AI Academy

追加: 2026-05-31

[00:00:01]All right, good morning, good afternoon, good evening, everyone.

[00:00:05]Um so, we will get started shortly. I know we have folks still joining. Um but thank you for attending um our latest session on the AI Academy series. Uh I know we haven't had one since Knowledge, so welcome back.

[00:00:20]Hopefully everyone had a great Knowledge, whether you were able to attend in person or attend online. And hopefully you got a lot of great information from that.

[00:00:29]Um so, for this session, uh we will be speaking to AI Control Tower, which was um one of the main uh topics at Knowledge. Uh and we're really going to be digging into um your AI asset inventory.

[00:00:43]Um so, what is the concept of managed versus unmanaged assets? So, we'll get into that in just a little bit regarding the agenda.

[00:00:50]I'm going to go ahead and start off with some housekeeping. Um so, you know, you've seen this slide if you've seen this series before. Um especially you saw it at Knowledge. So, we will maybe be speaking to some roadmap uh in this presentation, whether it's in the presentation itself or via answering questions. Um so, please just don't make any um purchasing decisions based on forward-looking statements or roadmap.

[00:01:12]Uh and, you know, make those decisions based on what's currently in the product today.

[00:01:19]So, a few more housekeeping items. So, we do have the Q&A button at the bottom of your screen to ask questions.

[00:01:25]Um we'll try to answer them throughout the session and we'll leave some space at the end. Um you'll also see at the bottom of your screen a reactions button. So, you know, give us a a quick reaction to know that you found it. Um you know, if not, uh we do have the chat available as well. So, uh you know, if you do have, you know, things that aren't Q&A, but you do want to chat with other attendees, you do have that chat option available.

[00:01:51]Um we will be recording this presentation uh and sharing it on YouTube and linking it on the community after the event. Um and after this session you will be prompted to fill out a short survey about the session. Um your feedback is super valuable to us.

[00:02:07]We do take it into account and try to improve our academy um as far as it runs and the topics that you want to know about as well. So if you do have time, please fill out that survey.

[00:02:20]All right. Um so a few more housekeeping items. Um so today's session, like I mentioned, will be available on demand.

[00:02:28]Um you use that Q&A console and that chat for any discussions. Attendees will be muted during the session. Um this just helps us keep everything together.

[00:02:37]So we will have someone moderating and looking at that Q&A. Uh and if we need to answer that question out loud or ask the presenter out loud, the moderator will do that. Um you feel free to use that reactions tool to engage with the speakers. Uh and like I mentioned that that survey at the end as well.

[00:02:57]So our presenter today, um so you do have me. Um I'm Ashley Snyder. I'm an outbound product manager here at ServiceNow. Been with ServiceNow for uh 4 years. My main areas um of focus are uh AI gateway, so governance of MCP servers, um as well as the MCP server console and data handling and security.

[00:03:18]And I'll let Mike uh introduce himself.

[00:03:20]>> Hey everybody. Mike Beltrancho. Pleasure to be here. Uh so I'm on the same team as Ashley. I uh support AI control tower as well and I focus on AI discovery and the AI asset inventory.

[00:03:29]Um been with ServiceNow for way too long. Um and but happy to happy to engage and chat with y'all.

[00:03:38]>> All right. Um I do see that we have some chats about the audio. I would say try and leave the audio the computer audio and rejoin it or you may have to um rejoin the Zoom, but it looks like uh the audio is working for some folks. So just some quick troubleshooting you're not hearing audio or maybe hearing duplicate audio.

[00:03:57]And one more thing before we get started. So, you may have noticed that we've rebranded. Um so, live on ServiceNow is now ServiceNow Exchange.

[00:04:05]Um so, ServiceNow Exchange is where we bring together events like this one um as well as in-person and virtual roundtables, different product academies. So, if you're looking more for specific like ITSM or HR academies, they are out there um underneath ServiceNow Exchange uh as well as on-demand content to help you learn, connect, and get more [clears throat] out of ServiceNow. Um so, you you do have a QR code if you want to scan that.

[00:04:29]Or if you go up to the community, there is like a tile that says ServiceNow Exchange. All that information will be there as well.

[00:04:39]All right. So, like I mentioned, today's session um is focused on one specific concept within AI Control Tower, which is the difference between managed and unmanaged AI assets. Um it does sound like a small toggle, but it's actually the mechanism that determines um governance, you know, life cycle controls, and compliance tracking um across AI assets in your environment.

