How AI Is Changing Hiring in Insights & ResTech

In this episode of the Greenbook Podcast, host Karen Lynch sits down with Joanna Byerley, founder of Talent Pools AI, to unpack how AI is reshaping hiring, leadership, and careers in the insights and ResTech space. Joanna explains the shift from traditional executive search to talent intelligence—a more strategic, data-informed way to map markets, pipeline talent, and benchmark salaries before a single role is posted.

She and Karen explore the emerging reality of jobless growth, the erosion of entry-level roles, and what that means for the future leadership pipeline. They also discuss how to build AI-native roles, what separates the 5% of successful AI initiatives from the rest, and why human judgment remains the irreplaceable core skill for insight and strategy teams navigating ambiguity.

Key Discussion Points:

• From search to talent intelligence: How market mapping, talent pipelining, and salary benchmarking create smarter, less transactional hiring strategies for insights and ResTech leaders.
• Jobless growth & entry-level erosion: Why AI-enabled productivity is decoupling revenue growth from headcount and what that means for early-career researchers.
• Careers as lattices, not ladders: How AI and role convergence (product, consulting, insights, tech) are reshaping career paths and the skills that matter most.
• What successful AI leaders do differently: Governance, focus on a few high-impact use cases, realistic 2–4 year horizons, and a culture that tolerates disciplined experimentation.
• The one thing AI can’t replace: Human judgment—the ability to weigh context, nuance, ethics, and risk in turning AI outputs into business decisions.

Resources & Links:

• Talent Pools AI – Executive Search & Talent Intelligence
• Joanna’s article: Jobless Growth: The 90-Day C-Suite Playbook #25
• Insights Career Network (ICN)

You can reach out to Joanna Byerley on LinkedIn.

Many thanks to Joanna Byerley for being our guest. Thanks also to our production team and our editor at Big Bad Audio.

Transcript

Karen: Hello everybody. Welcome to another episode of the Greenbook Podcast. I’m Karen Lynch, and I’m so happy to be hosting today, which I think I say all the time, but some days, I just approach these podcasts with a bigger smile than usual, and today is one of those days. I’m talking to Joanna Byerley, who I’ll introduce to you in just for a moment, but I first came across Joanna at our IIEX AI event when I noticed her engaging in the back room throughout the course of the event. And I hadn’t heard of her name before, so I looked her up, and I’m like, “Oh.” So, she’s the founder of Talent Pools AI, which is, you know, kind of, talent solutions, but data-informed talent solutions in this era of AI. And so, not only does she kind of frequently voice her contributions on air, she is a thought leader in this space, and I’m always happy to talk to a female founder. So, we’re going to get into all of that, but Joanna, let me just pause for a moment and say, welcome to the Greenbook Podcast. 

Joanna: Well, thank you so much for having me. I’m really excited to be here and very honored, so thank you.

Karen: You’re quite welcome. You’re quite welcome. So, before we get into the conversation that you and I have planned, let’s just, you know, kind of level set for a minute, and I’m going to allow you to kind of expand upon what you’re doing at Talent Pools AI, and kind of why you started the company. But also, I just want everyone to know—listening—that this is my first time actually having a conversation with Joanna, so this conversation might be a little different because I will truly be going where my curiosity leads me in this conversation. I have so many questions for you. So anyway, so everybody be forewarned, this is a podcast episode that I’m particularly excited for because I’m curious about it. So Joanna, tell us a little bit about yourself, and then what you started building at Talent Pools AI.

Joanna: Yes, sure. Thank you. So, hi everyone, I’m Joanna. I’m the founder of Talent Pools AI, as Karen said, and what I do is help ResTech, Insights, and Strategy startups and scale-ups with data-informed, human-led talent solutions. My work sits at the intersection of executive search, talent intelligence, and AI capabilities. So basically, to cut the jargon, I help founders and C-suite executives build their leadership teams, you know, finding the people, the unicorns, who can actually execute on the big transformations they are promising, today, investors and clients. In each project I undertake, I utilize a robust search methodology. So, all my assignments are tailor made, and it can range from, say, helping you to hire senior executive, through bringing on board this hard-to-find niche talent, which apparently is my specialty, to market mapping, talent pipelining and salary benchmarking. So, when I set Talent Pools AI, my mission was quite simple. I want to help companies to hire better and think a little bit more clearly about talent. And yeah, it means retained executive search, but also talent intelligence, tracking who’s moving where, how is AI reshaping these new roles that we have, and where the next wave of leadership capability is coming from. So, I started in a very, sort of, traditional market research, but a lot of my work now is with AI-enabled insight platforms and strategy firms. So, I’m looking at both the tech and the human side: what skills are we using, what new roles are emerging, and how leaders can grow their businesses without actually burning out their teams?

