Last year, I was catching up with Lisa, a VP of Operations we placed a few years ago at a mid-size logistics company. Brilliant executive, twenty years of experience, could solve complex operational challenges in her sleep. But when it came to AI, she was completely lost.
“I keep hearing about AI,” she told me during our conversation. “Everyone’s talking about how companies need to embrace AI. But honestly? I have no idea where to even start.”
Sound familiar? Lisa’s story is one I’ve heard from dozens of executives over the past year. Smart, experienced leaders who feel like they’re falling behind because they think they need technical expertise to leverage AI effectively.
The thing is, they’re approaching it all wrong.
The Breakthrough: From Overwhelmed to AI-Forward Leader
Lisa’s transformation didn’t happen in some fancy AI workshop or through reading technical papers. It happened when she became curious and started thinking differently about her daily challenges.
We talked about all the processes that she found mundane and repetitive. She told me about preparing for a quarterly business review, buried under spreadsheets and reports, trying to identify trends in their supply chain performance. She had spent three hours manually combing through data that she knew could have been analyzed in minutes.
“What if I told you there was a tool that could read through all of this and tell you exactly what patterns to look for?” I asked her.
Her response was classic: “Yeah, but I’d probably need to learn Python or something, right?”
That’s when I realized the problem. She was thinking about AI like it was programming, like she needed to become a software engineer to get any value from it.
AI in Leadership Starts with Mindset, Not Tech
I gave Lisa a simple reframe that changed everything: “Stop thinking about AI as machine learning. Think about it as having a really smart intern who loves to do tedious work.”
Before jumping into tools, I wanted her to build a foundation. She needed to understand what AI could do, what it couldn’t do, and where it excelled versus where it struggled. Lisa started reading industry posts, watching YouTube videos, and listening to podcasts that covered AI applications, various tools, and real user experiences. She familiarized herself with the terminology, finally understanding concepts like prompt engineering and the difference between large language models and neural networks.
As she immersed herself in this learning, she became more curious about AI’s practical applications. She experimented with different prompts to fine-tune outputs, tested various tools to create useful analysis, and spent time with her teenage daughter exploring how younger people naturally use AI in their daily work.
This foundation gave her the confidence to start applying AI to her own challenges. We began by making a list of everything that annoyed her about her job, not the strategic work she loved, but the tiresome tasks that somehow always consumed her time:
- Writing executive summaries of operational reports
- Preparing talking points for leadership meetings
- Analyzing vendor performance data
- Drafting policy updates
- Creating project status updates
- Reviewing contracts for key terms
“Pick the one that annoys you most,” I told her. She chose executive summaries, something that took her four hours every month and felt like pure busy work.
The Three-Hour Experiment
I gave her a simple challenge: spend three hours over the next week learning to use AI for this one specific task.
Hour 1: She started with ChatGPT, asking basic questions about the task and how it could be automated. This helped her understand the tool’s capabilities and limitations for her specific use case.
Hour 2: She fed her operational data into an AI tool and asked it to identify key trends and write a summary. What normally took her two hours was completed in about five minutes, and the output was surprisingly comprehensive.
Hour 3: This is where the transformation began. Lisa started thinking bigger. “If it can do this with operational data, what about vendor contracts? What about employee feedback surveys?” She spent the hour experimenting with different types of problems, developing intuition for what worked well and what didn’t.
After this experiment, Lisa wasn’t an AI expert. But she had something more valuable: practical understanding of when AI could help her work more effectively.
Teaching Her to Ask Better Questions
The most important skill I helped Lisa develop wasn’t technical – it was learning how to frame problems so AI could provide meaningful solutions.
Her initial instinct was to ask vague questions like “How can I improve our logistics process?”
I taught her to be specific and provide context: “Our delivery times have increased 15% over the past quarter. Based on industry best practices, what are the top factors that typically cause delivery delays in regional logistics operations?”
Instead of “Help me write a performance review,” she learned to frame requests like: “I need to give feedback to a warehouse manager who’s excellent with team leadership but struggles with inventory accuracy. Help me structure this conversation to acknowledge his strengths while addressing the accuracy issues constructively.”