[00:05:00]So, we will cover each of these topics uh within the allotted time. Like I mentioned, feel free to ask Q&A along the way, and we'll try to answer the question in line with the topic. And we'll also have some um time for Q&A at the end. With that, I'm just going to take a moment and hand over control to Mike, and he's going to go ahead and get started with our first topic.

[00:05:21]>> Absolutely.

[00:05:22]Yeah, perfect.

[00:05:27]So, I'm going to set the stage here real quick. I think I think all of us feel this in our lives today, which is is AI is here to stay, and it's it's everywhere. Um I know our jobs have have become largely centered around AI, and a lot of a lot of the work we're doing is with AI and about AI. So, we're all feeling that, and it's one of those things that we we are seeing play out in the industry as well, where um we're seeing the call for 1.3 billion agents in production by 2028, just a ton of experimentation going on as far as AI and what and what's what's happening.

[00:06:01]But all of that is leading to attention in organizations, right? You've got one side of the house saying go faster, let's get this done. You've got you've got people who want all of this capability available now. And on the other side of the house you have security, you have governance, you have other teams saying, "Whoa, slow down.

[00:06:17]This is new. This is novel. We we don't we don't have a handle on this yet." And so, that's really where we saw the rise of these centers of excellence for AI, right? Trying to get these disparate teams to come together and get some kind of governance over what's going on with AI in their organizations.

[00:06:34]And that's really where we focused with AI Control Tower.

[00:06:38]So, I'm going to let this build out real quick. So, AI Control Tower is really what we've what ServiceNow has put together to help drive this this governance challenge for AI in organizations. And really what we what we're looking at is across as as we start to look across the the estate of what what kind of AI you've got, we need to be able to discover all that, right? So, that's where we start. We we look at discovery and we we pull in all the AI assets that you have in your organization. Once you have those, you're able to govern them, secure them, observe how they're behaving, and also measure them, right?

[00:07:12]Are you getting the the value out of the things that you've rolled out?

[00:07:16]All of that information is flowing into the CMDB, and that's how we're tracking a lot of these. Now, that's and that's where we're really going to kind of focus today as giving you this kind of setup so you have the landscape of what AI Control Tower is and what it does.

[00:07:29]But we're really going to kind of drill in now to the AI asset inventory, the CMDB, and what what these managed versus unmanaged assets are.

[00:07:39]So, here's a real quick look at um what part of what we announced at at knowledge, which was the all new AI Control Tower interface. So, if you've seen AI Control Tower demos before, this may look very new to you. Um but one of the things that you'll notice is that right in the screenshot, we've got the managed AI assets versus the unmanaged AI assets. And this was really driven by feedback from customers, right? As we started to work with customers to pull all this information into the CMDB via discovery and and populate the populate the the various CI classes, it became very clear that um there was too much, right? There's just too much stuff flowing into um AI Control Tower and uh it was information that that you didn't really need all the time, right? There were only certain things that you wanted to manage or have visibility to. And that's really what we've addressed with the managed versus unmanaged AI assets.

[00:08:33]So, I'm going to focus in on discovery just for a second and kind of talk to you about we'll we'll continue back into um the managed versus unmanaged assets, which is Discovery is is how we're pulling in a lot of these different things. We've built out uh host of different connectors. Uh most of them are API-based today. Uh so, what you'll see is we started with the big hyperscalers, uh you know, Azure, GCP, um and AWS and and pulling in those assets and then we've moved into SaaS applications and other spaces as well.

[00:09:01]And what you'll see is that we'll continue to release various connectors uh as quickly as we can. We're we're we are on a monthly release cadence for uh AI Control Tower and we're constantly rolling out new connectors um as we as we can build them. Um this is based on our Service Graph connector technology, so it is a a a well a well um well-known and used technology within the ServiceNow platform.

[00:09:24]And uh it's one of those things that you'll see us continue just to add new capabilities as we go and new connectors.

[00:09:32]So, this is again the new new look of what the AI Control Tower looks like. I wanted to show you the screenshot just to kind of call out all that information that we've we've discovered is flowing into this AI asset inventory. On the right side, I've already toggled the action there to show that you can move these to manage move them to unmanaged and various other kinds capabilities, right? So that you can see what what's going on here. And we're going to dive into what that really means, right?