Karen: There’s so much to talk about, and I’m just so excited that we’re having this conversation. I want to just go back to the phrase ‘talent intelligence.’ People that listen to me, you know, like our producer Brigette is probably like, oh, Karen sure loves that word intelligence, and I do. I love customer intelligence. I love product intelligence. I love anything—strategic intelligence—I love anything that kind of points towards it’s a step beyond insights into to, you know, kind of greater knowledge, a greater knowledge base. So, talent intelligence feels like something different to me that I want to just pause on a minute. And when I say different, to those listening, I’ll just kind of clarify my own word. It sounds different from traditional executive search, this idea of talent intelligence, so please talk to me about that, which we didn’t really, you know, go into in advance. I didn’t kind of say, hey, I’m going to ask you that on the spot, but how do you define talent intelligence?

Joanna: Okay. So essentially, I tell you when it all started because initially, I’ve worked in a more traditional search and recruitment and—just to make it clear for all the listeners—I’ve been in the beautiful world of market research and insights for a very long time, since the beginning of my career, really, hiring talent acquisition. But post-Covid restructures, lots of redundancies that we’ve seen in the marketplace, all the pressures that we have from sort of economics and political point-of-view, and also explosive growth of AI recently helped me really rethink traditional search. So, I’ve moved from just filling mandates, search mandates, to asking what capability really, sort of, [unintelligible 00:06:01]. Also, when elastic platforms started to scale, I find myself working with people I’ve never worked before, like engineers, product leaders, data scientists, who are trying to embed insights into enterprise workflows, rather than treating insight function as a separate function in a business. So, I realized that people actually do not want to hire often, straight away. What they want to do is explore the market, especially, you know those new ResTech companies, say you know, they started their life in Europe, and they are expanding to the US or to the UK market, they want to look into the market, and first of all, see, you know, do we even have talent in this market? What’s the talent like? So, that’s the idea of market mapping. Then the talent pipelining is when you actually start identifying those individuals in this particular market, or pair a particular brief with a client that might be the right people before you even engage them. When you start doing talent engagement, we call it talent pipelining, when you actually speak to people and start engaging them. And you know, and a lot of occasions as well, I help with the salary benchmarking exercise. So, the whole intelligence, think about the market research and what you do with consumer in terms of intelligence; the same applies to hiring. You need to do your research before you actually go out to the market and engage your candidates, for instance. Yeah. 

Karen: It’s such an interesting, subtle shift in how we think about search, and yet it feels so intuitively important right now, and I’m so glad we’re having this conversation. The analogy that’s popping into my head is, well, of course that’s what everybody should do before they start of the hiring process is map the market. It reminds me again, the analogy is of selling one’s home or buying a new home, which I just had some experience within the last few months where, of course, we want to understand what’s happening in the market. You know, how many buyers are there? How many sellers are there? What’s competitive pricing? How long were things on the market for? Of course, we want to look at all of the data before we make such a big decision about buying and selling a home. Of course, we would do that with human beings in a search process. So, it’s very intuitive what you’re talking about and, you know, I think so critical. So, kudos to you for building something. Talk to me about then, you know, what was the moment, the aha moment for you, when you’re like, I’m going to build a business? 

Joanna: Oh, that started many, many years ago. Actually, interesting fact, I lived in South Africa for a while, and I started my first business in South Africa. And the reason why I did it because I’ve always been very passionate about working in partnership with clients, I never really believed in sending CVs and you know, seeing what catches. I wanted to deliver good service to all the parties, candidates and clients. And without partnership with your client and understanding their business inside out, you cannot deliver service to candidates. So, that’s how I started. I just wanted to be really pioneered as non-transactional partnership-led, relationship-building in this industry, which often is brushed, you know, with one stroke and saying, you know, all recruiters are rubbish. And then, as you know, market has changed, I started seeing that candidates and clients wanted a bit more. And that’s when the search, sort of, recruitment changed into search. And I think you know from, sort of, service point of view, this is the way that everyone loves working when they, sort of, get to know you and understand what do you mean, actually, when you provide search rather than recruitment. But like, you need to educate your clients all the time because they do not understand and often they wouldn’t have a good experience with recruitment partners because they work on transactional basis.

