Ep 123: Operationalizing Agentic Workforce Intelligence with Noelle London

[00:00:09] Hey everyone, it's Bob. Welcome back to Elevate Your AIQ, your go-to source for insightful conversations on human-centric AI readiness, talent transformation, responsible innovation, and the future of work. I'm thrilled to welcome back Noelle London, founder and CEO of Illuminous, an AI-native workforce intelligence platform that helps organizations connect their people data across HR, finance, and operations into a single, secure, governed view.

[00:00:35] Noelle joined us just over a year ago, she's back today to catch up on what's happening in the AI in HR tech space and to tell us about the exciting agentic workforce intelligence capabilities that Illuminous launched just this week. We get into how decision cycles are compressing across organizations, why the gap between AI experimentation and enterprise-ready deployment is where so many companies are getting stuck right now,

[00:01:00] and how proactive agent-driven insights are starting to replace the static dashboard as the primary way leaders stay on top of workforce change. This is the kind of conversation that reminds me why I do this show. A brilliant female founder building something that is reshaping how leaders make decisions about their people, and I loved hearing about her forward-thinking customers already taking full advantage of these new capabilities.

[00:01:24] If you are a people leader, a finance leader, or honestly any executive trying to make better decisions faster, this one is for you. Thank you as always for listening, and let's go talk to Noelle. Hey everyone, it's Bob Pulver. Welcome back to Elevate Your AIQ. Today I am excited to welcome back to the studio my friend Noelle London. How are you today, Noelle? Doing great. It's a big day. Thanks for having us. Absolutely. Yeah, looking forward to getting into the conversation and hearing about all the great things that you've been building

[00:01:54] and piloting with some of your forward-thinking customers perhaps. But for those who did not see your original episode back in May 2025, I want you to just give my listeners a little bit about your background and what Illuminous is all about. Yeah, absolutely. Thanks for having me back, Bob. I always really appreciate these conversations and feel like your perspective, there needs to be more of these kinds of conversations.

[00:02:21] So, you know, when we had some exciting things to share on what's new, you were one of the first folks that we came back to. But hi, everyone. My name is Noelle London. I'm the founder and CEO of Illuminous. Illuminous is an AI-native workforce intelligence platform. Essentially, what we do is we pull together various different data sources across HR, operations, finance data. We pull all of that together and provide kind of a secure, governed, single pane of glass

[00:02:50] so that organizations can more easily make decisions based off of context around their people. So prior to launching Illuminous, I had worked with a number of startups and entrepreneurs over the years. Most recently, I was on the management consulting side at Accenture. And while I was there, I was owning our HR technology partnerships. What was interesting about that is that there were so many interesting technologies that were within the market,

[00:03:19] doing things like AI upskilling and reskilling, doing things like workforce planning kinds of and scenario planning solutions. But there are kind of two big observations that we were noticing while we were there. One of those is that it's great when you have interesting technology that has AI forward-looking technology embedded.

[00:03:43] It's less helpful when those things are not actually connected to your systems to actually help you to understand how to make decisions. Otherwise, they're kind of in a tab and you're kind of trying to bring all of those various tabs together. So as we were sitting back and, you know, sitting with CHROs talking about HR strategy, workforce transformation, they had pieces of the puzzle, but puzzle pieces weren't talking.

[00:04:08] And so that's first and foremost, you know, it doesn't really help to have something very interesting if it's not connected together for you to have this holistic context. The second thing that we were noticing is, you know, being at a place like Accenture, you're working with very large companies. The kinds of resources and tools that support those very large companies traditionally have been luxury items that haven't been available to companies that are smaller,

[00:04:33] that don't have those budgets for enterprise technology tools, that don't have entire teams that spend their time poking around and trying to figure this out. So I think kind of both of those pieces together are why we launched Illuminous, essentially to connect that data foundation so that you can do really interesting things, getting into those second, third tier metrics, what's causing some of these things, how are these decisions correlated?

