I recently went one-on-one with Valerie Capers Workman, Chief Human Resources Officer of Empower Pharmacy. Valerie previously served as Vice President of People at Tesla, reporting directly to Elon Musk, and as Chief Talent Engagement Officer at Handshake.
Adam: Long before you were leading HR in the world of pharmacy, you were leading HR for a company that everyone in America knows, probably everyone in the world knows, and that is a company called Tesla. Can you take us back to your days leading HR for Tesla? What did you learn from that experience? What were your biggest takeaways from your time at Tesla?
Valerie: Adam, I have to tell the story of how I got that job because I’m not HR by trade. That was never my career plan. It wasn’t in my game plan, and it definitely was not on my bingo card. When I joined Tesla in 2018, I joined as head of compliance to build out the company’s HR compliance function, which, honestly, was a dream job for me. I had a great two-year run there doing everything from domestic compliance work to supporting the Shanghai Gigafactory work in China. If you know compliance, doing compliance work in China is a major achievement in itself.
During those two years, though, I started hearing more and more things that I thought we could be doing better for employees. I became executive sponsor for Black at Tesla, one of the engineering ERGs, and employees raised thoughtful concerns and opportunities around how we could improve, not just for Black employees, but for employees globally. At the time, we were around 50,000 employees, and they made compelling points about areas where we could do better. I became vocal internally about some of those opportunities, but in a very Tesla-specific way. That distinction matters because every company has its own culture, and Tesla had a very particular way to speak up and be heard.
Soon after that, I got a text saying, “Elon wants to see you.” During that meeting, he said, “I hear you think we could be doing things better,” and I said yes. I opened my laptop and started walking him through my ideas, and when I finished, he said, “Okay, you’re running HR.” I remember thinking immediately that I did not want that job because it wasn’t the direction I saw for myself professionally. At the time, the company was actively trying to hire a CHRO, and as you can imagine, finding someone who wanted to report directly to that particular CEO was challenging. But I also believed that if you speak up and say something should improve, you should be willing to help solve it.
So in December 2019, four months before Covid, I suddenly found myself leading HR at Tesla, overseeing a 700-person organization across recruiting, HR, learning and development, talent, DEI, workplace, and physical security. That was the beginning of my HR career.
Adam: Wow. A lot to unpack. Start me off with the fact that you had an accidental entry into HR, and it really started with you speaking up, but speaking up, in your words, the right way to do it at Tesla. As you inferred, there’s a right way to do it where you can get promoted very quickly and a wrong way where your career can get torpedoed. What’s the right way and what’s the wrong way?
Valerie: I love that question because it’s different in every company. What was right at Tesla would have been completely wrong at companies I worked at previously, like Wyndham. At Tesla, though, the right way was always to point out a problem, but also to come up with a solution. You were almost expected to identify issues regardless of your level in the organization, but you couldn’t just complain. You had to say, “Here’s what I think we should do.”
Whenever I raised issues or opportunities, whenever I said there were things we could be doing better, I also paired them with recommendations. It always came from a positive place because I genuinely loved Tesla. I loved the company and I loved what we stood for. I wasn’t criticizing the company because I thought it was broken. I was speaking up because I believed we could be even better.
That was the Tesla way. You raised issues, but you also demonstrated ownership. You showed that you cared enough about the company to help solve the problem instead of just pointing at it.
Adam: You mentioned that in different companies, there’s a different way to do it. In one company, something can be the right way, while in another company, the exact same thing can be the wrong way. How do you figure out what the right thing to say is in the particular company that you’re in?
Valerie: You have to understand the culture. You have to understand what actually moves the organization. If I think back to a company like KPMG, for example, when you raised an issue, it was much more collaborative. You weren’t expected to be the one person walking in with the answer. You raised issues by bringing people together, forming committees, discussing ideas, and collectively deciding what might work and what might not work.
Companies like KPMG, at least the way I remember them, were not places where you unilaterally declared that you had the answer. Some companies are hybrids of those approaches. That’s why it’s so important when you join a company to spend time learning the culture before trying to make major changes. You have to understand not just what the company values, but how things actually get done.
There are companies where what I did at Tesla would not have landed well because it could have come across too strongly, almost like I believed I alone knew the answer. But at Tesla, that approach worked. Even if the solution itself ultimately wasn’t adopted, it still mattered that your thinking was grounded, reasonable, and backed by a real point of view. That was the right way to operate there.
