AI can already draft, summarize, analyze data, write code, and find patterns faster than most of us. So as these capabilities become increasingly commonplace, what will make you valuable?

In this episode of Career Sessions, J.R. Lowry talks with Liat Ben-Zur, former senior executive at Microsoft, Qualcomm, and Philips and author of The Bias Advantage, about how AI is changing the skills that matter most—and why some of the experiences that once made your career harder may actually have prepared you for what’s coming next.

Liat argues that the answer isn’t trying to compete with AI at what it does best. Instead, we need to double down on capabilities machines struggle to replicate: judgment, adaptability, empathy, ethical decision-making, creativity, influence, and the ability to see what others miss.

They also discuss why hard work alone isn’t enough to advance your career, how to make your impact visible without becoming overly self-promotional, why nonlinear careers may become increasingly valuable, and how to use AI to strengthen your thinking rather than replace it.

In this episode, you’ll learn:

• Which human capabilities will become more valuable as AI gets better
• Why career setbacks and being an outsider can build unexpected advantages
• How to make sure your contributions are recognized—not just delivered
• Why career range may be more valuable than narrow specialization
• How to use AI as a thinking partner without weakening your own judgment
• What leaders will need to do differently as AI reshapes organizations

If you’re wondering how to remain relevant, differentiated, and valuable as AI transforms the workplace, this conversation offers a practical roadmap.

Check out the full series of “Career Sessions, Career Lessons” podcasts here or visit pathwise.io/podcast/. A full written transcript of this episode is also available at https://pathwise.io/podcasts/liat-ben-zur/.

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Listen to the podcast here

How To Stay Valuable In The Age Of AI, With Liat Ben-Zur

Artificial intelligence is changing the workplace faster than any technology we’ve ever seen. A lot of the surrounding conversation is focused on what jobs are going to disappear, what new skills we’re going to need, and how organizations can adopt AI more quickly, more thoroughly, more effectively. What does AI mean for leadership itself? In particular, is it changing what is required to be a successful leader? Is it simply exposing some things that were already broken?

How organizations make decisions, who gets promoted, what voices get heard, and what we value in our leaders? My guest Liat Ben-Zur argues exactly that in her book, The Bias Advantage. Drawing on decades of leadership experience at companies like Microsoft, Qualcomm and Philips, she makes the case that many of the people who’ve spent their careers navigating uncertainty, overcoming bias, and succeeding as outsiders may be the best prepared to lead in an AI-driven world.

In our conversation, we’re going to talk about why merit alone has never been enough, the leadership skills that AI will make even more valuable, how to build influence in organizations and why some of the very qualities that may have held you back in the past could become your greatest competitive advantage in the future. I’m J.R Lowry and this Career Sessions.

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Liat, thank you for joining me.

Thanks for having me.

We are going to talk a bit about your book, but also about your career journey. You’ve led a bunch of technology shifts over the course of your career. Why does this AI moment that we’re experiencing feel fundamentally different to you?

A lot of the previous shifts changed how we worked. AI is changing how we think. I think about when we went from desktop to mobile, from on-prem to cloud, we were changing a lot of the plumbing of how we worked or where we worked. It was about speed, access, connectivity, convenience, and all those things but AI is different. For the first time, technology is not just giving us this faster tool or shovel. It’s moving up the cognitive food chain, if you will.

To some degree, I feel like it’s commoditizing basic intellect like drafting, summarizing, and coding. As more of that intellectual work becomes automated, the new skills that are becoming more valuable shift towards judgment, ethics, creativity, empathy, and humanity. This transformation is forcing us to rethink where we, as humans, add the most value. That’s why it feels so personal because for a lot of folks, their work is their identity. That challenges our identity in some way.

That’s true at the individual and the leadership level because it is changing what we do as individuals. It’s also changing the way that people lead. Let’s talk about those two things. Maybe we’ll start with the leadership piece because you make the point that AI is not just changing leadership. It’s exposing leadership and it’s making visible some things that have been there all along as flaws.

Exposing Organizational Bureaucracy

I do think that AI exposes bureaucracy and you might have experienced with some of the companies you worked at. Companies could hide their slower decisions, weaker judgments, decision avoidance behind committees and processes and these endless meetings. AI is compressing the time between decisions and their consequences.