[00:10:02]So what you end up with is is one inventory of the two states. And so when we start to talk about manage, what that really means is that those those are actively governed, right? So those are the things that you have flowing into your AI asset inventory that you want to do the risk assessments on. You're going to get value measurement on all those kinds of things.

[00:10:21]Um when it comes to how we are um pricing AI control tower, it will the the managed versus unmanaged assets will also come into play there, right? And we'll kind of talk a little bit about that, but what I would encourage you to do is uh engage with your account team on the specifics. Uh these things do change. It really depends on where you are in your contract and what you've done.

[00:10:45]So on the unmanaged side, this is going to give you visibility only. It's going to have all of those things still in the CMDB. So they're there. You can see them, but they are excluded from subscription counts, right? So you can think of this like you you can discover all the things, you can see all the things, but you are going to choose what you actually want to have visibility into and apply all of the kind of process that we talked about in those different pillars uh for the AI control tower setup.

[00:11:11]So what does that look like? Um let's kind of build this out real quick.

[00:11:19]This is going to give you a view of what what we're really kind of counting in there. And it's it's going to be your AI systems, your AI models, your data sets, or your MCP servers. Those are the things that we're actually going to be counting as far as managed assets.

[00:11:33]So, as you choose to to manage those AI assets, one of these four will will trigger that as a count, right? And then what you can see is how these different things will play out in the like grouping them together and pulling them all in.

[00:11:55]All right. And so, Ashley, I think this is where we're going to transition into the what it actually does.

[00:12:01]>> Yeah. Um while I'm sharing, we do have some questions. Um it looks like two of them are about licensing and maybe we can just answer both of those out [clears throat] loud as far as how attendees can get resources on licensing. So, um we have one. Um So, you know, I'll just read it out. So, is this part of the Discovery license or AI Control Tower license? And then to piggyback off that, the second question was about the licensing model. So, just want to see where attendees can get this information.

[00:12:33]>> Awesome. Yeah. So, the for the for the for the Discovery license, it's actually part of the AI Control Tower licensing, right? So, the Discovery connectors are um they're included with with any AI Control Tower license, which you get when you purchase any any AI from ServiceNow essentially.

[00:12:50]Um so, that that's how you'll you'll get access to the Discovery connectors.

[00:12:53]They're not they're not licensed separately. Um that will feed the information into AI Control Tower and then the actual licensing is predicated on what you choose to manage or have unmanaged.

[00:13:04]>> Cool. And then we have another question.

[00:13:07]So, can AI Control Tower discover an AI agent running inside of AWS EKS ecosystem?

[00:13:17]>> So, today we support Bedrock as is the AWS environment that we can discover in.

[00:13:23]We are working towards being able to discover deeper in other environments.

[00:13:27]That's really where you'll see us go in the second half of the year is more kind of network-based discovery and proxy-based discovery to be able to find things that aren't in the I'll just call them call it the happy path of like where those agents are defined, right?

[00:13:39]So, if something's being built in a more kind of um loose fashion, if you will, right?

[00:13:47]They're bringing together different technologies, build that agent, and it just happens to be the AWS environment.

[00:13:52]Those are things that we're working towards being able to discover. Today, we're really kind of focused on um the the big kind of happy path use cases of this is Bedrock. It's been offered to build agents on the AWS ecosystem, that kind of thing.

[00:14:04]>> Cool.

[00:14:05]All right. Thank you. Um so, again, if you have more questions, please put them in the Q&A panel, and we'll answer them after the section or in line. Um but, I do want to switch gears and and just talk about, you know, what uh managing an AI asset unlocks. So, you know, why does it matter uh matter? And what do you actually get when you flag an AI asset as managed? Um so, you can think of it as turning on four different layers of oversight all at once when you decide to manage that asset. Um so, the first being um you know, our our governance or our risk and compliance.

[00:14:41]So, the moment that you mark something as managed, um controls that are aligned to built-in frameworks such as the NIST um AI uh risk management framework or the EU AI Act um or also activated. So, if your uh organization falls under um you know, purview of those compliance frameworks or such as the EU AI Act or you are, you know, adhering to the risk uh AI risk management framework, we have all that built in, so you don't have to build um those controls and how to track those from scratch. Um our teams have done that, and your AI assets um automatically map to those frameworks.

