070: Making AI Actually Work

[00:01:04] Hey, what's going on everybody? David Noe with SpeakEasy HR, presented by Payroll Partners. We are back with a brand new episode, Episode 70. Cannot believe I've gotten to Episode 70 this fast, but here we are. And we are thrilled to have someone on the other side of the world from Kentucky. We have Alex Meyer all the way from Germany. Welcome, Alex, to SpeakEasy HR. How are you today?

[00:01:30] Alex Meyer Hey, David. Thanks so much for having me. Very happy to be here. David Noe. Absolutely. Yeah, I was going back to when we first started connecting on LinkedIn and it was back in October of 25. And you were asking a couple questions about what I've been up to. And I said, well, I started this podcast. And so we connected and picked a date. And here we are. It's April 27th. And have a lot probably since the first time we talked and now where

[00:01:57] AI has gone and, you know, what you've learned along the way. So for those who do not know you, Alex, if you want to just kind of tell people about your background and what you've been into and, and, you know, what you're now focused on in your in your work. Alex Meyer Yeah. Thanks so much, David. Yeah. So I'm Alex. I'm an AI consultant. I'm the founder of Dualis Studio. And we help specifically leadership teams get AI right. So the mission is fairly

[00:02:24] simple. AI is a buzzword. There's so much fuss around this. And yet most companies are not really getting the value out of it. People are scared of it. People don't really know what to do with it. The media is obviously it's they're pushing AI all the time. There's so many potential, so much potential there, but it's not really yet working. So our mission is to bridge this gap and help companies, especially small and medium sized companies from any industry

[00:02:49] at this point, to really make AI work for them, build AI strategies and really get the mouse out of it without putting their data or the people at risk. So that's what we do. My background is I'm an engineer by education. I've been in corporate for quite a few years. I've done startups. I've been a consultant for many years. And yeah, now since two years, I have started my own company and we're very passionate about what we do. And I do think AI has a lot of potential, but it's not without risk and it's not without challenges. And that's

[00:03:17] why we're here to help you. Yeah. So what got you to the point of wanting to start this as like a new venture? Like, was this something that you thought about for a while or was it like one night, one day you just had like a napkin idea of writing down some, some things that you wanted to focus on? So what got you into that kind of mindset? It's interesting because my, the business I started

[00:03:40] previously was on executive coaching and leadership development. And one of the modules I was offering as part of that was AI and helping leaders get AI right. And I noticed that more and more, the demand for specifically that module kept getting bigger and bigger. And people were specifically asking about AI. And since I've been an engineer and I've been working in AI for a while, I've been doing projects with AI. I was thinking, well, let's, you know, take one plus one and take this demand and, you know, expand

[00:04:10] on it. And, uh, yeah, later I built a studio and we started doing formal consulting projects, lots of training. We train a lot of companies on AI, uh, AI and critical thinking, AI and problem solving, these sorts of things. And yeah, that's, that's where we are today. Yeah. Okay. So you are in Germany. Have you, have you traveled and moved or have you kind of stayed where you grew up? So it's funny. I, I, I'm now actually in Spain right now. I am German. I've lived most of my life in Germany, but I've traveled a lot. I've, I've been to the States a lot

[00:04:38] of times. I have a lot of American clients that I work with. Um, uh, yeah, I've lived in Australia for a time for some time. Uh, been in the UK and Spain now. So I've been, I've been pretty much around and, um, yeah, that's right now it's, uh, I'm in Spain. Yeah. One question I love to ask every guest is about their advice they have received in their career. So if you think back on the people you've

[00:05:02] been around, you know, what's the biggest piece of advice you've received? Yeah. So I'm big on working with mentors. I think everybody should have a mentor and everybody should have a mentee where they can also pass on knowledge. I think that's a very good setup to have in my case. I was lucky. I had very good mentors. Um, I've got lots of good advice. One of my favorite pieces was, um, which I think my mentor actually got from Richard Branson, but he told me, um, when someone gives you

[00:05:29] a great opportunity, just say yes and then figure out how to do it. So even if you're not sure if you can do it, but it's great and it's sort of aligned with where you want to go in life and your career, uh, just say yes and you're going to figure it out. And so far that's, that's, that's how true for me of, uh, you know, another mentor of mine used to say, put on a t-shirt with a size that's a little bit too big. So you can grow into it. And yeah, I think that's a great advice. If you want to go somewhere, take on these challenges and then, then figure out how to do them. And that's the

[00:05:59] fastest way to grow. Absolutely. No, that's great. I really appreciate you sharing that. And, um, I always love to hear what people say in that, in that question. So, um, all right. So shifting gears a little bit as we transition one more, uh, fun little game about, uh, an envelope icebreaker game. I started back when I thought about a podcast and I'm going to transition. So think of a number one

[00:06:24] through 15, and then we're going to get to that number. All right. What number you got, Alex? Uh, number 10, number 10. All right. There we are. Number 10. So question in the envelope is

[00:06:50] you're in charge of renaming HR. What's the funniest yet oddly accurate title you give it? Wow. That's a good one. Um, so for HR based on my experience, um, which have been mainly, mainly been good. Um, but I know it's not, I mean, it's not the same for everybody. Um,