We’re seeing markets move faster. We’re seeing products launching faster. Mistakes are scaling faster. The security blanket is gone and AI is holding this mirror and exposing which executives are able to lead through all these changes, which ones are highly paid gatekeepers who know how to manage processes well, but they freeze when they have to make a fast high stakes judgment. To your point, I don’t know that AI breaks the organizations, but it shows everyone where the bureaucracy and leadership weaknesses are.

At least in your circles, do you think that the senior level people and companies realize that this is what’s going on or are they still having the wake up call themselves?

They’re having a wake-up call. Many of them are still used to the old way of how things are being done.

What about at the individual level? This is a big identity shift. How do you see people responding to that?

AI is shifting power at two levels, a societal level, and then also at a business and individual level. At the societal level, AI is increasingly impacting who gets hired, who receives a loan, whose medical claims get approved or denied or who’s getting removed from a certain platform. As I explain in the book, AI is not neutral. The algorithms are only going to be as neutral as the data that trains them. Since a lot of these systems are learning from the past, if the past was biased, we’re going to scale that bias at machine speed.

If we’re not careful, the result of this is that we could accelerate inequities and create an even bigger disparity between the have and have nots at the societal level. Inside companies and at the individual level, AI is also shifting the balance of power. What we’re seeing there is just flattening hierarchies. I see this a lot in the boardrooms. A junior engineer with access to an LLN can build a prototype in an afternoon that used to require a seasoned team of twenty and a million-dollar budget.

Powers are flattening and democratizing in ways that destabilize leaders who are used to their own positions of power. AI can democratize power and it can concentrate power. That’s why ethics and fairness can’t just be bolted on because by then the damage already scales. It’s very interesting how AI is changing and impacting us at different levels.

One thing that’s interesting on the societal level. I thought you were going to say that what you didn’t say, is just the fact that AI is also creating this incredible wealth opportunity for a very small number of people to the degree that we’ve probably never seen. We talk about how much money Elon Musk has, but we could potentially create ten or 100 Elon Musk in the next five years with AI. I feel like that’s a societal impact that people don’t fully appreciate. The incredible amounts of money going into this and the valuations that are being put on AI technology. Things that didn’t even exist five years ago are just mind-blowing for me.

That’s a good point. On the one hand, we can say it opens it up for a small group of people. On the other hand, you can also make the argument that opens up to anyone that wants to start to play with the tools and use the tools. The nuance is in the details. A lot of what I talk about in the book is some of those details. I’ll give you a simple example. I’m trying to create some business pages for the book, like Facebook, Instagram, all those things to build awareness campaigns. I use AI. I use my agents to help me set those things up.

Meta decided to block me from building those because it probably noticed that there’s AI agents that are helping build those pages. The pages get built a little bit faster than you would if you were doing it by yourself. The irony in that is like, everything Meta is trying to do is push their AI and have humans use their AI. When a solo entrepreneur is trying to use AI to empower herself to be able to get these pages built faster and accelerate my about page, it’s blocked. That’s the societal thing that I’m talking about in terms of impact.

He holds the power of the platform to make these decisions. Another good example of that is healthcare. There’s a lot of data that’s showing the number of denials from insurance companies is going up because they’re using AI. They’re using AI at a platform scale. Individuals get a rejection later. They don’t understand. They don’t all have AI to fight back. There’s good intentions everywhere but if we’re not very thoughtful about the ramifications, it’s not always fair.

Leadership Skills In AI: Overcoming workplace challenges builds an invisible muscle that transforms early career friction into long-term strategic advantage.

In the scheme of things, we’re seeing a lot of those things that you’re talking about. It is becoming harder to fight the onslaught of companies using AI. You see the same dynamic in recruiting. It’s okay if the recruiters use AI to interview you. It’s not okay if you use AI to work on your answers to those interview questions. Professors can use AI. The students can’t use AI. It’s created different power dynamics that we’re still working our way through.

Let’s come back to AI a little bit later, if that’s okay. I want to talk a little bit more about your book, The Bias Advantage. The title of your book is provocative. Given that most people think about bias as something that limits us, you’re making the point that it can prepare us to lead. That it can be beneficial. Talk a little bit about how you came to that point of view and made it your title.

