Ep 130: Charting Career Reinvention and Prioritizing Responsible AI with Erika Oliver

[00:00:09] Hey everyone, 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. Today I'm sharing my recent conversation with my good friend Erika Oliver. She's the founder of Newton House, a boutique consultancy. She's an executive analyst at Aptitude Research, former executive recruiter and chief product officer, and a longtime executive coach and advisor.

[00:00:35] Yes, Erika has worn many hats and that gives her a broad perspective across the talent and talent technology ecosystems. I asked Erika for her candid view on where AI is actually landing in HR right now, the pressure to show real value after a year of pilots, the build versus buy debate keeping leaders up at night, and the very human question of what happens to the people caught in the middle. Erika and I covered a lot of ground, yet a part two is already in the works to recap the 2026 conference season and go deeper on her talent and talent.

[00:01:05] Intelligence and workforce analytics work. So think of this is part one. Give it a listen. And as always, thank you for spending time with us. Hey everyone, welcome back to another episode of Elevate Your AIQ. This is your host, Bob Pulver. And today I am lucky to be talking to my friend Erika Oliver. How are you today, Erika? I am great. It's so good to see you. Thank you for having me.

[00:01:27] My pleasure. My pleasure. This is an overdue, recorded conversation. I know you and I have talked plenty of times off the record, but glad to pin you down and we've got a lot to talk about. I was thinking about some of the topics we wanted to cover today and I'm like, we might have to have a part two. I don't know if we're going to get through everything, but that's kind of how we roll anyway.

[00:01:47] Pretty much, yes. Erika, tell my listeners a little bit about your background. I know you spent a lot of time in big enterprise, as did I, and then you ventured off on your own. So give us the backstory of that.

[00:02:03] Oh my gosh. You know, I could take a whole hour doing this and I'm going to try really hard not to, but I will get into some details because I do think it's relevant to folks that are in this what do I do now moment. I think of their careers and in the AI innovation cycle.

[00:02:23] So long story shorter, my original plan. So, you know, as you know, I was in leadership hiring for Meta, executive recruiting for Indeed. Prior to that, I ran talent acquisition for a professional services firm.

[00:02:42] My original plan, Bob, and you know, I've been an executive coach for a long time and not like someone who plays one on TV. I've gone through multiple trainings, most recently at Brown University in 2023.

[00:02:58] So my original plan before I went to Meta was actually just to coach full time. Okay. So I was running talent acquisition at this professional services firm. I was doing executive search there. They were actually the ones that were like, you'd be a great executive coach. They paid for my initial training. I had like a really strong practice. I had an office at WeWork in the same, it was just a whole thing.

[00:03:25] So my original plan was just to go part time there and then continue to develop my business. And in, I think, late 2019, yeah, late 2019, early, early, early 2020, Meta reached out to me about an executive search role. And I did the thing that you're not supposed to do. And I did not respond to the recruiter for like an entire month. And my sister's like, listen, if you want to be an executive coach,

[00:03:54] like, what better experience are you going to have than recruiting leaders? Back then, what was it? Not Mag7. What do they call it? A FANG. For a FANG company. And I was like, I had to make a good point. And the money was great. So I did that amazing opportunity. Still have a lot of great relationships that are still there. Folks that have moved on.

[00:04:15] And then I, while I was there, I was approached by a former colleague who had moved on to Indeed, who was spinning up an executive search firm. And I love executive search, Bob. I don't think we've ever talked about this, but I love executive search. It's, that's actually what I thought I'd be doing now. And so anyway, she recruited me out of Meta to Indeed, one of the best jobs I've ever had, one of the best teams I've ever been on.

[00:04:43] And, you know, you hear things about like a team being like a family. And then it's kind of like, wait, is there toxic coding in there? This like legitimately, I am still good friends with every single person and my boss from that team.

[00:04:59] So I did that. And then in 2023, Indeed had their first mass layoff ever. And, you know, 2023 was doing what was doing and recruiting, period. And, you know, people were slowing down hiring, people were slowing down executive hiring.

[00:05:15] I mean, we, we had been on hiring freeze for probably six months. So that department was decimated. I think they kept two people, folded them into general TA, everyone else was laid off. And that manager was moved to another part of the company.

[00:05:34] So I was literally day two of doing the training at Brown University for refreshing my coaching certification. Because, you know, you want to stay modern with this. It's not a build once and deploy many.

[00:05:51] So then, so it was like the day I found out on a lunch break, a really good friend of mine who was the very first talent intelligence person on our team who he and I were fast friends. He messaged me and he's like, I was impacted. Were you impacted? I'm like, I was impacted too. And he's like, want to start our own thing? And I was like, F yeah.

[00:06:12] And so we literally spun up a consulting firm between our two LLCs. I still had my coaching LLC. He had his own thing on the side too. And we started reaching out to clients. And so that is how I landed at that UK based labor market intelligence firm. So we had used their platform at Indeed for external labor market intelligence.

[00:06:38] We just reached out to a bunch of folks at our network about, you know, any kind of consulting we could do. Landed them as one of our clients out of the gate. And keep in mind, Bob, in my mind, I was like, okay, got a great severance package. Indeed was very generous. Plan was, I'm just, you know, the bloodletting happening in talent acquisition at that time. I'm like, I'm just going to ride this out until I get back into executive search.

[00:07:05] So we did consulting for this company. The very first deliverable call, my friend who is now in talent intelligence over at Comcast got called into jury duty. And he was like, hey, do you mind running this first kind of deliverable call with the client? I was like, yeah, no problem. And I was like, I'll dust off my old, like, you know, account management task or what will be. It's fine. If I can't answer something, I'll defer to you.