[00:07:16] I mean that the obvious one is talent management, because I do think that nowadays it's all about talent and, you know, human resources sounds like, you know, I don't know. It doesn't sound very, very appreciative. I think it's about talent. We need to have great people to make things work. Yeah. Um, so yeah, talent management, it's, it's kind of boring, but I think it's, it would be a good, good, good renaming. Yeah. Before I got an HR, it was called like personnel

[00:07:41] or something back way back. And so I'm, I'm kind of, uh, dating myself, but being in HR for 20 years, I think people look at HR and they might have bad experience, good experience, or if they've started a career in HR, they've seen a lot, uh, going through a pandemic. So yeah, it is, it is one of those things that as we dive into your topic here in a little bit more about AI, it's kind of like the human connection within the organization and how you treat your people, how you treat your staff,

[00:08:11] how they feel waking up on a Monday morning and coming to work, you know, that, that is so critical. So yeah, well, thanks for playing the envelope icebreaker. Um, a couple of rapid fire questions to go through and, uh, and then we'll hear a couple of messages from our sponsor payroll partners. So, all right, Alex, so AI buzzword, you're already tired of hearing. Yeah. So I'm tired of hearing it because AI is such a broad term. There are so many technologies

[00:08:39] and use cases and things underneath it. And people just say the generic term AI for everything and every product, every business sprinkles AI on everything now. So it's kind of tiring. It's, it's, it's not very nuanced. Many things are called AI, which are not actually AI. So yeah, a little bit tired of it. And I think we should clean up the space a little bit. Alex Bitts Biggest mistake companies make with AI? Not having a strategy, not having a clear plan. Alex Bitts Biggest mistake companies make with AI.

[00:09:05] One tool you're already actually using right now. Alex Bitts Biggest mistake companies make with AI. I use a bunch of tools. I've tried so many tools. Um, I have a bunch of subscriptions right now. Gamma AI is really helping me create presentations. Alex Bitts Biggest mistake companies make with AI. Okay. Okay. So hype or real AI will replace jobs? Alex Bitts Biggest mistake companies make with AI. Alex Bitts Biggest mistake companies make with AI. It's a, it's a mix of both. So, so some, some jobs will disappear. We're seeing that many jobs,

[00:09:29] if not most jobs will transform. The bar will be set higher. Um, yeah. Uh, but, and yeah, it's, it's definitely more hype than what we're actually seeing. Yeah. First step for a company just starting with AI. Appoint, appoint the right person to drive AI. If you don't have somebody driving it, and if it's just with the normal leadership team, they likely won't have the expertise and the drive to, to, to push it. Yeah. What scares people most about AI?

[00:10:01] Alex Bitts Biggest mistake I think it's falling behind. People are overwhelmed. There's so much happening. So falling behind is a real, real, real, um, fear that I'm seeing at the moment. Yeah. Okay. Final one, one word to describe the future of AI in HR. Alex Bitts Human AI collaboration. Alex Bitts Perfect. That is great. So as you might think, this topic that we're going to get into today is a little bit about AI and, and what Alex does on a daily basis with his

[00:10:29] organization that he started a couple of years ago. So just a couple of messages from our sponsor, payroll partners, and then we're going to dive into the topic here. So grab a drink and we'll be right back. Finding a new employee takes time and money for one position on a single job board. You could easily spend a minimum of five to $10 a day advertising a job post. It's worth noting that the average time it takes to hire a new employee is 36 days. However, it can take longer than that.

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[00:11:25] bank. And this feature lets you e-blast your network, hosting jobs on multiple boards at once, all managed in one location. Payroll partners, elevating your payroll and HR experience. There's a lot of technology out there, especially in HR and payroll technology. There are tons of platforms. And at the end of the day, it's a matter of who's the right partner for

[00:11:51] you and your business and who's utilizing those systems to make it a really good experience for you as the company and the employees you employ. So if you are looking and evaluating systems out there and partners, reach out to me on LinkedIn, look me up on LinkedIn. Payrollpartners.net is where you can find more about us. So we'd love to connect, hear about your situation and if we can help you along

[00:12:15] the way. So, all right. Now your top shelf topic, Alex, we are live on LinkedIn and YouTube. So if you are watching live, have a question for Alex, now's the time. I have plenty of questions. So if you are just listening along the way, but if something pops up in your head, throw it out in the chat, would love to hear from you. So Alex, what does making AI actually work really mean inside of an

[00:12:43] organization? Very good question, because that's exactly the question with which we started. When people say it doesn't really work for us, what does it actually mean? And the way we define it is, does AI give you ROI, return on invest? So of course, when you're starting with AI, you're going to have to invest a little bit. You have to invest some time, some, you know, to figure it out. You have to invest some money. You might have to hire people. You have to try tooling. So you're investing in AI. And at the end, what are you getting out of it in terms of dollars,

[00:13:11] in terms of time spent, in terms of maybe employee motivation? So there can be different types of returns. But you want to make sure that when you start with AI, you have an idea of what you could be getting out of it. And you want to have a way of measuring it. And yeah, many people, many companies are not getting it right. So what we're seeing is roughly 70% of companies currently are using AI already. But based on a recent McKinsey study, most of them, roughly 90%, are not seeing the expected ROI.