[00:15:21]So, again, you're not doing that manually. It's something that we've built in as part of AI control tower.

[00:15:27]Um second, you know, we have the life cycle of that asset. So, all of the the intakes, um the the approvals or the playbooks that it has to go through. So, all of the tracking, um we have different stages such as assess, um build and test, and deployment. And there are various tasks and sign-offs that need to be done throughout that life cycle in order to deploy that uh asset to production. And you will have um auditors, especially if you have assets that fall underneath different um regulations looking at that playbook and who kind of sign off on these things and how do they get to production. So, that is part of it. Um as far as uh other things as far as change control uh and retirement, that is built in. So, you have that kind of full auditability of what happens to that asset when either you manually entered it or we discovered it all the way throughout its life cycles and and everything that was kind of signed off on and everyone who looked at it um throughout that entire life cycle. So, that gets orchestrated from end to end.

[00:16:32]Um so, this really takes that out of tracking it in spreadsheets or emails or kind of just disparate systems to understand that full life cycle of that AI asset.

[00:16:42]Uh the third is monitoring. Um so, you get continuous visibility into access, um posture, and guardrails. Um so, for example, let's say an agent goes dormant um or has um more privileges than it actually needs. So, an agent would be considered uh an AI asset itself. Uh you can track that within the security and privacy tab and kind of understand what your risk posture is when you look at those certain assets. You understand, do I need to remove some roles from that agent? Or do I have AI systems that have um you know, live credential access to maybe MCP servers or different tools and they're just kind of sitting there. Um do I need to remediate that? So, you you get that when you manage those assets.

[00:17:27]Uh and the fourth is value tracking. So, um your return on investment, so your productivity hours, you know, how many uh human hours would have a certain task taken that was done um with AI. You can see that within the value tab. You can see your top-performing systems, so the ones that you would want to scale. You can see different departments and locations. And really understand kind of in your organization, um who's using AI? Maybe who isn't? Um are you getting productivity out of it?

[00:17:59]Uh and what AI systems are you going to want to maybe retire or maybe do a whole other kind of adoption campaign for? And what are your top-performing ones that you want to make sure that you um keep an eye on and want to scale out to to further uh organizations or departments within your organization.

[00:18:17]So, these four pillars are what uh managed assets unlock.

[00:18:21]Um none of them are available on assets that are left as unmanaged. Um so, that that is intentional. And I'll kind of explain why uh in this next slide here.

[00:18:33]So, um unmanaged, it's not it's not a problem to fix. I know Michael alluded to this um earlier in his slide. It it is really a design choice, right? Like as we mentioned, we had uh in our first iteration, there were a lot of assets coming in. There was a lot coming your way. Um just like you know, AI, we have proof of concepts, we have pilots, we have things that may or may not go into production.

[00:18:53]Uh and you need a way to understand, you know, how you should govern those. You have assets that are going to be more important than other assets um in your organization. So, you know, this is a design choice as far as unmanaged. Um the idea here is visibility without overhead. So, every AI asset that is discovered, it does land in your inventory regardless of state. You can see it, um but if it doesn't need active governance yet, you don't have to run it through that full life cycle workflow.

[00:19:22]Um there are four good reasons to kind of leave something unmanaged. Um first, of course, inventory completeness. Um even assets that you're not actively governing are visible and you have a record that they exist. So, you don't have that shadow AI out there. You know that there is a record in there and you can see it inventory. And if you wanted to see manage it in the future, you can.

[00:19:44]Um second is lower friction for early stage AI. So, if your team, again, is running that pilot or that sandbox, um project or an experiment experiment, they don't need that full life cycle um on day one, right? That's kind of a little bit of overkill there. So, unmanaged just keeps them on the radar, keeps them in the inventory um without the overhead.

[00:20:04]Um then we have cost control. So, unmanaged assets are excluded from subscription counts, so you're not paying for governance on things that don't need it yet. Um and like we just mentioned in previous Q&A for questions on life cycle and subscription, just reach out to your account team. They can go through all that uh with you.

[00:20:22]Uh and fourth, um you know, unmanaged assets are still audit ready. So, this is a really um important point, right?

