as I go out and I start talking to customers, partners, everybody's asking these questions. We're all hearing it and whether it's in an interaction with us about our technology, whether it's with another vendor, or whether it's within your team and your executive leaders, right? They're asking these questions. Hey, ChatGPT, Claude, if your solution looks like that, why can't I use those to solve these business problems? If AI can generate anything, why can't I just vibe code Anaplan? Seems pretty logical and I'm sure there's a lot of executives that are pushing down these edicts of, hey, go experiment, go try this. So this is a real conversation we're having right now. If everyone has AI, where's the advantage? If you think about it, AI is this race to get this competitive advantage, to not miss out on the next big thing. So why do we feel Anaplan's AI strategy is differentiated in additive to all of this noise that's going on in the marketplace?  

 

Joe Horsey 0:47:09.0: 

So let's break it down to a couple of things. One is business context, that AI can act on. I thought the video, the storm video, which, by the way, is amazing production, is the perfect example of what business context looks like. The decisions that were being made by our analysts were connecting the supply chain team's decision to the finance team's decisions to the HR team's decisions. All interconnected and they knew the implications of making one decision or another. Think about trying to solve that problem, if everybody was using their own general-purpose AI that didn't have that business context, understand the data and how all that data was interconnected. You'd get an answer, it might be probably correct, but it doesn't understand the implications of some of the decisions downstream or upstream. So that's business context and I think it's a big differentiator for us in the marketplace with our AI. Anaplan is real-time calculation engine. At the end of the day, we can calculate across massive datasets, do it in real time. So we've been doing that for a long time. Do it in a reliable - and I'll even change the word reliable to accurate. We need to be precise, we can't be directionally correct. So the fact that we have this calculation engine at the heart of Anaplan allows us to then not only leverage the value of these probabilistic LLMs, to get scale, to get the value out of what they're bringing to your business process. Also to ensure that you have confidence that it's not a hallucination. 

 

Joe Horsey 0:48:43.7: 

You understand the math behind there. No one wants to get into linear algebra, right? We want to be accurate and we need data lineage to be able to do that. So massive calculation, precision is what I'll call out as a differentiator for us there. We can give you exactly why we decided to tell you that this was the right scenario to execute, to mitigate a margin profit - or a margin issue or to drive a new run of some inventory where you have an inventory shortfall. We're already highly integrated into your business processes. All of you are using Anaplan across finance, workforce planning, supply chain, right? Sales and marketing. So we're already connected, we're already bringing workflows to that decision making platform today. So you don't have to tell it the context, you don't have to feed it the data, right? You can activate AI to accelerate that decision making process across your lines of business. I think the other thing too that gets lost a little bit is every time we're making a calculation, or creating a scenario, that's new data in your environment. New proprietary data for your business that no one else has. That's a competitive advantage. So I think that's another thing that I'd just like to call out, that the more you can activate the enterprise-grade decision making that we can do in Anaplan with AI, it's just going to accelerate that proprietary data and your competitive advantage in the market.  

 

Joe Horsey 0:50:12.7: 

So let's break down AI at the core of our platform, and I'm going to start at the bottom with a foundation. Right, core technology, and it was interesting, we're trying to - if you've ever heard our chief product officer, or if you saw Charlie Gottdiener, our CEO's CNBC interview, we like to talk about linear algebra. Foundationally Anaplan was built on linear algebra and vectors and matrixes and calculations. You put that into Claude, Claude comes back and says, 'Well, linear algebra is the steel if LLMs were a building,' right? So foundationally, we're built on the same kind of core technology. So it's very symbiotic in the way that you use them together, right? So calculation engine is really, really able to take advantage of what these LLMs are bringing to bear. Our traditional AI, so we've been doing time series machine learning AI forecasting for a long time, right? We've had that embedded with our Forecaster technology, which has been to market now for over six months, I think, and that's an evolution of what we've been doing for over ten years. So being able to forecast and drive optimization algorithms has been at the core of our platform. So we've done that. To be able to help all of you answer demand forecast questions, right? Which five products are going to be in demand in the northeast, right? Then, more importantly, understanding where you have a deviation or variance from that forecast and then execute scenarios to mitigate that deviation. Whether it's a supply chain disruption, a storm that knocks off a bunch of containers of tires, etc., right? 

 

Joe Horsey 0:51:50.1: 

Then now what everybody's talking about is this generative AI, right? Really it's about providing role-based agents to allow you to activate, analyze, build models at the pace of AI. That your business is demanding, that your leadership is demanding of you, and, quite frankly, what the market is demanding of us, right? So being able to have natural language AI agents throughout our platform, to allow us to not only build models but, as EJ was showing, interact with the data to generate insights. There's a lot of data, these massive datasets can be quintillions I think was the number that EJ talked about. Imagine trying to process that. Data's going to continue to grow, as processes become digitized, you're going to have to use AI to make sense of all the data that's in the platform. We're going to show both CoModeler here in a second in a live demo. So let's talk about two anchor agents that we launched about three or four weeks ago, at our New York event. Our Analyst, right? That are role based. So you have a finance analyst, a supply chain analyst, and then we've introduced the ability to create your own analysts with our Agent Studio. So whether you deploy an application, one of the 30-plus applications that we're bringing to market, that will be configured within Analyst. Or you want to configure and deploy an analyst against your models that you've already created. We'll allow you to do that, right? So we'll show a really deep dive demo on that with one of our solution consultants here in a second. 

 

Joe Horsey 0:53:16.5: 

Then the other one, CoModeler. Who went to the CoModeler lab yesterday? Show of hands. All right, I like it, hopefully it was a good experience. Yes, okay. So CoModeler, another agent that we brought to market targeted at the COE. At the model builder. How do we accelerate what you do when you're trying to prototype a new model? Business user comes to you with a request, I've got an idea for a model. Great, let's bring up our agent and let's just prototype it, let's talk about it, extend your models, extend the use cases in the application and then optimize those models, as well. So we'll walk through some of that, as well, right now. Actually, let's do it. So what I want to do is I want to bring up one of our amazing senior solution consultants, Gloria DiNardo, to the stage, to help me walk through some demos. [Applause]. Gloria's been at Anaplan for almost four years, probably over four years now, supporting some of our largest, more complex technology customers here on the West Coast. Based in San Diego, I'm very jealous, [unclear words 0:54:22.1] San Diego. So thank you for joining me, Gloria. Awesome. So why don't we jump in? We talked about the analyst first, why don't we walk through what the analyst can do in the context of workforce and building and hiring AI talent? 