Karen: Well, and let’s talk about, kind of, the elephant in the room, which is AI. So, obviously you have—you know, if AI is part of your company name, Talent Pools AI, it’s at the heart of much of what you’re doing. And obviously that is an era that is, I am sure, consuming the search world right now, on some level. There’s a lot of opportunities it brings, and also some challenges. So, what are you seeing out there? How are leadership teams, kind of, adapting or struggling in the, kind of, AI era? 

Joanna: Oh, I love this question. Okay, I’ve said I started in legacy research, and now because I’m working with a wide range of stakeholders, ResTech companies, insight teams, and those research firms that I’ve established relationships with many, many years ago, I basically have a front row seat, if you like, watching leadership, either working AI out or really struggling with this. But I’m seeing a few clear patterns. The best teams—I don’t know if they the best, but like, probably the most confident teams—are honest about the gap between hype and reality with AI. They know AI is powerful, but they also know that probably 90-plus percent of test runs do not deliver return on investment straight away, and those leaders are very strategic in their approach. They put their money on the, sort of, few solid use cases, and they give themselves two to four years’ time to make it work. And also, importantly, they hire people who can live with this ambiguity. On the other hand, you have struggling teams who often would use AI as a cost cutting exercise rather than the full transformation strategy. So, what you see—and it’s not relevant only to the insights market, but also across the board, technology market—entry-level roles are disappearing, right? Hiring freezes get justified by efficiency gains, but there is really no plan how does this going to change the leadership pipeline, let’s say, in five to ten years time. So, that’s the idea of jobless growth that I know we’re going to be discussing later on.

Karen: ...too, yeah [laugh]. Well, I want to put a pause in the conversation for a minute and ask you about two things that you said. One, you mentioned the importance of kind of comfort in ambiguity. And I want to just pause there for a minute. Again, not part of our brief, but how might one, if you are not naturally kind of accepting and comfortable living in a state of ambiguity, how does one develop that, the ability to navigate ambiguity or again, live in it, accept it? Do you have any, kind of, pro tips that you’ve seen that help people, maybe candidates that aren’t there, but they need that quality? Or is it something that you think is innate in people; they either can or can’t live in that space?

Joanna: I think they can with training. And I think what you see now is blurring of boundaries in the marketplace. So, for instance, I’ve seen a lot of senior management consultants moving to ResTech. Some of them, you probably have them on Greenbook, they are ex management consultants who have launched ResTech businesses. And then you’ve got product leaders who are moving to insight businesses. And then you have insight consultants who go and work for tech company in the product area. So, I think that the businesses and the person you know in the same way that will adapt the best is someone who can, like, embrace this convergence. And sort of, do not think, “I’m just a market research person,” “I’m a marketing guy,” or, “I’m a, you know, sort of product person.” So, I think it comes with learning. And, you know, we always learn. And at first, you might not be comfortable with ambiguity. But what I’m seeing Karen is a lot of clients who come to me now ask for candidates who will be able to thrive in ambiguity.

Karen: That’s really interesting. I’m remembering when I had—I was self-employed—sidebar—I was self-employed for much of my early career, like, 17 years self-employed before I went full time and I remembered speaking to somebody at the time, I was like, “I am six months into this job, and I feel like I am still trying to learn. Like, six months in, and I still don’t really know what I’m doing.” And we had a big laugh about, you know, this idea that that change should have happened. In my head, change should have happened faster, but I stayed in this state of uncertainty, and I switched my career up. Then I switched my career up again. Now, I’m at Greenbook. I switched my career up again. Now, I’m very comfortable in ambiguity. And what’s interesting to me is I don’t think I was, you know, eight years ago when I first made the biggest change. And I think maybe that’s the lesson, right, for people is, you know, taking incremental—or making incremental changes and taking those leaps of faith teaches you to live in the uncertainty that’s ahead because you have more experience living in the uncertainty, and then you trust that it turns out, okay. Does that resonate with you?

Joanna: Absolutely. And as we move forward with the whole, you know, transition of the job market with AI, ambiguity will be a skill that—or, you know, a state of mind, [laugh] if you like—that is going to be really in high demand.

