Josh: Hello everyone, and welcome! Today, we're jumping into a topic that's both fascinating and a little<break time="0.4s"/>… mind-bending: What happens when artificial intelligence starts taking on leadership roles? I mean, Drew, could you imagine your next boss being an algorithm? Drew: Oh, fantastic. I'm sure it'll totally understand why I need, like, three coffee breaks before 10 a.m. <break time="0.6s"/>But seriously, Josh, are we actually ready to put robots in charge? Last I checked, empathy wasn't exactly AI's strong suit. Josh: Exactly! And that's the central tension we’re going to explore. We're diving into Leadership by Algorithm by David De Cremer. <break time="0.6s"/>This book isn't just asking if AI can lead, but more about introducing a hybrid model--humans and machines partnering up to drive innovation. De Cremer makes it very clear: AI's incredibly efficient, yes, but it can't replace those oh-so-crucial human qualities like empathy, ethical judgment, and emotional intelligence. Drew: So, it's less Terminator, more<break time="0.4s"/>... team-building? Great, I'll bring donuts to the next AI-officiated meeting. Josh: Something like that! Today, we’re going to cover three key ideas based on De Cremer's insights. First, we'll explore how leadership itself is evolving in the age of AI. <break time="0.5s"/>Second, strategies to make humans and algorithms work together seamlessly, you know, like the ultimate office dream team. <break time="0.7s"/>And third, why having an inclusive and ethical workplace culture is “more” important now than ever, especially when you throw AI into the mix. Drew: Alright, so we're tackling the what, the how, and the why of AI-driven leadership. Got it. <break time="0.6s"/>Let’s just hope "algorithmic collaboration" doesn't mean we're all taking orders from our refrigerators. Josh: Stick with me, Drew, because this is about leadership that’s actually smarter, more innovative, and ultimately, more human, even with AI at the table.

Josh: So, let's dive into this hybrid leadership concept De Cremer talks about. It's basically about shifting from traditional leadership styles to a mix of AI and human strengths. AI “really” shines when it comes to crunching data, spotting patterns, and making logical decisions. But when it comes to actual leadership, it's still all about human connection, intuition, and deeply held values. Drew: "Complementary strengths," huh? Sounds like a nice way of telling us our jobs are on the line, Josh! I mean, AI's already beating us at strategy, predicting markets, and managing workflows. What's left for us to do? Give pep talks? Josh: Well, that's where De Cremer's argument gets interesting, Drew. He's not saying AI is going to replace us completely. It's about changing our roles. Let AI take on the data-heavy, tedious stuff, like scheduling or sales forecasting. That frees us up to focus on what it can't do — resolving conflicts, sparking creativity, and making those tough ethical calls. Drew: So, you're saying AI is the data geek, and we're the office counselors? Okay, I'll bite. What happens when AI screws up? If an algorithm makes a disastrous decision, are we humans still responsible, even if we didn't fully grasp what it was doing? Josh: Absolutely, and De Cremer stresses the importance of accountability. Leaders can't just treat AI as a black box and wash their hands of the outcome. He emphasizes transparency. We need to understand how these algorithms work, explain the decision-making to our teams, and ultimately take ownership. It's more about collaboration than delegation. Drew: So, leaders become translators, explaining to their teams why the algorithm wants us to push purple widgets this quarter? Still, Josh, AI can't handle everything. Soft skills, for instance. Could an algorithm handle layoffs with any kind of grace? Would it just fire people based on a productivity chart and send a generic "Sorry!" email? Josh: Actually, De Cremer touches on emotional complexity with a great example – Google Duplex. Remember that AI system that could make reservations? Technically impressive, but put it in a situation with human emotions – a last-minute cancellation, a frustrated client – and it falls apart. AI just doesn't understand tone, empathy, or how to smooth things over. Drew: Oh, right! I saw a demo of that. It was amazing…until the conversation went off-script and it sounded like a broken robot. "I’m sorry, I cannot compute your emotional state. Please reboot." It’s funny, but also a little scary. Josh: Exactly! That's why human leaders are still so important. Empathy, emotional intelligence, trust – these are vital to leadership. An algorithm might know what needs to be done, but humans understand how to do it. Take healthcare, for example. AI has transformed diagnostics by finding patterns in patient data. But when it comes to delivering bad news or building trust for life-changing decisions, no algorithm can replace a doctor's human connection. Drew: Okay, my takeaway is this: AI is like a super-competent intern. Great with data, logic, and spreadsheets, but not ready for the corner office, you know—the one that requires humanity, heart, and a good dose of wisdom. It's kind of humbling, realizing we might not be obsolete… yet. Josh: Exactly! and he advocates for hybrid leadership. It’s more of a partnership than a takeover. He even suggests ways to create that synergy, like collaborating