 

Gloria DiNardo 0:54:36.0: 

Yes, so coming into our organizational homepage, let's say I'm an organizational leader, I can see my key KPIs, I can see my strategic initiative. So on this case we are working on an AI initiative to build out a new applied intelligence team. Typically, if I wanted more information on this I would have to reach out to someone on my team, I might have to stitch together reports, look through my emails. We're going to go ahead and take advantage of our workforce analyst. So in this case I'm trying to understand where we are at with hiring for this team, so I'm going to go ahead and ask Analyst, what are the biggest hiring bottlenecks for this initiative? Analyst is going to go ahead and repeat that question back to me, to ensure that it's understanding it correctly, and right away we can see we have some gaps within our software engineering team. If I want to find that little bit more information about this, I can go ahead and visualize it as a chart, drawing my eye right away as to where we need to take action. Now, the great part about Analyst is you have full traceability into any of these responses. So I can see the source for it, I can validate exactly where this answer is coming from. When we think about our financials with workforce data, it's really important that we're able to understand where that information came from and that it's correct. 

 

Gloria DiNardo 0:55:58.7: 

Analyst is also prompting me to look at an additional page for more insights. So let's take a look at our hiring insights page. Here I can see my software engineers, so this is the area where we saw that gap. I can see that those positions have been open for over 200 days. I can seed any additional details, things like our location. So maybe Boston isn't the best for hiring, so we know we can find additional talent in the Bay Area. So let's go ahead and update this to San Francisco. I'll also update our compensation package to reflect that and then we can go ahead and notify our recruiter. Now, the amazing part about Anaplan is you're not working in silos. So when I make a change like I just did upstream, everyone else downstream is going to see the impact of that. So I'm not having to go to different reports, I'm not having to update San Francisco on ten different pages, it's all done for me automatically within Anaplan. Now, switching lenses, I am going to come in as a workforce analyst. Right away I can see I have some major gaps here. I have some - my forecast, I'm over budget, I can see I have an overall skills attainment of only 45 per cent. So while I know this is a large skills gap, I don't know exactly what's that made up of. So I'm going to go ahead and ask Analyst, what are the significant skill gaps on my team? 

 

Gloria DiNardo 0:57:29.4: 

I can see that prompt engineering has only a ten per cent attainment, so that something we need to focus on and ensure that we get it corrected quickly, so we can continue on with this project. I'll go ahead and ask Analyst how long it would take to train for prompt engineering. So instead of relying on a volatile hiring market, we can go ahead with that build-first approach. I see it takes about two months, but now I want to understand, what are the cost implications of this? So just like we would have a normal conversation with someone on our team, with an analyst, we're able to do that right with the Anaplan platform without ever having to leave this page. I can see my total cost and I would find some budgetary savings here, but I'm going to try one last thing which is finding out if there are any good internal candidates with a strong skills match. So Analyst is searching through my skills database of all of my employees and I can see both Emily and Jason came back as a match. I'll go ahead and utilize our org charting capabilities to look them up, that way we can dive a little deeper into what their skillsets are. So I can see Emily is a 71 per cent match, I can see her skill details, and this seems like a good fit, so we're going to ahead and scenario plan to move here and Jason over to my new applied intelligence team. 

 

Gloria DiNardo 0:58:53.2: 

As I move them over, we will see our skills attainment update based off of those skills that they are bringing to this team. Right away I can see that now I have a 74 per cent attainment, but we're still over budget here, so we can go ahead and close out our open software engineering positions, to get us back into the green. Now, I want to emphasize what we just saw. We saw, first of all, figuring out what sort of problem we had, then we utilized Analyst to come up with multiple solutions to that problem, and we were able to solve that with just within minutes. 

 

Joe Horsey 0:59:28.0: 

So we're not just searching against models, we're not just doing a Google search against the models, you're actually driving a conversation. The Analyst is augmenting your decision making and you're able to run through scenarios. Do I hire somebody externally? Do I put them in Boston? What's it cost me to put them in San Francisco? Do I have skills on my team? So it's amazing that you walked through all that. If you all think about the different teams you'd have to engage to get the same insights and same answers, right? So I love that flow. Well, how about we switch gears, right? We've got a bunch of COE team members, we've got a lot of model builders out here. Let's switch gears and run through some CoModeler scenarios of how we could use CoModeler in the context of building out a new model, first, I think. 

 

Gloria DiNardo 1:00:08.4: 

Yes, so why don't we take a look at it for building out a supply chain prototype? So if we have any model builders here today, which I imagine we do, you all know that this is not always so straightforward, right? There's a lot of back and forth, there's a lot of iterations, this can take days of manual work. So we're going to go ahead and utilize CoModeler to help us get a fast start here. Now, we can approach this in one of two ways. The first way being through a CSV file. Think about this as more of a prescriptive approach where we're building logic in, we're adding hierarchies, we're adding any information we want CoModeler to build with into this CSV file. We'll package that up, enter it into CoModeler and let it do the heavy lifting from there. The second way we can go about this is just entering in a natural language prompt. So I'm going to go ahead and send a little bit of direction to CoModeler, saying we want a scalable model that connects things like our supply contracts, our finished goods, all of this across multiple regions, multiple products. Right away we can see CoModeler has come back with an implementation plan. This is very important, because it means you are in full control over the process, so CoModeler is not going to take any action without us being there to either approve it, to ask for further refinements, or completely reject it and start from scratch again. 