[00:13:36] So they're not getting the return for what they invest. And that's a crucial gap. And that's something that we're addressing. Yeah. So where do you see most companies really getting stuck when trying to implement AI? Yeah. So there's a couple of things. I think the first thing that I hinted at earlier is they don't have a strategy. So what we see a lot of companies start with, they buy some tools. The typical thing is, look, we have a co-pilot license now. We're spending a bunch of money on it,

[00:14:05] but somehow we're not seeing any result, right? You know, we're not some, some people are using it to research. Some people are using it to write some emails, but most people are not even using it. So they're not seeing the return. So that's one thing where they're getting stuck. They don't really have a clear plan. They also don't have the priority. So the executive team, usually they're happy with having a license and they then kind of move on. They don't make AI a priority. They don't have anybody driving it who has the expertise to actually implement AI in different parts of the

[00:14:35] organization. And if they want to do AI, often they have so many other priorities that they keep moving it back. And then also it doesn't, it doesn't really work for them. There's many other things that I'm sure we're going to touch upon, but those are the main things that we keep seeing. So what separates organizations that are successfully using AI from those that, that aren't? Yeah. So it's usually, so it's, it's, it's, it's having the clear plan, right? The strategy,

[00:15:01] and that's, it has several components. So the first component is responsibility. So you have somebody who's driving AI, you have what's called maybe an AI champion or an AI officer or an AI coordinator. There's different terms for it. Maybe some even I call it the chief AI officer. It doesn't really matter what you call it, but you have to have somebody who knows AI well, who knows, who understands the business to some degree, and then who works with different functions in the business to find use cases, to address the bottlenecks and then help the help these functions,

[00:15:29] you know, actually solve their problems with AI. And at the same time who educates, people who educates the leadership team and drives things. All right. So that's, that's the first thing that people, that companies that do well with AI that they have in place. The other thing is, they also have some sort of governance. So AI comes with a certain risk, which is one of the reasons why companies sort of shy away from rolling out AI on a broader scale. And that risk can be mitigated and managed if you have proper governance set up. So that's another thing that should be driven by the

[00:15:58] head, by the head of AI or whatever you want to call it. Right. Another thing is use cases. So, you know, many think AI is just chat GPT, we get a chat GPT or a copilot license, and that's it. That's far from the truth, right? Really, there are so many things you could be doing. And companies who are successful with AI, they break down all these different use cases by, you know, business function, they have the different processes, they look at what are actually bottlenecks, they understand the business challenges, they have a responsible, responsible person for each process. And then they find, okay,

[00:16:28] they look at all of these processes and bottlenecks, how can AI actually help with this particular process? And that's how they find use cases. And then they also align AI with their business strategy, which will then help them define, okay, these are the use cases that actually make sense for us, right? Because whatever you want to do, you want to make sure it's aligned with your overall goals and strategic vision. Yeah. So how do you get buy in from leadership and teams who may be still skeptical

[00:16:54] and kind of overwhelmed? Yeah, that's, that's a huge topic. We see this like this overwhelm, especially skepticism to some degree as well. Sometimes it's resistance, even. Usually, the best way is you just, you talk about the consequences of not doing AI, because at the moment, there's only like, you don't really have that much choice, do you? Right? Because if you don't, if you don't do AI, you know, your competitors might be doing it, and they might be getting some advantage that you're

[00:17:22] not having. Your customers might be expecting you to have, you know, offers that are getting better with AI. You know, if you're in a 2026 organization, and you're not doing anything with AI, your customers might be wondering, okay, they're a little bit behind, right? Or also your talent, your employees, right? We're talking about HR here as well. We, there, there was a recent study, I forgot who, which institution it was by, but you can probably Google it or find it on perplexity,

[00:17:47] whatever. The, they, they showed that nowadays employees, they expect their employer to provide AI tooling and to provide AI upskilling. So if you don't have AI as part of your business strategy and part of your operations, you risk that some of your talents might be irritated and might even be leaving you when you become less attractive for talent. So it is also a real challenge there. So there's many reasons why you should be thinking about AI in a structured manner.

[00:18:15] And that's usually, that's, that's how you get by and you understand, you explain how, if they don't do it, these are the risks that they're running, right? And that's apart from all the inefficiencies that they could be solving with AI. Yeah. Yeah. So this, this wasn't really necessarily a question that I had thought of up until this point. So you, you bring up a good point on if people are not getting the right support, they might decide to leave a job. And I've heard all the ideas and things about why people leave jobs. You know,

[00:18:43] it's the manager, it's the pay, it's the work environment, you know, the, the main reasons. But when you talk about like a company, not supporting a team or work team, you know, work function with the right proper AI tools. So like, are you seeing where companies are investing a certain percentage, kind of like a marketing budget or a advertising budget or any kind of other

[00:19:07] budgets, right? Like, is there like a baseline where you're like, okay, if, if you have a company with 50 employees and you should be using X amount of your budget towards AI technology, is there, is there something that from a baseline perspective you are seeing or hearing from companies about that? So it's, it's, you know, of course it always depends. That's the standard answer of, you know,

