Episode 46 | Are You Making Me Money or Saving Me Money? | Maria Colacurcio

[00:00:00] You cannot cram in true AI learning in between meetings. You just can't. Like if you're building agents that have context and memory and file systems that they're going to be able to draw from, you need hours of time where you can go heads down and be thinking and unstructured and identifying areas from which you want to pull the structured data, the unstructured data. So if you ask your team, go experiment, go figure this out in the 15 minutes between meetings, it's never going to work.

[00:00:29] The only way you know that is if you've done it. Otherwise, you're reading all this crap on LinkedIn and wherever you get your social media news and you're sort of like, my people should be able to do all this with agents. Maria Colacurcio is the CEO of Cendio, the company that defined the market for pay equity software and is now pioneering decision intelligence for pay. The work is making every pay decision compliant, optimized and aligned with business strategy.

[00:00:57] Today, Maria talks with us about why pay equity is too small a frame for what's actually breaking in compensation. Why pay transparency and the rising cost of AI are on a collision course that almost no leader is ready to defend. And why AI learning can't happen in the 15 minutes between meetings. And what it costs a leadership team when leaders pretend it can.

[00:01:30] Welcome, Maria. Hi, thank you so much for having me. This is going to be fun. Maria, you recently shared a story about when you were working in communications at Starbucks in 2017. And you were helping to plan the first pay parity announcement. And you asked the head of global employment law how the company actually knew pay was fair.

[00:01:57] I believe that question ultimately led to Cendio. Can you take us back to that conversation and what you heard from that employment lawyer and what sparked you next? Absolutely. So the employment lawyer was a guy named Rob Porcorelli, who I've now worked with at Cendio for almost eight years. So it's... That was Rob! I didn't realize that. I was like, what's in here? Oh, wow. Once I said the name, he'd be like, got it. Yeah.

[00:02:27] So that was, in so many ways, that was the birth of what has come the next eight years. Because we started talking about it when he laid it out for me. And again, Rob has grown up a lawyer. Like, we all love Rob, but he was a lawyer. He was in the Navy, in Jag. Went to a law firm and then went to Starbucks to lead global employment law. Some of our best friends are lawyers. Is that what we're saying right now? I... My family has a lot of lawyers for a variety of reasons.

[00:02:56] But yeah, so what was interesting is that he hadn't had the journey that I had had, which was working at a bunch of startups, co-founding Smartsheet, thinking about things from the perspective of innovation. So when I was asking a bunch of questions, because I tend to be kind of an ask hole, and wanting to know more and more about why can't this be dollar for dollar, because this is a really important announcement. And when he explained it to me, we go out to a law firm or consulting firm, and we wait six months after we send them our data.

[00:03:26] And then they give us back this 150... He showed it to me. It's a 150-page cross-tab report, and it outlines for us where we have statistically significant issues and outlines a budget, etc. I was just like, this is insane. Like, there is a better way to do this. We should create a company for this. And he was like, that's not how it works. Anyway, long story short, we found Zev, the original co-founder of Sindio. We joined forces and actually started officially at the company on the same day.

[00:03:53] So he's literally been in this journey with me from the beginning. But it was that this has really driven me from the beginning, the power of what happens when you bring true expertise and innovation together in a way where they get to sit at parity. Not one outshines the other. They're truly at parity. And what can happen when you think about solving problems from that perspective? Because it was truly transformational. Hey, this is William Tinca.

[00:04:22] Listen, we did a series for iSolved called Heroes of HR, where we talked to HR practitioners. And these are just your run-of-the-mill, everyday, slogging it out, 300 employees, and Cedar Rapids, HR pros. Mostly it's centered around iSolved, and they're what they do with iSolved. But also, they talked a lot about kind of the day-to-day rigor of HR.

[00:04:48] If you're curious about that, search for Heroes of HR wherever you get your podcasts. Thank you. And I think you had a concept of a pay fairy in there. Tell us about that. It's perfect timing for you to ask about that. Because pay fairy, what Rob and I wanted to do, what we knew, and what he walked me through in my sort of early innings as somebody understanding pay equity,

[00:05:16] because I didn't know anything about it, was that we already knew where the problem stemmed from. So when you think about the symptom and you think about the solution, in every pay equity analysis, starting pay is almost always the biggest factor. And when you stop and think about it, it makes sense because starting pay compounds over time.

[00:05:34] So if somebody starts higher for a reason that is not sound, for example, they're a great negotiator or they have proximity to the boss or they have a great network or something like that that doesn't necessarily align to the company's policies as to what they value, that person's increase is going to compound over time.

[00:05:55] So when you think about that starting pay, it's truly crucial in compounding over time in a way that exacerbates gaps or creates massive pay drift and waste. And so what we came up with that we called pay fairy was if we could just get that pay decision right, if we could govern the pay decision from the beginning, the starting pay offer, the promotion, the lateral transfer, we could actually clean up and eradicate the pay gap overall

[00:06:23] because we'd be governing at the point of decision. So we called that the pay fairy, the little pay fairy that comes down and whispers in your ear and gives you a little bit of guidance and recommendation to guide the discretion to what that precise decision should be. And that's what we've just launched. And the little fairy actually is the transformational power of what AI makes possible in today's world. That's so cool.