[00:05:00] So it's not just looking at, you know, the standard things that are going to be within your existing tool set. So for us, I think chapter one especially was really about how do we connect the data and create a strong data foundation? That's where we've started. And I will talk to you about it in a little bit. But chapter two for us is really now that we've connected that data, how do we help organizations to actually be able to make use of that data,

[00:05:27] to make better decisions about their people, especially as we're thinking and just watching the workforce changing so, so quickly. So, yeah, that's a little bit about how we got started and, yeah, where we've come from. Yeah, absolutely. I think that, you know, as you're talking about, you know, management consultancies and, you know, some of the work that they do to try to pull all of that together.

[00:05:51] I mean, whether you intentionally did it or not, it seems like you could be sort of cannibalizing some of, or at least obviating the need for some of these companies to actually, you know, go and, you know, pull in that, you know, that horsepower from those consultancies because you actually have the ability, the visibility and the trusted data, you know, within your own environment.

[00:06:16] It's just one less thing that you need to go out and spend a big ticket, you know, expense on from a support standpoint. Yeah, it's really interesting that you say that because we actually work really closely with consultants. I'm a former recovering consultant. We don't do consulting services. We're very much a software, data, technology platform. But we actually are working really closely with consultants just because, just like I was mentioning,

[00:06:43] our customers' workforce is changing dramatically. The same thing with the consulting industry. I mean, everything is, you know, the word I can't not use is chaotic of, you know, even when you look at the consulting industry, you know, it's being challenged significantly now that you have AI tools and products that can just bring down the time. And when you think of even like the business model in the past was billable hours

[00:07:12] and the number of people that you have on projects, which just doesn't work anymore. So we actually are going in and embedding ourselves inside of consulting engagements to be essentially the horsepower behind the consultants. And then they're viewing their work less as, you know, hey, let's do an analysis in the PowerPoint. It becomes we've got the real time data and insights. And their focus is really on the change management and the strategy consulting around our platform. So it's interesting.

[00:07:42] You would think that, you know, it could be competitive. We're actually seeing it's developing into a really nice partnership. No, I could see that. Yeah, that makes sense, actually, because it makes then they can take on, you know, other types of I guess their role essentially changes a little bit, which is was going to happen anyway. Right.

[00:08:05] So now they have some of the, you know, data gathering and massaging and some of those fundamental tasks are sort of, you know, streamlined. And now they can act in more of an advisory, you know, capacity, helping to interpret things and be an additional layer of, I guess, decision support and interpretation of what they're seeing. Exactly. Some people may say like, hey, it's the end of consulting.

[00:08:30] And I don't think it's I think it's the end of everything is kind of the end of what we've known it now, though, especially when you think about, you know, maybe it doesn't make sense for a consulting firm to build their own products in the way that maybe it did previously. But, you know, change management with the amount of change and the amount of technology that companies are adopting to make sure that those adoptions go successfully, that you see usage and value out of that adoption.

[00:08:59] That's a really interesting place for consultants to play. So, I mean, since we've spoken, I mean, there's been a lot of you alluded to this before. There's been a lot of, I guess, evolution of some of the technological, you know, capabilities. Everyone's talking about Agentic, of course.

[00:09:18] And so I just thought you could sort of unpack some of your observations, you know, over the past year, whether that's through just your own, you know, research or ongoing conversations with some of your clients.

[00:09:33] And, you know, I guess I'm sort of leading into the impetus for this next phase for you and how you've sort of incorporated all that customer feedback to really bring something that's going to help people in a number of ways that I know we'll get into over the course of this conversation.

[00:09:52] But just a little bit of the backdrop of, you know, how this technological evolution has continued to sort of fuel, you know, the capabilities that you're building. Yeah, absolutely. And, I mean, I'll kind of thinking back on a year ago and you're saying it was May of 25. I was thinking, you know, in May of 25, you know, we were launching our AI native platform.

[00:10:18] And what that meant at the time is that, you know, previously when you think of HR reporting, when you think of dashboarding, oftentimes those things can be kind of like static.

[00:10:32] And at the time, what we were launching was the ability to essentially put any type of data analytics and intelligence into our customers' hands, where previously they may have had to get on the back of a roadmap to, you know, adjust something within their system.

[00:10:50] And what we essentially did is started creating our first agents back, you know, over a year ago at this point, where we were allowing our customers to essentially use our platform flexibly. So any data that was coming into the platform, they had a secure place to be able to ask any questions, essentially use natural language to start to visualize dashboards reports.