One of the things I learned at Tesla was that you also have to make sure you’re raising the right problem. That itself is a skill. There are always going to be issues, challenges, opportunities, and frustrations inside a company, but are you identifying the problems that truly matter to the business at that specific moment? Are they mission-critical? Are they connected to what leadership is actually trying to solve right now?
The things that matter deeply in 2026 may not matter in 2027 and may not have mattered in 2024. Timing matters, context matters, and relevance matters. So yes, everyone can identify problems, but are you identifying the right problem? Are you framing it correctly? Is your solution actually right for that company at that moment? There’s a lot that goes into consistently getting that right, and I think it takes experience to develop that instinct.
But when you can get all of those things right together, it can become incredibly meaningful professionally because you’re no longer perceived as someone complaining. You’re perceived as someone helping move the business forward. It’s the difference between someone who is simply naming problems and someone who is genuinely trying to help the company solve the right problems. People can feel the difference in intent, and leaders respond very differently to those two approaches.
Adam: You mentioned that you had an interesting experience working at Tesla, as one might imagine working with Elon Musk. What was his leadership style? What did you learn from your time working with Elon Musk?
Valerie: Interestingly enough, the more distance I get from that experience, the more perspective I gain on it. But one thing I will say is that I believe he was different with everyone. That’s why when you speak to different former leaders from Tesla, people often have completely different stories and completely different perspectives. I believe he managed the person, at least with respect to his direct reports, not necessarily the title.
I know my experience was entirely different from someone who may have been sitting right next to me at the proverbial table. What worked for me in my relationship with him was approaching everything in a very lawyerly way, even though I was leading from an HR perspective. Whenever I had a suggestion or solution, I framed it like a legal argument. Here’s the issue, here’s what I think we should do, here’s why, and here’s what the likely result will be.
That approach served me well. Meetings moved quickly, and I was perceived as efficient, even when I didn’t always get the outcome I wanted. But I always knew I was performing well because I was giving logical reasoning behind what I was asking for. Being a lawyer was incredibly helpful because it taught me how to structure arguments clearly, state my case directly, and support recommendations with logic and reasoning. That cadence worked very well in that environment.
Adam: What is the best lesson you learned from your time spent with Elon Musk?
Valerie: Oh man, the best lesson. You hear the phrase “first principles” thrown around constantly, especially in tech, but learning what first principles actually meant from an executive perspective was life-changing for me. It really comes down to understanding what you are actually trying to accomplish, what matters most, and whether you can boil something down to its simplest form.
I’ll anonymize the example because obviously I can’t share company information, but I remember one of my early meetings with Elon where I thought I had prepared the perfect case for why we should do something. I had the strategy, the vision, the steps, the rollout plan, all of it. I laid everything out and explained exactly how we were going to execute. After listening, he basically asked me, “But when are you going to tie your shoes?” It sounds simple, but what he was really pointing out was that I had built this big strategic vision while overlooking one very basic operational detail.
Most CEOs would have focused on the strategy itself. He focused on the missing foundational piece. I remember sitting there realizing immediately that I had missed something important. After that meeting, I promised myself that it would never happen again. From that point forward, I learned to understand everything from the smallest operational detail to the largest strategic objective because if you truly know every layer of the work, you dramatically increase your odds of success.
The good news is that it happened in my very first meeting with him, so I probably got some grace. But after that, I became obsessive about understanding details. If there was a “tie your shoes” question, I wanted to have the answer before it was asked. That experience fundamentally changed how I approach leadership, planning, and execution.
Adam: How have you seen that play out over the course of your career?
Valerie: It’s helped me tremendously because I miss fewer details now. Especially today, when we’re going through AI transformations and large-scale technology changes, the opportunity to miss things is enormous. You’re trying to understand current workflows, redesign processes, implement new systems, and translate all of that into entirely new operating models. There are endless opportunities for things to go wrong.
I was actually having a conversation with my team a couple of days ago around goal setting inside one of our platforms. We were walking through exactly how employees would interact with the system, literally click by click. As we got deeper into the workflow, we realized we needed to completely change our approach because the employee experience would have been terrible otherwise. Had I not learned to think through the “tie your shoes” details, we probably would have rolled something out that looked great strategically but failed operationally.