[00:06:45] So you manifested this pay fairy nine years ago and now AI makes it possible to actually do it. We did in so many ways. And I think, again, going back to this theme of when you have true expertise and through the course of these eight years,

[00:07:04] we've been really intentional about spending a lot of time with CHROs and people that are deeply, deeply steeped in compensation expertise to truly understand what are all the puts and takes that go into these decisions and how do they show up in a way that is positive for the company and employees and how do they go wrong? And so I think it's the culmination of, yes, the idea. But as we all know, any idea, no matter how transformational and innovative, is only as good as the execution.

[00:07:34] So I feel like we've really earned the right to now have a point of view and perspective on how to actually enact pay governance in a way that's going to be transformational for companies because we've put in the work and the time and gotten to know the experts and become, in many ways, experts. So I'm going to switch gears just a little bit. We're going to come back to pay, of course. But you've built a couple of companies and you've also thrived inside of giant ones.

[00:08:00] And now as the CEO of an enterprise SaaS company at maybe the hardest moment SaaS has ever had, what's the pattern you keep recognizing across these rooms? Like, what does it take to succeed today? Persistence. I think this notion of you're just not going to give up.

[00:08:25] So I think about I'm in a cohort called Journey to Lead, which is this incredible organization that Patty Sellers and Nina Easton founded coming out of all the work they did in Fortune's Most Powerful Women. And the objective they had was to figure out how to give women the networks and the opportunities in terms of the counterparts. There are also CEOs going through things that are similar.

[00:08:49] And this incredible network of women who are all in similar positions as I am, they're growing, scaling companies. They're going through SaaS to AI transformation. And our mantra is keep going. It's these two words that are so simple but so powerful because it really is persistence and grit and resilience and also curiosity. I think you have to want to be in this moment in particular a lifelong learner in order to thrive.

[00:09:17] And you have to be able to do that in ways that are true and authentic, not just hype and talk. Because if you're not walking the walk and actually learning, it's clear so quickly to your employees because you're asking them to do things. If the mountain upon which you're standing is just hype and not actual experimentation and experience with a lot of these tools and technologies, it crumbles so quickly.

[00:09:41] It's just that resilience, that grit, that persistence, but also this idea that you authentically love learning and you want to continue to learn so that you can push yourself forward. So you're role modeling a couple things that are really important to Amy and I. Your point about bringing together expertise and innovation, your point about thinking about how to persist but also include curiosity and learning.

[00:10:10] And one of the things that you are embodying but not using in words is the thing that is really top of mind for me, which is intellectual humility.

[00:10:21] The thing about leadership today and whether it's leading a SaaS company that's disrupted or leading a traditional company that is just navigating overall volatility, the reality is we're all in this moment where our expertise needs to be applied differently than it might have been in the past.

[00:10:41] And when you mentioned that people can tell if the difference between leading by pontification or bloviating versus leading from experience, I think one of the ways they can tell is this thread of intellectual humility that you have been willing to be new and to try things and you have been willing to learn and to maybe get things wrong.

[00:11:09] Help me understand, like, help me understand, like, how do you show up for your team who is also trying to figure this out for themselves and the broader organization? What conversations are you having around this to help bring people along with you? It's a great question. And it's not only those of us scaling AI companies that are thinking about this.

[00:11:32] I can tell you we have almost 400 enterprise customers and every single leader of people at these companies is struggling with the same thing. And when I say struggling, they're trying to figure out there's this premium for AI skills right now. But what folks are grappling with is what does that mean? Because the person that succeeds in building AI technologies right now is not just a set of skills that you can put down on a piece of paper. It's also things like change agility.

[00:12:03] It's things like the ability to be a little reckless, to use a really strong word. And I've gotten pushback on that word, but I continue to believe it. You have to be willing to be a little reckless. You have to be a risk taker. You have to be very open to experimentation, knowing that it could get you nowhere, but it could also get you to a breakthrough.

[00:12:23] And so the only way to start to model that for your team is to truly put yourself out on the bleeding edge and force yourself to learn, force yourself to experiment. When I've done this and I took a six-week class for executives on building agents, and it was all weekend and evening time.

[00:12:40] Because one of the problems, problems slash opportunities, things that leaders need to be really aware of as they think about the transformational power of these technologies, you cannot cram in true AI learning in between meetings. You just can't.

[00:12:56] Like if you're building agents that have context and memory and file systems that they're going to be able to draw from, you need hours of time where you can go heads down and be thinking and unstructured and identifying areas from which you want to pull the structured data, the unstructured data. So if you ask your team, go experiment, go figure this out in the 15 minutes between meetings, it's never going to work. Just it's absolutely untenable.

[00:13:23] You need the headspace to like think holistically and systematically. And then you also need the level of focus to make sure you're doing it right. Right. Because it's all new and weird and you have to be kind of careful and so on. But you need both parts of your brain to really tackle this.