[00:11:18] So what that meant is it gave everybody self-serve insights in their fingertips. So your chief people officer, you know, has the views that they have. They're shared with the chief financial officer. But everybody on your team is working in an aligned way because, you know, your talent acquisition person has access to the data and the insights they need. Your talent development person has, you know, performance and talent reviews in the way that they need.

[00:11:46] So everybody, you know, bringing this ability to have, you know, any data at your fingertips, the insights, really bringing up the data literacy within the team because it's everybody's job. It's not one person's job that's pulling that data. That was, you know, a year ago. I think that what we've continued to see within the market is just the level of change continuing to get faster and faster.

[00:12:14] So things that, you know, typically would have been, you know, we're looking at headcount budgets and, you know, we set that a year in advance and this is what it looks like. We're starting to see those cycles happen on a more frequent basis. We're seeing that with budgeting. We're seeing that with, you know, looking at what skills you need to hire for and kind of making those decisions not 12 months in advance.

[00:12:41] We're also seeing things like, you know, performance reviews that usually would have been like, you know, once a year because they're a huge pain in the back end. Like now those things are needing to happen on a faster pace because you can't have a sales team that's not performing and you're only managing that performance one time per year.

[00:12:59] So we're starting to see like cycles compress, which means that it's really important to be able to have that type of data at your fingertips instead of kind of being on your back foot when someone's going to ask because someone's going to be asking and someone's going to be asking with more frequency.

[00:13:17] Especially when you think about, you know, on the sales, on the marketing side of the house, like because of the technology that those teams have been implementing, the expectations for every single team across the organization has only gotten higher. And so everybody, you know, the data literacy, the do you know your numbers? Do you know what's going on? Like the expectations have gone up. The cycles we're seeing are continuing to go up as well.

[00:13:45] The other thing I'd say like outside of, you know, kind of like pace of change and just what's being asked for at a more frequent rate is, I mean, also I think that we've watched like AI adoption just dramatically pick up in the past year. In May of last year, we did not see the usage of tools that we see today.

[00:14:11] I think that's really exciting because of all the reasons that we just talked about where typically, you know, if you were a chief people officer, you're kind of on the back of the list in the back of the queue. Now we have all of these AI tools. It's really exciting. It's kind of like a kid in the candy store of like we now have the ability to do more, whereas we were previously reliant on the back of somebody's to-do list.

[00:14:38] On one hand, that's really exciting and it's really empowering, I think, for leaders to have access to these kinds of tools. We also are starting to see that I think that that's also creating like unintentional havoc within organizations as well.

[00:14:58] And so that's kind of, I think, what leads us into part of like, you know, what we're seeing just in terms of like that breakdown and how I think we want to help organizations move from, you know, let's do some experiments into, you know, we feel comfortable with using these kinds of things for our enterprise tools.

[00:15:20] Because I think there's a very, you know, there's a very important part of the process where somebody is experimenting and understanding their process and understanding, you know, like what's going on within the data and their systems. It's another thing when you start to move into kind of the impacts to the business and how you want to make sure that that's ready for prime time.

[00:15:45] So with some of the capabilities, the ability to sort of dynamically, you know, see all of this data, if you really want a dashboard, you could just say, turn this into a dashboard. You don't need to, you know, sort of pre-build anything. You certainly don't need, you know, static point in time information because now you've got it coming in and it's, you've just got all that, all that sort of real time or near real time signal.

[00:16:14] Coming in sort of centrally, but you've also, it seems like you've empowered more people across the organization. And so if that is true, I'm curious if this causes any adjustments into like how you approach the market in terms of who you could target for, you know, as a buyer.

[00:16:42] I mean, does it change how you think about your go-to-market strategy? Because you have just created this level of, I don't want to say autonomy, but you've created a whole nother level of capability and visibility and potentially trust, or at least the provider of, you know, trusted, comprehensive data to more people.

[00:17:07] And so you've got, you probably have more advocates within your existing clients already that are like, you know, this is, this is fantastic. It's this interesting place because, you know, there's, there's some tools in the market that want to do everything, right?

[00:17:24] They do everything end to end, but it means that, you know, the quality of everything that you're doing, you're, it's, it's challenging for you to be able to serve everyone very well with all of these features if you're kind of doing one thing for everybody. I think especially like the area that we are so focused on right now is those companies that are about 500 employees to about 7,500 employees.