Building Strength Through Friction

Bias itself is not the advantage. I’d never romanticize exclusion or being underestimated or being paid less but navigating bias can build an invisible muscle. That’s what the book is about you learning to read the room. You notice the gap between the official rules and the lived behaviors. You learn to sense what’s not being said, versus what is being said and to pull in voices and the data that may otherwise be ignored or missing. You tend to learn to adapt more quickly, to spot risks that others often miss because the system hasn’t always worked for you. The scar itself is not the superpower, but the muscle you build around it may be.

One thing that I was thinking about in going through your book was this idea about whether it’s about bias or about adaptability. Everything you’re just talking about is you going through adversity. You learn how to adapt. You learn how to overcome challenges. Is bias the way that people develop these capabilities or is there something that you think is more unique about bias in the way that it helps people develop the capabilities that you’re talking about?

I like that you added that. I do think it’s about both, but bias is like an intense school for adaptability. Ordinary adaptability can mean that you’re learning a new tool. You’re changing how you’re working. Navigating bias tends to be a little bit more personal. You might deliver great results and still be told that you lack, let’s say, executive presence. Which often means, there’s something about you that makes us uncomfortable but we can’t quite say what it’s. We don’t know how to put our finger on it. That experience teaches you to decode the context, to adjust your approach, to fit specific environments, and to build pretty high emotional intelligence and a thick skin because the stakes are very personal.

I think you’re right. I asked that question to get your point of view. I don’t have the same ability to draw on that as a White male. I just imagine that because it is more personal, because it hits more deeply than other forms of disadvantage that somebody might have to overcome. That it gets much more deeply steeped into who you’re and how you operate and how you see the world.

Therefore, you can approach these situations to be able to read the room, look at people more deeply, the things that you just talked about. For somebody who doesn’t see themselves as an outsider, what lessons can they draw from people who have spent their careers or their lives having to navigate these systems that weren’t built for them?

I would start by not assuming that the system itself is neutral because it’s worked for you for many years. I would encourage folks to just voluntarily put yourself in situations where you’re uncomfortable. Where you might be the minority, where you don’t know the rules, where your credentials suddenly don’t carry weight. Take on some of the messier, undefined projects, and then pay attention. Pay attention to things like who’s missing, who’s not being included in the conversation or in the data. Whose ideas get repeated, whose ideas get ignored, who tends to get credit, who tends not to get credit.

Your goal is to try to notice what the algorithms might miss. People who have navigated exclusion often develop a wider field of vision. Frankly, any leader can learn this discipline. First, you have to admit that your own field of view may be incomplete because you’re never going to correct a blind spot that you refuse to admit that you have.

That’s very true, but I also think it’s hard for people to see that for the very reason that it is a blind spot. One of the key things that certainly you try to teach people in terms of diversity type training is how to put yourself in somebody else’s shoes to see things through their eyes. To imagine what it would be like for them to understand all of the unconscious biases and the micro things that happen. I know we’re losing a little bit of that and calling it all woke but there is a lot to it.

In the workplace, even more and I’m sure, as someone who’s done a lot of consulting in your career, you’ve noticed this. When you go into a company, you are the fresh eyes. You’re the outsider going in and observing and giving feedback on the opportunities, the challenges. My analogy is, it’s like a fish that’s been swimming in the same ocean for years. They’re in that ocean. It does not occur to them that it’s salty, misty, dirty water, that they don’t have good visibility.

That all the other fish that are around them are dark and menacing. You come from another sea and you share with them like there’s other beautiful, light water, colorful fish, other places that have great visibility and reefs that you can go to. They look at you like, “What are you talking about? We know what the ocean is. We’ve been swimming in it for many years.” It’s not just a diversity story. It applies to everybody. It’s just that ability to think outside of your own historically peripheral vision.

Bringing this back to the AI topic that we started the conversation with. These skills that you’ve talked about. Again, reading the room, being able to understand others. When AI is taking away a lot of the rote stuff, all that stuff moves much more to the forefront. Let’s talk a little bit more about what makes somebody valuable in this age of AI. You talked about democratizing the idea of a developer being able to do in an afternoon what used to take a team of twenty months to do.

If AI is doing all of this drafting, summarizing, coding and content generation. What does that end up meaning in terms of how human value needs to evolve? For example, if information is becoming abundant and those capabilities that AI has are becoming more readily available. What’s becoming more scarce where that value is going to lie for the human beings in the world?