[00:07:29] I'll have this call with the CEO. And, you know, I was kind of framing up how we why we did something a certain way. And I said, I'm like, look, I was in sales before. I've been in account management. I've led account management teams. I know sales and account management always want wiggle room in terms of how they can frame something. And then he stopped and he's like, I think you blacked out. Didn't hear what I said. And he's like, I'd love to have an out of box conversation with you. And I was like, sure.

[00:07:58] So, Bob, I'm thinking that he's like going to want to hire me and my friend to do a search for him. He's like, yeah, you know, we've got this like VP of customer success and product strategy role that's been open for a long time. And he's giving me all the specs and so on and so forth. I was like, I'm like, OK, I'm like, yeah, I'm like, well, I mean, I'm sure that's something that so and so and I could work on for you.

[00:08:22] Do you mind sending over the job description? And he was like, actually, I was thinking about you for the role. And I was like, wow. OK, I mean, wow, I'm super flattered. Like, and I'm like, OK. And I'm like, I don't I don't have product strategy experience. I don't have product experience. I've hired a lot of product professionals.

[00:08:45] In executive search. So, like, I know, like, the remit. And he's like, no, we're looking for someone that has subject matter expertise like yours, buyer side, blah, blah, blah. So long story short, that transitions into a consulting engagement. And I'm like, look, why don't we just try this out? Try before you buy. I can make sure it works for me. My friend has an offer pending at Comcast. And I'm like, all right.

[00:09:09] So I consult with him for about a month one on one. And like week three, he's like, I want to bring you on full time. And I was just like, you know, I'm like thinking I want to go back to executive search so on and so forth. But then there were no TA jobs. And so I'm like, you know what? I'm like, gosh, I really appreciate the opportunity. Let's do it. And I think I literally said, I'm like, I mean, I need a full time job. Nobody's hiring in TA. I appreciate that, like the faith that you have to be. Let's do this.

[00:09:39] And at that point, like the final call again, I'm giving these details because some of your listeners are probably looking for a job right now. They probably have been part of mass layoffs. There's so many people looking for jobs right now. Like the jobs are out there now. AI is a part of it. This was an AI powered labor market intelligence firm. Didn't have any experience with AI. And I remember the final conversations with the CEO and the chairman of the board. And they were like, look, we envision you being chief product officer.

[00:10:08] And right then I was like, look, I will take I will take whatever you put my way. And I literally said to them, like, look, I am so flattered that you're thinking me this way. I've hired a lot of people at that level. I'm not that person. I'd be happy to find someone suitable for that role and and then help you in other ways. And the chairman of the board said to me in like headmaster glory, he was just like, you've got a product guy to the right to the left of you.

[00:10:35] You are exactly what we're looking for. Like you are perfect for this job. And so that's how I landed into that role. I've gotten that question a lot. And I went through all of that detail because whether I'm coaching folks at different areas of their career that are terrified about like what is next and how do they position, how do they pivot?

[00:10:57] Sometimes the opportunity that is for you is the one that you least expect, the one that you don't think you're qualified for. And you never know what a company is really looking for. So that's how I made the pivot out of talent acquisition that I wasn't looking to do. Now, how did I go into doing like Newton House from Bloom to Newton House and become an independent analyst? So I did that role for about two years. Again, UK based.

[00:11:24] I managed a team of six people all in Europe. And that, I mean, Bob, that was amazing. Like first world problems to have to go to Europe on a regular basis. But also hard when you're working 18 hours a day and with the time difference. And so, you know, it got to the point where I was just like, you know, how can I put this? You only have one life.

[00:11:50] In our off the record conversations, you and I have talked about this a lot. And, you know, I just kind of had a hard time with myself and my loved ones that were not getting a whole lot of time with me. And it was just like, you need to think about your priorities. I was like, you're right. So gave my notice, gave 12 weeks, as is typical for a role at that level. And I'm literally, I'm a planner, as you know, Bob.

[00:12:18] So I'm like, okay, 12 weeks. I'm going to transition as much as they need. And then I'm like, I'm going to network. And then I'm going to have actually the holiday season off for the first time ever. I was so looking forward to just like Thanksgiving and Christmas and not checking email and all those different things. And then the last, I'm going to say this part because it's an important part of the story. And I know you'll edit this as it makes sense.

[00:12:47] So six weeks into this transition, I end up, so, you know, you and I both are not in our 20s any longer. Okay. So as you get older, right. And I'm glad, like, I love my 20s, glad I'm not there anymore. And, you know, as you get older, it's like things get bigger, things get smaller. And so I thought, like, okay, I'm just, I guess I'm just going to gain weight in my stomach. You know what I mean? So, like, that's where I'm going to gain it.

[00:13:15] Like, once the holidays are over, I'm going to just be that basic person, blah, blah, blah. Turns out, and I'm giving this detail because I didn't give the details before. I mean, you know, but, like, I did it really publicly. And I think some people let their minds go wild. So it turns out I had a tumor the size of a cantaloupe. Thank God it wasn't cancer. It wreaked all kinds of havoc. Emergency surgery in the hospital for eight days.

[00:13:41] This matters because it impacts why I do what I do now the way that I do it. So great. Got rid of the tumor. Awesome. The problem is the pain caused my blood pressure to go to, like, crazy town. Like, there's, like, stroke and heart attack town. And then there's where I was, which they call hypertensive urgency. See? So the fact that I didn't have cancer, huge blessing. Again, tumor the size of a large cantaloupe. I'm five foot two.

[00:14:11] So that's a lot. Blood pressure, 242 over 150. Okay? So the fact that I didn't have a stroke, didn't have a heart attack, literally miraculous. The challenge is, while the surgery was successful, is, you know, when you're in that mode in ICU for that long, like, your organs start to shut down. And so eight days in the hospital, you do have time to think.