[00:17:54] That's really our sweet spot, again, because they don't have the enterprise tools that can really bring together platforms in a way to talk to each other and to understand across them. They also don't have the capacity to be able to build teams to solve for this. And so, you know. Welcome to this meeting should have been a podcast where we take on workplace issues from a skewed angle. No corporate buzzwords, you quiet quitters. And no strategic KPIs or dashboards.

[00:18:24] Get out of here. We're talking company culture, leadership, workplace. Guests who've lived through the chaos. So forget those slide decks. We're candid, clever, hopefully useful. We'll see you outside the boardroom. Go Lakers. Don't wear that. No, get out. Don't even wear that hat. It's this interesting place that we find ourselves in challenge because we're starting to get, our customers are starting to see the power of what it means to bring this data together.

[00:18:54] That they're starting to say, what else can we do? So, you know, when we work with a utilities company and we're looking at their workforce planning strategy, that brings operational data together with the finance data together with the HR data. And what we've built is very much like purpose built for how we build a platform that's flexible enough,

[00:19:18] where it's, you know, we're not going to hold you to the same models that one of the large HRS systems would. We want to give you the tools to be able to answer the questions you want. So we've intentionally built ourselves very flexibly where we're guiding you, but you can answer any questions that you want based off of the data that's fed into us.

[00:19:43] So I think we've continued to be very purpose built for how do we help you better understand your workforce. There are implications of workforce for every single leader within your organization. So what's been really exciting, we don't do, for example, seat costs have never been a thing for us intentionally because our goal is that we want everyone across the organization to be looking at this data.

[00:20:11] We had a chief technology officer from MailChimp, for example, invest in Illuminous in the early days because as a CTO, he didn't have access to the data that he needed to make decisions about succession planning and performance with his team. So I do think that this is something that expands beyond what we've intentionally done for the meantime

[00:20:35] and where we've focused right now is how do we help the leadership teams at every organization understand what's happening with their people and the implications that that means for the business. So what that looks like in practice, and I know we haven't really talked yet about, you know, what we're doing and launching around agentic AI today. But, you know, what that actually means is that with our existing customer base, you know,

[00:21:04] and one customer in particular that's been a very strong user of Illuminous while we've had this in beta is every single week, every single person on their executive leadership team are getting AI-driven insights into their inboxes to help them understand what's going on and to stay up with the pace of change. They're an organization that's in the insurance space. They're growing really quickly.

[00:21:29] So it's really important that their teams know when I'm hiring for critical roles and they're taking a little bit longer, maybe I need to revisit things like my location strategy. So those are the kinds of examples of, you know, how we're staying true right now to workforce analytics. Workforce analytics are becoming more important to ops teams. It's becoming more important to finance teams. It's not something that just lives in HR.

[00:21:57] We've concentrated on the workforce side, but we're seeing the usage across the organization. Yeah. No, I think that makes a lot of sense. So, yeah, you sort of teased it a little bit. So you've had this great solution around workforce analytics, workforce intelligence. And now, today, as we're recording this, you are launching basically this agentic intelligence.

[00:22:26] So you've got the workforce intelligence, but now you have more signal, more timely, and more like not just what happened and maybe a theory about why. But these are the correlations I see in the data.

[00:22:47] These are some things that maybe it doesn't seem that you've been tracking up to this point, but there's a relationship here across these data points that are worth additional scrutiny and discussion. Maybe there's some metrics that you haven't been tracking, but should because they're leading indicators of some other challenge that you want to head off.

[00:23:13] And leaning into, I guess, the proactive nature of what I think a lot of decision makers need as opposed to sort of reacting or just trying to predict what may happen. But this is the current course in speed of all these things and how they interact. So it's like, I don't know, that's like real-time money ball or, you know, I'll let you describe it accurately.

[00:23:39] So today we're launching a luminous agentic workforce intelligence, which is already in market. It's already used by our customers today. We've been building it alongside them. They've been shaping the product alongside us, and now we're releasing it to the public. So it's not something that we're talking about nine months down the road that we will think about building. It's something that people are already using and getting a lot of value and impact from.