Karen: Yeah. Oh yeah, it’s almost like, if I was a leader hiring—which I’m not—but if I—which I’m not hiring, but if I was, I might be looking for people who had made job changes. That’s new. Often we look for people who stay put, right, because that meant that they were loyal employees. But now, maybe I want the people that have moved a few times because they’ve practiced that muscle. Anyway, super interesting. Thank you so much for that. I do want to get into one other thing you said before we move into the jobless growth, which we’re going to talk about, but the entry-level positions is something that perhaps at the leadership level, it’s not that important, but I do always have concern for entry-level employees coming in from their education and having those jobs not be present. And here’s one of the reasons I’m asking. It’s simply because I have children who are still, kind of, in that phase of their lives. My daughter is still in college. My son is a recent graduate. Many of their friends are struggling already, so I’m talking about that, you know, 20 to 23 here in the US, that age group, and they are resisting AI a bit. They don’t necessarily love it. Some do, some don’t. My daughter loves it; my son resists it. Some of their peers love it or resist it, and they don’t know this yet about the change in the entry-level jobs. So, focus on that for just one moment. Tell me what you’re seeing, tell me how real it is, and then we’ll maybe get to some advice for younger listeners, who may be, you know, about to step into the industry.

Joanna: Well, this is a very close topic to my heart because my son is going to be 17, okay? So, he’s just started in the UK A-level education, and they are all focused now on deciding on university, job experience, et cetera. And I completely agree with you, they use AI perhaps, you know, just like to help them. I’ve introduced my son to Claude because Claude is a little bit more [laugh] natural than ChatGPT. But then, I don’t think they’re using it as much as we do, and I don’t think they realize what the future is going to hold. And it’s really difficult to estimate because in the past two years, the change has been so huge. I don’t know what my son is going to choose, you know, as a degree and whether three years down the line, this job will even exist. I mean, Elon Musk said that in five years’ time—I was listening to him one day because it always comes on my algorithm [laugh] somehow on Instagram—and he was talking that in five years’ time, we’re not going to have any apps. So, how does this affect young people? I don’t think they—I think it makes them feel a little bit uncomfortable, and I think once they get to actually choosing a university course, that’s where the focus is, I guess, you know, now, but today, I don’t know if you’ve seen, like, the news flashed for me—CNBC it was—that it’s a really dire market for young people to get into jobs. And I had some statistics, I just don’t remember now from the top of my head, but the numbers are increasing and increasing, and younger people, we call it in the UK, out of job and out of education because they can’t find a job. Yeah. But you know why it is? Because hiring is stopping at the bottom level because of productivity gains that companies can gain with AI. And if you have a pyramid, Karen, the foundation, you need to feed that foundation, otherwise the it will collapse from the base. And if young people are being replaced with AI because, you know, it’s cheaper and more productive shorter-term, we’re not teaching them, and they will never get this ambiguity that we were talking about.

Karen: Exactly. They need to start somewhere. Yeah, no, it’s very interesting kind of quandary. So, let’s go into this, this jobless growth, I think paradox is something that you called it. You know, the idea that the economy might be doing okay in some areas, the GDP might be increasing, you know, companies might be growing, they might be profitable, yet hiring is, sort of, on pause, so there’s jobless growth, right? That’s a very simplistic way to, kind of, put it out there. But just talk to me a little bit about do you think this is temporary? Do you think this is just based on the disruption with AI, or do you think this is here to stay, that we’ve got a real problem? What do you think? 

Joanna: Okay. A very good question. Look, I’m not an economist, I’m not a management consultant, but I read an awful lot, and what I’m seeing across multiple data sources—and also, most importantly, what’s happened in the real world, you know, talking to clients, talking to client that I’ve worked with who haven’t hired for 18 months, for instance—this is definitely the early signal of a lasting structural shift, I personally think, not the short-term correction. And let me explain why. You’ve mentioned economy. If there was just a blip, hiring would bounce back when economy does, but in the US—and this is what, sort of, prompted me to write that feature about jobless growth—because the economy is growing, but the hiring has dropped back to the levels of 2009. Goldman Sachs said it’s—actually Goldman Sachs and another source, they are outplacement firm in the US, they’re Challenger, Gray & Christmas—and they’ve concluded—and it was all over the media, that’s why I’m mentioning it—that you US hiring had collapsed to 2009 levels. It is a 58% drop. And we have not seen that kind of gap before, except from recessions. This came to light in October because you had, in the US at the time, government shutdown, which, of course, information blackout on official federal data, so you didn’t get the monthly job reports from Bureau of Labor Statistics, and that’s when this report was published. And it’s been picked up by news both in the US and the UK. At the same time, there were two other reports published in the UK. So, King’s College London did a very interesting study. They’ve analyzed every single job post on LinkedIn and across other sources since ChatGPT launch, and what they concluded that companies cut jobs by 4.5%, but the junior roles that we were discussing about were cut by 5.8%. And what they say it is, you know, still happening. The jobs are being eliminated, although revenues are going up. So, I think that it is early, but I think it’s structural. It’s driven by technology, cost pressure, culture shifts, and I believe it might be—as I say, I’m not an economist [laugh]—but it might be a new growth model, not a permanent—like, not a temporary slowdown.