[00:19:35] as you know, of a consultant, but, um, what I can say is that, so first, what it depends on, of course it depends on your industry and your business, right? If you're very heavy on information processing than AI, you know, you'd probably be spending more on AI because there's much more you can do with it as opposed to if you're a very traditional, very, you know, maybe manual labor type of company, there might be less, at least in the first, first sight, there might be less of what you can do with AI. Generally, the minimum also, if you would think

[00:20:03] about, you know, attracting talent and, you know, wanting your people to be, to be moving on with AI and getting ahead, you might want to ensure that everybody has a license to a, to a, to a, a large language model, right? So that should be the basic. And it's not enough to just have people use their private AI because many companies, they say, well, they have their own chat GPT, they can use that. That comes with risks because that comes with compliance risks. They might be, you know, putting data into their AI. That's not, you know, that's, that's maybe secret or they shouldn't be putting in there and that's, that's, that's real exposure. So that's not a good

[00:20:33] option. So a company in my view, they have to at least offer a license for everybody or at least everybody who wants to have a license, but generally everybody for an enterprise version of either chat GPT, enterprise version of cloud, co-pilot, one of these large language models, that's the basics. And then from there, depending on your business, if you create a lot of, you know, marketing material presentations and that sort of thing, you might want to have an AI that helps you build presentations and designs. If you are more on

[00:21:00] this, like if you have an organization that runs heavy on meetings and workshops and that sort of thing, you might want to have at least a meeting transcription AI that sends automatic follow-ups and that sort of thing. So it really then depends on your, your, uh, on your organization, what type of meetings and what type of, what type of work you do. Um, the sky's the limit, so you can always invest more, but the basics should always be give everybody a secure access to an LLM with which they can actually work professionally.

[00:21:27] Yeah. You might've already brought this up, but, but it's, it's maybe a follow-up question. So the most common mistakes you see that companies are making early on, uh, with, with this AI adoption. And yeah, so what I didn't mention yet, so a couple of things that I mentioned regarding the strategy, um, we can go into that in a second, even more, but another common mistake is that they're not

[00:21:51] upskilling their people. So somehow they, there's this belief that AI is just chat GPT and chat GPT is like Google and you can just use it. Like it's, you know, what, what's there to upskill. And that is a big problem because there's a huge gap between people who are very good with AI and people who are just using it like Google. Okay. Cause there's so much potential, right? If you have a, if you have licenses, for example, for Claude or enterprise plan or whatever, you can be using skills.

[00:22:18] You can use projects, you can use cloud design or you can use routines to automate tasks. There's so many things you can do that people just don't know, right? We can't expect people to just, you know, wake up and just know all of these things. So you have to upskill them. And that's also good for motivation. Um, we see that people who are, who get training on AI are more motivated and less scared of AI. So that's also good. Um, so that's one of these mistakes that we see that they're not, they, they're not upskilling them. They might have one seminar with like the basics.

[00:22:45] That's how you write a prompt and that's it. That's not enough, right? There are so much more you have to get into. And that's, that's, that's step one. And then the next step, that's sort of the next crisis that we're getting into. And that's going to be also very important for HR is critical thinking. So, you know, part of my USP or part of what we, what, what, what we sell a lot is training people on critical thinking in AI. So we are, we are seeing right now that over overly relying on AI tools is actually leading to an erosion of thinking skills. There's many studies on this by

[00:23:14] the Swiss business school, MIT media lab. There's a couple of institutions that studied this. And if people don't, if people more and more rely on AI, their thinking sort of gets worse. It's kind of like a muscle. If you don't use it, it kind of gets, it gets weaker. It's the same with the brain. So if you want to be good in life, you have to make decisions, you know, you have to figure things out and you need your, you need your brain for everything. And, um, if you, if you, if you just start using AI for everything, you might get worse at decision-making. You might get, you know,

[00:23:43] it is a real, real problem that we're facing. So we are actually teaching and companies should be doing that. Teach people to use AI, but also teach them to use AI with some good judgment and, you know, combine it with critical thinking and problem solving skills. Because at the end of the day, AI is becoming a commodity. At some point, everybody's going to be having AI, just like the internet or electricity, right? All of these base technologies that, you know, they're now comparing AI to everybody's

[00:24:10] going to have access to AI at some point. And once they do, they have to learn, you know, how to be better with it. And the, the old skills of problem solving and critical thinking are only becoming more important. You know, what's going to set you apart in a world where everybody is AI is your, your, your skill, your, your ability to ask the right questions, to scrutinize outputs, to, you know, use your judgments in order to make the right decision based on all these options that you have. So

[00:24:36] that's becoming more and more important to cut through the noise. Yeah. I am live with Alex Meyer, founder and partner of Dualis Studio. So if you are watching live on LinkedIn and YouTube, have a question about AI with your situation or just a made up situation, put it in the chat. Would love to hear from you. So Alex, when you're looking at HR and, and HR leaders, you know, how should they specifically

[00:25:03] be thinking about AI right now? Yeah. So one of the things is that there's several components to this. One is obviously if, if, if, if in HR, you're, you're also responsible for being attractive to talents and attract and maintain talent. So I think having a good AI strategy and positioning and, and, and tooling is one of the things that's going to be interesting for young talents coming in. Right. I have seen this firsthand as a trainer, when I train organizations where they don't have good AI

[00:25:31] tooling or they have lots of restrictions, people get frustrated, right? They, there are certain things they could be doing, but they can't because the company doesn't enable them. Um, that's a real frustration and they see it left and right. Maybe their friends and other companies, they can do things that they can't do. And yeah, it's, it's, it's, it's, um, it's a cause of, um, dissatisfaction at the job. So that's one thing. And there they have to work with the executive leadership team to address that.