That lesson from Tesla completely changed the way I approach execution. You have to test everything. You have to understand the details. You can’t just focus on the vision while assuming the operational realities will somehow work themselves out.
Adam: Before we can even think about AI transformation, we have to understand at a more visceral level how to create a culture of AI. How can you persuade your workforce to use AI, to overcome the fear associated with AI, and to understand AI as a friend and not a foe?
Valerie: This is what everyone is wrestling with right now. The first thing is that leaders have to live it themselves. You have to lead by example. If you’re leading a team and asking everyone else to embrace AI while you’re not using it yourself, your team will know immediately. One of the first things I said when I joined the company and met with my team was that if I assign you something, don’t bring it back to me if you haven’t run it through AI first.
People were a little surprised by that initially, but my point was that the work product would be better. We could get to the heart of the issue faster. That mindset didn’t stick overnight, but I reinforced it consistently because I could immediately tell the difference between work that had gone through AI thoughtfully and work that had not. Over time, we reached a point where essentially nothing comes to me that hasn’t gone through an AI-first process.
I think one of the biggest fears people had initially was that using AI would somehow make them look less capable, like it was a crutch. People didn’t necessarily want others to know they were using it. What I tried to reinforce with my team was the exact opposite. I wanted to see it. I would compliment people when I could tell they had used AI effectively, because now we could spend our time refining and improving instead of starting from scratch.
You have to celebrate the behavior you want repeated. When people use AI well, you have to recognize it publicly so the team understands that using AI intelligently is a strength, not a weakness. That’s how you begin creating a culture where AI adoption becomes normal instead of something people feel hesitant or embarrassed about.
Adam: Are you not concerned about the other extreme? I oftentimes see material sent my way where I say, “This was done by AI and is unacceptable.” And oftentimes when I say that, there’s pushback. “Oh, it wasn’t done by AI. It was 100% human.” Then I’ll get asked, “What tool did you use to check whether it was done by AI?” My response is, “The tool I used is my own common sense.” There’s this tension that gets created when someone is, in a sense, trying to hoodwink you by passing off work that was supposed to be theirs but was actually done entirely by AI.
Valerie: I think that distinction matters enormously. I wouldn’t say people should get in trouble if they’re not using AI, but I would say they’re not being efficient because AI is a phenomenal first-draft tool. It gets you to a stronger starting point much faster.
There’s a huge difference, though, between someone who uses AI to improve their thinking and someone who uses AI to replace their thinking. You can absolutely tell the difference. When someone uses AI well, you can see that they took the output, applied their own expertise, revised it, sharpened it, and made it better. That’s excellent use of AI. What doesn’t work is when someone takes raw AI output, doesn’t review it critically, and sends it along even though parts of it are obviously wrong, awkward, or nonsensical.
If you know AI well, you can spot that immediately. You can tell when someone used AI as a thoughtful first draft versus when someone tried to use it as a substitute for judgment. That’s why leaders themselves have to understand these tools. Otherwise, they can’t tell the difference between intelligent AI usage and lazy AI usage.
The goal isn’t AI replacing human capability. The goal is AI amplifying human capability. Those are two completely different things.
Adam: When you say “use AI,” that’s a pretty broad directive. What does that actually mean?
Valerie: That’s a great point because there are really two different categories of AI usage at work. One is the general chat tools like ChatGPT, Claude, Gemini, Grok, or Perplexity. The other category is AI functionality embedded directly into the platforms and systems your company already uses. Those are two very different things, but both matter.
In HR, we do an enormous amount of writing. We write policies, procedures, communications, feedback, documentation, emails, and training materials. So for us, a huge amount of our AI usage comes through chat tools that help us write better, think more clearly, organize ideas faster, and become more specific in how we communicate. Then separately, we also use AI capabilities embedded inside our HR platforms themselves. For example, there may be functionality that helps managers improve feedback, refine goals, or structure conversations more effectively.
That’s why when people say “use AI,” it can mean many different things. Sometimes it means using a chat tool to improve your work product. Other times it means understanding the AI capabilities already built into the systems your organization is investing in and learning how to use those tools effectively as part of your day-to-day workflow.
Adam: How can anyone become more AI literate?