[00:24:09] So with agentic intelligence, what I mean by that is that, you know, you've always been able to use a luminous to build flexible reporting, dashboarding views. We've been bringing together your different data sources for some time. Now what we're delivering is agentic intelligence. And what that means is we're actually delivering insights that we are developing.

[00:24:36] So they are, you know, while you sleep, you are having things that are delivered to you into your workflows directly so that you can keep a pulse on workforce and on anything that may be changing within your organization. So what that means is maybe there are certain depending on what you are focused on within the organization right now.

[00:24:59] Maybe you're in growth mode and you're really trying to understand where you are hiring and where, you know, there's recruiter productivity, where you're having some lag and finding and hiring critical roles. Maybe you're thinking about organizational change and you're thinking about, you know, where am I moving people into their next role?

[00:25:22] Am I upskilling and reskilling individuals into the workforce that I need to have in three to five years? Maybe you're thinking about workforce and headcount planning and you're thinking about am I on budget? Am I under budget? These are things that typically would have been hidden away in something like a dashboard. What we're now doing is delivering those things directly and we are developing context. So you're not getting graphics.

[00:25:51] You're getting the full understanding on are we good or is this something that we need to pay attention to? We're creating those key summaries for you. Your team can add notes and context to that as well. And then we're actually giving you those recommendations and points of attention that you should really be paying attention to. So it's no longer something that's living, you know, in a dashboard where you have to go look for the answer.

[00:26:17] Our goal is that we really handle the data side and make that easier so that it's much easier for you to make decisions and take that forward. I was thinking about the connection between, you know, people, leaders and, you know, in the business as well as HR and finance. Right. So that sort of triangulation.

[00:26:42] I mean, do you see this improving those relationships and maybe strengthening and expediting the development of business cases? Because I know that that has been a challenge, you know, whether that's a cultural thing or just, you know, the disparate, you know, data or lack of real time data or whatever.

[00:27:06] So is this helping to not just accelerate decision making, be more proactive, have, you know, trusted insights at your fingertips? But is it also just sort of operationally helping people, you know, get to more confidence in the business cases that they're building and making the right investments?

[00:27:31] Absolutely. Right now, what we oftentimes see is typical within organizations is that they there's there's a friction that is created by a lack of alignment around the data. Sometimes how we've seen this manage is when you think about for a finance leader, a head, end of month headcount report that is looking at budget.

[00:27:57] Are we on budget? Are we on, you know, like how are we doing relative to what we set out to do? Today, there are many different hands that exist within a spreadsheet that leaves a lot of room for human error. Recently, we were talking with a CFO that had their business partner on the call with them and we were looking at how they were managing headcount budgets today.

[00:28:24] Unfortunately, that one of the business partners said, oh, actually, that number is wrong as we're looking at the process and how it's done. So there's a lot of room for error. There's a lot of when did we pull this? What is the data definitions that it includes? You know, which employee types are in here? It creates a lot of havoc and too many wheels that are spinning just saying, can we agree on the number that we're going to use for this?

[00:28:52] Now, what we're oftentimes seeing happen within a luminous is you have different access levels down to the field level as well on who should have access to what. And what this is allowing you to do is come and have a trusted source of data. Everybody's singing from the same song sheet so that that back and forth activity that's wasting a lot of energy can actually just come through that single pane of glass.

[00:29:18] So I think that that is the alignment on is everybody working off of the same data so that we can actually trust the numbers. HR doesn't have a different version of headcount than finance does. Those are the types of things that very real, very real examples of where we've seen some of these data initiatives stall in the past. So I think it's yes, the alignment.

[00:29:42] I also think it's the self-serve nature because the way that a finance leader is going to want a headcount budget set up in a dashboard for themselves is probably a very different way than how HR or operations are looking at those numbers. And so everybody's using the same data, but you can build and customize the data and the way that's going to work for you with your role without messing up somebody else's view.

[00:30:11] So I think that's definitely a part of this. And just to take a step back or sideways, I just want to think about like the data types that we're talking about, like the actual scenarios that we're talking about. Because I think some people who might be in talent or HR somewhere, but they're not in, you know, doing workforce planning. They're not, you know, data experts or whatever.