[00:25:54] Another thing is HR in general is a function that deals with very sensitive information and decisions, right? Hiring decisions, um, sensitive information, like salaries, that sort of thing. So that's very important for AI. Um, meaning if you build agents and you automate processes and that sort of thing, which certainly is possible in HR and which you should be doing, you have to be very mindful of which part of the processes you're automating and which, which type of data you're sharing and which type

[00:26:24] of data you might have to anonymize first and which data you can never share. So the term here again is human AI collaboration, as I hinted at in the beginning. So when you set up agentic systems, for example, you want to make sure that you map out the entire process and the, the system at first on a, on a paper or whatever, and then look at, okay, these are the steps that AI can take, which are compliant. Here's the step where a human has to, you know, use their judgment, have to make a decision.

[00:26:49] So if that's human, then we can use AI again. So you want to put a, put together systems of human and AI collaborating on a certain process. And that's, that's what we call, um, human, human AI collaboration. Um, so I think that's very, very, very important for HR and maybe as a side note in the EU, we have the EU AI act. So that's regulation, which makes it illegal. For example, that AI makes, you know, hiring decisions like that's plain out, uh, illegal. So these decisions, they always have to be

[00:27:19] made by the human. I think even in other jurisdictions, you're going to get into trouble if, you know, if you don't hire somebody because chat GPD told you, so I think that's not, not going to be an option. Um, so yeah, human AI collaboration is important in any field, but especially in HR with sensitive data and decisions. Yeah. Uh, Nick is watching and posted this question. And, uh, so thanks, Nick, for, for watching and adding this to the conversation. So his is more, um, what does the

[00:27:48] future of HR look like and what should an HR professional do to stay valuable to an organization? I love that. Hey everybody. I'm Lori Rudiman. What are you doing working? Nah, you're listening to a podcast about work and that barely counts. So while you're at it, check out my show, Punk Rock HR, now on the Work Defined Network. We chat with smart people about work, power, politics, and money.

[00:28:13] Are we succeeding? Are we fixing work? Eh, probably not. Work still sucks, but tune in for some fun, a little nonsense, and a fresh take on how to fix work once and for all. Yeah. Excellent, excellent question. And it's, it's, it's a question that's, you know, that's, that's, that's the answer is also, it's, it's for HR professionals, but many others in, in white collar jobs as well. Um, these type of jobs, they transform, meaning you, the absolutely important thing is that you still make decisions,

[00:28:42] right? Your decisions will be more strategic. It's no point for the foreseeable future. AI is not going to be making major decisions, but AI is going to be helping you decide, like, uh, prepare decisions, strategic decisions, hiring decisions, uh, how to pay, compensate people, that sort of thing. Um, meaning as a HR professional, you still have to know these things. You still have to be part of the process. You just have to know, as I said, you have to know how to set up a human AI collaboration

[00:29:10] system where the AI maybe does part of the research, you know, what are maybe compensation standards for a certain role and that sort of thing. And then you make decisions or, hey, AI can help you screen through, you know, if you get thousands of applicants screen through these and show you which ones are most, most promising based on certain criteria, but then you still have to make the decision, right? Maybe HR becomes more strategic as an advisor to the senior management, right? Because HR, you know, you're going to have to be attracting talent, but you know, to attract certain

[00:29:40] talents, you have to have certain, you know, HR, uh, AI tooling and certain things in place. So, um, there's many aspects in short, a long, long story short, it's going to be more, more strategic. So practice your skills of strategic thinking and problem solving and judgment. Yeah. Um, so you can actually take on step in these more strategic roles, which will be expected of HR professionals in my view. A couple more questions from the live audience. So thanks for,

[00:30:08] for watching. This one is, will AI replace HR professionals or redefine their roles? Yeah. Good question. Um, again, I, I, I'm there, there's many different opinions because AI is a new thing and some people are saying it's going to replace everybody. Other people are saying it's not not going to replace anybody. I think the truth is somewhere in the middle. I think for HR professions, it's mainly going to be about transforming the role as I, as I, as I, as I explained, um, you're going to be more strategic. You're going to be more on the side of decision

[00:30:37] making and maybe even coaching, you know, uh, being a strategic advisor to the executive board. You know, there's a good example that I always bring from, um, Jensen Huang, the CEO of NVIDIA. Uh, he said he was asked if NVIDIA is going to replace their engineers, you know, their coders with AI because AI is really, really good at coding. Right. And he said, no, the opposite is true. He said that, um, NVIDIA is planning to hire more engineers because engineering is not about

[00:31:06] coding. Engineering is about problem solving and we need more problem solvers because there will be more problems. Yes. Maybe coding is one part of the job, which will be done by, by AI or to a large part of AI, but you still have to solve problems and that's the human part. And that's the same for HR professionals, some data processing and some administrative tasks can be done by AI, but you still have to solve problems. You still have to make sure that the company has, you know, a good human resources structure. It has, you know, happy employees. It has, it attracts talent or all of

