The case for using AI to rediscover your leadership strengths
AI can help turn years of vague feedback into a clearer picture of where you — and your team — create the most value.
Will Hodges is U.S. cross-commercial communications leader at PwC.
No Major League hitter has finished a season with a batting average above .400 since Ted Williams hit .406 in 1941.
For the non-baseball fans among us, that means Williams got a hit in roughly four of every 10 official at-bats for an entire season. In baseball, .400 has become almost mythical. For context, the batting average for the entirety of Major League Baseball in 1941 was .267.
Williams had a chance to protect his average on the final day of that season. He entered a doubleheader sitting right at .400 and was given the option to stay on the bench. Instead, he chose to play both games. He went 6-for-8 and finished the season at .406.
I’ve always liked that part of the story because I don’t think Williams was taking a risk. He was trusting a system he already knew worked.
Williams knew something about where he was most likely to succeed. In “The Science of Hitting,” he famously mapped the strike zone based on his batting average in different locations. (The man literally wrote the book on hitting.) He knew where he could hit .400 — and where his performance dropped dramatically.
That was part of what made him elite. He didn’t try to hit everything. He knew his zone.
Years later, Warren Buffett cited Williams’ approach as an influence on his own “circle of competence” investing philosophy: understand where you have an advantage and have the discipline to stay close to it.
That connection made me wonder: Could leaders map their own strike zones? And could AI help?
We already have more self-awareness data than we use
Most of us have taken some version of a leadership assessment: DiSC, Myers-Briggs, a 360, StrengthsFinder. If you’re anything like me, there is probably a folder somewhere on your computer full of the results. You read them. You learned something. Maybe your team talked about them for 90 minutes.
And there’s a decent chance — probably well above .400 — you haven’t opened that folder since 2017.
Over the past year, most of the AI conversations I’ve heard have focused on efficiency, automation and speed. I became interested in a different question: Could AI help leaders develop better self-awareness? More specifically, could I make my own use of AI less transactional and more “always on” — a tool that could help me understand where I create disproportionate value and do the same for people on my team?
To answer that question I started experimenting, and the process eventually settled into four simple steps.
- Gather the evidence
I started by giving AI information I already had: leadership assessments, manager feedback, peer observations and my own reflection. The goal wasn’t to collect another personality label. It was to look for recurring patterns.
If colleagues consistently describe you as collaborative or “good with people,” don’t dismiss those comments because they sound soft. Treat them as data. One assessment may tell you something about yourself. Multiple unrelated sources pointing to the same strength or observable soft skill are more interesting.
The question then became: What keeps showing up?
- Ask AI to interrogate the patterns
This was one of the biggest unlocks for me. Instead of asking AI to summarize everything back to me, I asked it to act like a leadership coach and question me.
Here is one example prompt that can be used as is, or as a starting point:
Review the feedback I’ve shared, identify recurring themes and then ask me one question at a time to test whether those patterns are real.
Some of the most useful questions were straightforward:
- What work energizes you?
- What work consistently produces unusually strong results?
- What drains you even though you’re perfectly capable of doing it?
- What do other people consistently trust you to handle?
AI wasn’t telling me who I was. It was helping me reflect on information I already had and surface connections I might otherwise have missed.
- Translate soft skills into something useful
This became the most valuable part of the exercise.
Leadership feedback is full of words like empathetic, collaborative, curious and calm under pressure. Those are nice descriptions, but they can also be frustratingly vague.
The clearest example for me is a line I hear a lot in development conversations: “You’re just good with people.” It’s a compliment — and I tell people on my team that if you’re going to be accused of anything, being considered easy to work with is not a bad place to be. But it’s also hard to build a career on. It’s almost the professional equivalent of a high school superlative.
With that in mind, I started asking a different question: If that strength is real, what does it allow someone to do better?
“Good with people” might actually mean an ability to build alignment among people or groups within an organization who don’t naturally agree. Empathy might mean people trust you when change gets difficult. Being calm under pressure might show up as clearer decision-making when things get messy.
That translation matters because most leadership assessments stop at describing personality and labeling strengths. The more useful question is asking what those traits enable you to do from a skills perspective.
- Make it reusable
From there, I started building my own version of Williams’ strike zone — a picture of where experience, energy and natural ability seemed to overlap. I also built a custom GPT using those initial inputs as the knowledge base and now use it as a thought partner throughout the day.
Then I began using the same questions in coaching conversations with my team.
Instead of starting every development conversation with weaknesses or gaps, I could ask: Where is this person already unusually strong? Are we giving that strength enough room to matter? And where might someone else’s capabilities complement it?
That can influence how you think about development, assignments and even team composition. It also creates a different kind of career conversation. Instead of telling someone simply to “work on executive presence” or “be more strategic,” you can begin identifying where they already create disproportionate value and how to use it more deliberately.
When I’m staffing a high-visibility project now, I’m less likely to ask who has bandwidth and more likely to ask whose zone this actually falls in.
Not every capable person should swing at every pitch
Being reliable creates an interesting career problem. If you are good at something, people tend to give you more of it. But over time, capable people can end up carrying a lot of work they do well, even when it pulls them away from the work where they are most differentiated.
AI will not solve that problem for us. It can find patterns that aren’t there, reinforce biased feedback and confidently offer an interpretation that doesn’t survive contact with reality. Human judgment still has the final say. But paired with honest reflection, AI can help us revisit years of forgotten feedback, ask better questions and put clearer language around strengths we may have taken for granted.
Ted Williams didn’t become Ted Williams by swinging at every pitch. He knew his zone. For leaders — and for the people we lead — knowing ours may be just as valuable.
