AI to Augment Leadership Decision-Making

Transcript

Ross Dawson: Lavinia, it is awesome to have you on the show.

Lavinia Iosub: Thank you so much for having me, Ross.

Ross Dawson: Well, we’ve been planning it for a long time. We’ve had lots of conversations about interesting stuff. So let’s do something to share with the world.

Lavinia Iosub: Let’s do it.

Ross Dawson: So you run a very interesting organization, and you are a leader who is bringing AI into your work and that of your team, and more generally, providing AI skills to many people. I just want to start from that point—your role as a leader of a diverse, interesting organization or set of organizations. What do you see as the role of AI for you to assist you in being an effective leader?

Lavinia Iosub: Great question. I think that the two of us initially met through the AI in Strategic Decision Making course, right? So I would say that’s actually probably one of the top uses for me, or one of the areas where I found it very useful. The most important thing here is to not start with the mindset that AI will make any worthy decisions for you, but that it will augment your cognition and your decision making when you are feeding it the right context, the right master prompts, the right information about your business, your values, what you’re trying to achieve, how you normally make decisions, and so on.

Then you work with it, have a conversation with it, and even build an advisory board of different kinds of AI personas that may disagree or have slightly different views. So it enhances your thinking, rather than serving you decisions on a plate that you don’t know where they come from or what they’re based on. That’s one of the things that’s been really interesting for me to explore.

If we zoom out a little bit, I think a lot of people think of AI as a way of doing the things they don’t want to do. I think of AI as a way to do more of the things I’ve always wanted to do—delegate some menial, drudgery work that no human should be doing in the year of our Lord 2025 anymore, and do more of the creative, strategic projects or activities that many of us who have been in what we call knowledge work—which, to me, is not a good term for 2025 anymore, but let’s call it knowledge work for now—just being able to do more of the things you’ve always wanted to do, probably as an entrepreneur, as a leader, as a creative person, or, for lack of a better word, a knowledge worker.

Ross Dawson: Lots to dig into there. One of the things is, of course, as a leader, you have decisions to make, and you have input from AI, but you also have input from your team, from people, potentially customers or stakeholders. For your leadership team, how do you bring AI into the thinking or decision making in a way that is useful, and what’s that journey been like of introducing these approaches where there are different responses from some of your team?

Lavinia Iosub: So we were, I’d say, fairly early AI adopters, and I have an approach where I really want to double down on working more with AI and giving more AI learning opportunities to those people who are interested, rather than forcing it on people who may not be interested. There are pros and cons to that approach—it can create inequality and so on—but I’m much more about giving willing people more opportunity, more chances, and more learning, rather than evangelizing AI. People need to decide their own take towards AI and then engage with that and go after opportunities.

As a team, as a company, we were early AI adopters, and as a leadership team, quite a few quarters ago, we actually went through the Anthropic AI Fluency course as a team, and then produced practical projects that were shared with each other. We got certificates, which was the least important thing, but we shared learnings and it sparked a lot of interesting conversations and different uses for AI.

Now, you also probably know that we’ve been running an AI L&D challenge for two years now, where, as a team, we explore AI tools and share mini demos with each other. For example, “I’d heard a lot about this tool, I tried it out, here’s what it looks like, here’s a screen share, and my verdict is I’m going to use this,” or maybe another person in the team finds it more useful. We found those exchanges to be really great for sparking ideas, not only about AI, but about our work in general. Because in the end, AI is a tool—it’s not the end purpose of anything. It’s a tool to do better work, more exciting work, double down on our human leverage, and so on.

We’re now running this challenge for the second year straight, and we’ve actually allowed externals to join in. It’s really interesting because it adds to the community spirit, seeing people from other areas of business and with different jobs, and seeing what they do with it. I think, and you may agree, Ross, that people think we’re in an AI bubble, but we’re still very much in an LLM bubble. When people say AI, 90% of them actually mean LLMs and ChatGPT. So it’s interesting to see what others do.

With the challenge, we’ve said every week you have to try different tools. You can’t just say, “Here’s the prompt I’m doing this week on ChatGPT.” No, it has to be different tools that do different things. It can be dabbling into agents, automating, or using some other AI tool that helps with your tasks. It can’t just be showing us your ChatGPT conversations or how it drafts your emails. We want to take it a step further.

It’s really helped us reflect on our own thinking and workflows and share with each other. It’s almost been like team building as well. For example, I was exploring a tool for optimizing—basically, geo, switching from SEO to geo, and seeing what prompts your company comes up in, and so on. It was pure curiosity, and now I’m having a whole conversation with our marketing manager about that, that I probably wouldn’t have had if we weren’t doing that.