[00:14:37] And the thing I was thinking about, is there anything that I haven't done that I wanted to do? Only thing that came to mind was, like, haven't been to Italy. I always went to Italy. Work didn't come to mind. And then once I got out of the hospital, was out of, like, the thick of stuff, my sister, my cardiologist, and my GP all had to sit down with me. They were like, look, we almost, like, you were lucky to be here. What are you going to do at this time? And I was like, I don't know what I'm going to do. I'm not going to waste it.

[00:15:08] So then what I originally planned on being like, look, you know what, I'm going to, like, push my coaching a little bit further. I'm going to take a step back and see what the market's doing. You know, I saw in 2023, Bob, to be frank with you, that AI was going to be a threat to some SaaS HR tech companies. It's like, if this thing can really do what we think it's going to do, there's going to be a lot of SaaS companies that are going to be under a tremendous amount of pressure.

[00:15:37] And I've seen this in the market. So in any event, so what I decided, I'm like, look, I need, I don't know how much time I'm going to need to recover. I have time that I wasn't supposed to have. I don't want to waste it. So I want to take a minute. I want to really take a look at what the market's doing. So I had an opportunity to become an executive analyst at Aptitude. Super grateful. What better way to be able to keep abreast the market? Executive coaching, you know, that's something I'm really passionate about.

[00:16:06] And, you know, it's something that, you know, I do commercially, but I also will do at no charge because I think we're in a time and space in this world where anyone who really wants coaching and needs it, I want them to be able to have it because I'm actually trained and I don't just play one on TV. Like I want to be able to help in that way. And then everything else that I do, whether it's board advisory work or consulting work, all of that's part of the wider ecosystem of like, what is it you want to do?

[00:16:34] How can I help you get there with an ongoing passion? As you know, workforce intelligence, workforce planning, people analytics. So that's a very long winded answer. But I think it's important because I don't know if you've heard this, Bob, but I, multiple times a week, I have conversations, whether they're coaching clients, people that I'm advising, people that I'm networking with.

[00:16:59] There's a whole lot of fear given the AI innovation cycle, what that means for the career, what comes next. So I wanted to share the details of that story where like regardless of the fear, regardless of the unknown, there is a path forward. You just have to be dedicated to seeing that through and what that means for you.

[00:17:19] Well, there's a whole bunch of threads that we can pull on from that, Erica, but I just certainly want to acknowledge that I'm very happy that you're healthy. Me too. And that you've had an opportunity to sort of reassess what your priorities are. I know you've had other things going on with other, you know, family or whatever.

[00:17:44] And it's just so important that you dedicate the time and attention to the things that matter. And sometimes it takes, you know, shocking revelation or event to make you appreciate some of the things that you have. So I'm glad that things are on course for you and glad you're here. Thanks, my friend. And thanks for being such a good friend during that journey. And it's funny, you know this about me. You know, normally I would have absolutely kept that private.

[00:18:11] But I think when you're you are so down for the count that it's going to impact how you show up. You you you either let people make their own assumptions about why you show up a certain way or you don't or you let them know ahead. And I think one person I think was done an exceptional job is Fiji Simo, who just stepped away from OpenAI because of some of her own health stuff. And so I just think, you know, do I think everybody needs to know every single thing about your personal business?

[00:18:39] No, like we can have a whole nother podcast about LinkedIn and what that's turned into.

[00:18:45] But I think Fiji is an exceptional example of, you know, when you're up against something, you know, you and I think a lot of your listeners have some version of that, whether it's health or whether it's family or whether it's job security or fill in the blank, where you have that kind of pivotal moment where it's just like, I need to make a hard call in a really fluid environment.

[00:19:16] How do I make the right choice? And, you know, what I would say to people who are listening is, you know, God forbid you find yourself in ICU staring out the window, reflecting on your life. Try to mitigate the like, please let your only I wish I would have been like, I wish I would have gone to Italy. Like, let that be the only thing that you wish you would have done. But that can be a subsequent episode.

[00:19:42] So, yeah, let's jump into some of the topics that I know are near and dear to you. You've been writing as an analyst for Aptitude Research, working with Madeline, who's awesome. You've been speaking at some conferences. You've joined different, you know, industry consortiums and collectives and things like that. And so I think we'll sort of hit on some of those just over the course of you sharing your observations and insights.

[00:20:11] But I thought we would just start with, you know, what's going on with AI and in HR these days and how you sort of frame and think about the, I don't know if it's phases. I don't know if it's, you know, the year of this or year of that, but I know you've phrased it in those ways. And so, you know, the evolution of AI has been coming fast and furious since, you know, I guess it's almost four years now since ChatGPT entered our lexicon.

[00:20:40] And so how have you thought about the last, say, you know, year and a half? Oh, wow. It's a great question. I mean, I think, you know, a lot of, I think what you and I both have seen in the market is like 2025 was the year of the pilot. And like you saw it all over the news. We saw it all over the market, nature of tech, where it's like, you know, from the CEO office and from the board level, it was just like you need to incorporate AI to drive more efficiency.

[00:21:09] Not really a clear understanding of how to get there, but it's like figure out a way, like that's where we're going to put the investment. That was a lot of 2025. I also think, and this is not really news to anybody, you know, I think, I know a lot of companies used AI as, I don't want to say excuse, but rationale for a lot of layoffs.

[00:21:39] And I think both to reach productivity goals, not productivity goals, to reach profitability goals, let's just keep it real, with the hopes that AI would be able to bridge the gap. And a lot of companies have reversed course from that. So Klarna is a great example. I don't know if you know that example where they, they touted the $10 million savings and then they had tons of complaints.