[00:31:36] these are problems to be solved. And that's, that's your job. And AI will take some, some, some parts of that, but you will take other parts. Yeah. Another good question, Albert in the crowd watching live is HR still a good career in the age of AI. I would say it's a good career if you again, true for many white colored jobs, I believe it's a good career. If you are enjoy strategic work

[00:32:05] and advising executive teams on, you know, strategic questions because HR, I think will become more and more strategic because big questions will be how much AI should we introduce, how like laying off people will be about, how do we upskill people, you know, in the age of AI, which skills are actually going to be needed. So there's many strategic challenges regarding people. And I think, I think good HR professionals who have a, you know, who obviously have a background in HR and

[00:32:33] but who also understand technology, understand trends, who understand strategy, strategy, they will be very, very important. So yes, it's a good, good, good opportunity if you want to be steering organizations and really make work on a strategic level. Maybe if you also enjoy setting up agentic systems in HR, that might also be for a short to medium, midterm opportunity as well. But

[00:33:00] if you are for standard HR work, where it's like the things where you just process data, that's going to be less relevant. But the strategic work, that's going to be more relevant, I think. Yeah, great, great questions. Appreciate you all adding those into the mix. And I would just add to that too, Alex, the future of HR, I think is still very important. So anybody that's getting

[00:33:26] into the career of HR, trying to start a career in HR, you know, I've been in it for 20 years, I've seen it change with technology and disruption with the pandemics and that kind of stuff will just make businesses have to evolve. So navigating change, navigating culture, and as long as a business has humans, you're going to need somebody to help with that side of empathy and that side of

[00:33:53] mentorship, right? Motivation. Having a mentor, you're not going to have a mentor like AI kind of situation anytime soon, in my opinion. I think it's somebody that you truly trust as your mentor to help you along the way. There's a really good book behind me, it's called Pathfinders. I'm interviewing Pete on Thursday at a live conference. He wrote a book about what you need to do to have a board

[00:34:19] of advisors for yourself. And part of that is to have a board of advisors of people in your life that are more mentors and a board of advisors for yourself, not just for a business. So... Yeah. If I may add to that, because I think you're touching on something very important, it's actually something we're seeing. Part of our job is also coaching, right? And I coach people on careers and things as well in leadership development. And, you know, AI could perfectly

[00:34:46] coach you. It has, you know, ChatGPT has more knowledge than me and more knowledge than any coach, right? But it doesn't have this judgment. You know, you need to find out which is the right question to ask. You can ask AI anything. And depending on how you ask and what you ask, you get a million different answers and they all sound good. But how do you cut through the noise? And what's actually important for you as a being? Like what's actually moving you inside? What are, you know? And sometimes we as humans don't even know what's actually triggering us, what actually

[00:35:13] we want. And we don't really know what the right question to ask is. And I think a good mentor, a good coach, and this could be the role of the HR professional, is helping people to find that out. Like what's actually moving you and what's the right question to ask? And then you can collaborate with AI to find options, to research opportunities for you. All of that is fine. But asking the right question and then using the judgment to find, to cut through the noise and understand what is now the

[00:35:38] right part of the answer for you or the right option from all the options that are out there. I think that's going to be super valuable. And that's something we're seeing. We're actually seeing more demand for coaching and cutting through the noise than less demand. Yeah. So if you have any other questions as we're navigating this conversation with Alex, founder and partner of Dualis Studio. Adam into the chat, we've had some great conversation and

[00:36:03] some great questions that have been added to the chat section. So Alex, where does AI create the most immediate impact across people, processes and productivity? Maybe something that you haven't mentioned already? Yeah. So it's usually it starts with, we differentiate between personal productivity and then, you know, organizational productivity. And usually you get the first quick wins and the first impact and personal productivity. So people get a little bit faster

[00:36:32] at things. They might, you know, get a little bit better quality of outcomes. You know, you can write emails now with AI or, you know, improve them. You can ask a chat GPT or whatever tool you're using. Look, I have the following situation. What's the best way forward here? And you'll get some, some impulses and you might, you know, manage certain situations better. And meeting transcription is one of those things that helps a lot, right? You have a workshop, somebody's taking notes, but it's not enough. You know, you lose, you always lose some of the context. If you have a transcript by AI, send an automatic follow up to everybody with all the,

[00:37:02] you know, the action items for per person, that's just valuable stuff. And it's, it just speeds things up. Right. Um, or, you know, you have a workshop, even if it's in person, you take a photo of the whiteboard where you have notes and sticky notes and stuff like that. You put it into AI, have it create the follow up for everybody. It just saves you hours. So these sort of things are, are where we see the direct impact. Um, obviously it's all the next step would be, you know, researching things that such as an internal knowledge base where AI can actually,

[00:37:28] such as co-pilot or a rack system or whatever can access internal knowledge and pull out data. It's very valuable instead of, you know, spending time on Confluence or SharePoint and looking through stuff. Uh, if it's, if it's well integrated AI can, can, can, can search the information for you. So these are usually the first things that we see the, the other things like on organizational level where we talk about use cases, you know, automating things, agentic AI, this sort of thing

[00:37:56] that takes a little bit longer. And we were in reality, we're not seeing that much of it yet. Uh, some we're helping some companies with it, but it takes some time. You have to really prepare these use cases. You have to have to map out the processes first. You have to understand who's doing what you have to keep in mind, compliance and data, data, data, data, privacy things. Um, so these things take more time, but obviously they have bigger leverage once you have them live. Uh, but then again, these, these are the things where companies are not yet seeing the return on invest because they're more complex and more difficult to implement than people might think.

