Is your job architecture keeping up with AI?

[00:00:00] [SPEAKER_00] Join us on a journey where we unravel the latest trends, tackle your burning questions and explore innovative strategies that are shaping the future of compensation, all with a coffee in hand. Hello and welcome to another episode of Comp and Coffee and today's topic is job architecture. It's being talked about a lot more in the context of a changing workplace where we're seeing work shifting quite rapidly.

[00:00:27] [SPEAKER_00] But today we're going to kind of start at the beginning and consider like what do we mean by job architecture? Our guest today is going to bring some of the research that they have been doing on job architecture and then we will move on to talk about the shifting nature of work and how that is impacting job architecture. So we know job architecture has long been the foundation of compensation strategy. It gives us the structure to understand the jobs, establish consistency and make informed decisions about pay.

[00:00:55] [SPEAKER_00] But traditionally it's been something that we build and then we put it in place and leave it largely untouched. And over time they clearly become outdated, increasingly disconnected from the work that people are actually doing. And that is a problem in today's environment. So it's not a surprise that I'm hearing a lot about a renewed interest in job architecture. I'm calling it job architecture 2.0.

[00:01:19] [SPEAKER_00] I think there is some of it is related to pay transparency and requirements, for example, of the EUPTD, where this notion of work value is becoming important. But also I think it's just people trying to understand the value of work as work is shifting. And we've gone back to saying what is the job architecture? What is the structure and how AI is kind of blurring potentially how jobs relate to each other? So that's what we're going to be talking about today.

[00:01:46] [SPEAKER_00] I'm delighted that we've got Sheila Sever from Deloitte coming to join us. It's our first time on Comp and Coffee. Would you like to introduce yourself and share a little bit about the work you do in Total Rewards and Job Architecture?

[00:01:58] [SPEAKER_01] Thanks, Ruth. Glad to be here. I'm Sheila Sever, a senior manager in Deloitte's rewards practice. I spend my days helping organizations build and maintain their job architecture and their compensation programs from job families and leveling to market pricing and pay structures. I've been practicing the art and science of compensation for a long time, and I serve on the faculty for World at Work. And I'll admit it, I am a job architecture nerd. It's the plumbing behind almost every people decision.

[00:02:27] [SPEAKER_01] And when it works, nobody notices. Right now, of course, AI is making people notice because everyone is asking whether that plumbing can keep up.

[00:02:37] [SPEAKER_00] And you also have been doing research, which we're going to dig into as well. Tell us a little bit about the job architecture research that you've been involved with over the last few years.

[00:02:47] [SPEAKER_01] Deloitte has worked with MSight these most recent years on the survey. We've now done about six different editions. We started back in 2017, did another one in 2020, 23. And then it's become what we refer to as an evergreen study since then. People can actually participate anytime. And then we annually produce a report that shares what those findings are. This year, we heard from 1,041 organizations.

[00:03:14] [SPEAKER_01] I'll be able to share some highlights as we go through today's podcast. One caveat, it is a snapshot of what organizations are doing. It's not really a prescription for what every organization should do.

[00:03:27] [SPEAKER_00] But a useful reference point. I'm looking forward to hearing some of the insights from that. Well, as I said, first time on Compom Coffee, we ask all our guests, are they a coffee or a tea drinker? What's their go-to coffee order? So what's your favorite morning beverage or what are you drinking right now?

[00:03:43] [SPEAKER_01] Oh, I will tell you that when it comes to coffee, I drink what my husband calls a warm milkshake. I take two Splenda and eight creams. Real half and half. No fake stuff. This morning, I've got some hot chocolate and I've got a cold iced tea. So when you're in the South, sometimes you have to go cold with the drinks.

[00:04:02] [SPEAKER_00] And I'm saying I have three drinks on the go. I have water. I have a green drink. And I have mint tea. So we're covering all bases today, but no coffee, as you said, Sheila. So, okay, let's get into it. We have a whole variety of people that listen into this podcast. Some of them are experienced comp pros. Some of them are people that are coming to Compom Coffee just to kind of learn about the art and science of compensation. So let's start by defining job architecture because it's a broad term and it can mean different things to different organizations.

[00:04:32] [SPEAKER_00] Do you want to give us the definition of how you would describe job architecture?