Prioritizing Human Discernment Over Output

A good analogy might be to think about it like music synthesizers. They made creating sounds incredibly cheap and easy, but that didn’t eliminate musicians. It raised the premium on unique style and curation, soul and that authenticity of the music. When content, code and analysis becomes infinite, and virtually free over time. The thing that becomes more scarce is also going to be curation, taste, ethical judgment, radical empathy and accountability. AI is going to give you ten options very quickly but the human leader has to have the discernment and the stomach to figure out which one is the best and to take ownership and accountability if it fails. You can’t blame the machines.

Using your music example. There are instances where people have put all of the biggest hit songs through algorithms and figured out what common traits they shared. They’ve created a song that includes all of those capabilities and people hear it and they like it. If I’m a music creator, that ought to make me pretty scared because it starts to beg the question of what’s going to be left for me.

First of all, it has made a lot of the music industry scared, and it’s also opened up a lot of new opportunities for the music industry. The same exact way that Napster and Spotify scared the music industry and then opened up a lot of new revenue opportunities over time. I would be careful about looking at those things and seeing them as a storyline that says it’s replacing artists. What you see there is the interesting way that the technology can show you which of the rhythms are getting the most traction, and how to put them together in different ways.

Most of the stories where those AI generated artists took off, it doesn’t last that long. Once people understand that it’s AI generated, there’s not that human connection. There’s not that sense of authenticity of soul or whatever. It’s usually a quick spike. That’s a lot of the essence of the book, too. From a leadership career perspective, you need to find that.

If you’ve defined your identity for 25 years as the expert who’s the best at analyzing these finances or writing this code, some of the tasks associated with that work may shift and go away. What are the human centric things that you bring to that table where you can oversee twenty of those versions of you doing the tasks and bring the human connection, the empathy, the accountability, and do the things that the machines can do.

I would certainly think that AI may be able to create the next hit pop song that sounds like what a lot of other people do. What it’s not going to do is to create a new musical genre that’s never existed before. It probably could, but you think about the things that have happened in the work world or the music world or any world. It’s like these brilliant strokes of creative genius that take something in a completely different direction than it’s gone before. When AI models, as they are now, are being largely trained by what’s existing in the past. They’re much less likely to come up with those creative things.

Bringing this back to your business point from a second ago. The idea of a leader who rests on their laurels, their title, the positional power they have, or whatever the case may be. This is going to expose that they need to be much more creative, bold, adaptable and fast moving. That’s what you argue is going to be the key in the future, correct?

That’s 100% it. That’s spot on.

transforms early career friction into long-term strategic advantage.
Leadership Skills In AI: Don’t let your critical thinking atrophy. Use automated tools to stress test your strategy rather than draft every decision for you.

How do we deal with some of the practical things that we’re seeing? For example, all the work slop that’s out there and the risk that people are overusing AI. They’re using it in the wrong way and it’s eroding their cognitive capabilities and their critical thinking because they’re using it to be lazy as opposed to legitimately making themselves better.

First of all, there’s a bunch of research out there that’s showing that this is a real thing, cognitive atrophy. GPS is like a great example. It’s a good analogy for us to use. It made navigation easier, but most of my friends can no longer find their way out of a paper bag without their GPS. To some extent, the same thing is happening to our brains with AI. If you are letting the LLMs write every single memo, make all your decisions, your critical thinking will atrophy.

Preventing Cognitive Atrophy

I’m a big proponent of using it to stress test your thinking. When I’m working on a strategy, use AI as a sparring partner more than, let’s say, a ghostwriter. Have it find the biggest holes in your plan, challenge your biases, expand your perspective, and sharpen your arguments. In the book, I talk about assigning a rotating devil’s prompt master role inside of your organizations. Their whole job is basically to challenge the AI-driven conclusion or recommendations that are coming out of the various product teams.

What that does is that makes dissent part of the quality control instead of treating the AI skeptic as that annoying person that’s holding up the meeting. The purpose of that recommendation, why I suggest it is not necessarily to catch AI making a mistake. It’s to keep humans willing and able to say, “This result may be mathematically clean. This recommendation may sound well, but it doesn’t make sense in the real world. Here’s why.” I’m already seeing teams where folks are afraid to do that. They’re afraid to question the machine. They’re afraid to question the results. They feel the AI said it must be true. We need to make sure that we’re not building AI so we stop thinking, but we’re using it to think better.