Again, I describe myself as AI fluent but very much people-centered. To me it’s always, the goal is not AI fluency or AI use. The goal is, how do we work better with each other as humans, and do more of the work that excites us and provides value to our stakeholders? All those different things definitely help with that.

Ross Dawson: Yeah, well, it obviously goes completely to the humans plus AI thesis. I think the nature of leadership—there are some aspects that don’t change, like integrity, presence, being able to share a vision, and so on. But do you think there are any aspects of what it takes to be an effective leader today that change, evolve, or highlight different facets of leadership as we enter this new age?

Lavinia Iosub: I would say so. If we think of the different eras of leadership and what it took to be efficient—well, I don’t want to go into the whole leader versus manager debate—but when you look at the leaders who were succeeding in the 50s, there was a command and control model, certain ways of doing things, and it was largely male, especially in corporate leadership. That went through some transformations over the last few decades, and I think what’s happening right now with AI will trigger, or perhaps augment, another transformation.

In this next era, the key to leadership will be blending systems thinking and AI automation—at least being aware of what you can do with it—with empathy, discernment, connection, and clarity. Sorry, just needed a sip of water.

Secondly, for a very long time, when we talk about knowledge work, the biggest competitive advantage has been talent—who you can attract to your team or company. Technology, money, all these things were important, but they were also quite accessible if you had a good idea and good people. So much depended on who you could attract, so it was a lot about talent, or what we otherwise call human resources.

Right now, we’re evolving into an era where it’s becoming about—I’ve tried to coin a term to make it easy to understand—people and AI resources, or PAIR. It’s a good acronym because it’s a pair: two different types of resources, people and AI, that have to work together. The most important thing, which I don’t see enough talk about, is not only about these two resources, but crucially, the quality of connection between them. How do you make them work together?

We’ve seen, and you and I know well, these now-famous cases of corporations that fired their entire CS department and made AI do it, and then the clients were all upset, it wasn’t working, and they had to bring the people back. Now you’ve got people who are laid off and disgruntled, and a botched AI implementation. So now you have a whole other problem.

When you hear of cases like that, what’s really obvious is that there was no connection between that pair of resources—people and AI. Depending on the industry—some are more conservative and will take a while to implement these things, others are faster—but the measure of success in leadership will be about how you understand what’s going on in AI and automation, what’s happening with people, how they’re concerned about their jobs, how that’s changing the psychological contract between employee and employer, and actually manage that situation so all these things blend and work together. And basically organizational design: how do you make all the resources you have work together to do the thing your trying to do. Whatever it is in your organization.

Ross Dawson: Very interesting. So talent is still critically important. My thesis is actually that talent is even more important than ever, but the nature of that talent is changing, as you said. As we talk about how leadership changes, the nature of talent also changes. It’s your ability to work with others, including AI.

We used to have this syndrome in professional firms, or in many firms, where you had team players and solo players. The solo players, if they did well, that was okay even if they couldn’t collaborate. But now we’ve seen a lot of research showing that the ability to collaborate with humans, the ability to lead humans, is highly correlated to the ability to collaborate with AI and to lead AI or AI agents. So talent is still critical; it’s just that instead of raw intelligence, more and more we’re heightening the aspect of being able to work with other intelligences—human and other.

Lavinia Iosub: Absolutely, I fully agree with that, and that’s why “P” as in people still comes first in my acronym. People resources—human resources—although I don’t like the term “human resources” so much because HR has been trivialized and even disliked in some organizations. But people are very much the ones at the core.

I believe one of the skills of the future—and when I say the future, I mean 2030, just a few years from now—is AI orchestration. At the core of that will still be a human who does this AI orchestration. It’s important to resource your people with the right tools and access to technology, because AI reshapes opportunity, access, hierarchies, career progression, and all these things. We can’t ignore it and hope for the best, but at the core, it’s still humans. Investing in humans and augmenting them with AI is probably the way to go for many of us. That can mean small integrations and augmentations depending on the job, or it can mean that maybe now 80% of the job is done by machines, but the remaining 20% is crucial.

Ross Dawson: This goes to what you were saying earlier about knowledge work perhaps not being the best term to describe what is valued today. Building on what you’ve just been saying, what comes next? How do we describe the valued worker in this new era? What are their characteristics?

Lavinia Iosub: I really think it’s really hard to overstate just how much of our world is built on the idea of knowledge being a scarce resource. When you think of LinkedIn—the entirety of LinkedIn is based on knowledge as a scarce resource: “I did this course, I have this degree,” and so on. The entire recruitment process in most organizations is based on the idea of knowledge being scarce, and all of that is collapsing right now. AI models are swallowing entire fields overnight. If we sit here silent for 20 seconds, we can probably hear the AI churning through 20 years’ worth of specialized knowledge in any field.