 

Gloria DiNardo 1:01:30.5: 

In this case I like our plan, so I'm going to go ahead and approve that. Now as CoModeler is thinking through this, we can see the logic within my panel of exactly how it's going about this, and then within the model itself we can see those lists being built out. So I can see different attributes, so things like my top level, my parent hierarchy, any specific details or properties that we want to include, CoModeler is going to take care of that for us. So now that step one has been completed, we'll proceed onto steps two and three. So steps two and three, and if anybody is not familiar with the backend of an Anaplan model, it involves building out modules and line items. So a line item rolls up to your model and our line items are what hold our formulas, our calculations, which drives the logic throughout the entire model. So CoModeler is doing all of that for us, it's doing things like formatting. So it knows, intuitively, if we're looking at a region, to pull from our list hierarchies. It knows how to optimize our formulas, really getting best practices from places like the Planual, Anapedia, [unclear word 1:02:44.8], all of that being built into here.  

 

Joe Horsey 1:02:48.5: 

So this is amazing. So you very quickly now prototyped a model that can continue to go through refinement, go through your typical lifecycle management of models' deployment into production, and then also change the way that you can interact with your business partners. As you define this, there could be a lot of feedback with the business users. Still need that master Anaplanner knowledge to understand and refine this, for sure. So that was a new prototype. We all have hundreds and thousands of models already out in the wild, how can we use CoModeler to document those models so we understand what was built five years ago, if I'm a new COE member? 

 

Gloria DiNardo 1:03:22.3: 

Yes, good question. So let's take a look at our integrated financial planning model and we're going to go ahead and ask CoModeler to describe our business processes in this model and synthesize it into an overview as if I am a first-time user. So let's think about that. As you're a new user coming into Anaplan, what do you care about? The business summary, what are the processes, what's the logic behind this? So CoModeler is going to be able to give us a nice summary of exactly what this model is doing, but existing model builders can also take advantage of this. So let's say, for example, I have been working on this model for years, I can ask very specific, pointed questions to help better understand different parts of the process better, as well. 

 

Joe Horsey 1:04:08.6: 

When we did our beta program for CoModeler in Q4, a lot of our beta customers, this was the first thing they wanted to do. They didn't want to go build models, they wanted to do all the - there's like, I need to know what's in these models, I don't know why we built them the way they are, why the modules are there, where the data's coming from. So super, super valuable. So we've documented, so we've built a prototype, we've documented models. Invariably we're going to want to extend models, right? You always get the business requirements, the business plans are always changing, how can we use CoModeler to extend a model, to incorporate new functionality? 

 

Gloria DiNardo 1:04:39.0: 

Yes, so I mean we've heard a lot about applications today. So the beaty of Anaplan is you have these out-of-the-box applications, but then there's also the flexibility to extend and build on top of them just like you have within your core Anaplan. Now, we're taking that to the next level by having CoModeler actually do that extending for us. So I'm going to go ahead and ask CoModeler, within our integrated financial planning application, to build out a more granular functionality within the OPEX planning, right? So I'm happy with the IFP application, but I know my team likes to plan at the PO level. Now, CoModeler is going to come back, again, with that implementation plan, but this time it's asked a few clarifying questions. I'll go ahead and give it those answers and it's going to continue to build out from there and extend on my application.  

 

Joe Horsey 1:05:27.2: 

So very quickly, you're now extending functionality there, building upon what you've built. So the last - look, we've been doing some really complicated stuff. You've built models, thanks for explaining to me what a model is. Modules and items, I appreciate that. Just something simple, can we just do like a bulk edit? A bulk change that's tactical, but it's just time consuming. Do an easy one. 

 

Gloria DiNardo 1:05:48.8: 

Yes. So this might sound simple, right? We're just going to change a couple of naming conventions on our list, how hard can it be? For my model builders out there, we know that this is not always quite so straightforward, right? If something breaks and we have downstream logic that changes, you're spending a lot of time trying to clean that up and fix it. So it's really important that we get this right and we get it right the first time, so it doesn’t impact any of our planning cycles. So let's go ahead and utilize CoModeler to make that change. I'm going to ask CoModeler to add a list property called customer headquarters to all lists that start with HLC. So this is a very specific ask, but I'm asking it to make it a bulk change throughout the entire model. If you noticed, I've also asked for best practices, as well as possible alternatives. So I know that CoModeler has all of those best practice approaches, it can optimize models for me. So I want to understand if there are any gaps that I'm not thinking of. CoModeler has recommended an alternative here, which is to create a dedicated module instead of doing this through our list items, like I would have originally done. I'll go ahead and approve that alternative and it's going to start to build this out. Now, when we think about time savings in our days as model builders, and where we can find efficiencies, this is really where we find value out of CoModeler, right? We're not spending our time being reactive, having to fix our models, we get to spend time being more proactive, doing more value add, more strategic work, just like we're seeing here. 

 

Joe Horsey 1:07:21.4: 

Yes, now this is awesome. So CoModeler's not just automating tasks and automating builds, it's augmenting you as a model builder. So giving you efficiency, productivity and recommendations. Hey, are you sure you want to do it that way? That's maybe how you did it three years ago, but there's new best practice that Anaplan's put out there. Maybe you should approach it this way because it's a cleaner, more efficient way to build the model. So really, really amazing. So Gloria, listen, thank you so much for coming up, walking us through Analyst, showing us how we can drive decisions from insights, showing all these amazing use cases for CoModeler. Can we get a hand for Gloria? She did amazing. [Applause]. 

 

Gloria DiNardo 1:07:54.8: 

Thanks Joe. 

 

Joe Horsey 1:07:55.3: 

Yes, thanks so much, Gloria. That's just a taste. That's just a taste of what we're doing. So let me drill down a little bit. Those were two agents that we've brought to market. I'm going to show you a little bit more about how our architecture, our agentic Anaplan architecture is evolving, and how we're going to start bringing more agents to bear that not only allow you to augment your role, but also to give you insights, to help you drive decision making. As well as to work on your behalf autonomously. We'll share some of that real quick. So if you think about the AI-driven planning platform, what we saw here was that natural language interface, right? Any AI platform, and you all have AI platforms in your environment, right? So I'll drive home a connection point about our extensibility here. So natural language is almost like the new UI. I don't want to go to a dashboard if I don't have to, if I can just interact with a natural language interface and get the answers, the insights, and take action, why do I need to go to the dashboard? Maybe you need to go there to refine the data or to get more details about the data, and we can do that as Gloria showed, very quickly. So we're going to provide this natural language interface, these will be role based, they'll have domain context out of the box. Then we know you will have other agents you want to create, so we'll providing Agent Studio to allow you to create your own agents and put those on top of your existing estates. 