[00:04:36] [SPEAKER_01] I think of job architecture as the operating system for how an organization describes the work. The building blocks, of course, are job titles, job families, career tracks, levels, job descriptions, and the pay structures that really connect to them. Among organizations with a formal framework in our survey, titles show up 87% of the time, pay structures and leveling 84%, and functions and families 75%.

[00:05:06] [SPEAKER_01] But the role that it plays really matters more than just a parts list. It gives everybody a common language. So you can compare jobs consistently. You can make pay decisions that you can defend and show people a path forward. When it's shaky, every decision is built on top of, gets harder and harder to explain.

[00:05:29] [SPEAKER_00] And so what would you say good looks like in terms of job architecture? It may vary from organization to organization, but generally, what does good job architecture look like?

[00:05:38] [SPEAKER_01] Well, good looks like three things. It's enterprise-wide, it's governed, and it's current. The encouraging part is 65% of the organizations that we surveyed have an enterprise-wide job architecture, and that's up from 61% in the 2024 study. But where organizations tend to fall short is really the upkeep. Only 22% update their job architecture frequently or continuously. Job descriptions are a good example.

[00:06:07] [SPEAKER_01] 83% of them only occasionally, and 58% are unhappy with how they're managed. A lot of organizations built a great house and then never really scheduled the maintenance. In fact, 21% even admit that they just said it and forget it.

[00:06:25] [SPEAKER_00] We'll come on to talk a bit more. I mean, that was obviously before AI started transforming work, and also AI transformed how we can actually write job descriptions. But maybe we'll come on to talk about that. One of the things I often observe, because obviously I'm talking today from London, based in Europe, is the difference in how the U.S. and Europe approach job architecture. What I see is like in the U.S., it's more based on leveling and maybe leveling to market data.

[00:06:52] [SPEAKER_00] In Europe, we're more focused on potentially formal job evaluation or point factor evaluation. Is that something that you see, and how would you explain the difference between those two approaches?

[00:07:03] [SPEAKER_01] Yeah, good question. With a caveat, about 85% of the organizations in the study are U.S. headquartered. So this answer really comes from my experience and not necessarily just the data. In the U.S., I think the center of gravity is market pricing. Lots of surveys available, lots of data, lots of great software for managing it. So you match the jobs to the survey data. You use a leveling guide to get it consistent.

[00:07:29] [SPEAKER_01] Europe seems to have a stronger tradition of formal points-based job evaluation, meaning structured, criteria-based, maybe points, approaches to job value, often shaped by collective bargaining or works council expectations. But the two really are converging. Pay transparency requirements, including the EU Pay Transparency Directive, push organizations toward objective criteria for comparing their jobs.

[00:07:56] [SPEAKER_01] So more market pricing-led organizations are taking a fresh look at how they document the value of jobs. You know, it's not legal advice, of course, but organizations should check with their council on what applies to them from a regulatory perspective.

[00:08:12] [SPEAKER_00] It's interesting because that's a similar shift that I'm seeing is like definitely more of a growing interest in the U.S. now and with our U.S. customers around a more, as you say, objective criteria-based valuation in order to think about that work of equal value concept. Okay, so you've given us a few hints of the research. Organizations we know approach job architecture and skills in different ways.

[00:08:39] [SPEAKER_00] And obviously, research can help them understand where practices are changing and where challenges remain. You talked about the annual job architecture survey. We will say that we're going to put a summary of that survey in the podcast notes. So you can, she's just going to share that with us so we can include that. But what you were saying is that's an ongoing survey. So if they want to participate in it, then they will also get a free copy of the full research.

[00:09:05] [SPEAKER_00] So maybe we include the link to the survey in the speaker notes as well. But what are the most important findings from the most recent job architecture research that you've been looking at, Sheila?

[00:09:15] [SPEAKER_01] First, job architecture is maturing. It's increasingly treated like a core HR infrastructure, not just a compensation tool. Second, there's a skills readiness gap. Only 31% of organizations link the skills and competencies to their job programs. And just 16% link any kind of skill proficiency to pay. So then a third observation, AI is still early.

[00:09:42] [SPEAKER_01] 65% of organizations rely entirely on traditional methods. And where AI is in use, it's practical. Language tools for job descriptions at 18%. Job analysis, 13%. Market benchmarking, 12%. So there's a lot of conversation about AI and a lot less actual adoption so far. So I think that's a useful reality check.