on decisions and upskilling leaders in tech. Leaders need to understand what AI can do to use it wisely, but also know its limitations. Those improvements will happen when human intervention enters: remember we talked about hiring bias in algorithms earlier? Drew: Oh yeah, it was HR who had to fix the algorithm that basically recycled historical discrimination. Fair point. You can't just let AI run wild – it needs a co-pilot to guide it toward fairness and inclusion. Josh: Exactly! The book mentions balancing productivity with workplace ethics. Algorithms can optimize performance, sure, but leaders have to ensure inclusion doesn't get left behind. Without leaders shaping those decisions, AI might just perpetuate the problems it was meant to solve. Drew: Alright, let me see if I've got this straight. AI is that hyper-efficient but tone-deaf team member handling the technical heavy lifting, and the human leader steps in to smooth over the emotional and ethical bumps, right? That makes sense, but it also sounds exhausting! Josh, are we going to have to essentially babysit machines on top of leading our teams? Josh: It can sound that way, yes, but if done correctly, this hybrid approach is not really about holding machines in check, it is about enhancement. AI improves our decision-making by offering insights, predictions, and precision that we'd never be able to get alone. And human leaders remain crucial because they can channel these tools into decisions that are responsible, empathetic, and aligned with the organization's values. Drew: So, instead of humans versus machines, we need to think humans plus machines—kind of like Batman with his gadgets. AI’s the utility belt; we’re still the ones swooping in to save the day.

Josh: Exactly, Drew, which brings us to how organizations can actually put these ideas into practice. Hybrid leadership isn’t just some abstract concept; it needs real frameworks and strategies to make sure AI helps, not hinders, human decision-making. De Cremer talks about things like clear governance, keeping humans in the loop, and tackling biases in algorithms as the first steps. Drew: Hold on a sec, about these frameworks. I get the idea of being transparent – no one wants to blindly follow a mysterious “black box”. But how do you actually make an organization transparent? Your average employee can't just open up the AI and understand it, right? Josh: Right, and De Cremer knows that's a challenge. Transparency doesn't mean everyone needs to become a programmer. It's about leaders making AI less scary by explaining how it works, what data it uses, and why it makes the decisions it does. A company might offer training to help employees understand the basics of AI—not writing code, but knowing enough to understand the outputs and question them if needed. Drew: So, like, "This algorithm predicts sales based on past purchases, but it might be wrong if customers suddenly change their behavior." Kind of like telling your team what the GPS is doing before it leads them into a ditch. Josh: Exactly! This kind of transparency isn't just nice to have, it builds trust. Employees need to feel like the AI isn’t coming for their jobs or conflicting with their values. Some organizations even present AI as an assistant, meant to make human roles better. That kind of message can reduce fear and encourage teamwork between humans and machines. Drew: Okay, transparency I'm on board with. But what about bias? You mentioned the "garbage in, garbage out" problem. How do leaders deal with algorithms that are trained on biased data? Josh: That's a tough one, but critical. De Cremer suggests a few things. First, organizations need to really vet the data they use to train their AI, looking for patterns that could lead to unfair results. Second, they should have checks and balances, like diverse teams reviewing the AI’s decisions to catch any biases it might have missed. It’s about creating a loop where both humans and machines are constantly learning and improving. Drew: Okay, so leaders have to watch the datasets, review the results, and make sure the AI doesn’t accidentally cause a PR nightmare. But, Josh, realistically, how many leaders have the skills or even the time for all of that? Josh: That's where education and training come in. De Cremer argues that leadership in the AI age isn’t just about traditional things like decision-making. Leaders need to be tech-savvy enough to understand the tools they're using. And it’s not as crazy as it sounds—a lot of companies are already offering AI courses for their managers. Think of it as another tool in their leadership toolbox. Drew: Alright, so we have transparency, oversight, and bias fixes. But what about the ethics? No matter how transparent or efficient an algorithm is, there will be times when its logic doesn’t match what’s morally right or humane. How do leaders step in and make sure we stay… human? Josh: That’s such a crucial point, Drew. De Cremer stresses that ethical decisions can never be outsourced to algorithms. An AI might suggest ways to boost performance by increasing workloads or cutting costs. What it may not consider is the human impact, such as burnout. Leaders must protect organizational values, ensuring decisions are fair and compassionate. Drew: So, basically, AI might say, "Cut 20% of your workforce to hit profitability targets," and the human leader needs to step in and say, "Not at the