[00:22:05] And so the cost to rehire was way more than the savings that they touted. Another example that has been well evangelized was the IBM example, where they were like, hey, you know, we're going to whatever efficiencies. And they paused and they were like, well, wait a minute, if we do this, we're not going to have any pipeline as far as like succession talent. And so they've doubled down on their efforts as far as entry-level hiring, which I think is wise.

[00:22:35] And I think IBM is going to end up on the right side of history of this because of that decision. Another one that's more recent was in June, which is Ford. So Ford, I don't, did you hear about this story where Ford had made massive cuts? Their CEO was just like, yeah, AI is going to like eliminate 75% of white collar jobs. They had some issues in terms of quality and so on and so forth. And the algorithms weren't doing what they thought they were going to do, not at the level.

[00:23:03] So they hired back 350 veteran engineers to like train younger folks, to train the algorithm. And so they went from like having subpar NPS scores, if you will, to like having the best profitability they've had in years. So I think 2025 was the year of the pilot by any means necessary. A little bit ear cover for like deep cuts.

[00:23:34] 2026, I think, is both pressing the unwind button on that a little bit where there have been failures. But I also think 2026 into 2027, what I'm seeing in the market, what I'm hearing from CHROs that I'm talking to in the market, folks in PE space. It is the A of kind of like, so we made all these investments in AI tools. Where's the ROI?

[00:24:02] And, you know, I've heard a lot in the market, especially in HR tech and in general about AI adoption. But I think the real conversation is where's the value that we're getting from the investments that we've made? So that's what I'm kind of seeing in the market there. What about you? Does that resonate with what you're seeing? It does. With the caveat that I think people are still trying to figure out what the ROI is. I agree that it's a goal.

[00:24:32] I agree that they have to say, OK, if we're going to move past a pilot and make this successful, you know, what is the ROI? Because maybe the business case for the pilot or the experiment was not, we didn't need that, you know, rationale. We're still tinkering. We still haven't, you know, wedded ourselves to a particular, you know, frontier model or, you know, any of those bigger decisions.

[00:24:56] But I also think, you know, this is going to sound a little sort of self-serving given the focus of this podcast. But AI readiness. Yeah. Specifically human-centric AI readiness, I think has taken on an important sort of board level, C-level, you know, discussion and importance.

[00:25:22] Because people are realizing, you know, perhaps to your point about the over-automation or the, you know, we can just deploy a bunch of AI agents to replace these people or to, you know, shrink our, you know, early tenure, you know, pipeline or what have you.

[00:25:41] And so I think they're realizing that, you know, if the people aren't ready, if AI investment in terms of the readiness of your people, if that's not part of your calculus and you're thinking about organizational AI readiness from a technology perspective or a data readiness perspective, you're missing what got you here and what's going to get you there.

[00:26:11] And so, as you know, I focus a lot on responsible AI. And then lately it's been more focused on human-centricity. And so those two things are not synonymous, although there is a lot of overlap. But I think everyone is responsible for responsible AI.

[00:26:28] But on the human-centricity side, if you don't recognize that you need to do everything in your power to augment the potential individually and collectively of your human beings that are part of your workforce, not just even your full-time workforce, but your extended workforce across all manner of, you know, legal affiliation and work that people are doing for you.

[00:26:53] So if you don't invest in that, then it doesn't matter how much you spend on tokens. 100%. Oh, my gosh. I have so much to say about this. So I want to dive into AI readiness, responsible AI, and then the human-centric part of that. Those are all, those are, it's funny, you were, like, I think very much ahead of your time, Bob.

[00:27:14] Like, I think these are things that you knew and I think the market inherently knew that needed to be addressed, but they've never been more important than the last couple of months, quite frankly. Starting with AI readiness. You know, I feel like when the conversation around AI readiness first came out in the market, it was more about fear mitigation and, like, how do we get people to adopt AI? Like, that was really more of the readiness conversation.

[00:27:42] Now what I'm seeing in the market and in the U.S. market, thankfully so, I think Europe kind of given their laws and regulations, they've always kind of been there. Readiness, I think security is a lot more part of the conversation as it should be. So it's not just are people psychologically ready or willing to adopt AI to drive greater productivity? You know, the readiness conversation is now like, are we, are we ready?

[00:28:12] Do we have security frameworks in place? Do we have the guardrails in place to be able to successfully and safely and responsibly deploy whatever AI tools that we decide to adopt? And I think that that's really come into laser focus with some of the, I mean, agentic AI has exploded, as you and I both know, over the last six months, let's say.

[00:28:42] And I love it. I love technology. I was an early adopter for AI all day, every day. But when you think about some of the security breaches that have happened with open AI and hugging face, Anthropic talking about three different instances, not necessarily disclosed, where those agents went rogue.

[00:29:01] You know, I think the responsible AI conversation has never been more important in thinking about how do you build, deploy, and let, whether it's an AI platform or an agentic ecosystem, run wild. And it particularly matters, I think, when you think about HR tech. There's a lot of sensitive information when you're talking about hiring and employee information and just HR data in general.

[00:29:30] I think the same holds true for finance and healthcare. And then the third part, so we talked about readiness, responsible AI, and then the human-centric aspect of AI. You know, again, I think you were ahead of the game on this. And I'm glad more and more people are having conversation around keeping the human as the center of the human AI dynamic. I think some of the security issues that we've seen, I think, is a 4C issue.

[00:29:57] Whereas, like, some, like, you know, I think when companies want to be as profitable as possible by any means necessary, I think now you're seeing, like, a line where, yes, but if you allow something to go rogue and you have a massive security breach, the cost of that could way outweigh the savings. But even more so, as someone who's been an executive coach for 14 years and who cares deeply about the human experience,

[00:30:26] and you and I both are in HR tech, you know, I think, or I guess I'm HR tech adjacent, and I think we're going to keep it real. You know, I think human-centric AI, I've heard a lot of the market about, and I'd love to get your perspective on this too, about what vendors are doing to, like, genuinely be human-centric in their approach and not just check a box.