To some degree, listening to what you just said, my immediate thought was people would do that with their bosses. It’s like, “The boss said so, we should just go do it.” We think it’s stupid, but we’re going to go do it anyway because we’re absolving ourselves of responsibility because it was the boss’s idea to do it. Now it’s the AI machine’s idea to do it. To me, it’s a lot of the same bad habits that people develop in their careers, which is not thinking for themselves. Not being willing to express an unpopular opinion. To your point, we need to be rewarding that more.

First of all, there are a lot of people in your career who are those types of people. The yes people and who just do what they’re told and don’t shout. You also know a lot of people in your career who weren’t afraid to challenge and ask the questions and ruffle the feathers and say the unpopular things. What the premise of this book is, in the AI era, some of the stuff that we’ve rewarded in the past for leadership needs to change. It’s because it is exactly these things that were not always popular in leadership circles in the past that we need more than ever to avoid some of the negative ramifications of AI usage.

What are some of the other capabilities that you would recommend to somebody who is in the first ten years of their career that they need to be over indexing on?

What I wouldn’t want them to do is try to beat AI at being an AI. Don’t try to be like a better expert because AI is always going to retrieve information faster than us. It’s going to write our first drafts better than us. It’s going to find patterns across massive data sets better than us and faster than us. Instead, it’s important to build some real depth in something. What I mean by that is, not just learning the basics because of an expertise in the data. It’s learning the exceptions, the failures, the messy realities that never make it into the textbooks.

Developing Cross-Functional Range

Since you’ve been working with it so deeply, you see the mistakes, the failures, how customers react to things. You understand incentives. You understand how decisions get made, be it internally or externally by customers, and why they’re making those decisions. After you become an expert and you understand some of these things deeply. Over time, you want to build a range. I encourage folks that I mentor to work across functions.

You want to get close to customers. You want to understand the product. You want to spend time with people who see the system differently than you do. AI does a great job at finding patterns in what already exists. Your advantage has to come from seeing what doesn’t fit the pattern. The non-obvious stuff. The AI says the customer is going to want a black box that looks like this, but you understand something about what’s happening in that customer’s personal life, family life, or geopolitical issues. That would be the reason why they don’t want a black box that looks like that. You can bring that nuance to the table.

Your point is well taken that we’ve got to encourage people to develop skills that are grounded in what AI can’t do in human-to-human interaction and the things that go with that. In thinking about the art of the possible, I hate that expression, but being able to think about what doesn’t exist. I wonder as a very simple example, whether an AI tool would have ever come up with the idea of getting into a car with a stranger and having them take you where you want to go or whether it never would even come up with that idea because it was an out-of-the-box idea when it was first developed. Those are the kinds of things that are going to be more necessary in a world where the machines get more and more capable to continue to be uniquely human.

Left on its own devices, it probably would. Add another horse to the carriage versus building a completely new machine. With the right leaders prompting it, guiding it, providing the right guardrails, and asking the non-obvious questions, challenging the answers that it gets back. It could very well also lead to the invention of some of these things that you’re talking about, the art of the possible. Even open it up to more possibilities.

It does require some of that handholding. That’s what leadership is. if it’s not fighting the machine per se, not rejecting the machine, and not ignoring the machine. Also, it’s not accepting everything that machine is using and allowing the machine to accelerate the existing processes and add another horse to your carriage.

It’s about shaping it to allow for some of the innovations that otherwise we would never unlock. Also, in a responsible, ethical and thoughtful way that humans need to be part of that loop to understand, to understand the downside bias that the machine doesn’t necessarily think through. The machine’s optimizing for a certain goal, optimizing for a certain KPI. It’s not thinking through those human implications.

I want to spend a few minutes before we break on some of the other things you brought up in the book around the myth of meritocracy and the fact that other factors play a key role in driving whether people move forward in their career or get stuck. You talk about the difference between performance and perception. Which probably makes people feel uncomfortable because it immediately evokes politics. What’s the difference between strategically managing your reputation and simply playing office politics?

First, I’ll say many of us have been fed a lie for many years that if you work hard, the cream is going to rise to the top. In corporate America, that’s not always true. If a tree falls in the forest and nobody hears it. It may have made a sound, but it’s probably not going to get promoted. If you’ve generated $10 million in revenue for your team, but the people that are making talent decisions don’t understand the role that you played in that. They can’t properly value your contribution.