[00:10:07] [SPEAKER_00] So the skills part is interesting in there. What was the figure? You said 30-something percent are? Yes, 31. So that's, again, what we're generally seeing about skills adoption, you know, as people think about integrating them into jobs and like mug and how they price work as well. So that makes sense. What are organizations telling us about their ability to keep job architecture current as work changes? Are you asking that in the survey?

[00:10:34] [SPEAKER_01] Well, honestly, it's mostly reactive. I think only 22% update their job architecture frequently or continuously. On organization design, 64% only revisit when a major trigger hits, like a new strategy, new leadership, or an acquisition. And just 11% actually review it annually. So that cadence works when work changed slowly.

[00:11:00] [SPEAKER_01] But if AI is changing the tasks faster than your review cycle, then the structure drifts from reality and you end up making pay decisions off of an outdated picture of the work being done.

[00:11:13] [SPEAKER_00] Yeah, and it becomes very difficult to value work as it's shifting. We had Jeremy on a recent webinar I did with Jeremy from Emsight and we were talking about how AI is changing work and it's not happening in a consistent way yet. You know, we're seeing maybe some roles being fully automated. We're seeing some roles being augmented and maybe like different skills, like more important skills like data analysis and being required and strategic thinking.

[00:11:41] [SPEAKER_00] And we're seeing some jobs where, you know, basically elements of the job are being replaced by AI. So, you know, there's a lot of shifting going on. And I think, I don't know, like if we've reviewed our job architecture, something that's been done like maybe on an annual or every two year process. Do you think it's reality that we would be able to update job evaluation like every day or where are we going to end up here?

[00:12:05] [SPEAKER_01] Good question. Well, I think the larger the organization, the more likely they are to have a full department that does nothing but job architecture. And they'll work from one function to the next to the next, looking at various groupings of job families along the way and really are doing some sort of job structure update all the time.

[00:12:26] [SPEAKER_01] The smaller the organization, the more likely that you're just going to see maybe a person that handles the job architecture, hopefully with a backup. Or even smaller organizations may just have a line on the comp analyst job description that relates to updating the job architecture.

[00:12:47] [SPEAKER_01] So I think it is going to be a big shift for even smaller organizations to need to address their job structure much more frequently so that they're able to keep pace with the changes that we're seeing. And, you know, sometimes jobs will have AI that really enhance or augment and help things go faster.

[00:13:08] [SPEAKER_01] In cases like that, you may very well need to look at upgrading the job because maybe they've got more time for that human in the loop strategic work based on the information they're able to get from AI. But if, of course, the AI is really replacing a role, like maybe it was heavily routine, transactional kinds of work, then you've got to look at reskilling your workforce and giving them something different to do.

[00:13:37] [SPEAKER_00] Yeah. And I know there are some organizations who are trying to map what AI transformation is going to look like. So, for example, they're taking all the jobs that they have today in their job architecture. So they're looking at how work is done today. And then they're trying to predict like what of that work, what do we think can be automated and then what work is left behind and how do we restructure that? I know AI transformation is a big focus area in terms of practice for Deloitte. Have you been involved in any of those projects?

[00:14:06] [SPEAKER_01] We actually have a tool called Workforce Analyzer that takes the tasks of a job and looks at which of them could be automated through AI. And it's able to actually tell you where the best investments would be so that you can see, you know, how much remains human only and how much of it really can be automated through AI.

[00:14:31] [SPEAKER_01] So it's a fascinating area. And, you know, we also have other tools that will help you take a look at what you may be able to see in the future. Like our organization, Digital Twin, will actually help you figure out what your new structure might look like in the future if you did automate a lot of things. And actually, it will allow a whole bunch of different levers. You know, if this happens in the economy, then what's going to happen?

[00:14:58] [SPEAKER_01] And just you can do multiple different variables to see how you may want to move your business forward. And by having it be a Digital Twin, of course, that means you're not really messing up your own real data. You're out there kind of playing and modeling and figuring out what might be that best approach going forward.

[00:15:16] [SPEAKER_00] That's really a new skill for the Comp Pro that we haven't maybe necessarily need to do before is, you know, we obviously modeled when we came to job architecture. If there was an M&A, we were like bringing the other organizations together. We were looking at where job architecture didn't align. But now we're trying to like predict what work will look like in the future and build a career structures and build job architecture for the future, which is an interesting challenge. It sounds like some of your tools have really helped with that.