Valerie: The first thing is just to start simply. Pick one chat tool, honestly, I’d recommend Claude, and begin experimenting with it in low-pressure ways that have nothing to do with confidential company information. Ask it to help you write an email. Ask it to summarize something. Ask it to write a paragraph about your favorite movie. Just get comfortable interacting with it and understanding what prompts actually are.
That’s really the first step because prompt engineering is not going away. Even with all these increasingly sophisticated tools, your ability to ask good questions still matters enormously. Once you start understanding how prompts work, you also start understanding how changing the quality of the prompt changes the quality of the output.
I also strongly recommend taking a couple of free courses. One should simply teach you how chat tools work, and the other should focus on prompt engineering. You can probably do both over a weekend, and you’ll already be far ahead of where you were before. There are so many free resources available now that the barrier to entry is actually very low if someone is willing to spend the time learning.
Adam: How do you actually drive AI transformation?
Valerie: Driving AI transformation is much harder because it’s not enough for one leader or one business unit to embrace it. The entire executive leadership team has to be aligned. Otherwise, individual teams either feel singled out or they feel like other groups are being left behind. The messaging has to be consistent across the organization.
I think it starts with executives making it very clear that AI usage is expected and that it’s becoming part of how the company operates going forward. You have to provide tools, training, examples, and clear expectations. Then, over time, eventually, AI usage and AI fluency become part of performance expectations as well. It slowly becomes embedded in how people work.
But I think one thing that’s incredibly important is reinforcing that AI is a tool, not the decision maker. Humans still have to be the final layer. Humans provide judgment, EQ, context, reasoning, and discernment. AI may have more information available instantly than I do, but I still believe I’m smarter than Claude because I bring context, human understanding, and judgment to the equation. That distinction matters enormously when building an AI culture.
Adam: I think a big word here is judgment. Recognizing that you can’t outsource judgment. You can outsource a lot to AI, but understanding what you can outsource and what you can’t outsource is the difference between winning and losing.
Valerie: I completely agree, and honestly, I don’t think judgment comes up enough in these conversations. The moment someone takes AI output and forwards it along without reviewing it critically, without fact-checking it, without applying context, without applying EQ, without applying subject matter expertise, that’s where things break down.
That’s why critical thinking matters more now than ever. We’ve somehow created this narrative that traditional foundational skills no longer matter because of AI, but I actually think the opposite is true. Reading, writing, reasoning, and critical thinking are becoming even more important because they’re what allow you to evaluate whether AI output is actually good or not.
The best organizations are not going to be the ones that blindly automate everything. They’re going to be the ones where humans use AI to accelerate and improve work while still applying strong judgment at the final stage. That last mile, the human layer, is where the real value still gets created.
Adam: What do you believe are the most important skills for the AI era and how can they be developed?
Valerie: Honestly, I think the most important skills right now are reading and writing, which is incredibly ironic given where we are technologically. But if you can’t read critically and write clearly, you’re going to struggle in the AI era because you won’t be able to evaluate whether what you’re getting back actually makes sense.
When AI gives you a response, you have to be able to determine whether it’s brilliant, partially correct, completely wrong, or just polished nonsense. That requires comprehension, reasoning, and critical thinking. If you can’t evaluate the output critically, then the technology becomes dangerous because you’ll mistake fluency for accuracy.
That’s why I think strong reading, writing, and reasoning skills are becoming more valuable, not less valuable. AI is amplifying the importance of human judgment, not replacing it.
Adam: I can’t help but laugh when you say that because I think back not only to my time in high school and college, but even to yesterday and the day before interacting with AI. I’ll ask a very simple question and suddenly get back what feels like a 5,000-word essay. To which I respond, “I’m not here to read an essay.” But to your point, understanding the right questions to ask and then being able to process information quickly and discern what matters is incredibly important. A lot of the skills we thought were secondary skills are suddenly becoming essential skills.
Valerie: Exactly. And what’s funny is that even though many of us are typing less because we’re speaking directly into these tools, the underlying skill is still rooted in reading and writing. The reason someone can create a strong prompt is that they know how to communicate clearly. They know how to frame a question properly. That ability came from developing strong communication skills long before AI existed.