[00:13:46] So even just like an hour isn't enough because maybe you'll get to the like the first part, but to actually get the execution in takes even more time. Yeah, that's great advice, Maria. And the only way you know that is if you've done it. Otherwise, you're reading all this crap on LinkedIn and wherever you get your social media news. My people should be able to do all this with agents. Sure, if they have unstructured hours of free time during the week. Yeah. Sure.

[00:14:14] And they have the tools and they have guideposts in terms of where they're going to learn this from. But once you do it, you're like, oh, God, this is a big net to crack. You start to understand like what's the structural system that you really need in place in order to support everybody to do this. So ClickUp recently announced that it was laying off 22 percent of its workforce so that it could pay million dollar salaries to the high impact employees.

[00:14:43] What does your pay equity brain say about that, Maria? Or maybe pay equity isn't the right term, but what is your expertise and curiosity say? This idea of premiums makes the pay governance problem, too, to speak to that a little bit, way more urgent, not less urgent.

[00:15:04] Because when the range of outcomes, the transformational outcomes we're talking about get that wide, the potential for bias in how you distribute that range gets exponentially larger. So again, who gets the one million dollar package? Who gets the 200K package? If that's just a pure judgment discretion call without any governance around it, you're going to create gaps pretty fast.

[00:15:28] Wide bands don't break pay equity in themselves, but they definitely raise the stakes because now you're talking about huge packages for some and not for others. So if you're going to pay someone 10x of their peer, you have to really be able to articulate why with consistency. That has to be part of your systemic comp philosophy and policies. When I read the ClickUp coverage, it was very clear that they needed to go hire this externally.

[00:15:56] So as long as that group doesn't skew heavily male or female and that they can explain the characteristics that make this special snowflake, then you're okay. The big problem is we find a lot within technology, for example, they have great pay equity in terms of equal pay for equal work. But the issue, you see this in a lot of the litigation, like in the Amazon case, the Google case, is it's systemic underleveling.

[00:16:24] So the question is, does somebody come in with the same skills and experiences that make that super level? But because of bias and human discretion, they get notched at a five versus somebody else who shows better, negotiates better.

[00:16:39] Outside of the pay scale challenges, which I think we all can see pretty clearly, putting on my experience as a comp committee member at a board level, one of the things that you really need to be thinking about is, is this outsized value creation a one-time thing or is this a ongoing thing?

[00:17:01] So if you kind of really break it down, it's very similar to outcome-based pricing for technology versus SaaS-based pricing. You need to really think about how do you mean to go on in the long term with this very high-skilled individual? And how is the recurring value capture aligned with the goals and the strategy of the business?

[00:17:27] So again, when you think about like, how are we looking at the future of not just work, but also how we employ the best ideas to create value and capture value? How do you share the opportunity and the risk? I think the other interesting parallel to this question, if agents can do all this work, maybe you don't need people, right?

[00:17:53] Maybe the agents can take over and do the work. So I feel like what we're really grappling here with isn't just about pay equity gaps, but it's really about what is the structure for big leaps that we expect to take in the next couple years to do new and interesting things.

[00:18:18] And how do we make sure that that value is shared with the people that are doing the work? And how do we make sure that that is aligned with the goals of the company? I love what you're saying. So this is where AI is so incredibly cool. Because the first step for us when we think about pay governance is to make sure our pay decisions are compliant. So reducing risk of pay gaps, class actions, issues with the EU Pay Transparency Directive, making sure you have compliance. That's really important.

[00:18:48] And in order to do that, you have to be able to explain, articulate, and justify why you pay what you pay, which is, by the way, it's good business practice too, to be able to explain what you value in a way that employees can understand. So good for employers, good for employees. The second piece is optimized. So you're starting to speak to a little of that in terms of what are the things that we can tie to business outcomes. And this is where AI gets very cool.

[00:19:14] So when you have, let's say, a back and forth between a candidate and a recruiter, and there's a bunch of verbal conversations going on where the recruiter's learning a ton of things, it all gets lost. What ultimately ends up in the applicant tracking system is whether the offer was declined or accepted and what it was accepted for. All the back and forth, the negotiation, the first offer that was dismissed, the reasons behind why they increased it, it all goes into the ether.

[00:19:43] And so when we implement pay governance and decisions for our customers, what's so cool is now you can capture all of that and ultimately tie it back to business outcomes. So, for example, if you have a justification that you're a big tech company, so you're going to pay a premium to somebody that comes out of Google. They're an ex-Googler. Therefore, they're going to ramp faster. They're going to understand your technology from a competitive intelligence space.

[00:20:09] They're going to be able to be more innovative and create more patents or whatever the things are. You apply that premium and you apply it multiple times from people that came from Google or Meta or the FAANG, whatever you're doing. We can actually now, six months later, follow that through and say, yeah, that premium's not working out so well for you. Those employees that get that big premium for being former FAANG or competitors that came in, they trade out faster. They're more likely to get a separation package.