Karen: Yeah, that’s interesting. And, you know, the connections I’m making in my head right now—and I want to kind of talk about that pyramid, and if we’re truly eliminating a bottom rung, right, we need to, you know, kind of pull up the ladder, figure that out. But let’s talk about that in a moment. It sounds to me that if these entry-level jobs are kind of being eliminated at a higher rate than just jobs being eliminated in general, it would seem that there’s more opportunities for people that, you know, are more seasoned in their careers, whereas I think that it used to be that companies would hire younger because they’re a little less expensive than somebody who has more… [laugh] you know, more years under their belt. So, I think that that’s a big change. Are you hearing that, that the appeal of hiring younger because they’re less expensive has shifted also, or is it—because, I don’t know, that to me, seems like a fairly big change.

Joanna: I think there are lots of different forces in play that are causing this disconnect, if you like, and it’s not only that you rightly say the bottom layer of junior people are affected. Obviously, we’ve had massive redundancies across both sides of Atlantic over the past 18 months. But I think it’s all down to AI-enabled productivity. And actually, I was reading something, I have to read it out to you. There was a conference in the UK, not going to mention the names because we are supporting Greenbook here, but [laugh]—

Karen: No? Thank you [laugh].

Joanna: This is from one of the CEOs talking about growth and hiring, and I’m quoting, “But we also said that AI should enable us to double revenues without changing headcount. Part of the reason for saying that was partly to say we are very ambitious about this, and it was also to say no one is going to lose their jobs. But you see where the paradox comes from because you are doubling your productivity, but you are not hiring because you’re not replacing people who are simply leaving, so therefore those more experienced people are also affected.” So, you know, it’s not only entry-level roles, but I think entry-level roles, from this strategic point of view of talent acquisition, it’s really important to understand that if you do not invest into this level of hiring, in two or three years’ time, you’re not going to have mid-level people. And currently, I’ve seen it with my own eyes on LinkedIn. Someone was talking about, why is it so difficult to hire SREs, core SREs? Why? Because you haven’t— 

Karen: Yeah, because they haven’t been raised up, so they don’t exist. That human being is not out there right now because they aren’t—I think that is so interesting. So again, to the leaders listening, we have a lot of people who are at the leadership level who are listeners, in addition to, you know, newer players to the market, but if you’re listening the—I think—big takeaway for me would be, it’s urgent to hire those people, even if you don’t think you need them for efficiency sake at the moment because somebody at a higher level can use the tools that are available and do that work. However, then in a few years, if that leader moves on, or if that person moves on, there’s no one to fill their shoes. That is a big quandary that’s ahead. People can’t see it, or people are so focused on the now, what do you think? What’s the lesson for leaders there? How do you help them see that future?

Joanna: Look, one of the pressures that leaders have is margin, isn’t it? It’s a margin pressure because, you know, we’ve been in this volatile economic environment for quite a while, so the efficiency has become like a driving force. If you can grow revenue without growing headcount, it’s brilliant. You have narrative now to justify that. But, you know, it’s also interesting? I think the other reason is cultural. And I’ll tell you what I mean by this. In the past, you know, I’ve been hiring—I’ve hired 20 people this year. You were growing business because you were growing headcount—

Karen: Headcount, yes.

Joanna: —but now leaders are prioritizing lean, efficient teams. They want agility. We even have an advert from Mishcon de Reya, which is big law firm in the UK, “Leaders want agility. They do not want scale,” they say. And that is the mindset that alone is holding back hiring across all the levels. And look, you know, if the economy can expand through productivity and technology, then some leaders, especially those who have been suffering because their budgets have been cut with their clients, they want to grow their workforce. So, that’s a disconnect, right, [crosstalk 00:30:01].

