HR veteran Karl Ahlrichs—human capital consultant and risk management expert—joins host Nichol Bradford to discuss why critical thinking is essential as AI transforms the workplace. Learn how human decision factors can prevent AI overreliance, improve hiring outcomes, and prepare managers for high-performing teams. Ahlrichs offers practical strategies for balancing AI's analytical power with human creativity and emotional intelligence—a crucial equilibrium for HR's future where AI becomes invisible, but human skills remain indispensable.
HR veteran Karl Ahlrichs—human capital consultant and risk management expert—joins host Nichol Bradford to discuss why critical thinking is essential as AI transforms the workplace. Learn how human decision factors can prevent AI overreliance, improve hiring outcomes, and prepare managers for high-performing teams. Ahlrichs offers practical strategies for balancing AI's analytical power with human creativity and emotional intelligence—a crucial equilibrium for HR's future where AI becomes invisible, but human skills remain indispensable.
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Nichol Bradford:
Welcome to the AI+HI Project, a SHRM podcast. I'm your host, Nichol Bradford, executive in residence for SHRM. Thanks for joining. Each week we speak to experts to uncover strategic insights, practical tips, and actionable strategies for the integration of AI in HR. This week we're going to be discussing critical thinking for human resources, future skills for HR in an AI world. And HR is at the fulcrum of implementing AI in a sustainable framework. And this episode offers actionable tips for HR professionals to be the human decision factor in a more automated world. With AI on the horizon, no skill is more important in business today than the ability to understand, analyze, and act on information effectively and responsibly. And HR professionals who are also savvy, sharp critical thinkers can cut through the ambiguity and information overload to quickly zero in on what's important. So this episode covers cognitive techniques and the way to think about critical thinking tools to enhance decision making.
Joining today we have Karl James Ahlrichs to talk about this subject in depth. Karl specializes in helping organizations solve their talent shortages. He is a national speaker, author, and consultant. He's worked with a variety of consulting firms and he has been the SHRM Human Resource Professional of the Year for the state of Indiana. So we're very lucky to have Karl James Ahlrichs here with us today. Hello.
Karl Ahlrichs:
Hello. And thank you for having me. This is something I'm very passionate about.
Nichol Bradford:
Well, I really appreciate your coming here because I'm so curious about your body of work around critical thinking and human decision factors, especially in the age of AI. Will you say more about that?
Karl Ahlrichs:
Of course. Let's step back for a second and agree that everybody at this point has used AI briefly and found it exciting. But it's a creative nonlinear tool that gathers a lot of things that aren't connected and creates a new thing or report or an analysis. Critical thinking is a linear tool that is a scientific tool, if you will, to look at observation and analysis and presentation of ideas and be open-minded to maybe we were wrong, maybe we were right. So the whole scope of AI, let me make kind of a positive contrarian statement right here at the start. If we in HR don't keep our critical thinking skills sharp, the AI may get out of control that it's going to be a very powerful, creative, productive process that's being added, but there's real risks with it. And those risks are well mitigated by having the human decision factor watching what's coming out of it and deciding, "That's good. That's bad. Let's stop that. Let's do more of this."
Nichol Bradford:
Yes. One of the things that we think about when we think about AI is that it's just another member on your team. And one of the things that made me very curious about your work is people have been talking about the difference between autopilot and copilot. And so copilot is when AI is supporting you with that decision making process, allowing people to know what their next best action is and to take it. And autopilot is when we stop bringing our critical thinking skills to bear on the outputs that we see.
Karl Ahlrichs:
Well put. Human resources, I've been in HR for decades. We've been using decision assistance tools that in their basic format are AI. We've been using them for decades. We've been using predictive data analysis and benefits to take a look at the data and say, "Hey, wait a minute, your self-insured employees are visiting the emergency rooms a whole bunch on Sundays when it's really expensive. And it's usually the spouses and partners. Okay. That means we can now take action on better communicating the benefits plan to spouses and partners." Okay. That's a good use of data analysis by some helpful predictive analytics. So that's copilot. Autopilot is where the analysis would be run and a custom generated communication would be sent to the employees that are overusing the benefits. And it's sent, it goes and pulls what their age profile is, what's their gender profile? What's all of this data about them and crafts a custom letter written in their dialect, in their language to talk about this issue. And has it delivered in a way that they read?
Well, it could be text, it could be a short video clip with an avatar that is speaking to them. And the avatar is created, it looks like them. But the avatar isn't a person, it doesn't exist.
Nichol Bradford:
And so that's autopilot.
Karl Ahlrichs:
That's autopilot. See, this avatar has now been created and HR may not know what was said.
Nichol Bradford:
Right. It's the principle of human in the loop.
Karl Ahlrichs:
Right.