[00:15:44] [SPEAKER_00] Back to the research before we move on. What findings or trends from the research do you think compensation leaders should pay particular attention to as they plan going forward?

[00:15:56] [SPEAKER_01] Well, I'd watch three things. One, the link between skills and pay. We've been seeing that going up as we've had many years of doing the survey. Only 16% of organizations reward new skill acquisition. And most of those do it through base pay and merit increases. So really, that's kind of an open question for our profession. You know, how do you value the skills? How do you do it the right way?

[00:16:22] [SPEAKER_01] The second thing I'd watch is how AI shows up in roles. 25% of organizations are embedding AI skills into their existing roles rather than just creating dedicated AI jobs. So compensation teams, I think, will need a point of view on what that means for leveling and for pay. And then finally, governance.

[00:16:44] [SPEAKER_01] You know, with 58% dissatisfied with how they control their job descriptions, you know, speeding up change without a clear decision right really can get risky. So I think it's important to watch governance as well.

[00:16:56] [SPEAKER_00] I can imagine, you know, each time you're like hiring for a role and then you realize that you have to like shift the role. You know, there's a lot of like change happening within the job framework. What we've seen customers talking about is like, how far does a job have to change for you to recognize that that is genuinely a shift in grade level or in your job architecture? So I think I love the idea of the governance, like building a governance around that rather than just letting it happen.

[00:17:25] [SPEAKER_01] Yeah. And I can't wait to see what the survey houses do with AI. You know, are we going to have to have each match with an AI prominent statistic and a low or kind of normal haven't really automated kind of statistic? Can't wait to see which direction that goes.

[00:17:45] [SPEAKER_00] And the other interesting aspect of that is it can be incumbent specific as well, because what we're really, I mean, I see it in how we're using, we use Praud here at Payscale, but you know how Payscalers are adopting using co-working tools to amplify their work. And we have people who are embracing it. We are people who are like getting around. We've got people who are slower to embrace it.

[00:18:07] [SPEAKER_00] And it's the incumbent themselves actually augmenting the role based on their capacity to adopt these skills and like change the way that they're personally working.

[00:18:18] [SPEAKER_01] Totally agree. Have lots of different AI tools. Some of us use Claude, some use Copilot, some are Gemini people, you know, sort of the pick your favorite tool. And then based on your level of interest and passion around getting some of that automation help, you're exactly right. From one individual to the next, even in the same job title, it can look very different. Yeah. So it just feels like a lot is in flux at the moment.

[00:18:44] [SPEAKER_00] Okay. So we defined job architecture. You helped us to do that. You gave us some great insights from the job architecture research. Let's get to the topic that we kind of started with at the beginning. Shifting workplace, roles changing. How do we keep up and what should we do about managing job architecture in that context? So what do you see are the biggest challenges facing organizations when it comes to maintaining job architecture?

[00:19:07] [SPEAKER_01] Oh, capacity, ownership and tools. I think teams are really stretched. I mean, we're kind of going into a busy season of the year now. In our survey, a total rewards professional supports about 1,200 employees on average. And a compensation specialist supports around 880. So I think, you know, having the capacity and the time to do it is one big challenge. Then ownership, that's kind of scattered as well.

[00:19:36] [SPEAKER_01] 20% of organizations keep multiple versions of their job descriptions out on individual HR practitioner computers. So a lot of things are kind of scattered out and not with a clear owner. And then finally, tools. They're still mostly foundational. You know, the human capital management systems at 65%, spreadsheets only at 59%. So maintenance is largely manual. AI is about to increase the pace of change on top of that.

[00:20:06] [SPEAKER_01] So the real challenge isn't the initial design. It's really upkeep. Right.

[00:20:11] [SPEAKER_00] So it's recognizing the problem. But then how do I, as a compensation professional or leader, like to try and tackle those? And how do you think organizations should approach job architecture when responsibilities and skills are changing faster than a formal job description?

[00:20:28] [SPEAKER_01] Well, I certainly wouldn't try to rewrite every job description in real time because you'd lose that race. Start where change is the most visible. Pick two or three job families and use that work to build a repeatable refresh process. So tie your updates to triggers like a new tool rollout or a team restructuring rather than just the calendar. And keep the structure stable while the content flexes.

[00:20:57] [SPEAKER_01] So most human capital management systems are built around a job code. So a practical approach is to keep the job codes and the levels that your pay programs rely on and then layer your tasks and skills information on top of that.