[00:30:37] But when we talk about combining all of this data, it's not just like workforce headcount numbers and, you know, projects and stuff like that. I mean, you just talk a little bit about some of the other aspects. I mean, it might be like maybe location data or author skills data in there, or is it only, you know, full time talent or is it the whole talent ecosystem with contingent?

[00:31:03] You know, could you just talk a little bit about like the scope of the data we're talking about? Yeah. Typically, everything that you just mentioned, those are absolutely all components of what we're usually touching. I, in terms of like the data sources and the critical tech stack that we oftentimes touch is bringing in your HR system data, bringing in your talent acquisition data, engagement data.

[00:31:30] So having that qualitative piece, looking at performance data, bringing in budget data, increasingly with workforce planning and just flexible workforce models, people are really wanting to understand operationally, what do we need? If I had X amount of engineers previously and I'm offloading a part of the company, what do I need in the future? So we typically touch all of those pieces of the puzzle.

[00:31:58] That way, that data is being ingested into a luminous. It's normalized so that apples talk to apples. We connect that employee journey. So you're going to be able to say, you know, this was Noelle's time at the organization. This is how long she was in role. She did training and showed, demonstrated these particular skill sets. All of that is coming together so that you can truly track someone's journey within the organization.

[00:32:24] And then all of that data is coming in so that the user has the opportunity to be able to ask anything that they want to ask of the data. So typically, those are the types of data sources that we're bringing in. Again, increasingly, we're starting to get more requests for operational data, customer-facing data as well.

[00:32:46] Because being able to interact and understand, like, how does employee engagement, how do employee initiatives translate into things like store revenue, CSAT scores, that kind of thing as well. Yeah, I'm glad you mentioned talent acquisition because I feel like that's still a bridge that needs to be. Hey, y'all. I'm Lee Cage Jr. And I'd like to invite you to listen in to my podcast, 15 Minutes With.

[00:33:13] 15 Minutes With rising stars and seasoned disruptors, thought leaders and change agents who are sharing how they're reshaping work, rethinking worth, and reimagining what's possible. This is fast-paced, hard-filled, and unfiltered. These aren't just conversations. They're catalysts. So tune in, elevate, share the shift, 15 Minutes With, wherever you get your podcasts.

[00:33:37] You know, crossed in terms of looking at the total talent ecosystem, not just the different pools of talent in terms of contingent, contract, consulting, and full-time or part-time labor. But you like the end-to-end cycle. Here's a pipeline of potential new, you know, talent. What skills do they bring?

[00:34:03] Is that the right way to actually, you know, sort of backfill some people that are leaving? Or what do we know about what, you know, just looking at attrition data and exit interviews or whatever. This is what we know about what's actually, what we're losing. And then how do we, well, I guess the first question is, do we backfill that person? And if so, perhaps, is there an AI agent that could do that work?

[00:34:31] Or do we need another human being? And if we need another human being, what pool of talent that may come from? But the point is, it's all, it's like a big organism, right? I mean, you still need to understand what the inputs and outputs are. And you need to have that total talent visibility to get to this total talent, you know, intelligence. Yeah, for sure. And to your point, I mean, we've talked about organizational data being siloed.

[00:35:01] And like finance data isn't talking to HR data. Ops data is definitely not talking to HR data. You know, we talked about organizational silos. But really, even within the HR function, there are a lot of silos that exist. And a lot of that comes down to like differing incentives. And those problems can become aggravated when the data is sitting in different systems.

[00:35:28] So if, you know, you've got an, if I'm chief people officer and I'm wanting to build this modern HR function, if I've got my talent acquisition team and the primary things that they're being focused on are like time to fill. All right. But like talk to us about the quality of that hire. Was that person successful?

[00:35:49] If we've got somebody that is a great fit for to internally fill that role, hey, this is a new opportunity. This is kind of a growth role. But this person's a hypo. And we want to make sure that we're retaining them. You know, incentives aren't always set up to have that TA leader not look outside of the organization and to really look inside of the organization.

[00:36:15] So what this is, you know, doing is creating some sense of alignment or, you know, so that you have that chief people officer has one view on what's going across the HR function, what's going on and how those different activities are related to each other. So it's not just talent acquisition. It's also let's look at this from like a talent management and talent development perspective. Yeah. How do we bring that all together so we can make more contextual, better decisions?