[00:38:23] Yeah. Um, a couple of things to bring up as we're getting to the end, I started a new newsletter on LinkedIn. It's called the top shelf insider. So it's a monthly newsletter. If you are interested going out there and following my content with speakeasy HR, the HR channel is a brand new show app basically on Roku and Amazon fire TV, several other podcasts are out there with me. So check out

[00:38:51] the HR channel. If that is something that you're wanting to get more of and, and obviously looking at HR in general and, and hearing from other, uh, voices in HR, there's so many out there with this great, uh, platform. So check that out. So Alex, um, how do you balance speed of adoption with, with the risk compliance and culture and how fast everything is changing?

[00:39:17] Yeah. I think the, the key is really upscaling, upscaling yourself and upscaling your team. And again, as teams have so many things going on and leadership teams have so much going on, so many priorities, you want to have somebody in the company who's driving this depending on the size of the company, one person, two people, whatever, maybe just a fractional role is also fine. I'm doing that for smaller companies. Um, and then you want to make sure that, you know, that person knows what's going on in the market, knows what trends are happening,

[00:39:46] knows new technologies. Cloud, cloud code was a big trend recently, open clause, these, all these agents that people run on their own devices. So there's a lot of things going on. You need somebody who makes sense of all of this, who understands these trends and synthesizes what this means for the company. Right? So that's one important thing. And that person must also be aware of, you know, governance and regulation so that the risk stays low because there is real risk associated with this. So you have to, have to make sure that that's addressed

[00:40:12] as well. And that brings me back to the, to the, to the pillars, right? Of AI strategy. You want to make sure you have the people upscaled. You want to make sure that you have some governments and governance in place. You want to make sure that you find the right use cases in order to find the right use cases. We used the spark framework, maybe something I can also share here, the spark framework, uh, assistance for specify the problem. So you always want to start with understanding a business problem. So you, we don't do AI for the point of doing AI, right? That's very important. That's

[00:40:40] how you don't get AI, uh, ROI. You have to first understand what is actually a problem in our business. What's a bottleneck. You know, customers are not, are complaining. The NPS is too low or our revenue is not as high as we want it to be, or whatever it is. Like there's some business challenge. That's where you start. Then you break it down into, you know, into different processes. What are the different processes? And then you look at, okay, so that's, that's the S, specify the problem. P then when, once you have the problem specified for different processes,

[00:41:06] you propose P a, a, an AI solution. So how might AI be able to help you solve this problem? You brainstorm certain solutions, right? And then a, uh, stands for assess readiness. So you have a couple of, you have thought of a couple of AI solutions, a couple of use cases where I can help you. Then you have to understand, are we actually ready for this? Because one of the big bottlenecks is that companies, they're not ready for AI both in terms of data. So they don't have the right data in place. That's the, that's the biggest bottleneck we see currently. The data is not, you know,

[00:41:35] accessible or the quality is too low or it's, it's outdated, or, uh, there's six different dimensions of, of, of data quality, usually two or three, or even all of them are failing. So then AI doesn't make sense if you don't have the data or organizational readiness. We also have to understand, are our people system readiness, are our tools connected? Do we have the right software integrations and that sort of thing? So a, we have to assess the readiness before starting with any of these use cases. And

[00:42:05] we might have to figure out how to get ready in the first place. And once we're ready, we go to R in the spark framework and R stands for run a pilot site. So once you have, you know, you've identified a problem worth solving, you have found an AI use case to solve the problem. You have made sure you're ready for it. Then you run a pilot, maybe just one process, maybe just one small part, just to see how it works. You iterate and then K in spark framework, you kill or you keep meaning

[00:42:33] if the pilot wasn't successful, you feel like it doesn't really make sense for your company, kill it. You know, there's no point holding onto it, but if it, if it, if it's promising, then you can keep it, you can scale it and develop it. I love that. So spark, I, I, I didn't write down what you said, but. It's S P A R K. Yeah. Specify the problem, propose AI solution, assess readiness, run a pilot and then keep or kill decision.

[00:43:02] And data. Yeah. Because Sandro is putting that in the chat. Yep. Data readiness is the biggest bottleneck that we're seeing at the moment. Yeah. Yeah. He, he brought up a conversation he's reached recently had, and it was about AI taking over ACA filing. I know. Well, I definitely know that process. And if, if AI will be able to do that and, and basically at the end of it, uh, you know,

[00:43:27] it's, it's still data humans make mistakes, but getting that good data. And if that's something that AI can actually take over and, uh, basically handle for them. So it's, it's, um, it's going to constantly change. And I had someone on Alex, my first season in 2024 and she had talked about the job market in 2030 and what it will look like.