 

Joe Horsey 1:09:18.6: 

At the heart of this is an orchestration of agents. So our strategy is very grounded in industry standard protocols, we're leveraging heavily the agent-to-agent protocol, which was an industry standard that was brought forwards by Anthropic, to allow interconnectivity between agents, right? So we saw the analyst agent, we saw the CoModeler agent, think about a scenario agent. Think about a data anomaly agent. Think about an ADO agent, right? Just for data transformation. So you're going to see an orchestration of these agents that start to build out over time throughout the rest of this year and into next year. More importantly, those agents are going to be built on industry standards. So eventually maybe in that supply chain storm scenario, where a decision was made on sourcing a different tire, that supply agent needs to talk to the SAP agent. Because the SAP agent is actually the one cutting the PO or interfacing with the provider or the supplier. That is where that extension - and so the agent-to-agent protocol we feel really futureproofs a lot of our investments and your investments with AI to make sure that we're not overriding the investments you've already made. Also enabling end-to-end agentic frameworks and automation. We talk about bring your own LLM, that's another big topic we get. Hey, I've already invested in Gemini, I've already invested in - we're a Claude shop. Well, you might be a Claude shop today, right? If they start changing their economics or if another LLM comes out that we don't even know about yet, it's more efficient, more cost effective or more accurate. We want to have that flexibility for all of you. 

 

Joe Horsey 1:11:08.6: 

So our platform will be flexible and extensible to be able to bring the LLMs that bring the best cost, are the most accurate and most efficient. Then all of this interoperability in this platform is going to activate against the models, the data, the logic that's built into the foundation of the Anaplan platform. You'll see the MCP, the model context protocol. We have a very different approach, our strategy is really geared on A to A, so we get a lot of questions. 'Hey, when are you going to expose your MCP? When can I get access to your MCP?' Well, that's great, we could do that, that's like giving you a chainsaw and saying, 'Hey, go cut the grass.' MCPs are very special built and either you overload them with tools or you make them very special built and special fit for purpose. So what we fundamentally believe is that if we can encapsulate this MCP specific domain tooling into an agentic framework and use the agent-to-agent protocol as the conduit to those tools, we can drive better governance, we can mitigate the risk that potentially opening these all up brings to the table. Allows for a much more scalable architecture. So fundamentally we have some thoughts on that architecture and we're happy to drill down on more details later today, or follow up with our teams. It's a really important conversation, because the market is moving so quickly and you need to understand our thoughts and our strategy there. So this platform's going to accelerate agentic applications, it's going to enable this open agent integration. It's not a one or the other, it's not us or them. It's a combination, those architectural conversations are really deep. They need to be integrated. I just want to close with just summarizing, again, why we think we're differentiated in this world of AI and why the SaaS-pocalypse is not going to particularly wipe us off the map. Which is a scary, scary proposition, being as I work in Anaplan. 

 

Joe Horsey 1:13:10.4: 

So domain knowledge, with context, so that business context is critical, the real-time calculation engine, the accuracy imperative is critical. We can't just be directionally correct when we give you a financial decision or supply chain decision for you to make with your business, or a hiring decision, right? So we need to be accurate, we need to be reliable. Obviously highly integrated. It's not about just activating AI on your data, it's about fundamentally how are you changing your business processes with AI? Are you driving that efficiency? Because, eventually, someone's going to ask you, 'What's the ROI of the AI investment I made?' I'm sure we're all tracking that, right? We have a business case that we can come back to our finance folks when they ask us why we're paying so much for AI. So that highly-integrated piece of it, being able to government it and measure it I think is really, really critical. Especially as you drive more automation into the business. Then, again, it's a competitive differentiator for all of you. You're generating new, proprietary data for your business that's going to give you a competitive advantage. So anyways, this was just a taste. Thank you so much for your time, what I'd like to do right now is welcome Ed Majors, from Deloitte. He's got an amazing customer panel coming up with our team from Synopsys. [Applause]. 

 

Ed Majors 1:14:28.7: 

So first of all, it's great to be with you guys. As I'm reflecting on this presentation, 45 per cent. So 45 per cent of my career I've spent working with Anaplan to deliver some of the most remarkable solutions in the world. For the largest companies, the largest integrated, the largest connected thing ever. 45 per cent, it's just phenomenal. 45 per cent I've worked with Anaplan to scale out our Deloitte practice into 35 countries now. I was the first one to hire anybody in India and train them on Anaplan. It's been a remarkable journey. We're now operating in 35 countries around the globe, which is just phenomenal, and we've opened up new markets with Anaplan in the Middle East, Africa, to name others. We're getting very big in the public sector now with Anaplan. The expansion has been phenomenal and I think 45 per cent of my career is at least ten times more fun than the 55 per cent, because I've been working with Anaplan as a partner. They're a remarkable partner. So the core for Anaplan's success, and Joe just said some great stuff around what the up and coming - the AI, EJ always does an amazing job. The core to Anaplan's success has always been connected planning, that's the DNA of Anaplan. The DNA of Anaplan, and that's the spark, and that's why we were able to do such amazing things with Anaplan since I started working with them in 2012, at this point.  

 

Ed Majors 1:16:08.1: 

That's where their growth was. Now, everything that Joe just described and EJ described is just putting more velocity onto what's currently out there. It allows us to innovate quicker and I always say it's something that used to take six months is going to take six weeks. That's what I challenge my teams. Six months is now six weeks. Everything has to be in one ecosystem and talk to each other. So I am very pleased to have an amazing client that I've been working with for now, I think, close to three-and-a-half years, that has been on this remarkable connected planning journey, leveraging all the greatest innovations of Anaplan. Synopsys. So if you guys don't know Synopsys, we can thank them for designing the chips that are in our iPhones. So think about the software that designs all the chips, tests all the chips, every major chip maker is using Synopsys and it's been a remarkable journey with them as they've gone through the hyper growth since I've been working with them the past three-and-a-half - four years. So if we can introduce them on stage, we have Raj, that is our transformation officer. Christine, that represents the overall program management. Anish, that represents marketing. Heather, that represents the sales. I'm going to screw it up again. Urvashi, yes, that represents our finance group. So please, Raj, you're the boss, you can sit wherever you want. Okay, so just an absolutely remarkable group that we've gotten together. 