[00:36:45] And we're talking cross industry, right? I mean, it doesn't really matter what industry or companies. And you've got these functions and you've got, you know, cultural or data maturity, you know, barriers, just like anybody else. But is there any particular industries where you've found that this is solving particularly, you know, acute challenges? Or is it more like just culture and organizational structure and what have you?

[00:37:14] Yeah, I think for us, what we've tended to see is I think that that company size component that I talked about earlier, 500 to about 7,500. That's really an area where I think that this pain is pretty acute for organizations. We don't have an industry, you know, we're not in one industry or two industries.

[00:37:37] It's really a matter of typically these are companies that the companies that we work really well with are companies that are experiencing a large amount of change, which is a lot of industries. But when you think of some of those like key activities, like, you know, hey, I'm going through an M&A transaction and bringing multiple HR systems together, potentially.

[00:38:02] I am moving to a new HR system and I don't want to lose my historical data. And I want to make sure that I've got one place where everything's coming together. I'm in high growth mode. I am looking to do some type of like large transformation restructure. I want executive reporting that's automated because it's taking too much of my time. Those are typically some of the areas where we're working with organizations most.

[00:38:32] We, you know, it's again built flexibly so you're able to add in any type of use that you want to see. But technology, sports and entertainment, insurance, retail, we have experience, you know, kind of across the board on those industries. The defining thing that's different is that we're typically working with the modern version of the companies within that industry.

[00:38:58] So these are the ones that want to be breakout, that want to be kind of ahead. That's typically what we see in terms of the type of company. And then just how a decision maker would interact with this. You mentioned that someone could just basically get, you know, an email or some notification at whatever frequency, you know, they want. Here's what's going on. Here's some things to look out for, you know, things like that.

[00:39:23] But is there is there another sort of like how do you go and interact with it? I'm guessing there's still some assistant type interaction like you would have with a typical, you know, AI assistant or AI agent as well. Right. So and I think that's one thing where, again, we built this in a flexible way because, you know, this is powerful to bring these kinds of insights to your leadership team and to bring this kind of data to your leadership team.

[00:39:52] People may be at different parts of their journey of their level of comfort with doing that. And so we built the platform where you have different options for how you're delivering this information. Ideally, there are key topics that you want to deliver on a regular basis and those are put into your inbox.

[00:40:13] You can choose to give a very like varying levels of information behind that, whether that's something like, you know, PDF explanations of the data that's there. You could put somebody into the platform itself with very limited viewership on what they're actually able to see and dig into. There's PowerPoint downloads that would allow you to deliver this in like a QBR board report type of way.

[00:40:42] So it's really dependent on the culture of your organization and how you're sharing information and the platform set up to support you through that. I'm just thinking about like how the market is sort of maturing. Like you alluded to this at the beginning around people are increasing their AI readiness. Of course, what I refer to as AIQ, but really people are becoming a little bit more savvy.

[00:41:11] They're using it in their personal lives. Leadership in general, I think is one thing, you know, we could probably talk about just because I've seen leaders recognizing that they need to be, you know, conversant and more sort of literate with AI in general. To be able to know what questions to ask or what am I overlooking?

[00:41:38] How do I think differently about this challenge that's been plaguing, you know, this organization? And so have you seen similar observations where leaders, you know, probably a year ago, if we had talked about this on our last recorded conversation, like I think we would have seen that individual contributors were far ahead of leadership in terms of their AI, you know, literacy and readiness.

[00:42:06] And I do feel like leaders have sort of caught up, which I think, you know, you and the team at Illuminous would be beneficiaries, right? Because now it's like, oh, now they're more, you actually would welcome a more sort of discerning, you know, buyer and user. Because now it's like, well, now I get it.

[00:42:30] Now I get the power of AI is not just about, you know, streamlining, you know, existing, you know, processes, which is probably going to have diminishing returns. But this is about how do I, how am I a more effective leader? How am I making more data informed decisions? And what else can I do?

[00:42:52] It just seems like you've sort of lit a fire under some of these, you know, leaders that coincides with their increasing AI readiness themselves. I think we've seen a lot more, we've seen so much more adoption in the past year within organizations. It's, hey, you know, learn AI skills or else, you know, we've seen that happen quite a bit. But that's all positive.