Nichol Bradford:
My question for you is your career has spanned HR and operations and consulting. What made you focus on human decision factors and critical thinking? What is your why? And why are you interested in that with AI? Because you've been doing what you've been doing a long time.
Karl Ahlrichs:
I have. I've earned these gray hairs.
I had a job that let me look at the mistakes. For 10 years I was in corporate sponsored outplacement. For 10 years I was the person when someone was terminated, I was the first person they met, and I did the triage of why are you coming out? And what do you want to do next? And I got to observe the impact of poor job fit on humans. And realized that the hiring process was deeply flawed. And I wanted to use my common sense or uncommon sense because apparently common sense isn't very normal now, to try and figure out a better way to get job fit. And that's how I kind of got to this. So I started using advanced assessment tools. I started using value-based interviewing and achieved a better fit. I've probably been a part of between 9 and 10,000 terminations. That's where the why came from.
Nichol Bradford:
That's a lot of terminations.
Karl Ahlrichs:
Did you see the movie Up in the Air with George Clooney? I had that job.
Nichol Bradford:
Got it. Well, so as the way that you define critical thinking, and in the context of HR, how is that becoming increasingly critical as AI adoption grows?
Karl Ahlrichs:
When kids got Velcro shoes, they stopped learning to tie their shoelaces. When I got a calculator, I stopped doing math in my head. When we get AI to be creative for us, we're going to love doing that, and we're going to stop being critical about the results. And so the check and balance is going to go away. So we have to consciously keep practicing our critical thinking skills as we go forward. Really the value we had and say, "Well, let's get HR to the table of the executive suite." Well, great. While we're there, let's join the CFO in being the positive contrarians about what's going on, thinking, "That may be a good idea, but how's that going to affect who we're hiring? How's that going to affect the workforce? How's that going to affect our clients in the customer experience we're going to be providing?" And the people who are all gung-ho will say, "Wow, we didn't think about that."
Nichol Bradford:
Well, we have seen in some early studies where there is what is being called an over-reliance.
Karl Ahlrichs:
Yes.
Nichol Bradford:
So people check the output first time, second time, maybe third time, and then they stop checking output a little bit more.
Karl Ahlrichs:
Yeah. It's going to be fine. That's what I'm talking about.
Nichol Bradford:
So what do we do about that?
Karl Ahlrichs:
Well, I go into organizations and train this subject. I teach ethics and critical thinking together because there's big ethical issues that come up alongside the critical thinking issues. And also, I train leaders and managers to appropriately have a lot of teaching moments where employees are allowed to have small failures so that they learn from these failures. Not failures that will cost a client, but failures that may cost 30 copies of a report that you need to have that feedback loop cycle of, "Ah, I didn't get it right, I didn't pay attention to the critical thinking steps. I'll do better next time."
Nichol Bradford:
So I'm hearing that for HR people across the country and around the world, I'm hearing that one of the things when they're bringing in AI is to have a process in place or a guidance in place that allows people to understand and experiment with failure.
Karl Ahlrichs:
Yeah.
Nichol Bradford:
And then also to see that the outputs can be flawed. Is that the...
Karl Ahlrichs:
Well put. That's exactly right.
Nichol Bradford:
So how would someone implement something like that in their process for an AI implementation?
Karl Ahlrichs:
Oh, interesting. I think everybody's watching the leaders. So to have the leaders become transparent about their critical thinking steps, their critical thinking skills, so that everybody downstream from the leader is watching somebody who when they get it wrong, they don't hide it, they deal with it and explain how to deal with it. There's a real generation of trust that's been lost because of all of the noise in the system. And we get our cues from social media, where I am an influencer, everything is perfect. The world isn't perfect. And so for a realist to be a leader and a realist who's a good communicator, and then work with training and development to have baked into the lesson plans of the skill builds in an organization, have built into there, "Here's what we're doing. Here's what we'll get when it works. Here's what happens when it goes wrong. And here's how we hope you fix it."
And to include that last step is often missing. Let's be realistic about what we train on and that people are going to stretch too far and fall. These next couple years are going to be messy because we are implementing everything all at once all the time. And there's going to be some failures.
Nichol Bradford:
I think also just the speed of change right now is sort of inherently messy. And additionally, we have different generations of AI. I love what you said earlier at the very beginning about how AI has actually been in HR for a long time.
Karl Ahlrichs:
Oh, I think since 1970.
Nichol Bradford:
Yeah. Across the board. So predictive machine learning, different variations of it. What's different about this version is that, or of the generative version of it, is that it's something that can extend the individual in a way that they can see and prompt and change.
Karl Ahlrichs:
They can leverage that power.
Nichol Bradford:
Yeah.