[00:30:54] But as someone who's come from the vendor side, it's as much the responsibility of the buyer and the enterprise. I'm not hearing as much language around that. What are your thoughts on that? Because I can pontificate on that for a day. Yeah, well, I think as legislation has started to evolve, I think people are realizing that it goes back to, you know, we're all responsible.

[00:31:22] I don't mean all us, like, everyday, you know, users. I mean everyone who wants to consider themselves a trusted, you know, stakeholder and contributor in any ecosystem. Then you've got to take that responsibility seriously, and you've got to think about being responsible by design. And, you know, there were versions of this that go back, you know, decades.

[00:31:50] When you think about, like, computer researchers looking at, like, human-computer interaction and, you know, man plus machine, you know, all kinds of modalities of what that actually means. And human-centricity can go in, you know, a number of different directions depending on the stakeholder and the domain.

[00:32:09] But certainly, you know, ethical and respectful treatment of people across the talent lifecycle is vitally important. We're talking about people's lives and livelihoods that we're making judgments against. And, you know, we don't have to get into a whole debate about, you know, human bias versus, you know, AI's, you know, exhibition of bias.

[00:32:33] Yes. And, but I think that we've got to really think more deeply about what everything that we do means, the impact that it's going to have, the ramifications of, you know, if we outsource this task to a, you know, some non-human, you know, entity, an agent, a workflow, you know, automation routine, you know, these types of things. And what does that mean?

[00:32:58] And so somebody might look at that open AI, you know, hogging face situation and say, well, you know, basically, you know, hogging face, you know, they left a door open. They left a back door open and, you know, the open AI, you know, agent, you know, exploited it. It was, you could argue then that the agent was being resourceful. Well, we say this, you know, candidates say the same thing when they're using AI inappropriately in a job interview. Oh, I was just being resourceful. You didn't tell me not to, I was just being resourceful.

[00:33:27] Like sometimes you got to just hold yourself accountable for using AI where you should, not wherever you can. And so we've got to think about responsibility, not, and it's not even just solution providers because we're all builders. Yeah. Right? I mean, I literally have an old IBM t-shirt from my last job there. It says, I am a builder. I love that. Yeah. Because that's what we were doing with IBM Watson in 2015, 2016.

[00:33:57] We were educating all IBMers across the world on what does it mean that we are now, you know, that Geneva Medi declared we are now a cloud and cognitive computing company. What does that mean? You think somebody who's working on the manufacturing line in Fishkill, New York knows what that means? Does every salesperson know what that means? Does that, like, am I, does this change what I'm selling? Does it change what I have to know?

[00:34:23] Does it change the skills that I have to, that I have to acquire to be competitive if I want to continue on this career path? Or can I pivot? And maybe somebody takes a chance on me, like many times, as you told us, Erica, people have taken a chance on you. They've recognized your transferable skills. They recognize your attitude and your, you know, empathy and your collaboration style, your leadership style. Like, how do we, those things will come from a resume, right?

[00:34:51] And so there's just so much more that we need to think about what people are capable of, starting with ourselves. Do we know what we're capable of ourselves? And so AI is an opportunity to jump in and see how can I customize my own AI to make me more effective, to augment my capabilities and increase my, you know, potential to succeed. Bob, I want to stand up and slow clap at what you just said.

[00:35:17] Because what I was literally thinking is, you know, it's, and I, maybe part of this is human nature. Maybe part of it is, you know, before the pandemic, gosh, I mean, you know, I was at Meta and Indeed, you were at IBM. Like, that was the era of where, like, people were throwing money at you. They were feeding you breakfast, lunch, and dinner. And like, like, the perks, it almost hurts to think about now.

[00:35:48] But I think you bring up a really good point that the responsibility starts with us. We can't just wait. Like, yes, do I think we need legislation, stronger legislation in the U.S. around guardrails? My humble opinion, yes. Not to be in a way that, like, impedes, like, innovation, but, like, let's not wait for something massively bad to happen to be like, okay, now we've got to do something.

[00:36:17] But when I think about the three things you mentioned, so responsible AI. Like, you know, don't wait for someone to tell you what the right thing is. Like, look inside of yourself and think, like, hey, does this, and not everybody has a moral compass. Like, let's be honest. But most of us do.

[00:36:36] So it's kind of like, as you're waiting for the full edict from your company to be like, here's our position on how we're going to be responsible with AI. I think it's important to think about that in and of yourself. Don't let somebody else tell you solely how to be responsible. Thing one, the human-centric part of AI, you know, as I alluded to before, and I feel really strongly about this.

[00:37:04] I mean, yes, we want and would hope that vendors would be thoughtful and mindful and not just check a box there. But, like, the real owners of whether or not AI is going to be leveraged in a human-centric way, it is going to be the buyers. It is going to be, like, the people responsible for the humans at companies, typically HR organizations.

[00:37:25] But really, the whole organization, that onus is with them in terms of are you going to deploy AI in a human-centric way or not? And then the last thing I'll say on that, last but not least, when I think about AI readiness, and I'm going to lump in AI adoption with this. Because, you know, I almost wrote something about this, like, a month ago, Bob, and I, like, have it on my list. So don't be surprised if I write about something sooner rather than later.

[00:37:54] So when I hear in the market, you know, the rightful conversation about AI adoption and AI readiness, you know, the same conversation has existed within SaaS technology, period. Like, being on the vendor side, you can, like, have a product that does everything and slice bread.