 

Ed Majors 1:17:54.9: 

It's hard enough to get you all on a Zoom call together, let alone be on a panel. So this is absolutely phenomenal. So Raj, we started this journey a number of years ago and you - whatever you were thinking about probably four years ago versus what you're thinking about now has evolved dramatically. You understood the need to implement the connected planning, the connection between different functions. Do you want to talk about your strategy for - quite frankly, you're the best salesman I've ever seen, because you have to go reach out to each of these different leaders, get them on board and deliver this. Maybe could you chat about that a little bit? 

 

Raj Budhrani 1:18:32.4: 

Not sure if I can - it's a compliment or so but this was not on the list as a question. It's a tricky question. So I think three years ago, when we started this journey, we created the holistic plan, like where we want to be in the next three to four years. It was mainly around the typical Anaplan models, like how we can start. Where should we start? So for example, we started with the expense planning and some of the other corporate planning models, but then the goal was how can we bring in the existing ICM and the TNQ models into the connected planning roadmap. The idea was also how we can bring in the non-financial business unit models together, and make it more driver based, rather than just forecasting historically. So we started with the basic idea of how we can really create a connected planning models, across the company. Not just in finance, or not just in GTM, but across the company. Today we are at a very different level. So we are still working on the connected planning models, but the big difference is we are leveraging a lot of machine learning nowadays, which has accelerated our journey and it is giving completely different results. So that's the difference between where we started three years ago versus now. 

 

Ed Majors 1:20:02.6: 

We weren't thinking about the whole concept of AI three years ago. Just trying to get the stuff put together. The first level was to get the foundation, get everything laid in and get the data right. Then the speed of innovation I think has been dramatic. 

 

Raj Budhrani 1:20:17.7: 

Yes. That's the most critical one. I think data was a big problem, obviously, everyone knows that. So the data was all in Excel sheets and I'll let the team talk more, because they are the ones that have done the work, I have not. So they will talk more about how things were in Excel sheets, sitting in different places, having different versions, and then how do we even bring these things together? So as a part of the transformation journey we were not only working on Anaplan, but also working on the processes and the core systems, like transactional systems, in the back. So that was critical for us, like how we can bring the data together at the end of the day on the data lake and then start [unclear word 1:20:59.9] on the Anaplan. So it was a lot of work and I don't think we should underestimate that effort. 

 

Ed Majors 1:21:06.3: 

Yes, data's never the fun part. Christine, Raj comes up with the next best idea every week, as far as [unclear word 1:21:15.7] wants to go into next. You've been tasked with trying to make this whole thing happen and tie these different pieces together. The way I see it, there's like three different types of use cases. One, you've got the core, obviously for finance we need to do P&L, from sales we need to do the comp. Then, around that, you've got the edge, which I think, Raj, you've been very successful in pushing out into the organization and those are the ones that are really delivering the value. So expanding beyond finance into the different functions. Then, Christine, you've got that third one which is something's very important for an organization, but it's got a limited life, right? Maybe we need to - instead of doing stuff in Excel with the 1000 spreadsheets, create something that is going to serve its purpose for six to eight months and then it's no longer needed. Maybe you want to talk about how you were able to pull this together, because it's been remarkable, especially with Raj's next great idea every week. 

 

Christin Chon 1:22:14.1: 

Yes, well, first I have to manage Raj on a weekly basis. I think it's really about driving effective change management, right? Taking people on this journey that going from Excel spreadsheets, like they're both mentioning, to moving to a very foreign model. People don't want to change, right? They're very comfortable. So it's really instilling into them the why. Why are we on the journey? What is it in for you? Then taking them on the path of connected planning and what it can provide, not just for your function, but across all the different functions that we have. So we have really, really focused on this drum beat of change management across all the functions, so that people are bought into the idea. We work with them really closely throughout the development, but even post development I think we work really closely. I probably talk to Urvashi every week, daily sometimes at quarter end, just to make sure that she's supported. Even though a lot of her models are already live, I would say, so yes. 

 

Ed Majors 1:23:17.1: 

That's great. You make it seem so simple! 

 

Christin Chon 1:23:20.4: 

No, it's… 

 

Ed Majors 1:23:22.0: 

I tell you, she is a bulldog, so she does not let any test or anything fall behind. So thank you for that. Let's talk about marketing a little bit. I know we're very passionate about marketing and sometimes it's not something that generally is top of mind, but I think there's tremendous value of what you'll be able to realize. 

 

Anish Jariwala 1:23:42.2: 

No, absolutely. So first and foremost, when you think about marketing, the biggest billion-dollar question CMOs are asking into this world is what's truly working in marketing? Probably COs are asking that question to CMOs, as well. The answer to that question, in my opinion, goes through Anaplan. Every dollar that we invest in marketing programs, how many dollars are we getting in pipeline and [unclear words 1:24:16.3] revenue? That measurement helps showing the value marketing can provide. So that's the billion-dollar question many organizations, especially CMOs, are asking these days. I still recall when I came to Synopsys a year-and-a-half ago, in the first week I asked the team, 'Can I get a list of all the marketing technology or marketing apps that we are using?' As I received the list, I was going line by line and I started asking a question, can Anaplan do it? If Anaplan can do it, we can move into Anaplan and we don't have to use the individual SaaS application, which is siloed and the data is stored within those apps and we cannot create value. So we are on the journey of moving as many things as we can into Anaplan and the biggest impact Anaplan provides is this honeycomb of use cases, right? If you think about the honeycomb, in a nutshell, it's a network effect. My usage of Anaplan is only as good as my colleagues using Anaplan, right?  