The pattern was straightforward: provide context, be specific about desired outcomes, and frame the interaction as collaboration rather than simple delegation.
Watching the Transformation
Within a month, Lisa had fundamentally changed how she approached her work. She wasn’t using AI for everything, but she’d developed strong intuition for when it could add value.
Before important meetings, she would use AI to help anticipate questions and organize her thoughts. When analyzing performance data, she leveraged it to spot patterns she might have missed. For difficult emails, she would draft them with AI assistance and then edit for her personal voice and style.
The real breakthrough came when she started seeing opportunities beyond her own responsibilities. “If this can help me with vendor analysis,” she told me, “imagine what it could do for our procurement team’s contract reviews.”
It turns out Lisa wasn’t alone. Many forward-thinking leaders are beginning to reframe how they make decisions with AI. This Forbes article highlights how AI is helping executives lead with sharper insights and faster decision making.
That’s when I knew she’d become truly AI-forward. She wasn’t just using the tools, she was thinking strategically about how they could transform her entire department’s effectiveness.
The Ripple Effect
Lisa’s transformation didn’t happen in isolation. As she became more comfortable with AI, she started having different conversations with her team. She began encouraging them to think about automation opportunities and helped guide their own exploration of AI tools.
Six months later, her department had automated approximately 30% of their routine operational tasks. This wasn’t through some massive digital transformation project, but through dozens of small experiments that accumulated into significant efficiency gains.
Her CEO took notice. “Lisa’s team is moving faster than anyone else in the company,” he told me. “Whatever she’s doing differently, we need to understand and replicate it.”
A Framework for AI Adoption in Executive Roles
Looking back, here’s the approach that transformed Lisa from AI skeptic to AI advocate:
Understand the fundamentals. Getting a high-level grasp of what AI is and how large language models work helped her understand the tools’ capabilities and limitations.
Start small and specific. Rather than trying to revolutionize everything at once, she focused on one specific problem and became proficient at using AI for that task.
Focus on augmentation, not replacement. Lisa never felt threatened by AI because we positioned it as enhancing her capabilities, not replacing her judgment.
Build confidence through incremental wins. Each small success gave her the confidence to attempt something slightly more ambitious.
Think in systems, not just tasks. Once she mastered individual use cases, she naturally began seeing how AI could improve entire workflows.
Stay curious and current. AI evolves rapidly, with new tools and updated models appearing regularly. Staying informed helped her identify emerging trends and potential new applications.
What I Learned from Working with Lisa
The executives who successfully become AI-forward are those who embrace experimentation and aren’t afraid to learn through trial and error.
Lisa succeeded because she was willing to invest time in learning and wasn’t deterred by initial imperfect results. She didn’t wait for complete understanding or comprehensive training, she simply started experimenting and iterating.
The leaders who struggle are typically those who want to understand everything before taking action. They read articles about AI strategy and attend webinars about machine learning, but they never actually open the tools and begin testing them with real problems.
The Conversation That Confirmed Her Progress
Three months into her AI journey, Lisa called me with an interesting development. Her company was considering hiring a consultant to analyze their supply chain efficiency. The proposal was $50,000 for a six-week engagement.
“I fed our operational data into an AI tool over the weekend,” she told me. “The insights I got were honestly comparable to what I’ve seen from expensive consulting reports. The whole analysis took me about two hours.”
They still hired the consultant because external perspective and deep industry expertise have distinct value. But Lisa entered those meetings armed with her own analysis, asking more targeted questions and pushing for more specific recommendations.
“I’m not trying to replace human expertise,” she explained to her CEO. “But I’m not going to make important decisions without leveraging tools that can help me better understand our data.”
That’s when I knew she truly understood it. AI-forward leadership isn’t about replacing human judgment, it’s about making that judgment more informed, faster, and more confident.
The Broader Pattern
Lisa’s story isn’t unique. I’ve helped dozens of executives make similar transformations, and the pattern is consistent: start small, focus on real problems, embrace experimentation, and maintain curiosity.
The leaders getting ahead with AI aren’t necessarily engineers… they’re explorers. They stay open, stay hands-on, and engage with what’s possible.