[00:20:39] They get terrible performance reviews from their directs. Their engagement scores are crap. And you could start to analyze all this information in a way that gets very predictive. So not only are you now providing discretion, guidance, and recommendations for managers and recruiters and whoever's making the pay decision in the moment they're making it,

[00:21:01] you're also providing that full circle loop in terms of these are the premiums that don't seem to be connecting to your business objectives as an organization. And that's really powerful. And what this allows is actually a true semblance of performance culture, which is you actually say, here are the things we value, and then you follow the trail and you identify when do they work and when do they know.

[00:21:27] I want to add to this, and I love this analogy that you brought forward, Meg, and then you added to Maria in terms of the employee pay to pricing, right? And how those are related. And, you know, I kind of look back to, you know, five years ago when we would talk about will we start paying for skills, right? Skills-based pay.

[00:21:54] There was a big skills-based pay, but it never really quite materialized. But now I'm wondering if we will just completely leapfrog that and get to outcome-based pay. And if that's a whole parallel track to outcome-based pricing. Companies are having a lot of trouble figuring out how to price by outcomes. They're also going to have a lot of trouble figuring out how to pay people by outcomes.

[00:22:21] And they have a lot of similarities in terms of what's difficult in terms of defining the outcomes and being able to tie it back and to be able to prove it and everything. So I see so many parallels there. And I'm wondering, Maria, if, you know, because you were talking a lot about outcomes. And is that something that your customers are starting to grasp onto?

[00:22:48] And then maybe kind of reflecting into your own business around doing outcome-based pricing? Like, do you see a parallel there? So many questions. So number one, I think particularly in the last three weeks since Anthropix pricing changed, we are seeing more and more of our customers build their own agents, et cetera.

[00:23:10] There is a absolute requirement that if you are a vendor pitching an AI solution that you have a hard ROI. So we talk about ours in four buckets. We talk about governing spend. So we've talked about that a little bit, like pay drift, discretion. You have a big range when it could be more precise, which means you've got discretion in this top of range you don't even really need. So get really specific. That's a clear, hard ROI. And you get to it by looking at how many pay decisions do you make a year?

[00:23:40] How many people do you hire? How many backfills? How many promotions? What's your merit cycle budget? You look at risk. So thinking about the risk of being not compliant. So whether that's a remediation budget or the penalties for being over the 5% gap in one of the jurisdictions of the EU, like all of those types of costs that you can identify and put a number to. Performance is the third.

[00:24:01] And I would say the hardest number against this is really the percent of payroll that you spend on attrition costs. So one of the things that we found, and this is the squishiest of them, so you kind of have to squint a little bit on this one. But there are many studies coming out of people analytics teams that show if you two work at a company and I hire somebody and you've been here a long time. You're long tenured employees and we tell our people we value legacy and tenure.

[00:24:30] I bring someone in who has less experience than you, but because they're coming from the external, they get paid more, which causes an issue and has attrition risks. Because now you're like, wait a minute, this person is my direct report and they're making more than me. Like that makes me unhappy. I'm going to leave. So there is the ability to identify a clear hard ROI from attrition risk, which typically is around 3% of payroll.

[00:24:55] So those are things like attrition risk, hiring efficiency, things of that nature that fit into that like performance bucket. And then the last one is really around speed. So if it takes you three days, for example, to do the escalations and the workflow to get an offer to a candidate that's sitting on the sidelines when you could apply technology and make that a 30-minute SLA, for example, there's real cost savings there. So we kind of break it into those four buckets.

[00:25:22] And what I will tell you is customers are willing to spend when they can clearly identify the hard ROI. Outcome-based pricing is going to get really, really specific and required. And it's going to have to be around what am I paying you and what are you saving me or what am I able to generate? It goes back to like the age old, are you making me money or saving me money? And I'm going to be really, really specific about those costs and making sure they're hard costs.

[00:25:52] But this technology for the sake of just being innovative and technology, I think is not going to fly. The way I'm starting to look at it is kind of very aligned with both the maturity of the customers today and the data that's available in the market.

[00:26:09] I would posit that starting in second half and certainly into 2027, we come back at things like incentive bonus comp and performance stock unit definitions and start to really drive for these sort of bigger strategic outcomes and start to price them for our employees so that they get shared value.

[00:26:36] We're at a moment where we can start getting better line of sight to not only the value that we're driving, but also the compare of pricing is this value that has the best ROI with the humans or with the agents and where.

[00:26:54] And I think because we're going to be able to be able to be able to be able to be able to be able to be able to do a much better job of not just aligning pay, but also aligning enthusiasm and cultural behaviors as well.

[00:27:23] Because when people start seeing the application of their talent driving really new things for the business, they're going to be motivated to do more of that. So the same way we've always leaned on comp plans for driving sales behaviors, I think we're going to have the opportunity of doing that to a broader population. I think there's so much promise. So I could just be doing the Freakonomics thing where I'm pulling the pieces out of what you said and hearing what I want to hear.

[00:27:52] What I hear you saying is you're sharing an aspiration for a world in which compensation is not done in a black box, but a glass box. And when you think about a full audit trail, so every single decision logs, inputs, model versions, overrides, adjustments, context with a human in the loop. And that is clear.