[00:38:18] But if your users are not willing to be properly onboarded, if the company is not willing to drive adoption, then the adoption of any tool is not going to be there. So some of the roadblocks around AI adoption are similar issues that existed in non-AI products. So I think that's something that's getting lost in the conversation. I mean, I often make the same observation when it comes to just transformation in general.

[00:38:47] I mean, I guess our adoption is part of that. But, you know, we've known, I spent most of my 22 years at IBM going through some form of transformation, right? And then the entire three and a half years at NBC and Versailles going through transformation. They still are. Yeah, IBM is probably still going through the same SAP transformation that I went through starting in 1998. But, yeah, you're constantly transforming, right? But you're transforming as an organization from a cultural and a behavioral standpoint.

[00:39:15] And technology is going to, you know, come in waves, right? And we all have to sort of adapt to that. And adaptability is one of the key, you know, elements of AI readiness. You have to, yes, you have to sort of be willing to outsource certain aspects of what your job used to require. Those tasks may still be on your plate, but now you are managing, you know, a set of agents that might execute them more consistently.

[00:39:44] And 24-7 or, you know, whatever it is that you're doing. But I just think that organizations really need to think deeply about, you know, what made them successful to this point? What's going to, in the long term, make them successful going forward? And treat, you know, people as an important segment of your AI investment. It's not just about the technology itself. So, so true.

[00:40:13] And I do think where, you know, transformation, the AI addition is different, is that this is an innovation cycle that we haven't seen since the Industrial Revolution. And so it's, I think it's going to force the, it's going to force a needed issue to be, like, dealt with, quite frankly.

[00:40:37] And I do think that there are, I think, I'm sure you've seen this on the news, but, like, you know, what was it, like, last month? It was like, oh my gosh, saspocalypse, I can't even say it right. But I do think it's going to be, like, it's something to pay attention to. It's like multiples are not necessarily what they used to be from a PE perspective.

[00:40:58] I also think, you know, people, you mentioned something earlier, and I love the fact that in your IBM days you had a t-shirt that said, you know, we are all builders. It's almost, like, kind of prophetic because we're in that world now. And I love, I think, those of us that know you, Bob, know that you were part of Watson in the early days.

[00:41:18] I think, you know, maybe folks earlier in their career that are maybe listening to this may not understand how much of an impact Watson actually had and has had on AI overall. I mean, I remember when I was at Meta, our head of AI, we scooped out of IBM Watson and it was a big deal. It was a really big deal.

[00:41:42] And so I think, you know, what is that going to mean in terms of readiness, human-centric mindedness and adoption and transformation? I think part of this is going to be TBD. But I think the work that you're doing and the work that you've been passionate about for years at this point, I think is only going to become more important.

[00:42:07] And, you know, I think of the work that I do from both an executive coaching perspective and an advising perspective. Like, you know, I get, I'm up close and personal about the human impact at various stages of folks' career. And then, you know, it's like, and I love your perspective on this, you know, the products that make it and have a chance to be a real unicorn or to have a chance for investment, like, the bar is way higher than it used to be.

[00:42:36] There are a couple of companies. One, disclosure, I'm advising them. The other, I just like a whole lot. Mirovi, they're like a talent evaluation platform. I became an advisor for them because I fell in love with the platform and doing like a briefing with them. It was just like, there are a bunch of folks from a well-known executive search firm. They built the thing that they needed. And it's fire.

[00:43:01] And I think it's a great example of how do you, like, executive search is always going to have to have a human nature to it, just given the confidentiality that's there, given like whole other podcasts there. But being able to leverage AI and technology in a way to make that process more efficient, I think, is long overdue. And I think they've done a great job on that. You should have them on your podcast. The other one, I just don't like these guys.

[00:43:30] So it's GoGig. I'm actually talking to them again later on today. The only part that I've seen of their suite of products is a veteran. So I don't know if you know this about me, but I have a huge, huge, huge place in my heart for veterans, for active duty military and folks making the transition.

[00:43:50] And I don't advertise this, but I regularly have folks that I coach that are either active duty or transitioning veterans. I just think it's important. And the reality is, you know, finding a job once you get out of the military, especially if you've been there for, if you've been a career military person, it's hard because it's hard to like translate what have you done to what do you want to do. GoGig has absolutely crushed it.

[00:44:20] So check them out when you get a chance, Bob. Also, we're having them on your podcast. They have developed a product that has exceptional product market fit, where basically it translates skills of active duty military and veterans to particular jobs that you like pop into their product or it'll go through career paths.

[00:44:45] And, you know, when I think about my days at Meta and Indeed, there was a passion, you know, in kind of the DEI era. Veterans are very much a part of that. I think most companies did a really bad job of being able to translate what is a major in the Army that's done X, Y, and Z. How does that translate to this role that they're applying for? These guys have done a great job in solving for that.

[00:45:13] Other fun fact, I actually, before I got sick, I like would speak on a regular basis at West Point. Huge privilege to their data science classes. And, you know, it's when you have folks that both are dedicated to like kind of serving our country, regardless of what that means. But then don't even have any idea of what life is like as a civilian. Like they don't even know how to think about that.

[00:45:43] So anyway, two things I feel like from an AI perspective that's getting it done right. I just wanted to shout out those two companies and add that to your list of people who have on your podcast. Yeah, I'm a big fan of anyone who's building solutions for candidates. Yeah. Certainly those who've observed and any, you know, historically disadvantaged, you know, populations. Huge supporter of. And that includes neurodiversity. Yeah, absolutely. Right. I think, again, a little bit sort of slightly selfish, but not really.

[00:46:12] I mean, that's partly why I felt so strongly since I can remember, since I've thought about people related decisions, even back to my IBM career when I was not in HR at all. It was more work around, you know, collective intelligence and just human potential and, you know, playing into our strengths and not focusing on weaknesses.