[00:43:21] I think that we've seen leaders really pressing their teams on, hey, experiment, can we do this with AI? What I think we're also seeing is like, a lot of this is coming to a head where it's great that you've got ICs that are experimenting. It's good that leadership is pressing of we need to see more adoption within the organization.

[00:43:50] It also is starting to, you know, those are all good things. What we've also seen happen is like where this starts to hit a wall within organizations where you've got someone that, you know, wants to start their own initiative. You know, to say, like, look at what I built. You start new initiatives, but somebody's got to maintain those initiatives.

[00:44:15] And so I think that that's kind of the part of the evolution that we're coming up on where, you know, how do you make sure if you're using AI within the organization, it's governed properly so that you're not using these tools as a pass through for people that shouldn't have access to information?

[00:44:37] How do you make sure that there's the proper levels of security around this so that you're really careful about what's ever dropped into an LLM model? We're never putting data into an LLM model. The data is not ever used to train our models. And so those types of things where, oh, like we actually we push the gas. We actually need to think about how we're doing those things. Access levels.

[00:45:04] So as you're creating more, you know, visibility across the organization, how do you make sure the access levels are set up in such a way where the right people are getting access to the right information? It's, you know, the maintenance of these. You know, I'll tell you, I, you know, can build clawed agents myself. But when it's not working the way that I want it to work, like, how do I maintain that?

[00:45:30] If I'm building integrations once and I'm using something on the back end to connect these systems like a Zapier, if it breaks, I don't necessarily always know that it's broken. So those are that type of like monitoring and infrastructure that makes great software. And that's really important. Those are the components that are really necessary.

[00:45:52] So I think, you know, the a lot of the modern organizations that we've been working with, they're doing the experimentation, which is great. And it's really important. They also are now using Luminous because it's the secure governed space that allows you to experiment. It allows you to get comfortable and understand your data. But it's doing so in a way we're not exposing the organization to risk.

[00:46:19] And so that's kind of what we're seeing and why we launched the product that we did launch. You know, how do we help organizations that are building these initiatives to maintain and scale them? How do we support you as you are implementing this technology, implementing this level of like data transparency within your organization? Yeah, no, I'm glad you brought up the responsibility pieces because I think that's part of AI literacy and readiness, right?

[00:46:49] Like that's not just what it can do, but like, is it doing it within the constraints of, you know, ethics and privacy and security? Can it explain why it's making these, you know, recommendations? All these things are important. And I think it's increasingly concerning when we do get to, we hear the word agentic. It's like, okay, wait, so that means agents are talking to agents, right?

[00:47:14] So how do I know that the agents are only sharing the right, like who's asking for this? And before I do an agent to agent sort of communication or workflow, do I understand all the elements of who the requester is? Should they have access to this information? Well, if I take that, if I say take certain data elements away,

[00:47:39] does it almost negate the ability for it to actually generate, you know, insight? So there's all these little things that need to be, you know, thoroughly, you know, thought through and certainly evaluated before these things can go into production. The other thing that I was thinking about when you were talking is just like the scalability of some of these things.

[00:48:04] I mean, if one decision maker and user of the Luminous is doing something and they built something that they thought they got a lot of value from, you know, we still need to understand, is that something that could be potentially shared with others or are there other limitations based on that, you know, privileged, you know, information? And so you've got to think through a lot of that.

[00:48:31] This is a lot of things different than building traditional, you know, rules-based software. Yeah, for sure. Well, I always appreciate our conversations. Likewise. Yeah. So thank you for coming back on. And congrats on the launch of phase two of Luminous and your agentic work. I think it's really providing a lot of value to leaders who are sort of on this AI, you know,

[00:48:57] journey and breaking down silos across a lot of organizations so they get that real-time insight. And yeah, congrats on all the work that you're doing. Thanks. It's an exciting time. And yeah, would love to show you more at some point. Yeah, that'd be great. In the meantime, I will make sure I have the updated, you know, website and all your new capabilities in the show notes of this episode. And yeah, I think that's it for today. Until next time. Until next time. Thanks again, Noel.

[00:49:27] And thanks everyone for listening. We will see you at the next episode. All right. you