Even if one day we get to the point where we’re simply thinking questions instead of typing or speaking them, the quality of the thinking will still determine the quality of the response. You still have to know how to ask good questions. You still have to know how to evaluate the answers you receive. Those foundational skills are not going away.
That’s why I think critical thinking, reasoning, reading, and writing are still the basics. The technology changes, but the human skills underneath the technology remain incredibly important.
Adam: How, as a leader, can you evaluate whether someone possesses those skills?
Valerie: I evaluate it through work product, just like leaders always have. When someone brings me something, I’m looking at the quality of the thinking, the quality of the communication, and the thoroughness of the work. The difference now is that I know what level of quality should be possible because AI exists. I know when someone likely used AI thoughtfully, and I know when someone probably didn’t use it at all.
Earlier on, before everyone became comfortable using these tools, there was almost this hesitation where people felt like they needed to prove the work was entirely their own. There was this “show your work” mentality that many of us grew up with in school. But that’s no longer the standard for excellence. The standard now is whether you produced exceptional work, whether you used the tools available to improve it, and whether you applied your own judgment to make the final product better.
You can actually see that process reflected in the work itself. You can see when someone thoughtfully used AI to sharpen ideas, strengthen analysis, and improve clarity. You can also see when someone either ignored AI completely or relied on it blindly without adding any human judgment of their own. That distinction becomes very obvious over time.
There’s also a generational component to this. Some people are still much more reluctant to use AI because they worry it somehow diminishes the value of their own thinking. That’s why leaders have to actively reinforce that using AI intelligently is not a weakness. It’s becoming part of what high performance looks like.
Adam: I think about it like a calculator. When I was growing up, I was told that calculators were bad because they prevented you from learning math. Then, as you get older, you realize calculators are actually incredibly useful because they help you do math better. Both things are true. You still need to understand the fundamentals, but once you understand the fundamentals, the tool becomes incredibly powerful. When you use AI without understanding the fundamentals and outsource all your thinking to it, the work product is usually mediocre at best. But when you bring your best judgment to the table, your best knowledge to the table, and your best understanding of the tools to the table, and marry it with these amazing tools that we have at our fingertips, that’s when you produce exceptional work.
Valerie: Absolutely. One of my favorite examples of this involves my sons. I have three sons, they’re all in tech, and they’re all founders. I’m constantly reviewing agreements and legal documents for them. At one point, one of my sons had a disagreement with a co-founder, so I uploaded the founders’ agreement into Claude and asked whether my son had certain rights under the agreement.
Claude came back and said no, he had no rights. Immediately, I knew that the answer was wrong because I’m a lawyer with more than 20 years of experience. So I pushed back and essentially said, “No, that’s incorrect.” Then Claude revised the response and came back with a much more accurate analysis that properly identified the rights involved.
That example perfectly captures the issue. AI gave an authoritative answer, but if I didn’t possess the legal knowledge and judgment to challenge it, I could have delivered terrible advice to my son. The value came from combining AI with actual subject matter expertise. AI helped accelerate the process, but human judgment was still absolutely essential.
That’s why I think domain expertise matters more than ever. AI is incredibly powerful, but if you don’t actually understand the subject matter yourself, you may not even realize when the tool is wrong. That’s where people can get into real trouble.
Adam: You bring up such a great point, which is how AI can lead us astray. It’s such a powerful tool, and like any powerful tool, it can be used in incredibly productive ways or in incredibly dangerous ways. How can people use AI properly?
Valerie: One of the most counterintuitive things I say is that people should primarily use AI in areas where they already have real expertise. A lot of people assume AI should be used to learn entirely new things, but I actually think it’s most powerful when it’s amplifying knowledge you already possess.
For example, I’m not going to ask AI how to design a rocket because I have absolutely no expertise there. Even if the answer sounds convincing, I would have no idea whether it was correct. But I do know law. So when AI gives me legal analysis, I’m capable of evaluating it critically, refining it, and identifying mistakes or gaps.
That’s why I think AI is best used to elevate and accelerate expertise, not replace expertise. The strongest use cases happen when knowledgeable people use AI to improve the quality and speed of their work, not when people blindly rely on AI in areas they don’t truly understand.
Adam: Do you have any other tips on AI transformation?