expense of our people or culture.” Sounds like the leader is the referee here, making the calls the algorithm can’t. Josh: Precisely. And De Cremer gives a great example of this in healthcare diagnostics. When algorithms helped doctors identify cancer with fewer errors, the results were amazing. But delivering that diagnosis? Explaining the treatment plan? That’s where a doctor’s emotional intelligence comes in. It’s a powerful reminder that while AI can improve accuracy, it doesn’t replace humanity. Drew: Yeah, and let's not forget that story about those recruitment algorithms going rogue – rejecting candidates based on biased old data. If HR leaders hadn't stepped in, that system would have just made existing inequalities even worse. Josh: Exactly! Those kinds of moments shows where human leaders are so valuable. Organizations that use AI need to put ethical guidelines into their operating principles so those values don’t get left out. And it’s not just about avoiding bad outcomes, De Cremer argues it’s about creating workplaces where AI and humans work together for shared goals, based on trust and inclusion. Drew: Okay, I can see the vision now—a partnership where AI handles the heavy tasks, but humans step in to keep things ethical, empathetic, and aligned with values. Still, Josh, it’s a delicate balance, isn’t it? I mean, if we mess this up, won’t it just make inequality and distrust even worse? Josh: That’s why this hybrid model isn’t just about technology—it’s about leadership. Leaders have to ensure that AI solutions amplify human potential rather than undermining it. And by fostering a culture of collaboration, transparency, and ethical accountability, we can strike that balance.

Josh: So, understanding this balance is key before we even start thinking about how AI fits into our culture and ethics. Which brings us to a super important part of De Cremer’s book—how leaders can build a culture that’s both purpose-driven and inclusive in a world increasingly run by AI. This is where the "why" of all this becomes clear, you know? It's not just about making things more efficient with technology. It’s about making sure that as AI advances, it actually helps people instead of leaving them behind. Drew: So we're not talking Skynet here, right? Good, because I’d really prefer my future with fewer killer robots, and maybe one or two robots at the coffee machine. But seriously, what does it even “mean” to build a "purpose-driven culture"? It sounds like one of those corporate clichés, the kind you hear at team building events right before they hand out the company-branded water bottles. Josh: I get that it sounds kind of abstract, but De Cremer actually makes it pretty concrete. He says purpose-driven leadership means making sure that everything you do—with both people and AI—is aligned with clear values like being inclusive, fair, and building trust. Leaders can't just manage people, they also have to guide the technology they're using to make sure it sticks to those values. The main goal is to create a work culture where AI actually supports things like human dignity, creativity, and teamwork. Drew: Okay, dignity and creativity sound great, but is there a risk of it just becoming a checklist item? Like, "Our AI is ethically certified! Trust us!" How do you avoid that? Josh: That’s a great question, actually. De Cremer offers some really practical ways to make purpose part of your everyday work. Let’s break it down a bit. First, there's “continuous education”. Leaders have to realize that AI can be a little scary for some people, right? Instead of just throwing a new AI tool into the mix and expecting everyone to get it, they need to invest in teaching people about it. Giving employees workshops on how AI works, what its limitations are, what biases it might have—basically, helping them see that these tools are there to help them do their work better, not take their jobs. Drew: Right, so you're turning employees into informed users instead of just paranoid skeptics who think they're one wrong algorithm away from being replaced. Education makes total sense. What else is there? Josh: De Cremer also talks a lot about “transparency”. And he means real transparency, not just marketing buzzwords. If you're using an algorithm to make decisions—whether it’s promoting employees or optimizing supply chains—leaders need to explain exactly what's going on. People need to know how those decisions are being made and what data is being used. When people understand how the system works, they're not only more likely to trust it, but they're also more likely to actually use it effectively. Drew: So, transparency is like turning on the lights in a room that everyone's afraid to walk into. But what happens if turning on those lights reveals something… ugly? Like built-in biases or flaws in the system? I mean, Josh, we've seen how AI can sometimes just amplify the worst parts of the data it's trained on. Josh: Exactly! That’s why another key thing is “bias mitigation”. Leaders have to actively look for and fix the biases that AI picks up from its training data. De Cremer talks about how some companies' recruitment algorithms were accidentally reinforcing existing biases – like filtering out minority groups because they were underrepresented in past hiring data. One company fixed this by forming a team of HR experts, data scientists, and ethicists to review the algorithm’s decisions