Karen: Yeah, yeah. What would you say? Like, I’m thinking now specifically of some of the startups in our ecosystem that, you know, they’ve just gotten funding so therefore they can grow their teams. And you think that—I shouldn’t say ‘you think;’ not everybody does—I think [laugh] that funding means they can hire to grow and scale up. That sounds all of a sudden like that might be kind of an outdated model of thinking, that funding means headcount for growth to be able to scale up and manage the growth. But is it a totally different model now? Are funders expecting the teams to not scale up that way, but to just use the money wisely? So, is the pressure coming from the external funders in the startup space, or is it their mindsets? Because a lot of these individuals don’t have the history with a different way of being?

Joanna: Yes, absolutely. And I think it really depends what sort of investment you got. You know, you have some players with $57 million investment. And then, they—you know—and I still—I’ve been watching these people in this space, and I think there’s a different way of also approaching hiring. So, all this, you know, companies that got this huge investment, they have to hire, but they’re also trying to, like, hire themselves, and they’re presenting themselves very differently, like online. You know, I know, you know, preferences of some of these founders’ wives, for instance, because they’re talking about their personal life on LinkedIn. But productivity is moving faster than hiring because of speed. Okay, we have AI tools who are cheap, accessible, and widely adopted. And I’ll give you an example of some clients I work. So, you know, without mentioning exact investment, but they, let’s say, over $10 million, and they still haven’t, like, expanded. They’re very cautious about who they hire, how they hire because they know that they can make those efficiencies through, you know, utilizing AI tools at this junior level. Look, all the roles, you know, like in the past, summarizing, drafting, sorting, analyzing, now it can be done in minutes, and that removes that immediate pressure to hire.

Karen: Yeah, yeah. And the more important skill than all of those skills which were requirements for entry-level jobs in this industry, it’s really now more about ability to manage products and organizational and critical thinking and these other skills which come with experience, which is fascinating. That dichotomy is fascinating to me because how will those people, how will they learn those skills that they need for the jobs they need today? That’s rough. That’s a rough one.

Joanna: And you know, on that point. I also think that organizational structures haven’t caught up with, sort of, the speed of AI, and that means that leaders haven’t redesigned the workflows yet at the companies and career paths. Because what should happen with this entry-level roles? And I actually was thinking about the things you’ve said when we talked about our children, is creating, like, AI native entry-level roles, but instead, they are not opening them at all because of that, you know, efficiency mode, not expansion mode? So, I think that, you know, a lot of companies haven’t yet figured out how to build AI into, say, job design, developing people, planning their work first, long-term.

Karen: And mapping out career paths. I hope—I’ll be optimistic here for those who—[laugh][unintelligible 00:33:53] will like it when I get gloom and doom, probably because I like to be optimistic—but I would hope that right now, the you know, the disruption has made it hard to see into the future, as we know when we started off talking about ambiguity, but hopefully within the next year, that shifts a little bit, and people are confident again that there is a future, that they can, like, say, okay, I now I can see where we’re going to be in five years, and they can start to build out that early career path for some younger hires. That would be my, kind of, aspirational wish to put out there. Do you think that’s possible, or am I living a dream [laugh]?

Joanna: Look, I think the hype, the fear, the urgency, it’s what also dictates a lot of those sort of movements in the market. Because you have leaders and competitors announcing those AI initiatives all the time. Investors are talking about AI capability. And the press is to blame also because they treating AI adoption as some sort of a proxy for innovation. So, one of the statistics I—

Karen: I feel a little seen in that statement, I just want to say, as a media company, I feel a little bit like I might be contributing. Anyway, please go on [laugh]. 

Joanna: Basically, you know, Deloitte, I read, has done a study, which I have mentioned in one of my newsletters, that, “Leaders do not want to fall behind.” That was the quote. So, it’s created a bit of a psychological shift because, like I’ve mentioned before, it gives execs this narrative to say, you know, if we invest early, we gain efficiency, productivity, and competitive advantage. But they are not looking at data, some of them, you know, and it’s all driven by hype. So, that hiring that you’ve mentioned, you know, if you’ve got a lot of, you know, investment, you need to hire, hire, hire, but it’s often the hype as well, I think that comes into force. 