Valerie: One thing I’ve learned from doing transformations for years is that the fundamentals actually haven’t changed as much as people think. I joke that I’m dating myself, but I was involved in major systems transformations long before AI became the focus. The lesson then is still the lesson now. If you don’t truly understand the workflows and work processes you’re trying to transform, you’re going to build something that fails.
That’s one of the biggest reasons many AI transformations are struggling right now. Companies buy impressive tools, leadership gets excited, and then deployment falls apart because the actual workflows weren’t mapped properly. The people designing the transformation often don’t fully understand how the underlying work is actually being done day to day.
The subject matter experts have to be deeply involved in the process. It can’t just be IT teams or transformation teams operating in isolation. The functional experts, whether they’re in HR, finance, supply chain, legal, or marketing, have to work side by side with the technical teams because they understand where the real operational friction exists. They know where the workarounds are, where the inefficiencies are, and where things are currently being held together with band-aids.
That’s why I go back to first principles so often. Before you can AI-enable anything, you first have to understand the workflow itself. If you don’t understand the process deeply, you’re going to automate confusion instead of improving performance.
Adam: Do you have any other tips on how to excel in today’s workplace and in tomorrow’s workplace?
Valerie: The first thing is becoming AI fluent. That’s no longer optional. And by AI fluent, I mean understanding prompt engineering, understanding the major chat tools, and understanding the top AI tools being used in your own field. If you’re in accounting, what are the leading AI tools accountants are using? If you’re in supply chain, what are those tools? If you’re in marketing, what are those tools?
You don’t need to become an expert in everything immediately, but you do need awareness. You should know what your company is already using and start becoming comfortable with those tools. Within a weekend, someone can become significantly more knowledgeable than they were before simply by learning basic prompt engineering, experimenting with chat tools, and familiarizing themselves with the AI landscape in their field.
Then the next step is getting involved. Every company right now has some version of an AI committee, AI deployment team, AI testing group, or transformation initiative. Volunteer for those projects. Raise your hand. Companies are actively looking for people who are willing to spend time learning, testing, experimenting, and helping shape how these tools get deployed.
That’s one of the best ways to accelerate your growth because now you’re no longer just learning AI conceptually. You’re becoming part of how your organization is actually implementing it.
Adam: You shared a really important point there around volunteering and raising your hand. Why is that so valuable?
Valerie: Because that has always been one of the clearest signals of a high performer. The people willing to step up, take on projects, and help solve problems are almost always viewed differently inside organizations. That hasn’t changed. What’s changed is that AI projects now have company-wide visibility and impact.
In the past, if you volunteered for a project, maybe only your immediate team or business unit noticed. But AI initiatives are different because they touch the entire organization. When you raise your hand now, people across the company see it. Leadership sees it. You become associated with innovation, learning, adaptability, and forward thinking.
You’re also building expertise in the exact tools and systems the company is investing enormous resources into. That creates visibility, credibility, and opportunity. It puts you directly in the middle of where important decisions are being made.
Beyond that, volunteering signals something important culturally. It shows curiosity. It shows initiative. It shows that you’re willing to learn new things instead of resisting change. Those qualities matter enormously right now.
Adam: Do you have any other tips on either how leaders can create a culture of AI or how individuals can better utilize AI in their careers?
Valerie: From a leadership perspective, one thing I don’t think people fully appreciate yet is that leaders themselves are going to have to become genuinely knowledgeable about AI. You can’t outsource this entirely to IT teams or external consultants. Leaders are going to be responsible for helping redesign workflows, evaluate platforms, make purchasing decisions, and guide transformation efforts. That means you actually need enough knowledge to evaluate whether what you’re being shown is real or just marketing.
I’ll give you an example. When we were evaluating HR systems, every vendor claimed they had powerful AI functionality. Every single one. But when we started testing those products live and asking detailed questions about what the AI capabilities actually did, the differences became obvious very quickly. Some platforms genuinely had meaningful functionality. Others were mostly using “AI” as a buzzword.
If I didn’t understand AI well enough to evaluate those systems critically, I could have made a terrible decision for the company. That’s why there really isn’t time anymore for leaders to avoid this. Leaders have to become fluent enough to ask smart questions, challenge assumptions, and understand what they’re actually buying or implementing.
From an individual perspective, I think people need to stop thinking about AI as optional. It’s becoming part of the baseline expectation for how work gets done. The sooner people become comfortable using it thoughtfully and strategically, the stronger position they’ll be in professionally.