[00:21:12] [SPEAKER_00] Yeah, that's good advice. And then coming back to the topic we talked about, like governance, how do you determine when a role needs to be redefined or re-leveled versus not? Kind of talked a little bit about this earlier, but what's your advice there, Sheila?

[00:21:27] [SPEAKER_01] Yeah, well, I'd break that into maybe separate parts. You know, redefining has the actual content of the work changed, meaning the tasks, the tools, the deliverables. Then, you know, re-leveling has the scope changed, such as decision-making authority, impact or complexity. And then re-evaluate or reprice a job. Has that job's value really changed internally or in the market?

[00:21:54] [SPEAKER_01] For example, because a skill now has become scarcer. So in either of those last couple situations, re-leveling and re-evaluating, a key indicator is if AI is enhancing or helping to get the job done. And if so, it's quite possible that that role will need to be upgraded for a higher level or a larger scope, since incumbents should be equipped to get more done and use higher-level human thinking and strategic skills.

[00:22:22] [SPEAKER_01] And then finally, build some triggers that prompt those questions. So incumbents doing work that no longer matches the description, hard-to-fill roles, shifts in the market data. AI can help you flag when things are drifting, but people should really make the final call on these things.

[00:22:41] [SPEAKER_00] Yeah, definitely. I mean, that was some of the advice that we had given in that webinar that I mentioned with Jeremy. We'll link that webinar in the podcast notes as well. We talked about where do you start because going from the structured static job architecture that most organizations have today to moving to a fully flexible architecture is hard to imagine exactly what that would look like yet.

[00:23:04] [SPEAKER_00] It's really kind of just, as you say, trying to identify, okay, start by looking where you're getting the signals that work is changing the fastest. And quite often that does happen in the hiring conversation where you're seeing that the role is hard to fill, as you say, or, you know, that the hiring manager is flagging that they need a specific new set of skills that potentially weren't in that job before.

[00:23:27] [SPEAKER_00] It's often in that hiring moment that we identify that, okay, actually the job has that we were looking for or the person that we are looking for has completely changed. So let's think about like takeaways from today, Sheila. What would be one practical step you would advise our listeners to do today to prepare for their job architecture and pay strategy, thinking about the changes that are ahead?

[00:23:50] [SPEAKER_01] So many things. I think picking two or three job families where AI is visibly changing the work and run a pilot refresh, draft the updated tasks and skill profiles, then have the managers validate them with you. You'll come out with better content and more importantly, a repeatable process. So write down who decides what, who approves the changes to the families, the levels, any exceptions. A couple of cautions, though.

[00:24:19] [SPEAKER_01] Keep a human in the loop on any decision that touches leveling or pay and check your data with the vendor terms before putting sensitive job or employee data into any tool. So small, intentional and well governed.

[00:24:35] [SPEAKER_00] We are, I think you mentioned earlier, we're going into busy season for most compensation pros. They're going to be going into their year-end planning cycles in many organizations. That's going to be a time where maybe some of this job shift is also going to come up. You know, they're going to be thinking about how to allocate pay and how to retain people through that merit process.

[00:24:58] [SPEAKER_00] We may start to see some signals coming out of that process as well that will say work has shifted or these worlds now need these type of skills. Is that something you're seeing?

[00:25:07] [SPEAKER_01] Yes, absolutely. People are coming into busy, busy times, and I think they're likely to get new light bulbs coming on and ideas about what may be a good path forward. You know, you certainly want to keep that job structure as fresh and accurate as you can because you're making pay decisions,

[00:25:29] [SPEAKER_01] and you don't want it to be based on a stale foundation, which could give you, you know, misleveled roles, inconsistent pay comparisons for similar work, you know, internal equity issues. Certainly, refreshing the career paths as you see those changing would be a good thing as well. And, of course, cost. You might be overpaying for work that's changed or underpaying for skills that are becoming scarce.

[00:25:55] [SPEAKER_01] So, being on the lookout for all of that as you're going through this time of reviewing and upgrading your overall compensation program is very important. And, of course, you need credibility. If people stop trusting your job framework, then they'll fall back on old spreadsheets and one-off exceptions. And, you know, with pay transparency expanding, an outdated architecture is also harder to explain, harder to defend.