Karl Ahlrichs:
I'm glad you brought that up. There is a good parallel between what happened in auto manufacturing and what we're doing in HR. An organization like Toyota invested a bunch in robots and got it wrong. They had too many robots. And so they kept the robots but changed what they did. Instead of building the cars, the robots are now assisting the humans that build the cars.
Nichol Bradford:
It's one of the reasons why I was curious about your human decision factors, because as we are reinventing work and reinventing roles, there's at this moment what we are beginning to do is to pixelate the roles to pull them apart. And to see-
Karl Ahlrichs:
I like that term, pixelate them.
Nichol Bradford:
Thank you.
Karl Ahlrichs:
And to see what can be automated? What can be augmented? And what can be enhanced? And so it's your AI is your newest team member and which pieces do they take? And so I'm really curious about what are the human decision factors? And how do they fit into what can be automated? What can be augmented? And what can be enhanced as the roles are being pixelated?
Karl Ahlrichs:
Great. Let me get on my soapbox briefly and talk about scientific critical thinking, linear thinking, and a way that may fit HR better. Okay. So first we're going to talk about scientific critical thinking, where my father was a scientist. So I am a creative thinker who was raised by a scientist. So it's like a blending of both worlds. They observe, they then interpret the data they get, then they analyze what do those numbers really mean? Then they pull inference from it. This plus this and this happened, so we can infer that these are connected. Okay. So that makes a big difference, they evaluate it. Process, process, process. Here you hear it. Then we have to explain it to others. So we have to have communication skill and say, "We think plate tectonics really works and that the surface of the earth is moving, and here's why."
By the way, plate tectonics was only agreed upon in my lifetime. It's something we all think it's always been through. No, open-minded scientists had to realize everything they thought about the earth was wrong in about 1955. And then having a feedback loop of self-regulation when you get it wrong. Okay, that's very scientific. What's missing from that? Why won't that be useful in HR? It doesn't have the human factor. This is very cold and logical. There is... Okay, so the second thing I want to talk about is a resource that HR could use. Psychologist Edward de Bono created something called the Six Hats Thinking Style. And if you hit Amazon and type in Six Hats Thinking, there's the book. And it's congruent thinking that includes human emotions, where people go through the steps of finding the facts, fine. But then let's have new ideas. Let's have some benefits statements, let's have some emotions. And once we've done that, then turn the critical thinking process loose. But let's build the human factor into the front of this.
Nichol Bradford:
So human factors are specifically the emotional context.
Karl Ahlrichs:
Yep. The thinking of the benefits, but also I would say that being creative is a human factor of this plus this, plus this, thinking outside the box. Although my CFO friends, I work a lot with CFOs, say that, "Karl, when you're thinking outside the box, you're just thinking in the larger box." Okay, fine.
Nichol Bradford:
So it's those three.
Karl Ahlrichs:
Yeah.
Nichol Bradford:
And then are there more?
Karl Ahlrichs:
Those are the core.
Nichol Bradford:
Great.
Karl Ahlrichs:
Yeah.
Nichol Bradford:
Yeah. It's very interesting. One of the things I spend a lot of time doing is looking at what makes an implementation, an AI implementation, what has made them fail? And what has made them succeed?
Karl Ahlrichs:
Interesting.
Nichol Bradford:
And across history, especially, all of the... many of the large scale implementations that you referenced, machine learning and the predictive implementations, failure had a lot to do with not including the human factors.
Karl Ahlrichs:
Right.
Nichol Bradford:
And failure had a lot to do with, you can get the algorithm right, but then the implementation goes wrong without buy-in, without the iteration that comes from having buy-in plus human creativity, so you can train it better and things like that. So I am curious, so we can get a little specific. For the HDF that you've been teaching and advocating for, in terms of AI implementations, what are some of the ROIs that you've seen? What are some of the things you've seen people do so far, tangibly?
Karl Ahlrichs:
Let me give you some quick talent management wins. There was a grocery store that had 80% turnover within six months, no matter the hire, they were gone. And we used some predictive analytics to figure out what was different about the people that stayed? Adjusted the screening process and raised the screening standards. So there were actually fewer applicants, but they were aligned with the values of the grocery store and they had a couple of key attributes that could be measured. And so by implementing that, within nine months, they had flipped the metric from 20% were kept to 80% were kept. The volume of their hiring was way down because people weren't leaving. Another thing I found on the human factor was I helped to design, put in this new high standards, high ethics, high achieving hiring model. And it worked. And the new hires were real high performers. The managers weren't ready for them-
Nichol Bradford:
That's interesting.
Karl Ahlrichs:
... and the high performers quit. Because as a manager, is it more challenging to manage a high performer? Yes. They're always questioning, they're never fully satisfied, they've got a new idea they'd really like to try. It's exhausting. And so when you raise your standards and hire higher quality people, I'd say the first step is first upscale your managers in active listening, in all these human factors, so that when the new ones arrive, the managers are ready for.