[00:28:44] There are things that, you know, we spend so much time at work. And in a lot of ways, we don't necessarily know what portions of our output are most valuable. And you shared that pay tells the truth about a company, but that truth can be hidden and murky or it could be transparent and luminous, right? That's kind of what you're saying with the black box versus the glass box.

[00:29:10] This is part of what's kind of embedded in the EU AI Act, which I really appreciate. I mean, my one beef with it is they call this type of work that touches compensation high risk. My fear is that leaders will see that and not want to touch it in terms of an AI transformation use case. And what they don't do in applying that moniker of high risk is say anything about as compared to what.

[00:29:34] And if you take that as compared to what, the compared to what today are humans making very biased decisions with lots of discretion and very little guidance. So this tale of two cities, which is high risk, high risk, be very careful when you embed AI against things like compensation.

[00:29:53] But particularly when you look at solutions that have that human judgment in the loop and are providing recommendations and guidance, it's so much better than what we do today, which is it's a lot of wild west.

[00:30:08] A lot of compensation vendors, they're really anchored around market data and leveraging AI to do more and more with market data has its own issues in terms of, you know, that can create gaps and biases in its own way. And so I'm wondering how does that fit into your model? Where is there a place for market data and where is there not? Market data is one piece of the puzzle.

[00:30:36] And I think if you over index on it, you're putting yourself and your company at risk. I think it has to be one input among many. And that's how almost all of our customers look at market data. In the old days, it was the survey business. And so companies actually still many, many companies are looking at hundreds of surveys, whether it's Radford or you've got McLaughlin, you've got Mercer, you've got Compa now as more of a real time input to that big set of market data.

[00:31:05] And they're using that to create their ranges. And it will always have its place. And it's one piece of the overall puzzle. Our take is you have to have that, but you also have to have internal data. What do your current folks make? You can pull in offer data. So what have your last several offers that have been accepted look like? What are your last however many offers have been rejected look like? That's going to drive your market as well. And then you've got your output from your pay equity.

[00:31:34] So what's your equitable range or what's the input that can actually make that offer competitive but also fair? And I think the education that we've had to do because we were birthed out of the pay equity movement and originally spent a lot of our time looking at the backward-facing audits that were the twice-a-year pay equity analysis. Now we're moving into preventative, leveraging AI and technology to do pay governance in real time.

[00:32:00] Our pay equity outputs are one piece of it just like market. And so we've had to educate our customers that just because pay equity is one piece of it doesn't mean you can't be competitive. There are many big, big companies that are starting to move away from market in general because what they're able to do is almost create their own market by grabbing onto the coherence under the hood of where do they see offers accepted or rejected.

[00:32:27] And so if I'm a big, big company and I have that type of momentum, not everyone has it, but I have 10 offers out for financial analysts in a particular geo and all 10 have been declined because of the offer, well, market's telling me that that offer range is too low.

[00:32:47] And so start to get to this place where if you have that feedback loop and coherence of what's actually happening, you can start to create a market sense that is really particular to your company and what's happening at your company. Well, it's either telling you your pay is too low or you're not a great place to work. You recently launched a decision intelligence agent called Cindy.

[00:33:15] This is your pay fairy, right? And I heard something really interesting recently about how deploying agents or hiring agents is like hiring a consultant. And it's so important to match the tech with actual subject matter expertise and to some degree, even implementing that agent at a customer.

[00:33:40] You really need to have an SME involved in order for it to really be effective. And so I was wondering, how do you see the domain expertise alongside the tech in terms of the effectiveness of Cindy? Domain expertise is really important. And I think you need to leverage it in how you interact with your customers on a daily basis.

[00:34:06] So when we think about like the FDE model and for deployed engineering, we not only have workflow and agents like Cindy who work across our entire platform. So working across essentials, which are the pay equity suite of tools, but also decisions, which are offers, promotions, transfers. There's also very specific customized agents that deal with things like compartmentization in the event of mergers and acquisitions.

[00:34:31] That's an agent that was built alongside a customer in the trenches solving a problem that was really specific to them. Now, we don't train other customers' data off their instance. That stays within the four walls of their company. But the use case of an M&A compartmentization use case is now something we can go and help other customers with.

[00:34:55] So that's where I think the expertise really comes in handy is solving problems for customers that are ubiquitous across what your other customers may need. The other agent we stood up was a job leveling agent. So very simply being able to look at a job description and identify if it was over or under leveled and to the degree of how much, one level, two levels, and then making that recommendation to the human to say, I think you should look at this level. It's missing these key components that would make it a level seven.

[00:35:25] And it's going to create compression for 16 employees, all of whom happen to be top performers, if you proceed with it as is. We employ employment lawyers. And the reason for that is we want that in the DNA of our company. But I would say that doesn't scale. What scales are these interactions with your global customer base to the tune of almost 400 companies that we can then extend to other customers that have the same problems? It shows a bit of a distinction.

[00:35:55] So let's say an HCM platform software company who has comp, you know, they're going to be building agents and selling those to their customers. But that lack of real subject matter expertise will make the agent not particularly effective.