and change the process to line up with the company’s diversity goals. Drew: That's smart. So instead of pretending that the algorithm is neutral just because it's a machine, they actually brought in people to dismantle bias, piece by piece. Makes perfect sense. But here’s my question – how do you get regular employees to buy into all this? Because if you tell me, as an employee, "Trust this algorithm, we’ve audited it," I’m still going to wonder, "But what happens when the algorithm really screws up?" Josh: And that's exactly where “ethical leadership and communication” come in. Leaders have to show humility and empathy—not just as a show, but as a real way to create an environment of psychological safety in the organization. For example, if an algorithm suggests laying off employees to cut costs, a leader needs to step in and say, “Wait a second, does this align with our company’s values? What will be the human cost?” Ethical leadership means keeping values front and center, even when efficiency might push you in a different direction. Drew: I guess it's about being that referee we talked about earlier, stepping in and saying, "Hold on algorithm, humans have values here!" Honestly, Josh, trying to keep all these values in mind in the face of all this tech pressure sounds exhausting. Is there any hard evidence that all this trust-building and collaboration stuff actually works? Josh: Absolutely! De Cremer cites research and even brings up Warren Buffett’s famous quote about trust being like the air we breathe in an organization. When it’s there, you don’t even notice it. When it’s gone, you’re suffocating. Studies clearly show that trust directly affects things like morale, productivity, and innovation. For example, employees in high-trust workplaces report being 74% less stressed and 50% more productive. And then, you factor in AI—if teams trust the AI tools they’re using, they’re much more likely to integrate them smoothly into their workflows. Drew: Well, trust might be the air we breathe, but I can tell you, a lack of trust definitely creates a storm. So, can you give me a real-world example where this whole purpose-driven culture thing has actually worked? Josh: Sure, there’s the example of Stitch Fix, which we mentioned before. They use AI to suggest clothing recommendations based on customer data, but where the magic really happens is when human stylists step in to fine-tune those choices. This makes sure the final product incorporates both the AI's precision and the stylist's intuition. Employees feel valued because they’re not replaced by AI. Customers get a service that’s genuinely personalized. Drew: Are you telling me that my impeccably styled outfits aren't entirely the work of some heartless algorithm? Shocking. But I get your point. Stitch Fix didn’t just throw AI into their system and call it a day – they made sure that humans and machines were co-creators, and that they each added unique value. Josh: Exactly. And the bigger picture here is this: leaders need to set the tone for a culture that’s inclusive and purpose-driven, where AI is used as a tool for collaboration, not just automation. Trust, transparency, and a clear shared purpose create a work environment where technology amplifies what humans can do. Drew: So, leaders of the future aren’t just managing people – they’re managing relationships between people and technology. They’re translators, referees, and culture builders all rolled into one. No pressure, right?

Josh: Alright, so to bring everything full circle, we've been discussing how AI is “really” changing the game for leadership. It's pushing us towards this hybrid model, right? Where human creativity meets the precision of algorithms. AI is fantastic at data analysis and system optimization, but it's still human leaders who bring empathy, ethical considerations, and emotional intelligence into the picture. Drew: Exactly. And I think the big takeaway here is that it's not an either-or situation. It's not humans versus machines. It's about forging partnerships. AI can handle the logical stuff, the number crunching. But humans? We're there to make sure decisions are, you know, emotionally intelligent, ethically sound, and inclusive. Like we said before, it's Batman and his gadgets, right? Josh: But for this hybrid model to “really” work, leaders have got to prioritize transparency, educate their teams, and actively, proactively manage biases in AI. As De Cremer’s book highlights, trust and a clear sense of purpose, those are the foundations for making AI a collaborative tool rather than a source of conflict. Drew: So, here’s the message for all the leaders listening: Don’t be intimidated by this AI revolution. But at the same time, don't blindly trust it either, okay? Be that bridge between technology and humanity. Because, let's be honest, no algorithm can lead with heart or replace a well-thought-out vision rooted in values. Josh: Absolutely, Drew. Leadership in the age of AI isn't about letting technology take over the show. It's about enhancing our humanity. Making sure that the future is smarter, more inclusive, and driven by a purpose. That’s our challenge, and it's also a huge opportunity. Drew: And, you know, as long as the coffee machines are still operated by humans, I think we’ll manage just fine. Josh: Thanks for joining us, everyone! Until next time, keep thinking, keep leading, and most importantly, stay human.