[00:20:29]You do have the discovery metadata and the history um of that asset and it's retained regardless of state. So, if you do need to do an audit on those unmanaged assets, they're all within that inventory for you and you don't have to, you know, go scramble to find them as soon as an audit comes up. Um so, one thing worth calling out that, you know, unmanaged is not a way to tag something as proof of concept and move on. Um even proof of concepts will need oversight. Um so, you can use the life cycle state to track proof of concept versus production uh and not the manage flag itself. Um so, you'll see that in the inventory um, and you know, just want to make that important point about this proof of concept um, type of AI projects.

[00:21:17]All right. Um, so how do you actually make the call on managed versus unmanaged? So this slide kind of gives you a simple decision guide. Um, feel free to screenshot it uh, and you know, we'll have more information on the community about this. Um, you want to flag something as managed when at least one of these four conditions is true. So you have a risk owner that's been assigned, so that tells us that, you know, this asset has to undergo some type of compliance. Um, and life cycle or audit controls are required. Um, if this asset has access to enterprise data, so is it using MCP servers? Is it using um, different kind of API connectors to enterprise data? That's going to be something that you're going to want to govern. Um, and you know, runtime guardrails will need to be uh, in place for those type of assets because it is consuming your enterprise data. Um, if it's in production uh, where a quality regression would have real impact, you're going to want to mark it as managed. Or if you need to uh, track the business value uh, and tie it back to the specific asset, this is a also a good case for managing it.

[00:22:26]Uh, as far as unmanaged uh, you can leave it as unmanaged um, when these certain conditions apply. So you know, if it's in a sandbox uh, or proof of concept like we just mentioned before or something still in evaluation, um, you can leave it unmanaged. Like I mentioned, you can always move it to manage if it starts to meet those other four criteria. Um, this also could be a retired asset that you're just keeping for audit reference. So it's visible, but it's not actively govern >> [clears throat] >> or actively governed. Um, so the rule of thumb is simple. Um, promote to manage um, the moment an asset starts being trusted with real work uh, or is even connecting to enterprise data.

[00:23:06]And when in doubt, you know, ask yourself if if someone relying on this in production, if so, it should be managed. And I would also even cascade that down to sub-production, especially if it's connecting to enterprise data. Maybe you have some developer tools that are using MCP that are going out to your SharePoint or other enterprise systems.

[00:23:25]Even if it's in sub-prod, that's a good case for it to be managed as well.

[00:23:33]All right. So, let's talk about who actually makes these decisions and what happens when they do. So, our AI steward is the authorized person who controls the managed life cycle. The AI steward is our main kind of persona and role for AI control tower. So, an AI steward can move an asset between managed and unmanaged. That is the only role that can do so. So, this is, you know, controlled. It's not a free-for-all setting that anyone can toggle. There is that certain person in your organization that should be designated to do so, and there is a role in ServiceNow to allow them to do so.

[00:24:10]A few things to understand about how this works in practice. When you promote when you promote an an asset to manage, so let's say that you have an experiment that's ready for production or let's say you start connecting that enterprise data, a fresh governance review begins. So, the full set of the four pillars that I mentioned previously activate.

[00:24:31]And here's the important part, the prior history doesn't disappear. So, everything that happened while the asset was unmanaged, such as that discovery metadata or any prior assessment, all that information stays accessible and associated to that asset. The reverse is also true. So, if you demote an asset to unmanaged, maybe it's because it's been retired or it has lower priority. Any active workflow task are canceled, but they're not deleted so we still retain that history just like most things in ServiceNow. We still retain that history for you so that record stays intact and you can understand all the history on that asset. Um this is what makes the system audit ready regardless of state changes. So governance history persists across the full life cycle of the asset even if it moves between different states. So the AI steward isn't just flipping a switch or you know marking manage versus unmanaged. They're making deliberate governance decision with a full audit trail behind it.

[00:25:38]All right. So um before we get to some questions just a few things to remember and some actions that you can take if you are using AI control tower and building out your asset inventory. Um on the takeaway side you know we have one inventory with two different states.

[00:25:53]Every AI asset is visible the moment it's discovered or you know manually entered into ServiceNow and you your AI steward can decide which ones to actively govern.

[00:26:04]So this distinction is intentional.

[00:26:07]Managed assets unlock control different risk controls life cycle workflows runtime evaluation and value tracking and these only activate on assets that you explicitly has flagged as managed.

[00:26:21]Unmanaged assets get visibility but not that governance overhead.

[00:26:26]Um the AI steward you know holds the keys to the castle here. They can promote and demote as assets mature or retire and that history persists across every state change so you never lose that audit trail on the record itself.