How is that going to help you move forward? If you’ve solved a major problem for your organization, but nobody outside of your immediate team knows about it, understands or can properly value that work and then reuse it in other areas, how does that limit your impact across the organization? The real purpose of that conversation around performance versus perception is not to encourage people to become some self-promoting peacock.

Translating Results Into Reputation

God forbid. There’s so many of those already. I don’t want anyone to tap that as a takeaway. It’s to point out that reputation management is about translation. It means making your impact, the things that you’ve done, clear and connecting those things to what the company cares about and what the people who need to understand them can impact your future pathways. Know about them at the right time, in the right place, and in the right way. There is an art to that.

Oftentimes, when you’re early in your career, no one talks about that. They just say, “Keep your head down. Do the right things and everything will come.” For many people, especially those who don’t necessarily fit into the system or are outsiders, that’s not true. That’s why there’s a section in the book about that.

It’s especially not true for people who are underrepresented. I think it’s true for everybody because at the end of the day, I feel like when somebody tells you, “Put your head down. Do good work. Think good things will happen.” It’s almost like what they’re saying is, “Leave me alone. I can’t do anything to help you. Go off and do work because there’s nothing more that I can do to be beneficial to you.”

I like that. I recognize that, too.

I certainly have seen that a lot in my past and some of the people that I continue to coach and mentor who feel stuck. They get told, “Be patient. The opportunity will come.” My advice to them generally is leave because what you’re being told is, “We don’t see it. We’re unlikely to see it.” Unless you completely reinvent yourself, which most people have a hard time doing. We’re never going to see it. What I say to people, if you believe in yourself, you should leave because essentially, you and whatever organization you’re working in are not gelling and you should go find someplace else that will be more appreciative of what you think you can offer but don’t be patient.

test your strategy rather than draft every decision for you.
Leadership Skills In AI: Delivering strong results isn’t enough on its own. Strategic reputation management means translating your impact into the language your leaders care about.

I’ve given that advice plenty of times

You also argue and you talked earlier about the idea of learning multiple functions. What are some of the other ways that you would propose to people to embrace nonlinear career paths?

Leveraging Nonlinear Paths

This is an important concept that may not be so obvious to a lot of folks, but what AI is doing is collapsing the boundaries between functions. Between sales and marketing, finance and sales and customer support. The hardest problems to solve are no longer sitting very neatly inside of product or finance or marketing. People that have had very nonlinear careers are often very good translators.

They’re able to connect ideas across domains and see waste that insiders who have only lived in their function for a very long time stop noticing. If you’ve had this zigzag career, say that you were in engineering and then you were in marketing then you spent a year in a nonprofit. You build this unique cognitive fingerprint. You can import your ideas from one industry to solve problems in another industry in ways that aren’t always obvious in other industries.

Consultants are a great example of this and see these patterns matching all the time. When things are very stable, like from a geopolitical perspective, from a career perspective, having that winding path can sometimes look unfocused. I’m sure you’ve met folks who you’ve looked at their career path and you’ve even given that advice to them like, “You’re so unfocused.” I’ll tell you what, in a very rapidly changing world, which is what we’re having with AI. It builds range.

Range is what you need when AI is collapsing the boundaries between these functions, because you need leaders who are able to look at problems and ask, why do we need to do it the way that we do it? Why don’t we get rid of all that and do it this new way? Everyone will nod to it and go, “We don’t want to accelerate or build new processes.” Easy to say, very hard to do because people have a very hard time imagining things when they’ve been doing work a certain way for fifteen years and it works for them. It’s one of the hardest things in AI transformation that I run into again and again in a lot of my clients.

This idea of range can be valuable for a couple of reasons. One, you can connect the dots, as you were just describing. In some ways, it’s also a bit of a portfolio play on how things are going to play out in the future. If you’ve got range, you have more situations, more functions, more corporate situations, more whatever that you have some relevance in.

If some of those get cut off because of AI or other external factors that you don’t have any control over, you still have other things you can fall back on. If you are single threaded, you’re putting yourself in a very precarious situation. Last question. If readers were to remember one thing from your book and how you’re thinking about everything around your book. What would you want that to be?