 

Anish Jariwala 1:25:32.3: 

So we have been on that journey for the last year-and-a-half and, on a side note, whenever we move things into Anaplan, we save costs, and Raj is paying for Anaplan, I don't have to pay for it. So I save money, right? So we have been on the journey, we have two compelling use cases that we have gone live. One is about event planning and rationalization, and in that case we do so many events across the globe. Not only in marketing, but various teams, engineering, product management, so on and so forth. So we said, 'You know what? Let's bring everything into Anaplan, so everyone can see events across different teams.' For each and every event depending on where they are there are various workflows for approvals that we have put in Anaplan, as well. There's only finite dollar, so all the leaders can duke it out who gets what in one single, centralized place, and that is Anaplan. The second use case we have which is - we are using now, is around budgeting and planning and forecasting, right? We have been on that journey, as well, and I think we have made rapid progress in that area, as well. So all in all, marketing can have their own honeycomb and we are on that journey. 

 

Ed Majors 1:26:59.6: 

So every CFO needs to know what the ROI on all those big marketing dollars are, and you're giving them some level of appreciation. 

 

Anish Jariwala 1:27:07.6: 

Absolutely, because if you think about it, any SaaS companies you go to and the number one question they ask, 'Hey, I gave you $200 for marketing program, out of those $200 for marketing program, what kind of pipeline did you generate and what is the impact on [unclear word 1:27:27.4] revenue?' So in the end, we can ask for even more budget from marketing standpoint. 

 

Ed Majors 1:27:33.6: 

Yes. 

 

Raj Budhrani 1:27:34.6: 

I'm more interested in the billion dollars he mentioned. 

 

Ed Majors 1:27:41.1: 

Urvashi. So I'll have to apologize about this question, but I am an accountant we heart. The problem we have in finance is we're always seen as the scorekeeper, right? The person that got invited to the party that nobody understands why they're there. As opposed to a business partner. As we think through obviously there's certain things that finance has to do, but then there's also how do you expand beyond the scorekeeper into a true business partner? Were you delivering the value for the organization? Truly that, or helping them make the right decision? I think you've got all sorts of experience with different use cases on how you've expanded beyond the scorekeeper. 

 

Urvashi Desai 1:28:30.0: 

Yes. So finance in Synopsys has always strived to be more than a scorekeeper, but because of the way our finance operations were set up, planning tools, data was spread across multiple platforms. A lot of it was in Excel spreadsheets, we have to work with facilities and HR and the business. Everything was coming in, in drips and pieces, and we spent a lot of time and effort just consolidating that to get our base off of which we could actually provide business and strategy inputs, right? Ever since we've moved into this Anaplan connected universe and we have a couple of our key components and models now in Anaplan, things are really different. So the first piece that was critical and that moved into Anaplan was our expense planning model. We're talking about an expanse plan that covers over 20,000 employees and their costs all rolling up and then also… 

 

Ed Majors 1:29:32.2: 

We'll say cost is like everything - employees are everything for your costs. That's such a critical thing to understand. 

 

Urvashi Desai 1:29:38.4: 

Right, like 70 per cent of our spend is driven by employee costs, right? So getting that right and getting it efficient, because we run forecasting cycles, monthly, quarterly and annually. So getting that set up in Anaplan was huge, right? So now finance delivers its monthly, quarterly and annual spend plans via the expense planning model in Anaplan, right? So there's that piece and then we also implemented a position planning model in Anaplan and this ensures that we're aligned on all hiring, right? So planned hiring, actual hiring, everything happens through this model. It's connected to our expense planning model, so whatever budgeted spend we had for hiring, the two models are connected and we make sure there's no planned hiring happening outside of budgeting. The other piece here, which we have connected with HR, and HR's organizational and structural plans are also in this model. So now we have this one location in Anaplan where all hiring plans and approvals go through and it's connected with all the budgeting requirements and the organizational structural requirements. Then we have a third critical piece of this connected finance universe in Anaplan is our location strategy model. This model is built in partnership with facilities and we capture all our location costs and also our current and future site capacity in here. So the three models are connected and we make sure that our hiring plans are, 1) within budget, and 2) they're also aligned with the company's global footprint strategy, right? 

 

Urvashi Desai 1:31:25.0: 

So today all these three pieces sit within Anaplan. A finance leader can go in, just a few mouse clicks switch across models, make sure everything's aligned, within budget, we are aligned with HR strategy, we're aligned with our global footprint. Now they have this really solid base to go talk to the business and advise them on what to do next, right? The other piece, we need AI and ML, right? So we have Precision View, which is your Deloitte predictive analytics product, that's been customized for us. It's linked to our data source and it sits on top of all these models, right? That it uses a lot of machine learning models to - uses our source data and it provides us with key drivers and also scenario planning opportunities, which enable us to fine tune our forecasts more. We're specifically using this with headcount forecasting, so our analysts do a headcount forecast with expense planning tool, but now Precision View is using the same inputs and coming out with machine learning outputs of where it believes our headcount forecast will be, right? So we're getting that additional insight to make sure we fine tune our headcount forecasts better. 

 

Ed Majors 1:32:46.7: 

So it's absolutely remarkable, given that so much of your cost base, 70 per cent being employee related. I remember back when you were talking about the location model, you can't hire 5000 people in India if you don't have an office space for them, right?! So how do you do the build out? How do you look at the average time they're going to be in place to make sure you have the facility? Then how do you then tie your smart - because you guys have been growing exponentially for many years. How do you make sure that when you hire people they're in the right location? You have everything set up for them? 

 

Urvashi Desai 1:33:20.7: 

That's where connectivity comes in with the model, right? So the location strategy model clearly states where capacity is, where capacity will be in two years for every site, right? So expense planning is connected and we can see between the two. Don't open 200 reps in India without making sure in the location model that we actually have that site capacity, or will have it in the next two years. So it's really easy to crosscheck between the two models and make sure you're hiring in the right place.  