So knowledge is becoming a lot less important. Accumulating knowledge, especially hoarding knowledge—in a lot of larger corporations, you see this behavior because knowledge is access to power and authority. People had the right incentives to hoard knowledge, but that’s switching. There are diminishing returns to accumulating and hoarding knowledge, and accelerating returns to skills. Some of these, I’m saying “skills” in quotation marks, because they haven’t been regarded as skills for a long time—like curiosity, for example. That’s a really important skill that I personally want to see in anyone joining our teams, because we’re building the car as we’re driving it. We can’t rely on degrees or previous knowledge. People being curious and having a mindset of continuous learning, unlearning, and relearning is absolutely key. Being comfortable with that, being comfortable with being proven wrong—like, this was the best tool yesterday, now it’s a new day, a new week, there’s another tool, and you can’t be attached to what you were doing before.

You need to get into a beginner’s mindset and start from scratch again. Discernment and things like taste and perspective—these are things that AI can help with but cannot fully form. This is where the value of lived experience is still going to be a competitive advantage for a while. Sometimes I watch really young people interacting with AI, and they don’t know how to curate, how to ask the hard questions, how to play devil’s advocate, or look at what it’s producing, because they don’t have that real-world experience.

In the humans plus AI community, there’s a big debate about junior jobs, mentorship, and all those things. This is where mentorship and watching more experienced people interact with hard problems and do their job is still going to be important for a while, because AI can only tell you what you ask it. If you don’t know what to ask, you’re not going to get it right. So things like discernment, taste, perspective—which come from lived experiences, connecting dots, and so on—are very important already.

Interestingly, emotional awareness and clarity will be very important as well. One of the reasons for that is the fact that right now, we have so much more speed for execution. I was watching a video the other day, accompanied by my AI assistant, about a guy who has created 24 startups with zero human employees. He’s basically created AIs that look into what’s missing in the market and create a product or service to meet that demand. The speed of execution for him is so fast. So instead of acting on impulse, being able to have the right discernment, emotional clarity, and so on to actually direct the AI—to orchestrate the AI, as we were mentioning—can probably save a lot of time and money, because the speed of execution is now so fast.

Before, if you took the wrong decision, especially in corporate, you could spend a few months executing 5% on it, get lost in bureaucracy, maybe it never happened, and you had a lot of chances to pull it back. Now, by tomorrow, you can have a working app, a whole business, a whole startup. So these things around clarity of decision making, taste, perspective, discernment, and so on become very important, because we can very quickly go in the wrong direction and find ourselves much further in the process than before, to correct course. If that makes sense.

Ross Dawson: I think there are a few different categories and ways. Curiosity is an attitude or propensity, which we can foster, but it is kind of a mindset. There are a number of these things that are just our attitude to the world or our mindset. Taste—a lot of people talk about taste as a real critical differentiator, and it’s interesting to compare the words judgment and taste. Judgment and taste are obviously closely related, but taste is more aesthetic—”this is beautiful”—as opposed to “this is right” or “this is a better assessment.” Judgment is still critical relative to AI. As you were saying, we need to be able to discern whether the AI is coming up with something useful or not, and judgment is a critical aspect of being able to be the pair—the people working with the AI.

That aspect of taste is interesting. Some people do have better taste than others; there are designers who get called for that work. But again, I think this is something we can foster and evoke. I think the set of capabilities you ran through are critically important.

But I do want to hop on to the Remote Skills Academy. I’d love for you to talk about what that is, where you are in that journey, and what you’ve learned—what are the lessons learned in bringing this to life?

Lavinia Iosub: Thanks for asking. The Remote Skills Academy is actually a project that was born at the beginning of the pandemic. For wider context, I run Livit, which is a support ecosystem for entrepreneurs, startups, and remote workers to do amazing, disruptive work in terms of their businesses, careers, ventures, whatever it is they’re working on. We do that in a variety of ways. We were very busy with that, but we also incubate internally—we keep tinkering with different products, projects, and services that sometimes turn into their own ventures.

The Remote Skills Academy emerged at the beginning of the pandemic. I had been thinking about it for a while, but it was a moment where that sort of project was highly needed. I live in Bali for most of the year, and at the beginning of the pandemic, on an island that depends pretty much 90% on tourism, you can imagine all the jobs disappeared. Everyone was like, “Okay, what are we going to do now?” because we depend on physical jobs within perhaps a few kilometers around us.