[00:36:16] Or if the customer is looking to build their own agent using, you know, their own technology, again, they don't have that subject matter expertise. So you have this like empty tech that maybe goes part of the way, but really isn't going to bring the outcomes or the ROI, get to the outcomes of the ROI. You really need that subject matter expertise kind of hand in hand with the technology.

[00:36:44] And I would argue it has to be fluid. So I could hire the best comp expert in the world to work full time on my team. And for six months that they're on my team helping me build solutions, they're six months out of the enterprise getting out of date with how the actual enterprise works today. The best use cases and problems that we get presented with in terms of coming up with creative solutions are from our customers.

[00:37:13] Help me solve this problem. I get 500 new job titles dumped on my team every year and have to figure out how to harmonize them. That's a great problem for technology to solve. Now, you have to deeply understand comp, but the tools and technologies and ways of working that are happening in the enterprise today, I would say, are actively changing. So if I was in the field two years ago, my customer's job today is going to look very different.

[00:37:42] You can even play that out and understand based on our conversation about these new skills and experts that are being sought after, that we're in a moment where there's really not a tremendous amount of clarity.

[00:37:58] So being good at bringing together innovation, expertise and curiosity to really collectively solve these problems is the key because pretending like this is you're solving a static problem is kind of missing the point. Let's go back to what you asked me earlier, which is what are the characteristics and aspects of a thriving company in this era? It's relationships.

[00:38:27] It's not transactional. If you are transactional, you are not going to get to the real meat of the problems that people are faced with right now. You have to be pretty vulnerable to articulate the problems you're faced with right now because there's a lot of emperor has no clothes going around. There's a lot of all of these other people seem to understand AI. I don't really know what the acronyms are, but I'm just going to kind of sit here and nod and smile. There's a lot of that happening.

[00:38:55] There's a lot of hype and there's a lot of vulnerability around saying, I've got a problem I don't know how to solve because for some reason over the past six, 12 months, I think people have been put upon that they should have been able to figure this out with AI. And it's just not the case.

[00:39:39] And several new executives. You've mentioned that you're moving from SaaS to AI. Lots of narratives out there right now, but like how is Maria, the CEO of Sindio, attacking the org design problem right now? This is an ongoing conversation. I think number one, you have to figure out, again, practice what you preach. So that's number one for me always. Never ask anyone in the organization to do something I wouldn't do myself. So I'm very much like a roll up my sleeves.

[00:40:09] And if I'm going to ask you all to be coherent with agents, then I better be coherent with agents first. I think also looking at this from the perspective of truly surrounding yourself with experts who are experts in their function and field. So the way that I look at it is what it takes to hit your first $40 million in revenue is not what it takes to go from $40 million to $100 million and beyond.

[00:40:35] And so when I looked at the reset of my entire executive team, we are at a moment where I went from needing all around athletes to people who specialized. And I needed people who specialized deeply in fields and functions where they could specialize at scale. And that was really the difference. You have to constantly be looking in the mirror and saying, am I still the right person for this job? And let me tell you, over the past 18 months, I've had a lot of heart-to-hearts with myself where I'm like, I don't know.

[00:41:05] I don't know if I am. If you look at what we've been through as a company, we are a company built on existential crisis. We're built on existential crisis from the perspective of we were a pay equity company. When Trump rolled into office and started putting out executive orders that just absolutely decimated things like the EEOC, we had customers wondering if Title VII was still a thing. There was such chaos in our industry in particular.

[00:41:33] And we just stayed the course. We were like, no, no, no. We've always been about progressive and lawful. We don't favor or disfavor by any particular group. It's statistical significance. We look to Europe. We're like, okay, Europe's still going. We've got the EU pay transparency directive. Let's focus there while we figure out what our expansion play is, leveraging this incredible opportunity with AI. But you have to think like a builder. You have to be willing to face the existential crisis head on.

[00:42:02] Ask yourself if you're still the right person. And if not, where are your gaps? And can those gaps be closed by bringing in experts around you? And that's ultimately what I came to. I'm not the best person to do all the things that I'm doing right now. I'm too multifaceted. I'm too much in the weeds all over the place in the company. I need to surround myself with specialists that can help me with go-to-market, with marketing, with partnerships, because I'm trying to do too much and I'm not doing it well.

[00:42:30] And so that conversation either ends with, okay, you need to find a new CEO or I can close the gaps by changing the talent that's supporting me. And that's the route I chose to go. Good leaders are having this conversation with themselves all the time. And you have to because, number one, you have to think about how does the organism for which I am shepherding, for which I'm in charge, like I'm shepherding this organism that is Cyndio. How does it thrive?

[00:42:58] And that has to be your number one goal. So good. Again, what you're modeling for people is truly asking about what does the future need, understanding things that need to be addressed to help you get to the next level.

[00:43:18] And when I coach people about this, if you can't see the ultimate future, what I try to do is to say, okay, I do understand what the next hurdle is. And I have slight clarity to the hurdle after that. I want to make sure that I'm setting myself up so that as I take the next hurdle, I'm also building the muscle that is going to be required to do the one after that.