Karen: Yeah. It’s so interesting. Let’s talk about because, you know, for the sake of time, I want to make sure we talk about something that also caught my eye—when I started to research you—caught my eye online, which is this 90-day playbook concept that you have because, you know, it’s a way that you kind of, you know, shared, you know, publicly, so I’m not kind of disclosing anything terribly proprietary for you, that you know C-suite execs can really tackle this. What are some elements of that playbook that you have? Like, what are some of the recommendations or steps that people need to be taking to even get to a place where they could develop a 90-day playbook?

Joanna: Yeah. So again, you know, the playbook wasn’t my idea. I’ve quoted and analyzed different data sources because I’m not an economist, but I think what I’ve read is that you need to first of all establish a cross-functional governance board, with the CEO, CFO, CHROs, CTOs, because AI project is not just an IT project; it involves all the senior levels within the company. And secondly, a lot of companies rush, rush, rush. They, you know, have this copilot and this chatbot and this AI here and one another over there. You need to have a very clear process for identifying and prioritizing the AI use cases that you really believe in. And you know they say 95% of tests fail. Only 5% actually survive. So, you need to focus, I think, you know, your 90-day playbook on three to five initiatives that you really want to undertake that will generally transform your business. And also you have to think in terms of performance indicators because you’ve got this pressure from boards and investors, but you have to think about ROI because you can’t measure those less obvious benefits. They do not show on your P&L sheet straight away. And you have to accept that some trials will basically fail. And you need to empower your leaders in the company—if you are, let’s say the board—that this is okay, you know? Obviously it has to be in a disciplined way, but you need to learn a lesson from it and really allow for experimentation because if you don’t, you’re going to be stuck in that, sort of, 95% you know, phase that never gets anywhere and actually do not get return on investment, on, you know, the AI use cases.

Karen: So, if we want to be one of these, you know, one of the 5% that’s successful, and that’s built for the future, and that, you know, kind of has this ability to move on and thrive, experimentation is, you know, is key. Are there any other values or anything else that separates those that are succeeding from those who are not, in your purview? 

Joanna: So, we’ve mentioned governance, right? I think it’s the focus and conviction that comes into fore because, like I’ve mentioned before, you need to treat AI as a strategic transformation, not a tech experiment. So, first of all, you know, those successful 5%, apparently they have five times more revenue, according to those statistics that I was reading. They make fewer but bigger bets, you know? So, that they sort of—the 5% choose a very few high impact use cases, and they just go all in. And they have everyone aligned from day one. So, the CEOs, like I mentioned, you know, CFO, CTO, CHROs, they all sit at the same table because they know that AI touches everything: talent—obviously that’s my space—finance, risk, product, and customer success, and experience all at the same time. And the other thing is that ROI that we talked about before, they actually commit to a longer time. So, I was reading somewhere, I think Goldman Sachs said, it’s a two to four year timeline. So, they understand the AI returns are exponential, not immediate. They’re patient, but you know, they disciplined. So, they cannot—in summary, those AI pilots cannot be treated as disconnected experiments. There have to be a long-term business strategy in place. 

Karen: Yeah, yeah. Yeah, it’s a lot of intense work at the leadership level, at the CEO level, for example, to not only you know, shore up, you know, kind of operation—become operationally sound, but also make sure those values are clearly communicated, that we are experimenting in very specific ways, we’re all-in very specific ways, and really mapping that out should be a part of that 90-day roadmap, right? Spelling out for your employees, this is where we stand during this time of disruption. Super interesting. Any other kind of pro tips for thinking, for insights and strategy teams, specifically?

Joanna: Well, yes, I do have one thing. I just want to say to everyone that your career is not going to be linear anymore. It will be less like a ladder and more like a lattice, okay, because we are going to be, with that ambiguity we were talking about, you going to be moving much faster. How the insights industry will be affected, and especially you know, what could HR leaders do with these entry-level positions is think about growth not, you know, how many tasks, for instance, that young person does on a daily basis. Think about how they can grow. And you need to teach these people skills that normally you had probably learned when you are the research manager or associate director level, commercial skills, speaking with your stakeholders, being able to communicate strategic insights. So, I guess the training has to be there in place, and it has to be structured around AI-augmented roles, but you also have to allow for more fluidity across the market because people, you know, you’re not going to have the same career progression as you had before. You started as an RE, you moved to SRE, you move to research manager, you move to leadership. It’s not going to be like that. It’s going to be very—it’s not going to be a ladder, put it that way. Yeah.