[00:43:56] Basically this was, if this was end of 2024. So projecting out five years from then. And she was talking about a staff that still sticks with me. And she was talking about the amount of jobs that will be available in 2030. 85% of those jobs haven't even been created yet. Yeah. And I don't know if that's some similar statistics and data that you've heard or what the job market will look like compared

[00:44:23] to now. Yeah. I love these discussions and I think about this a lot and discuss it with clients. And, um, I mean, you know, it's, it's, it's the crystal ball. Like it's very hard to say. Um, I do, I do agree with, I've also heard this number that I don't know, 80, 80, 85% of jobs have not even like in 2020, 30 have not created yet. I've not been created yet. Um, I do think that, you know, everybody will have an army of agents. So when you hire a person, you're not just getting a person,

[00:44:52] you're getting a person with a bunch of agents that they run locally and securely. So local models is the thing that's going to keep happening as the technology advances. So you have your own secure models at home, uh, that you can use. And then you hire somebody who's, it's not just a human, he's just the, the, the CEO, if you will, of their own agent, uh, force. And when you hire somebody, you're not getting, not just getting the person, but you're getting all their agents and you know, the quality of their orchestration. So that's, that's, that's something I'm seeing. And then

[00:45:21] of course it will be all these things that AI doesn't do, you know, the empathy, the human judgment, the emotional intelligence. So coaching people, being with people, uh, this will be huge. Um, decision-making as I, as I said, AI cannot make decisions because of the accountability problem. When AI makes a decision and it's bad, who's accountable, right? I mean, and then if your organization, your, your, your business is accountable, right? If your AI makes a bad decision,

[00:45:49] you're accountable. It's kind of like when, when, when, when, when you have a pet, a dog and it bites somebody at the end, you, you, you, you pay the price, right? And, um, so if your AI fails, uh, that, that comes back to you, there might be insurance, uh, to cover this sort of thing, I assume. Um, but, but you have to be aware of that. So you have to understand that you make decisions and you have to make sure that you orchestrate your AI in a way that it helps you with decision-making, but you are actually confident in making a decision and critical thinking again

[00:46:18] will be a huge skill to develop for that future. Yeah. So I will leave you with this question and appreciate everyone in the chat. Great questions, great conversation today, for sure. If you had to give a simple roadmap, what would the three, what, what, what are the first three steps to making AI actually work for a business? Yeah. Number one, uh, appoint a head of AI or somebody,

[00:46:45] some, somebody fractional or somebody who can help you figure this all out. Somebody who knows the market of AI and can help you synthesize all of these developments into concrete implications for your business. So that's secondly, an AI strategy. So not just don't just start with random tools. Like that's, that's going to be a waste of money, but have a clear plan, make sure it's aligned with your business strategy and your goals. And so we'll create the strategy as I explained.

[00:47:12] And then third pilot, the first use cases now, you know, and, and pilot the first use case, maybe that's the step number four step number three would be upskill the right people. Maybe find some champions, you know, you don't, if you're a bigger organization, you don't have to upskill everybody at the same time. Start with those people who are already enthusiastic about AI upskill them, and then they can bring it further into the organization. At some point you'll have to upskill everybody, but starting with AI champions, it's usually a good place. Yeah. So you have a newsletter and

[00:47:41] I'm going to bring that up here. So if anyone's watching, wants to scan it. So tell us about this, Alex. Yeah. So I share roughly every two weeks, um, uh, AI tips from my work as AI consultants, um, mainly on, you know, AI strategy, how to actually make AI work, um, short, like, um, AI failures is one of the things we address where AI goes bad. And also, so it's always very actionable. I always give, you know, one thing you can do this, this week, or one thing you can do today. I, I make

[00:48:11] sure it's very actionable. And we also address the topic of AI and critical thinking, what it means for your career, what kind of skill you have to learn. I'm actually building a product, um, a, a, a, a coaching app that helps you build your critical thinking skill for the AI age, which I think will be crucial. So, so yeah, there's many things coming and all of that I write about in the newsletter. And also I write my newsletter myself. I have AI help for researching and, and, and obviously I'm, I'm non native speaker. So I have AI for, you know, correcting and improving the way I write things.

[00:48:41] Yes. But I make it a habit to write it myself, um, based on my experience and AI is just my support. So that's something worth noting as well at this point. Very cool. Now this has been fantastic. I feel like we're going to be talking on this for quite some time. So definitely want to have you back on in the future with, with a future episode, because I'm sure things are going to change and, and you're, you're going to have to adapt and businesses will have to adapt. So

[00:49:10] definitely welcome back anytime on speakeasy HR, Alex. Definitely. I appreciate it. Thank you so much, David. Yeah, absolutely. Thanks everyone for joining today. I really appreciate your time, your questions, engaging with us. I'm taking it on the road this week. I will be at a conference, on Thursday in Cincinnati, Ohio. We have an all day HR collaborative, 11 great speakers. I will be interviewing, I think each and every one of those speakers for about 15 minutes to talk about their

[00:49:40] focus for the, for the presentation at the event and what they'll be diving into. So if you're not able to join, if you're not part of the Cincinnati Midwest region, you can join me and you can hear from the, from the speakers themselves and looking forward to taking this on the road. So again, thanks Alex for your time. I wish you all the best. Let's keep in touch and hope everyone has a great day and we'll talk soon. Thank you very much. Thanks everybody. Take care.