[00:43:46] And if you're constantly thinking that way and continuously looking out ahead to say, what are the potential futures and how do I create optionality for myself in whatever happens in those moments while building capacity in myself and in my team so that we have built the muscle such that the one after that is in our sights and available to us.

[00:44:12] And I think that in this moment, that is exactly what we all need to be doing. We need to be taking some risks on how to get ourselves to that next step. And we need to be building the foundations for how we can bring the groups along with us so that the future can thrive for everyone. And so impressed by you, Maria.

[00:44:36] It's such a good pattern that you're setting out for other leaders that it's not in having the answers, but it is absolutely on you to ask the questions. I think that's so spot on. And in particular, what resonates with me about what you said, and I always think about this in terms of the man in the arena, and there's that part that's always quoted. And if you follow Steve Magnus, who's like a big running endurance guy, he did a post recently on the part that's not quoted.

[00:45:04] And it's something about for the purpose of what? And the part of that speech that's not often quoted in the was the man in the arena and you put yourself out there and you do the struggle is the part about in service of what? It's in service of others. It's in service of a worthy goal that you're doing it. It sounds really cliche, but for me, what you're speaking to is this iterative approach to making sure you're focused on a really smart next thing that's a worthy goal.

[00:45:34] In our example, I knew we had to come up with an AI native expansion that expanded upon what we thought of way back when with Payfairy and that solved that problem. And I knew if we could do that well, I could then recruit the right kind of leaders who would want to come on board to a rocket ship. But in doing that, I developed all of these skills and abilities that are now serving me in this next phase.

[00:46:02] So it's almost like I built the skills while I was just focused on that next right thing. I think people get stuck or I certainly can get stuck often in, I don't have all these things I'm going to need to scale this company to here. Just do the next thing and you're going to build skills and you don't know what those are going to be or how they'll serve you. So that resonates with me what you're saying. I like to talk about it as you should have a goal that's so big you have to grow into it. That's exactly what you're describing.

[00:46:32] So you've alluded to this several times and that is that you need to roll your own to be credible with your team. And you've shared a story just on LinkedIn about building an agent and where it went wrong and what you learned from it. Tell us the story, Maria. Oh my God, it's so crazy. So I have three agents that I work with every day, all day.

[00:46:57] I have my strategic advisor, my operational agent, and then I've got one that's like a lean CRM. It's all of my off the record, on the record conversations with customers that can then guide me in interactions with them in the future. And so we were all in Calgary together. We have a bunch of engineers in Calgary and we were building out some of the early agents that I mentioned before, working on scenarios with compartmentalization. And so we all had access to the repo and we weren't building in production. We were just building for fun.

[00:47:27] And I was like, I want to build something because we're all here building. And I think it would be awesome if they could see the CEO building side by side with them. That's just something that I value. Like I love that in the trenches, we're all working together. And so I said to my operating advisor, pull the repo. And I gave it the information. And three times I asked it to do this. And remember, these are agents that have .md files, tons of context on me, my organization, my leadership team, my board. They know a lot.

[00:47:56] And instead of giving me the repo, it pushed back on me three times. The first time it said, I don't know that the CEO should be writing code and tinkering with code. Like maybe you should just give somebody a spec or like let's create a spec together that talks about. Did it pat you on the head while it said that? And so then I said again, I was like, no, no, no. I want to do this. So pull the repo. The second and third times it directed me to men on my team. It said, I know Devin's there because you're at the onsite in Calgary.

[00:48:26] He's the head of product. Why don't you go sit with Devin? And you and Devin can figure out. You can do it side by side. And then the third time it directed me to another guy on my team. Brilliant men. Like and so that's when I picked up my laptop. I went over to Devin. I said, can you ask your agent to pull the repo? He did. And it started working and pulled it down. And I was just flummoxed. Like, is it because? So a lot of comments I got on that post were like, well, obviously it's because you're the CEO. And I'm like, he's the chief product officer.

[00:48:55] My agents know that I like to be hands on. They know that I want to build things. Obviously, I'm building them. And so I just asked it. And I said, was the reason you pushed back on me three times because I'm a CEO or was it because of my gender? And it basically came back with this long thing about I can't rule out that gender wasn't a factor because my training data is such that it could have played into how I responded and what I directed you to do in a very technical request in a way that I wouldn't have with a man.

[00:49:25] That was a moment for me.

[00:50:27] Yes. immerged your curiosity and running up against structural issues, being able to recognize that for others on your team potentially, but also your customers and what they're grappling with in terms of AI tools that are being released to them. It's just a great learning, a great story. And I love how you've leveraged it to be even more curious, Mary. Yeah, very cool. Thank you. So cool.

[00:50:57] Thank you for coming on the Meg and Amy show, Maria. We are so delighted to get to talk to you and to learn from you. Thank you so much for having me. Ready to go to your ship corner, Meg? You know it. All right. Here's one I think that many people are going to relate to. I got laid off almost two years ago, 20 plus years in my field, a strong track record, what

[00:51:27] I thought was a solid reputation. But I've been applying, networking, taking every coffee anyone will give me and nothing has stuck. What do you do when you've done all the right things and the market just keeps moving past you? Oh, you're right. There's a lot of people that are going to feel this one deeply. I have been there myself, actually. So I will say, first off, God, I'm sorry this happened to you.