Adam: When you talk about the importance of leaders leading by example instead of outsourcing AI expertise to someone else on the team, there’s a real risk there. Everyone is suddenly claiming to be an AI expert. Everyone claims they have a groundbreaking AI solution. If you don’t understand the technology yourself, how do you know whether someone is actually telling the truth?
Valerie: Exactly. And when you’re working with IT teams or transformation teams, they know almost immediately whether you understand what you’re talking about. But beyond that, you simply cannot transform workflows and business processes if you don’t understand how those workflows actually function in real life.
There was a time when companies would choose a technology solution, hand it off to IT, give them a flow chart, and then wait for the finished product to come back for testing. That model does not work anymore. The subject matter experts have to be deeply involved in designing and building the workflows alongside the technical teams because the transformation itself depends on understanding the actual operational realities.
Whether you’re in HR, finance, legal, marketing, or supply chain, you have to be at the table. And honestly, there are too many free tools, free resources, and free classes available now for leaders to avoid learning this. You can become conversationally fluent in AI very quickly if you commit to it. At this point, there really isn’t another option.
Adam: You bring up something interesting, which is that there are so many different resources out there. There are classes, tools, videos, newsletters, podcasts, and tutorials everywhere. How do people avoid option overload? If you walk into a grocery store and there are three different types of cereal, you can make a choice pretty quickly. If there are 500 choices, you can stand there all day trying to decide.
Valerie: I love that analogy. I always compare it to cooking. Before you learn advanced techniques, you first learn how to crack an egg without getting shells into the bowl. AI is similar. People get overwhelmed because they think they need to master everything immediately, but they really just need to learn the basics first.
The basics are understanding chat tools and understanding prompt engineering. If you learn those two things, you already have a foundation strong enough to start figuring out where you want to go next. Then you can start exploring the tools specific to your industry or your company.
Honestly, one of the smartest things someone can do is ask AI itself to create a learning plan. You can literally open Claude and say, “I know nothing about AI today, but I want to become conversationally fluent within 90 days. Build me a plan.” The tools themselves can help guide your learning process.
The important thing is not trying to learn everything at once. Start simple. Build momentum. Then layer additional knowledge over time.
Adam: And the key is just getting started.
Valerie: Exactly. Do something. That’s really my biggest message because what scares me right now is how many people still haven’t started. The headlines create fear. People hear things like “AI is going to replace everyone” or “there’s no point in learning anymore because the machines will do everything.” That kind of messaging can become paralyzing.
If people become overwhelmed and do nothing, that’s probably the worst possible response right now. The people who are experimenting, learning, adapting, and becoming comfortable with these tools are going to be in a dramatically stronger position moving forward.
So my advice is simple. Start today. Open a tool. Take a course. Watch a video. Experiment with prompts. Volunteer for a project. Just do something because momentum matters.
Adam: And on the flip side, as a leader, it’s critical to calm that noise. Leaders need to understand that people are hearing these headlines constantly, and if you don’t address those fears directly, they’ll fill the silence themselves.
Valerie: Exactly. But I also think leaders have to be candid and honest. Nobody can promise perfect job security anymore. Tomorrow isn’t guaranteed for anyone in any industry. But what people can control is career security.
That’s the distinction I talk about with my team all the time. You may not fully control your job, but you absolutely can control your career development, your learning, your skills, your expertise, and your adaptability. Those things belong to you, and you carry them with you wherever you go.
That’s why I encourage people to focus on building their capabilities, building their brand, building their voice, and continuing to learn. The skills you develop now around AI fluency, adaptability, and transformation work will continue creating opportunities for you long-term, regardless of where your career takes you.
We also have to remember that every major technological shift creates entirely new categories of work and entirely new roles that didn’t previously exist. We don’t fully know what all of those future roles will be yet, but the people who become AI fluent now will be much better positioned for whatever comes next.
Adam: Is that your message to people who work within your organization and who are concerned about what’s happening?
Valerie: Absolutely. I tell people constantly to focus on their careers. Learn continuously. Build expertise. Develop a voice. Own your growth. Those are the things you can control. I literally shared that message with my team a few days ago during our onsite. Own your career. That mindset matters now more than ever because adaptability and continuous learning are becoming foundational professional skills.