 

Ed Majors 1:33:50.1: 

Yes. That was a huge part of your strategic - one of the things we talked about three years ago was we can't just hire wherever, we need to be very specific about it and having Anaplan allowing you to realize this strategic initiative has been, I'm sure, very valuable for your organization. 

 

Urvashi Desai 1:34:07.3: 

Right. Because even if an analyst goes in and puts in some plan hiring and expense planning, we - to actually hire against that, the hiring managers have to go into the Anaplan model for position planning, right? Right there, if you're trying to get a position approved for hiring in a location that's not aligned with our location strategy model, you get flagged. So it's immediately a red flag that, okay, you need to reconsider the location here. 

 

Ed Majors 1:34:37.0: 

That's great, that's amazing. Heather, so I won't say your life is easy, before you had a minor… 

 

Heather Russell 1:34:48.9: 

A little acquisition. 

 

Ed Majors 1:34:48.9: 

A little acquisition of 35 billion or 38 billion, I forget what it was. 

 

Heather Russell 1:34:53.5: 

Yes, easy-peasy, right? 

 

Ed Majors 1:34:54.2: 

She had a very distinctive go to market model within Synopsys and then Ansys just - again, it's all complementary, but the way that they sell their [?sole 1:35:06.4] structure, etc., is drastically different. Now you're tasked with pulling all this together and making it make sense. 

 

Heather Russell 1:35:16.6: 

Yes. So it has been a journey, for sure. Sales Comp was the first to adopt Anaplan at Synopsys. So we had the luxury of having time on our side. We have been using Anaplan for seven years now, and have built out numerous models, apps, connections with Salesforce, other Anaplan instances, SAP. So feeling fairly confident in what we've been able to achieve. When news of potential Ansys acquisition came about, we felt like we were going to be able to create a unified plan for the team. So we needed - once we closed on that acquisition we needed to quickly pivot from planning our future state to creating our future state. Bringing together two mature, but distinct go to market models with different sales roles, different compensation philosophies and enhanced planning became critical to deliver on that. We had our first focus was on creating clarity for sales. Wanting them to know exactly how we wanted them to work together as a unified sales organization, not just what we wanted them to sell. So we adopted a prime model and whichever side had the larger book of business would then be the account owner. That meant that we were going to continue having Ansys on legacy compensation plans, but also holding Synopsys quota, and vice versa. So needing to be able to share information between the sides. Because, of course, it wasn't easy. 

 

Heather Russell 1:37:16.9: 

We had 40,000 some sites that needed to be mapped, their customers were not necessarily the same naming convention as our customers. They were on ACB plans and we are on TCB plans, so very different. Differing fiscal years. They didn't have the same fiscal year as us, so needing to harmonize the fiscal years so that we could then work on connecting our data. Where ADO came to the rescue, my team had spent time getting certified in ADO, so that as soon as we had closed on the acquisition, we were able to quickly connect to their Snowflake instance and have data start flowing through. 

 

Ed Majors 1:38:05.5: 

That was with ADO? 

 

Heather Russell 1:38:07.3: 

Yes. 

 

Ed Majors 1:38:07.8: 

Okay, Adam will be happy, I'm sure he's listening to this somewhere there! 

 

Heather Russell 1:38:11.9: 

I'm trying to give you some plugs! Yes, so that was a huge win for us in being able to create those connections, because they were on a different ICM model, that we could not connect to. So ensuring that our compensation plans remained aligned with evolving business priorities, and supporting leadership on multiple levels, with minimizing disruptions for sales, was our main focus. I think we've done a pretty good job so far. 

 

Ed Majors 1:38:46.2: 

You've done an amazing job. That was [unclear words 1:38:48.4], and then obviously the full integration of everything else with the two organizations, tracking synergies. These are some of the use cases that Christine, you [unclear words 1:38:57.8] very quickly. We know that we don't have to worry about synergies forever, but you spun that up very quickly to solve the specific business problem and maybe make another big acquisition. I think [unclear words 1:39:08.0] tired after the last one, so maybe a smaller one. 

 

Heather Russell 1:39:11.6: 

Give us just a little more time on that. 

 

Ed Majors 1:39:14.2: 

Okay, Raj, I know you're passionate about - Raj shows up to every call we have talking about AI. He's a sponsor, he is driving the organization. There's a tremendous amount of foundation that's been built and your ability to innovative with AI, and I know a lot of the stuff is very confidential, but maybe just the broader discussion around your strategy, leveraging AI and what your vision is. 

 

Raj Budhrani 1:39:44.0: 

Yes, I think with Anaplan we had an interesting journey and this is where Christine and I debate a lot. There are different layers when we think about the overall AI, especially in relation to Anaplan, at least from our perspective. So the first is we have to keep these things separate. Anaplan models and modelling, we should leverage it to the most. That is where AI doesn't belong there, you still have to run the scenario modelling, you still have to create those models. The underlying data is very, very important. So without that data structure and clarity around how we are establishing the master data, how we are able to agile, in the case of Ansys, if we have different sets of accounts, different mapping has to be done. How can we quickly deliver that in the back? That is the must. There is no shift happening, at least for us, as in architecture. Where we are seeing that AI is playing a role is especially when we layer the decision making on the top of it, which is it's not just the scenario modelling, but the scenarios actually providing us better insights and running many complex calculations, both from the internal and external side. Like what are the internal factors that are influencing this decision? I'll take a few examples, like when Urvashi's talking about the facilities. 

 

Raj Budhrani 1:41:11.7: 

So rather than checking, okay, what is the facility available? The model should immediately check for us and share where the facility or space is available, or do I need to build a plan for the facilities and push it to the facilities? When it comes to the marketing side of it, how we integrate the GTM and the GMC together in terms of looking into the overall leads and campaigns, so that we can generate more value. So when we run these multiple factors, internal and external, that is where machine learning is playing a big role for us. It is providing the insights which we are - which generally a human does. Checking these different places and identifying the different parameters which will impact our decision making. So we are seeing a huge difference or growth in terms of - it's a shift in mindset, how we are looking into the same information, but insights are already there and you are making decisions much faster.  

 

Ed Majors 1:42:15.4: 

Based on hopefully better information. 