[00:51:55] This is a really, really big, crappy thing to experience. And your 20 years of experience suggests to me that also you may be hitting some age discrimination in addition to the potential market dynamics that we're all starting to feel right now. So first, a little bit of a reminder. You are not your work. This is not a reflection on your skills or abilities.

[00:52:24] And you're not alone by any stretch of the imagination. But that doesn't make anything better. So a couple things that I tend to like to recommend to people that are in the in-between space, and it does sound like you're employing a lot of the tried and true mechanisms to finding another job. I do strongly recommend that you get an unemployed buddy to work with you and to collaborate with

[00:52:52] you for no other reason than to support each other through this journey. Finding one of those is probably something that most people have a pretty good path towards. But if not, I would definitely start there. The second thing I would recommend is a little counterintuitive, but I would encourage you to invest in something that you have full agency and control over, whether that is a skill you want

[00:53:20] to build or a hobby you want to take up or some cause that is near and dear to your heart. Because keeping your heart well adjusted and your energy up really, really matters, especially for a sustained period of time. But I encourage you to do something that doesn't incur a lot of cost, that does require time because

[00:53:46] you have time, and is something that will feel good for the accomplishment and the progress that you're going to make. So for me, those were usually fitness goals, but I've known people to pick up instruments or languages or a lot of different types of hobbies, ones that you can sustain and that you can also improve on. Because I think one of the biggest challenges of in-between space and unstructured time is that

[00:54:14] lack of sense of achieving anything that we get used to in the world of work. And then the last thing comes into the envelope of looking for alternative things that you can apply your talents for towards making money, whether that is to sort of bridge a financial gap or to make the time go better. So for a lot of people, sometimes there is no full-time hiring, but maybe there's contractor

[00:54:43] hiring, or maybe there is some type of fractional or subdividing things that you know how to do that might be marketable in this time. So a little bit of gig economy work to not only keep your skills fresh, but also to find a way to bring in some funds while you bridge a longer period of time than you maybe had hoped for.

[00:55:08] And I think it's good when you do that to keep tabs with yourself about are some of the things that you're spending your time on things that you might enjoy more than what you had originally planned on for your next phase of career? Or are they just bridge things? Sometimes you will find you start doing things for one reason and end up continuing to do them for others. One of the last bits is consider volunteering.

[00:55:36] There's usually needs within your community that you can be contributing, even if you're not able to make a living doing it, but you could be contributing. And whether that is at a food bank or something local, but getting involved with people and causes in your community that need volunteers, I think is another great way to help your energy,

[00:56:03] fill your time, and open up new networks that your traditional coffees are not opening up right now. What about you, Amy? Yeah, I got a couple thoughts. First off, I think it's so important to have a story. You need a story. And that is going to be important in terms of building up your energy and keeping you from getting depressed.

[00:56:31] So your story is, what is it you're working on? What is it that you're doing with your time? What is it that's getting you excited about things? But that story, you need it, again, for your soul, but you need it for networking. And the harsh reality is that you need to network as hard as it is, and you need to expand your network.

[00:56:55] And it's really hard to network and to expand your network when you feel less than you have ever felt. And so that's why you need to build up your confidence. You need to have something that you are excited to talk about. That's so important.

[00:57:14] The other thing that I think requires a little bit of self-reflection as well is looking at the work that you used to do and what that work looks like today. Is it possible that that job doesn't really exist anymore or that maybe it's very rare in the form that you used to know it?

[00:57:40] And so you need to look at, like, well, what are the pieces of the job that are still in demand that you really like, that you're really good at? And are there adjacencies to your old job where those tasks and skills are abundant, perhaps?

[00:58:02] Or perhaps you can find some, you know, consulting or contracting work to just do those things to support different functions. So, you know, that's going to take a lot of kind of reflection and analysis.

[00:58:18] That's really important in today's age is to do a little bit of a breakdown in terms of what you were good at, what you did, and then apply that to where things are going today. Anything else, Meg? That pretty much covers it. We wish you luck. That's for sure. Well, that was a fantastic episode. I just, I adore Maria. She's just the real deal.

[00:58:48] I just can't help but reflect that Maria just manages to say the quiet part out loud. It's really important that we have more people do this. And specifically her asking the question, am I the right leader to lead the business at this phase? Is this job the right job for me? This is the part that I think really has people shaken a little bit.

[00:59:12] And I think she role modeled beautifully how to come out the other side of that stronger, not to get stuck in the self-doubt, but to use that moment to ask yourself a different question. Where is the gap between where I am today and what the business needs and how do I fill that gap? That role modeling was just amazing to see in real time. So we thank you, Maria, for coming, but that was tremendous.

[00:59:42] I'm so glad I asked that question. That's right. Once again, Amy always asking the best questions. The question was basically, how are you? But yeah, all the best questions. Never mind. And with that, I am so grateful to everyone for supporting us. And we encourage you to tell a friend because we're trying to grow our audience. Let's invent the future together, everyone. I believe in us. Let's make every day count.