[00:46:37] And then, you know, how some of these, you know, matching systems work, how a matching system, whether it's inside a, you know, internal talent marketplace or matching engines or, you know, project based ones in the gig marketplace.

[00:46:52] I mean, there's so many ways in which I feel like there's a ton of solutions out there, but yet here we are still having trouble, you know, matching the right people to the right jobs, which is why, you know, I'm at least today, you know, pretty sour on a lot of the talent acquisition space.

[00:47:13] Because I just feel like we're just keep throwing, you know, millions and millions, if not billions of dollars into these problems that just can't seem to go away. And yet it's still taking a concerted effort for us to think differently about how we're attacking the problem. I mean, this is a two-sided marketplace in talent, and yet most people are just serving one side and expecting magic to happen.

[00:47:40] And it's, I mean, it's pretty frustrating to witness as an advisor, as an analyst, as a, you know, talking head in the space and as an occasional candidate as well. I mean, it's infuriating sometimes. You know, Bob, I'm going to intentionally interrupt you here by saying I could not agree with you. Because, you know, when someone's like, I'm sorry for interrupting. No, they're not. So I'm like intentionally interrupting you.

[00:48:07] Because I so agree with your point on neurodiversity. You know, what I think, two things come to mind in particular. One would be the autism spectrum. I think, you know, just like with anything, sometimes, you know, people claim to be on the spectrum and, but never seek the diagnosis. I'm kind of like, go ahead and get the diagnosis, you know, kind of know where you are.

[00:48:34] You know, don't, don't just leverage that, you know, because that's a word right now. So I think it's a disservice to people that are truly on the spectrum. And the same thing goes for ADHD. People who legitimately have ADHD, like they're, I have folks that I coach and they all are either creative or technical. People, exceptional. But like that, there are real roadblocks that can come into play, both from the hiring process and being in seat that come to mind.

[00:49:04] I would, I don't know about you, but I would love to see more products in the HR tech space tackle that. Tackle fairness when it comes to the autism spectrum and like the ADHD spectrum. I think there's huge need for that. And I think candidates in general would be much better served if that were addressed better. Yeah. Yeah, absolutely. I guess one of the things that, and just to, I guess, put a pin in that, in the candidate side of things.

[00:49:32] I mean, I, I recognize that it's, you know, companies, employers that have the big budgets to buy these solutions in the first place. But, you know, we, you hit on the, you know, build versus buy a little bit before and, you know, I feel like now there's, because the, the time and investment level has dropped with the ability to build, you know, AI powered solutions.

[00:49:57] My hope is that we'll see more, more solutions to help on the candidate side, but not necessarily. So just to keep up with this, you know, cat and mouse game, you know, between recruiters and, and candidates. I mean, I think that is just this never ending and disappointing, frustrating story for all sides.

[00:50:19] And I don't know that people are using, right now, they're using AI, they're not using AI necessarily the most constructive ways, right? They're just accelerating. It's like faster and higher volume is not going to get us where we need to go. I totally agree with you.

[00:50:37] And, you know, kind of on the build by, I've heard like, I think it was last year at the Global Talent Intelligence Conference, Bob, Nick Kennedy, who I love, who runs like the Workforce Planning Institute. He like mapped out 12 different Bs. And I was like, oh my God, like, because I mean, there's so many different things that go there. You mean build, buy, bought, borrow, that kind of B? Yeah. And then, and then like eight more on top of it.

[00:51:04] I think the, right, I mean, seriously, it's just like we get, and I mean, again, like, you know, the build, buy, borrow, et cetera, et cetera, et cetera conversation has been around since the outsourcing days. The real conversation that I'm hearing in the market right now is, I mean, yes, borrow is part of it. Of course, bought is part of it.

[00:51:31] But the real conversation I'm hearing right now and the pressure on SaaS companies is the build versus buy conversation. And I think it's something, you know, for any vendors that are listening, it's going to be really important to pay attention to with an open mind. Like, sure, on one hand, it's like, is it easier, safer, more secure to go with a particular vendor solution?

[00:51:59] Maybe, probably. But going back to the beginning of our conversation, talking about from the C-level saying, like, you know, we need to pilot AI, you know, and kind of the transition to like, now we need to see the ROI. Sometimes it's AI pilots or like, the conversation is really around build. Like, do we need to buy this or do we need to build it? The pressure on SaaS companies, particularly in HR tech, it's like, hey, do we spend $100,000 on X, Y, and Z?

[00:52:29] Or do we try to build it in-house? Or do we renew our multimillion dollar contract with X, Y, and Z? That's all purpose. Or do we see what we can build and then fill in the gaps with point solution? So I think it's a real pressure point. That's like, we're living it right now.

[00:52:52] And, you know, while I think the argument is very sound about like the cost of maintenance and the cost of like getting it wrong versus getting it right from a build perspective, I think that's very real. But when I talk to CHROs and like workforce planning, like senior leadership, the conversation is not necessarily about perfection. It's a lot, it's often about is good enough, good enough.

[00:53:20] And so I think the build versus buy specifically conversation, I think that's one for vendors to really pay attention to as we start to head into Q4. And folks start thinking about Q4 renewals. Like, I think that's very, very real right now. It's definitely a very real scenario that people need to evaluate.

[00:53:42] But I want people to recognize that you were sort of alluding to this, but like there's a lot that goes into building like an enterprise grade solution, especially if you're in a regulated industry or in a place like HR. Where, I mean, I just had a software development company reach out to me on LinkedIn asking if I wanted to help building, you know, a custom AI powered HR solution.