Karen: Yeah, no. No, and I love the idea of lattice work. I used to use the analogy when I would talk to people that I would mentor, I just haven’t used it recently, but I used to use this idea of people who are rock climbers, and you know, they may have an idea where the summit is, but there isn’t a straight line up, right? Sometimes you have to pivot over here. Sometimes you have to pivot over here. Sometimes your arm is dangling over here, but you find the reach over there, like, you’re all over the place on the surface of a rock that you are trying to climb. It is not multi-directional. But what I’m hearing from you is that even that analogy, which was great maybe ten years ago isn’t quite right because the summit may not be the goal. The goal may be in the side-to-side work, right, and need to come up with a whole different approach.

Joanna: It’s also another thing that I forgot to mention. But you know, we’re not only having the jobs that we are having, replaced, if you like, you know, AI is creating new roles that didn’t exist, say, two years ago. And I think, you know, people who are listening would like to know what those roles are. AI Insight Strategist, you know? So, who is it? Someone who can translate AI outputs into business decisions. The one I’ve seen quite a lot is AI Creative Strategist, so someone who sits at the intersection of creativity, strategy, and technology. Hybrid role, emerging fast in agencies, consultancies. Another one I worked with recently was a head of behavioral AI, someone who combines behavioral science with machine learning insights. And the last one, I was very surprised when the client came to me and said, “Joanna, are we looking for an AI expert?” I was like, “What do you mean? Who is an AI expert?” And actually, there were legacy agencies, say, someone from Europe, and they were looking for an architect of the whole AI system, the person who knows how to use AI models, how to put them to work together, and how to find smarter ways to analyze data, but also work with the legacy research. So yeah, you know AI are reducing some jobs, but also creating some new.

Karen: Creating new ones. Well, I don’t know if you know, I sit on the board of the Insights Career Network, so a lot of people listening are probably in that space as well, and I am sure that was just like the money shot for this podcast is what are these new roles? I love that you’ve shared them. Thank you so much. Really interesting, and also indicative of how companies within the industry are growing to adapt to, kind of, this AI era that we find ourselves in. Thank you so much, Joanna. Is there anything else that I didn’t ask you that you wish I had? Because we are, as I said, every time I hit the certain time I’m like, I really have to wrap [laugh]. Anything you wish I had asked that I hadn’t gotten to yet?

Joanna: Well, we’ve covered a lot, and [crosstalk 00:46:25]—

Karen: We have, so much [laugh].

Joanna: —because I had so much more to share.

Karen: Of course. Of course. But I think, you know, one thing, I think maybe you could have asked me, it’s, what’s the thing AI will never replace? Mmm, excellent. Yes, please answer that question because I love it.

Joanna: Judgment. The answer is judgment because this is one capability that is becoming more valuable as AI gets better, the human ability to weigh context, risk, nuance and ethics, and for me, that’s the real differentiator for insight and strategy teams.

Karen: Yes, brilliant. Thank you so much. How many times does one say over the course of their work, like, “Use your judgment,” when they’re talking to somebody else? Like, “Please, just use your judgment in this case.” Or, “You know what, I trust you. Use your judgment.” And so, I think that is fantastic and it’s probably going to be a word of the year for me in 2026, so I love it. Thank you, Joanna, so very much. Where can people find you? How can they discover you? What can you share by way of closure here?

Joanna: Okay. Okay, so LinkedIn, obviously. That’s how you found me. It’s the best place to find me, so please just send me a connection request, or you know, schedule a Zoom coffee with me. I’m very happy to chat.

Karen: That’s fantastic. And you are London-based, which I will share with everybody so that they know in the US, have that coffee—your coffee might have to be a little earlier or move into tea time [laugh].

Joanna: I’m happy, I’m working with us clients, and yeah, so I am used to it. So, [crosstalk 00:48:11][laugh].

Karen: I love it. I love it. I love it. Well, I just want to thank you so much for your time, Joanna. I hope this was pleasant for you and positive because—spoiler for those listening—this was also her first experience. So, I’m so glad to have had you [laugh].

Joanna: Thank you so much. I really, really enjoyed being here, so thank you very much for having me. Thank you.

Karen: What a pleasure, what a pleasure. I’m so glad. And then also to Big Bad Audio for editing both our video and our audio, we so appreciate you. Brigette, thank you for your role in the production of this episode. And to all of our listeners, thank you so much for showing up for us time and time again. We value you, and we hope that we are bringing you valued conversations. We’ll see you next time. That’s all for this episode of the Greenbook Podcast. Bye-bye, everyone.