 

Raj Budhrani 1:42:17.7: 

Yes, much better information. 

 

Ed Majors 1:42:18.9: 

Having the machine challenge the biases that we all have as people, because we always think it's going to be the best-case scenario. 

 

Raj Budhrani 1:42:24.1: 

Correct. 

 

Ed Majors 1:42:24.9: 

It doesn't matter if we're in sales, it doesn't matter if we're in marketing, and that doesn’t work, so having a machine help us there. Okay guys. So the concept of connected planning, as far as number of companies that have realized it, the numbers are zero and it doesn't necessarily make sense to connect every function across the organization. What you guys have displayed here today has been tremendous as far as where it made sense to make that connection and where you get the value. So the one plus one equals ten, from an ROI standpoint. So figuring out where to make those connections, making the connections very quickly, you guys are much further down the road and we started with not a whole lot. You came out of the gate [unclear words 1:43:12.3], so if we had something, and then we've started with finance and that was a challenge to do the basics. Now we're at the point where we can innovate very quickly. I guess maybe start with Christine, let's talk about some lessons learnt. 

 

Christin Chon 1:43:27.6: 

Don't go on this journey! So I think, feeding off of what everyone's saying, the first that I probably share is ongoing alignment across the business. Our business has changed since I have joined to where we are today, this acquisition's completely changed us, we have new leaders. So bringing everyone along the journey into what our strategy is for the company and making sure that continues to align with our vision for what we do for Anaplan. I think that's really key, so we continue to be able to maintain the models without the unfortunate event of you working so hard on a model and then it not being adopted or used by anyone. Then, secondly, I would say, gosh, there's so many lessons learnt! I would say focus on impact. We get a lot of requests for new models, people want to automate things, right? There's so many opportunities and then, like Anish said, Raj is providing the budget, so people are jumping. 

 

Ed Majors 1:44:39.4: 

As long as Raj has money, we're good. 

 

Christin Chon 1:44:41.1: 

Yes, but we try to focus on impact, always. What is the impact that is going to drive - how does this then connect to other things that we can further do down the line by connecting or providing this data within Anaplan? 

 

Ed Majors 1:44:58.2: 

That's perfect. Hopefully she left you something to talk about. That's quite a bit. 

 

Urvashi Desai 1:45:03.5: 

I guess my biggest learning is the model is only as good as the data you're feeding it, right? So we definitely struggled with this at Synopsys, where users complained that the model was broken or wasn't working, but it was because our underlying data feed had an issue, right? So the key to successfully implementing Anaplan models and the connectivity is to first make sure your data platform is sound. It's working right, you have - also your data hub in Anaplan should have validations built in, because without that it doesn't matter how fast or how enhanced your model is, it's not going to work the way you need it to work. So I would say for me, in terms of trying to push this out into finance operations, the struggle has always been data. 

 

Ed Majors 1:45:56.9: 

Yes, absolutely.  

 

Anish Jariwala 1:45:59.9: 

Yes, so from marketing perspective, I think if you think about Anaplan for planning, I think it's a misnomer. Anaplan can do so much, right? That is the power of Anaplan, we can measure end-to-end marketing performance in Anaplan and that is what we are excited about as we go through the journey. You can connect the dots across the organization, as you can see on the stage, as well. I did not know how sales is using, I am also learning on the stage today. There is endless potential and we are really hungry and I think we have just started on Anaplan. 

 

Ed Majors 1:46:42.4: 

Yes, and just to go back to the data, I always tell my clients, 'Look, Anaplan's the easy thing.' I can build models, I don't build myself, but my team can build models day in, day out. Now with CoModeler maybe we can build ten models a day. The data's always the challenge, since everything around it, Christine, that you deals with, with the change, the data, and then the art of the possible, what makes sense. So these are great points. 

 

Christin Chon 1:47:07.5: 

Two big things. For us, I would say, spending the time to reimagine the possibility. Don't just recreate what you already had, so we spent about 18 months planning our transition to Anaplan, and it was time well spent. The other big takeaway that I would recommendation is as one of the model builders on my team continuously reminds me, minimum viable product. 

 

Ed Majors 1:47:38.7: 

That's minimum [unclear words 1:47:39.0] product. 

 

Christin Chon 1:47:40.4: 

Okay, we'll go with that, as well. When you first start you have big dreams of building out all sorts of things, build for the functionality that you need right now and then quickly plan out those enhancements in your builds. It will make you much more successful and quicker to be able to deploy. 

 

Ed Majors 1:48:04.1: 

That's perfect. Raj, take us home here. You've got a couple of lessons learnt, right? 

 

Raj Budhrani 1:48:12.5: 

I think that all these - I think the big takeaway, Christine highlighted this very clearly, at the end of the day it is a partnership. How we work together is very, very important. Every function participates, without the business function participating and driving this, we would not be successful. No matter how much money we spend, how much effort we put, it's not going to work. There's a lot of learning we - our priorities shifted over the last three years multiple, multiple times. I think there were good learnings in terms of bringing in the large acquisition on the Anaplan very quickly. I think the team did an amazing job, we were expecting it will take six, eight, twelve months to really bring the - such a big acquisition under Anaplan, so that we can plan together. It has been very, very smooth and really short period of time. I think in two-three months we were able to bring the Anaplan, whether it is synergy calculation, whether it is the headcount modelling, whether it is the leads and campaigns, whether it is on the ICM side of it. So it took just a few months to bring in, but all kinds of us - because of the underlying data architecture that we had. So you rightly said model building is very easy, but if you don't have the right architecture it's not going to work. 

 

Ed Majors 1:49:35.7: 

That's perfect, yes. Okay guys, we're excited. We're excited just at the speed of innovation and, quite frankly, what I mentioned up front, things that had taken six months should be six weeks. I won't say six days, I think that's a bit ambitious, because we do have the data to deal with. I think what you guys have shown and the foundation you've built is truly just remarkable. Having this variety and spectrum across different functional leaders on this stage, talking about how they're able to collaborate with the use of Anaplan is tremendous. So thank you for coming. 

 

Raj Budhrani 1:50:11.7: 

Thank you so much. [Applause].