[00:54:09] It's like, well, first, first of all, you didn't look at, you were trying to connect with me. You obviously didn't look at my profile. Well, and second, you can't just come out of the woodwork and build a secure, safe, ethical, you know, responsible solution where the legislation and regulations are changing, you know, day in and day out.

[00:54:34] Not to mention that, you know, the trust factor is huge. So if you think you can just pop in here and build me a bespoke, you know, talent solution without recognizing all that goes into that process and that buying process or whatever. Like it's just can't, it's just really ill-advised area to focus on. So I just think people need to be careful.

[00:55:02] You know, it's hard to just say, well, I could just build a bunch of, I've got some technical staff and bandwidth and some investment dollars. And I could just, yeah, and I could just whip up a new ERP system or some, you know, tentpole, you know, core system. I think if anything, you're probably better off building some niche tools that could plug in, you know, those point solutions that you were describing.

[00:55:28] Probably have more success trying to build a point solution that's custom to what that particular team in that domain or that geography or whatever really needs because it's going to, you know, when you've done the calculus and you can build it for X and maintain it for Y. And that still is half the price of, you know, the staff and the recurring license fee if I were to go buy it.

[00:55:53] But if for a company that's, you know, small mid-market, that doesn't have the technical, you know, chops and the budget for all that, I don't know. You just, I'm not saying one way or the other. You just got to, you go in with your eyes wide open and make sure you're doing a full total cost of ownership on what that's going to look like. I could not agree with you more. And it goes back to our conversation around responsible AI.

[00:56:18] And, and like, I think thinking about how to deploy AI responsibly should be a part of the calculus when you're thinking about the build versus buy conversation. True story. And I, I know, I see your face. You're like, I'm already buckling up. But it is a buckle up conversation.

[00:56:37] So I have a good friend who is head of HR, technology, all the decisions, large, well-known company that you would know, which is why I'm not going to say it. Literally, the founders went to him and asked him, hey, do you think that we could build something like greenhouse via Claude Code and just use that?

[00:57:04] So my friend had to respectfully, like inside, he's like fireworks going off being like, what are you talking about? That's ridiculous. So he had to respectfully say, there's a lot more to it than that. So he took the responsible approach and pointed them in the direction essentially to what you said. Like, let's start with more niche things. There's so much more that Greenhouse does rather than being an ATS. Again, founders are not that much in the weeds there. I love Greenhouse.

[00:57:33] They're an incredible company. And I don't think there's anywhere on God's green earth that someone could replicate that by building it in Claude Code. But these are the kinds of questions and requests that people are getting from the senior most level and the effort of saving costs. And they're like, well, why can't we build it?

[00:57:49] So I think that's a part where I think people are going to have to take ownership of defining responsible AI in their own day-to-day work, even if that has not been laid out as a codified framework for them.

[00:58:35] Yeah. They're going to have to take ownership of the future. You know, Dan Chay didn't just say, oh, you know what? I bet some of my guys could just vibe code a new voice agent. But no, they went to Ophir, you know, Samson and Ezra Labs and says, these guys, these guys did it right. They were responsible by design. They kept track of all the legislation around what it means to where do I store this data? How do I interpret this data?

[00:59:01] How do I not, you know, inadvertently disadvantage certain populations? They purposely don't look at the video because that could be give you a false positive on whether someone's looking at another screen. They may just be neurodiverse and they have trouble maintaining eye contact with the camera or all kinds of other reasons. So Ophir and his team thought deeply about that. And yeah, Dan, yeah, they could have gone off and spent time trying to figure that out.

[00:59:28] But they made the calculation that this was much this was a much better approach. So I think all that some of those fundamentals around, you know, M&A strategy and growth strategy still still hold. You've just got to be aware of the, you know, that what AI is capable of today is going to be a subset of what it's capable of three months from now. Isn't it funny now? I know I know we're at time, but isn't it funny that we are talking in terms of three months?

[00:59:58] Remember back on the day, Bob, and we were talking about the next three to five years? Or the next 24 months? Yeah, it's insane. I mean, even as I'm going through, so I'm in like a boot camp right now to elevate my own AIQ. Yeah. And it's, you know, the tools, I mean, we're like fixing the plane while it's flying, right? Like it's insane.

[01:00:21] Like every, almost every week, it's like, oh, here's something else that we may, you know, pull into our tech stack because, you know, it does this better than that or, you know, whatever. It's just like, it's a lot. It's a lot for people to try to absorb. But I still contend that you can't wait because we don't know if or when things are going to slow down. So you got to jump in. And because we can all be builders, all the more reason why AI literacy needs to be responsible.

[01:00:50] AI literacy so we can, you know, we don't turn into, you know, weak links in the organization. I totally agree. And I think to close this out on the human side of things, it's, there's a whole lot of building the plane as we fly now more than we've ever seen before.

[01:01:09] And so I think it's important that like the C-suite, business owners, executive leadership boards understand the impact that that has on people that are actually responsible for getting the thing done.

[01:01:28] And I think, you know, the human-centric AI mindfulness that we need to have is both with how do humans and AI platforms, agentic ecosystems, hold the blank, work together. But I think there needs to be a mindfulness of especially where we are right now in the innovation cycle of what is the load on getting that right?

[01:01:57] And the load is greater if it's done responsibly than I think a lot of boards and a lot of C-level folks realize. Right. I wish I had a mic to drop. I actually do have a mic to drop that if I did, it'd make a horrible noise. I won't do it this time. Don't do it. I'll save it for next time. Okay. Erica, great to have you finally on the show. Yes, we'll have to do a part two at some point in the not too distant future. But really, really appreciate you spending all this time with me.

[01:02:26] Some great takeaways for my listeners. So really, greatly appreciate it. Thanks for having me. Until next time. All right. Thanks, Erica. Thank you, everyone, for listening. We will see you next time.