The qualities that you always thought made you easy to overlook or not fit in. The qualities that sometimes pushed you to the outside may be exactly the very qualities that make you extremely valuable. I say this to a very broad audience of folks who oftentimes don’t feel like they’re AI experts. They are feeling like they’re left out of the AI revolution because they didn’t come up in tech or they don’t know all the latest and greatest with AI.

You have the skills we need more than ever to lead the AI revolution now. If you’ve had to learn how to read the room, how to question the rules, how to adapt quickly, how to build influence without authority. If you’re great at seeing what others miss, you’ve been training for this moment. The very struggles that made your path harder, have built what I believe to be the skills that we need more of in the era of AI.

Empathy, judgment, adaptability, ethical clarity, resilience, if anything, AI is making those capabilities more valuable. A lot of companies don’t even realize that. They’re still hiring and promoting based on the old leadership playbooks. Those of you who understand this, understand the shift, understand how power is changing, you can position yourselves now to play critical, valuable roles in advancing AI transformation in your company.

You’ve said it quite powerfully. I will not add anything on top of that because it’s a critical message that you’re leaving people with so thank you for that. Thank you for the broader conversation.

Thank you for having me. I appreciate it.

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A wide-ranging discussion with Liat. Let’s talk about what we can take away from that. First, AI is definitely raising the bar for leadership. The more AI takes over information gathering and routine analysis, the more valuable uniquely human capabilities become. Some of the things she talked about, judgment, curiosity, ethical decision-making, relationship building, adaptability, and the ability to make sense of ambiguity. That means that the leaders who thrive in the future aren’t necessarily going to be the experts, the ones who know the most. They’re going to be the ones who can think the best, make decisions the fastest, and adapt.

The second is the idea that your greatest career advantage may come from some of the challenges that you’ve already overcome. This is what Liat meant in the title, The Bias Advantage. If you’ve ever felt underestimated or overlooked or you had to work harder than others to earn credibility. Those experiences may have developed exactly the capabilities that organizations now need. These are going to become leadership advantages more so than in the past.

Third is that hard work matters, but it doesn’t speak for itself. The world has never been a meritocracy, and the idea of that is just a myth. Delivering results is essential, but so is making sure that the right people understand the value you’re creating. Building relationships, developing sponsors, communicating your impact, shaping your professional reputation is not office politics. It’s part of doing the right things for your career. You don’t want to be that tree that falls in the forest that nobody hears.

Finally, is the idea that you should not use AI to replace your thinking. I beg you not to use AI to replace your thinking. Use it to elevate it. AI is an extraordinary tool, but it’s still just a tool. Don’t accept its answers without question. If you do, your critical thinking will suffer. That’s not what you want to happen because it’s going to be more necessary in the future.

Use AI to provide challenges, add context, strengthen your judgment, and combine machine intelligence with human insight so that you can make better decisions. As always, I invite you to subscribe to the show on Apple Podcasts, Spotify or YouTube. If you found the discussion enlightening, sign up for my membership community, which is called PathWise and our newsletter PathWisdom. Thanks.

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About Liat Ben-Zur

Liat Ben-Zur is a technology executive, independent board director, and AI advisor with more than 30 years of experience leading product innovation, business transformation, and billion-dollar growth at Microsoft, Royal Philips, and Qualcomm. As Corporate Vice President at Microsoft, she helped turn declining consumer services into businesses generating more than $14 billion in revenue, revived Microsoft Edge, and led the launch of the world’s first commercial GPT-4 experience with Bing. At Philips, she led the Internet of Things and connected health strategy. At Qualcomm, she launched the company’s first open-source IoT initiative, AllJoyn, and helped build a global ecosystem across smart home, automotive, and smart city markets.

She holds a magna cum laude BSEE from UC Davis and an MBA from UCLA Anderson, holds multiple patents, and serves on public and private boards. As CEO of LBZ Advisory, she advises executive teams and boards on high-stakes AI decisions across strategy, governance, and execution.

A gay woman who built her career in rooms not always designed for her, Liat learned early that friction can sharpen vision. That lived experience, combined with frontline AI deployment at scale, makes her uniquely qualified to write The Bias Advantage and show how unconventional leaders can win when the rules collapse.

Liat lives in Seattle with her wife and their two children.