Nichol Bradford:
What's so interesting about that, one of the questions I love to ask guests is about the unexpected challenges.
Karl Ahlrichs:
I know.
Nichol Bradford:
And what you've just laid out is that as we bring in more AI into HR and we have more predictive, and we bring in more generative, and let's just assume we get the pixelation right.
Karl Ahlrichs:
Yeah.
Nichol Bradford:
So people really get augmented and they really get enhanced. The unintended consequence of that is that you end up with a higher number of high performers.
Karl Ahlrichs:
Yeah.
Nichol Bradford:
And so your managers have to be ready for that.
Karl Ahlrichs:
They better be good.
Nichol Bradford:
Oh, that's great.
Karl Ahlrichs:
And the number one skill... I've done a lot of work on this and people have me come in to teach managerial training. Number one skill to start with is active listening, because we have over time have this model of the ideal manager of being an extrovert who doesn't listen but just tells what to do. No, the manager of an AI future is an introvert because they listen.
Nichol Bradford:
That's interesting. So I do want to circle back. So you gave a very good predictive example. Have you seen yet, and I know it's early, but have you seen a great generative AI example of an implementation where the ROI is going in the right direction, whether it's strategic or economic?
Karl Ahlrichs:
Yeah. Let me step back a second. Human resources as a function has about, I don't know, 10 internal departments. There's 10 slices of human resources. Everything from workforce, prediction and organizational design to compensation and benefits to employee communications to what I call... they call employee relations, I call fixing broken people and getting the right people in the right place. Each of those has to be measured to a different ROI. So I can't say HR in general. I gave you an example of predictive analytics helping that grocery store. Okay, so that's on a talent sourcing type of thing. I gave you an example of the benefits analysis of the overuse of the emergency room on weekends. And that's an example of analysis and the benefits component. So the implementation's going to be different for each of those departments and the outcome's going to be different. Can I make a prediction about one that I think is coming? We've got the issue of burnout. To have on the way in employees take a multi-phase, multi-scale psychometric assessment of who they are, what's driving their behaviors. Okay, we've got that.
We can take that data and put it into a generative AI engine and run the machine that is this person, if you will, in a virtual environment. Run it for five years and have it predict which of these factors is going to cause them to burn out first.
Nichol Bradford:
That is interesting because you can basically run those five years in an hour.
Karl Ahlrichs:
Exactly. And learn that this person will overuse their positive communication skills and not be transparent about the negative. And so build into their onboarding heads up about this because it's going to be a problem. What will the metric be on that turnover, turnover of high performers?
Nichol Bradford:
That's so interesting. We do have, there's 10 different areas, and to your point, it's so clear there's going to be different implementations and different ROI metrics for each one.
Karl Ahlrichs:
For every slice of the-
Nichol Bradford:
Yeah. For every slice of the pie. But overall, what I'd love to... And we've talked about the unintended consequences of the people are definitely not talking about how every level of leadership actually has to get better.
Karl Ahlrichs:
Yeah. And if we run it on autopilot, I know of an organization that was almost crashed completely by autopilot.
Nichol Bradford:
Yeah, autopilot rarely works. Last question, future outlook. Where are we in 10 years? 5, 10 years, what has happened? What does HR look like? How has it been transformed? What does the enterprise look like?
Karl Ahlrichs:
Look around you. Everything you see will be different. Think back 10 years and look where we are now and speed that up. Okay. So when the internet arrived, everybody taught courses about the internet and everybody talked about what was going to happen. Some people got it right. We no longer talk about it because it's like oxygen.
Nichol Bradford:
Yeah. We don't talk about mobile either.
Karl Ahlrichs:
No.
Nichol Bradford:
So AI will be electricity.
Karl Ahlrichs:
AI will disappear. And our critical thinking skills are going to keep us on the right path. So that's why I'm confident for the rest of my career, I want to come into organizations and help them be sustainable, have better work-life balance, become better humans by using these as leverage tool and not as turning the keys over to a robot that's going to drive.
Nichol Bradford:
Great. Well, thank you so much for your time today. I really-
Karl Ahlrichs:
Thanks for having me, this has-
Nichol Bradford:
... really appreciate it. I think your work on human decision factors and keeping those front and center is very critical. And that's going to do it for this week of the AI+HI Project. A big thanks to you, Karl, for coming in and sharing your insight with us.
Karl Ahlrichs:
Thanks for having me.
Nichol Bradford:
Before we say goodbye, I want to encourage everyone to follow the AI+HI Project wherever you listen to podcasts. Also, audience reviews have a big impact on the podcast visibility. So if you enjoyed today's episode, please take a moment to leave a review and help others discover the show. And we'll see you next time on the AI+HI Project.