[00:26:42]Um so some actions that you can do when you leave the academy today is first start identifying your AI stewards if you haven't already in your organization. So who should be the person responsible for deciding which assets are governed or not? Um second, you know, start running discovery on one connector. So, if you have those external assets and you know, you are licensed to do so, uh you can run that connector and start bringing in those external assets. So, even one hyperscaler is enough to start populating your inventory and making the managed to unmanaged decision feel real.

[00:27:19]Uh and third, you know, pick a few candidates to mark as managed. So, you know, three or so candidate assets and practice the decision on a very small set of assets and understand you know, those four pillars that we just talked about and how they work before you go in and commit to scale. So, you know exactly what to know what to expect when you start marking those assets or your AI steward starts marking those assets as managed. It'll surface new questions and edge cases that are much easier to resolve early.

[00:27:50]Um so, with that, I'll go ahead and open it up for some Q&A here. I know we have a few questions. I think Mike's been answering some questions in the background, too. But Mike, were there any that we wanted to really speak to out loud before we close up today?

[00:28:06]>> Uh we had a few questions on discovery.

[00:28:07]I think I think for the most part, uh we've we've tried to address them. If we just want to talk about it in you know, here. Like we've got another one that just came in about are they using the SGC technology and are they different connectors? They are different connectors from our our traditional SGC connectors that you have out there. Um so, they are built for AI use cases. Um so, you'll you'll continue to see them evolve. Um so, they they are all new and they are built to do slightly different things, but they are based on the service graph connector technology.

[00:28:39]Um I think one one of the things I want to clear up is that they are based on the service graph connector technology.

[00:28:44]You don't have have be licensing iTom to use them. I think there is some confusion about about what you need to have in order to do that. AI control tower is largely a a self-contained kind of skew. So, it's one of those things where if you already have access to an AI skew, then you have access AI control tower and that that's what you need to to access most of the capabilities of AI control tower. You don't need a whole lot of other things.

[00:29:08]That's how we've tried to build it. So, there are exceptions to that, but for the most part that is how we've structured it.

[00:29:16]>> I think we have one more that came in and then we have a minute or so um as far as it if part of your standard is to use proof of concept to validate expected benefits to permit the move to production, then wouldn't all POC or proof of concept activities need to be considered for the managed space?

[00:29:40]Mike, do we have some kind of insight on that? I know we kind of spoke to POC a little bit.

[00:29:45]>> I I I don't. I was I was kind of contemplating like how it how it would be structured.

[00:29:51]>> Yeah, I think it really ties back to you know, this really is up to your AI steward. If that is if that is what your organization wants to follow, if you want to manage those proof of concept assets and you know that they're going to move into production, you can, but I that's a good question as far as you know, what are you doing with those proof of concept assets? How far are they going to get?

[00:30:17]You know, that really is up for your AI steward to decide, right? Because if you have some that really never make it past sandbox or experimentation. You would be using some of your subscription to manage those. So, that's a good question and you may want to go a little bit deeper with your account team on that and your solution consultants to kind of dive deeper on what your organization is doing and if you should uh those or not.

[00:30:48]Cool. I think that's all the questions that we have. So, we did pretty good on keeping on time. I do want to thank everybody. I have had this slide up for a little bit regarding some of the questions that we've had, right? We do have our customer success program to help you out to really get hands-on keyboard and dig through this. So, if you do need help as far as building your AI asset inventory and using those connectors, we do have customer success.

[00:31:15]We also have, you know, within that expert services, ServiceNow Impact for that advisory guidance, and of course ServiceNow University. I posted a link to training there. And then our partner ecosystem as well. So, we have a lot of resources for you if you need assistance on AI Control Tower or anything ServiceNow and want that advisory from Impact or if you want more hands-on keyboard help from expert services or partners, we do have that for you.

[00:31:42]And like I mentioned, any kind of licensing subscription questions, please reach out to your ServiceNow account rep. They can help you with that. And they can also help unlock, you know, more personal conversations on AI Control Tower and kind of get those meetings set up for you.

[00:31:56]But thank you everyone for joining today. We do appreciate your time.

[00:32:00]And you know, please join future academies. We have some great topics coming up. Check the community page for future topics as well as links to past topics as well.

[00:32:10]All right. Thank you, everyone.