For a long time, we had been building remote-capable teams and working digitally ourselves. Before it was cool, we were a bunch of weirdos. Now everyone knows about remote work, knows it’s possible, and everyone has an opinion about it, with return-to-office mandates and so on.

But the world has proven we can do that, right? We can work remotely and keep the world running a largely remotely, obviously with some notable exceptions in critical infrastructure, but professional services are largely remote-capable. We had been doing that for a long time, and we thought, “Okay, we’re building digital projects, working remotely with clients and team members from all around the world. How do we take what we know and make it more accessible to more people?” Perhaps people who are losing their jobs in tourism, or people who want to work from home for whatever reason, across Indonesia or elsewhere.

We had no idea what we were doing, to be honest. We just knew that you can work digitally and remotely. We thought, “Okay, how do we teach this and open a world of opportunity to people?” We started very small with a cohort of 20 people. It was very quickly, in a couple of days, oversubscribed, which was a clear signal that there was a huge need. I think I spent $10 on Facebook ads that I managed myself, so it probably wasn’t even very well managed. Then we were like, “Okay, close the gates, because we don’t even know what we’re doing, so we don’t want more than”—it was 23 people at the time.

We said, “Let’s teach people basic digital remote work skills and see what they can do with that,” not in a way where we’re promising anyone—maybe a non-techie—that they’re going to be a senior Python developer in three weeks, but in a way that just opens up a little bit of opportunity online, perhaps in virtual assistant jobs, community management, digital marketing, project management, and so on.

Long story short, we’re five and a half, almost six years in now. We have helped upskill 25,000 learners across three continents, a majority of them in Indonesia—about 95%. We’ve had partnerships with other countries and communities and opened it up to places like Thailand, Rwanda, Hungary and so on, but the majority has happened in Indonesia, and we’re pretty much fully focused on the market here.

Ross Dawson: Just pulling that to the present—so it’s been five and a half years now, and we’ve had the rise of generative AI, which obviously makes it very different now. As you say, it’s not as if you’re going to make people into developers, but presumably one of the key things you’re teaching now is how, as a remote worker across any domain, you apply AI to be a more effective freelancer. What specific skills are you teaching in order to help these freelancers to able to work well remotely?

Lavinia Iosub: Actually, it’s not only freelancers. This year, we have offered AI upskilling to 10,000 people alone—specifically AI upskilling. Some of those people have been freelancers, some have been job seekers or students wanting to increase their chances of getting a job, and some have been, for example, tourism sector workers or small business owners. We’ve widened the scope, because even if you’re a small business owner with a physical business, it still helps to have AI skills and digital skills generally, but specifically AI skills.

Just being able to, for example, know how to prompt an LLM beyond “draft my emails”—what else can you do with it? For example, something I’m working on for a session tonight is quick demos on how you can use it in project management. I’ll show how you can record yourself talking about a project you want to outline, then have an LLM put that in a structured format as guidelines for your team, and then use something like Manus AI—other tools do it well, but I find Manus does it better—to turn that into a Trello board, a Monday board, a Gantt chart, a timeline, and everything. Even if what you do is partially or mostly in person, it can still help to be able to do these things, and you can use free tools for it.

So it’s going beyond freelancers, online business owners, or remote workers. I believe we’ve got about two years until AI will fundamentally change the way we interact with the world. I don’t want to hazard myself to predictions, but I think when we look at the speed of AI adoption—that has been so much faster than Google or anything else, even simpler tools—and it’s now exponentially faster, I believe a majority of the world will be using or at least affected by AI. So it’s becoming much wider than freelancers, remote workers, or digital businesses. A tourism sector worker or a physical business owner has a lot to gain from being able to find their way around those kind of things.

In Indonesia, for example, a lot of business is carried out via WhatsApp. A lot of people just record voice messages, then feed that to an AI to turn it into documentation, project structure, organizational knowledge, a whole project management board, or whatever is.

Ross Dawson: That’s fantastic. So where can people go to find out more about your work and your companies.

Lavinia Iosub: I think we can link in the episode notes—a LinkedIn profile is probably a great way to connect with me, and the websites liv.it and remoteskills.academy are where we share a lot about what we do, as well as through socials. I’d love to connect with your listeners. We’re always open to ideas, partnerships, and so on.

Ross Dawson: That’s fantastic. Love what you’re doing Lavinia. Thanks so much what you’re doing and for sharing today.

Lavinia Iosub: And likewise, thank you for what you’re doing, and the amazing Humans Plus AI community that I recommend everyone to join if they can, and the fantastic podcast that I’m very honored to be on. Thank you.