[00:26:23] [SPEAKER_01] And, eventually, it just stops being a foundation and it starts being a bottleneck. And so, I think really important for compensation professionals to be alert, watching for what they're seeing, identifying how they can get a process established to be able to keep things more up-to-date,

[00:26:42] [SPEAKER_01] and to start having the conversations with your leaders about where you see compensation going as a result of all these AI changes and getting them to, you know, help you be on the alert for things like whether a job is really using a lot of AI and, therefore, it is enhanced and there's more strategic thinking and it's a bigger job now or watching for the other way.

[00:27:11] [SPEAKER_01] You know, it was such a transactional routine kind of role that the AI is just about replacing some of those old jobs. So, I think really, really exciting times for compensation folks these days because you're at the heart of the business. This is how the work gets done, how you ultimately add value to your organization and help achieve the goals.

[00:27:34] [SPEAKER_00] And it's interesting, too, that we've seen increasingly HR being asked to be like the owner of AI transformation and the transformation of jobs and work within the organization. So, you know, compensation professionals partnering with HR to help make that become a reality is really important. And for all the reasons that you've stated, AI changing work, pay transparency, increasingly people wanting to understand career ladders so that they can understand how to progress their pay.

[00:28:03] [SPEAKER_00] And also just generally like wage suppression, wages are not going up at the rate that they were. So people, again, have this keenness to understand how to progress their pay and you can't explain that. If you don't have career paths and job architecture to guide people through, like, how do you get from this level to the next level? We covered a lot. What resources would you recommend for our listeners, Sheila?

[00:28:27] [SPEAKER_01] Oh, absolutely. There are some great ones out there. I think starting with the Deloitte and MSITE 2026 Global Job Architecture Practices Survey would be a great one. If you participated, then, of course, you already have this resource. But if you didn't and you would like to, it's an evergreen survey. You can go to jasurvey.com, you know, job architecture, jasurvey.com, and participate. And then we will send you the full study.

[00:28:55] [SPEAKER_01] And then, of course, you'll be included in the 2027 report that'll come out next year. You know, for the AI angle, our point of view piece called AI and the future of job architecture, going from static frameworks to living systems, is a great point of view piece. And I can send that to you and you can give folks a link out to that resource, or I believe it's posted on Deloitte.com as well. And then the global human capital trends, we do that every year.

[00:29:23] [SPEAKER_01] It gives a much broader view on work and skills and how those fit into your human capital programs. Also, that's out on Deloitte.com. You can just search on human capital trends and it'll take you right there. And you can look at things that are for your industry, your size organization. And I see that you've linked a practical piece on job architecture in action in the show notes. So I think that would be a really good read for folks as well.

[00:29:51] [SPEAKER_00] Yeah, second, the global human capital, that's always been one of my favorite reports. And I look forward to that coming out every year because that gives you such a good idea of some of the big picture trends that are happening in our space. But thank you very much for all those recommendations. We'll make sure we get all of those into the show notes so that you can access them. Thank you for coming with all your insights as well today, Sheila. Really appreciated it.

[00:30:14] [SPEAKER_00] We've kind of discussed how AI is changing the work that gets done, the implication that has for organizations in terms of defining jobs, valuing skills, and ultimately making compensation decisions. So thank you so much for sharing your perspective. And to our listeners, we'd always love to hear from you. So you can tag us on LinkedIn with your questions. We'll be sharing some of the insights from this episode on LinkedIn.

[00:30:39] [SPEAKER_00] And then if you have any suggestions for future episodes of Compton Coffee, you can email us at coffee at payscale.com. But thank you very much, Sheila. Great to have you on the show today.

[00:30:49] [SPEAKER_01] Thank you, Ruth. Great conversation. I'd love to also hear what listeners are seeing in their organizations. Of course, you can find me out there on LinkedIn, Sheila Sever. It's S-E-V-E-R. Thankfully, it's a rare enough name that you don't have, you know, 100 of them when you're trying to search and connect. So looking forward to hopefully hearing some feedback from folks. We were trying to be just a teeny bit provocative in what we were, you know, saying that could potentially be the future.

[00:31:17] [SPEAKER_01] And would love to hear what kind of ideas are sparked and know what's kind of going on in other organizations.

[00:31:24] [SPEAKER_00] Great. Thank you very much. Well, goodbye, everybody. And we'll see you on the next episode of Compton Coffee. Comp and Coffee. Thank you.

[00:31:30] Thank you.