Ep 59 - Year End Reflections With AI: And It Has Notes

What if reflection wasn’t a year-end memory dump but a working system that sharpens judgment? We sat down to examine how AI quietly changed the way we think, plan, and lead—shifting focus from task speed to decision quality, from outcomes to assumptions, and from rigid plans to resilient learning loops. Instead of asking what happened, we asked who we grew, where we created real leverage, and how our narrative as leaders evolved.

We unpack the prompts that force clarity without comfort: where did our judgment create outsized impact, which decisions aged well given the information we had, and how well our calendars matched our stated priorities. Along the way, we show how AI reconstructs decisions at the moment they were made, turning private reasoning into an artifact we can analyze without ego. That distance unlocks clean counterfactuals—what alternatives were viable, which assumptions mattered most, and where risk was mispriced—so we stop relitigating ourselves and start improving the system.

From there, we build a true decision quality loop: track choices, inputs, confidence, and results to expose patterns in judgment. Strengths become repeatable, biases become addressable, and learning accelerates. The payoff isn’t just productivity; it’s resilience. AI lowers the friction around thinking, helps separate signal from noise, and makes it easier to update beliefs quickly. If next year looked exactly like this one, would that excite you or concern you? Press play to grab the questions, run your own review, and set a sharper direction.

If this resonated, subscribe, share with a friend who leads, and leave a review with the one question you’re taking into your year-end reflection.

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Chapters

• 0:00 Framing A Different Kind Of Reflection
• 0:52 AI As Mirror, Not Just Tool
• 2:36 Questions That Force Clarity Over Comfort
• 4:33 Separating Outcomes From Decisions
• 6:08 Externalizing Thinking And Counterfactuals
• 7:45 Building A Decision Quality Loop
• 8:49 Note: This timestamp is invalid and should not be here

Transcript

SPEAKER_00: 0:00

Welcome back to Inspire AI, the podcast where we explore how artificial intelligence is reshaping the way we work, think, and prepare for what's next. Today's episode is a little different. It's more reflective. Because as this year comes to a close, I've been thinking about something unexpected. A year ago, I thought AI would help me work faster. I didn't expect it to change how I think. Not dramatically, not overnight, but slowly for many small moments. Late-night questions using deep research, rewriting drafts, reimagining and building self-learning paths, troubleshooting problems, getting a second perspective when I felt stuck. So today, as we close out the year, I want to reflect on that shift. Invite you to do the same. Most of us reflect from memory, and memory is emotional, selective, incomplete. We remember the big wins. We often miss or forget the quiet progress. And like AI itself, this change didn't happen overnight. It took years, decades to build to this moment. This year, many of us started using AI as something different. Not just a tool, but a mirror. And we've asked questions like: based on our history, what would people say about me when I'm not around? Or what patterns do you notice in my thinking? And even what am I spending the most energy on? And how about this one? Tell me something about myself that I don't know. Suddenly, reflection becomes structured. It's less judgmental with more clarity. By asking these questions, AI doesn't tell us who we were. It helps us see who we've become. Here's the prompt I used. Based on what you know about me, what reflection type questions should I ask myself as a year in review? The response? Jason, a strong year in review for someone at your level isn't about what happened. It's about how your leverage, judgment, and narrative evolved. These questions are designed to force clarity, not comfort. And that line stuck with me. Because reflection at this stage isn't just about listing accomplishments. It's about understanding how I've changed as a leader. And here are the questions that followed. Ones worth sitting with anyway. First, where did I create real leverage? Not where I stayed busy, but where my judgment, decisions, and presence created outsized impact. Where was I operating at 10X? And where was I still doing work the system should handle? Second, who did I grow? Leadership isn't measured by what you shipped. It's measured by who's stronger because you were there. Who exceeded expectations? Who stalled? And what does that say about how I develop people and design teams? Third, how did my judgment evolve? What trade-offs am I proud of? Where did I move too fast or hesitate too long? Because maturity shows up as much in the decisions that we don't make as the ones we do. Fourth, did I stay technically and intellectually sharp? Especially in the age of AI? Did I use these tools just to move faster or to think better? Am I clearer on what truly matters now than I was a year ago? Fifth, what story am I telling without realizing it? What narrative do others tell about my leadership when I'm not in the room? Impact only scales when it's paired with clarity and communication. Sixth, how sustainable was my pace? Not just productivity, but energy? What fueled me? What drained me? And did my calendar reflect my real priorities? Finally, am I becoming the leader I wanted to be? If next year looks exactly like this one, would that excite me or concern me? Reflection like this isn't about dwelling on the past. It's about sharpening direction. Because progress isn't just growth. It's growth in the right direction. We often judge decisions based on outcomes, but wisdom comes from reviewing assumptions. One of the most powerful uses of AI this year has been its ability to help us revisit decisions without emotion. Here's why that matters. First, AI separates the decision from the result. If something worked, we call it smart. If it didn't, we call it bad. But that's a cognitive trap because outcomes are noisy and decisions are always made with incomplete information. AI lets us reconstruct a decision using only what we know at the time. The assumptions, the constraints, risks, and goals. And that lets us ask a better question. Given what I knew then, was this a sound call? Not did it work? Second, AI externalizes your thinking. When decisions live only in your head, they're tied to ego, identity, and reputation. But when you document your reasoning and have AI analyze it, the decision becomes an artifact. Not a judgment of you. You're no longer relitigating yourself. You're reviewing a system. Third, AI can explore counterfactuals without attachment. You can ask what alternative paths were available? Which assumptions mattered most? What risks were underestimated or overweighted? AI doesn't protect its pride. It doesn't rationalize. It doesn't rewrite history to feel better. It just explores the decision space. And that's incredibly hard for humans to do alone. Finally, AI helps build a true decision quality loop. Over time, you can compare decisions made, information available, confidence levels, and actual outcomes. Patterns will emerge. Not emotional stories, but signal. And it'll signal where judgment is consistently strong, where bias shows up, where uncertainty is mispriced. And that's how leaders improve decision quality, not just decision speed. The bigger idea here is simple. AI isn't here to tell us what to decide. It's here to help us learn from decisions without shame or ego. And that may be one of its most underrated superpowers. Because growth doesn't come from regret, it comes from understanding. If you stay on a learning path long enough, something shifts. You stop optimizing for rigid goals and you start optimizing for learning itself. What replaces linear plans is a loop. Envision. Try, reflect, adjust, and then try again. What AI changes is how fast and how honestly that loop closes. It helps separate signal from noise. It lets us update our thinking without ego. Instead of asking, did I succeed? We ask better questions, like, what did I learn? And what should I try next? That's not just productivity. That's resilience. If you pause for a moment, you might notice something subtle but important changed this year. You became more comfortable with not knowing, less afraid of asking basic questions, and faster at finding clarity. AI didn't replace your thinking, it reduced the friction around it. And that's a powerful shift to carry forward, especially where uncertainty is constant, but rarely normalized. As we close out this year, I'll leave you with this question. What did AI hope you notice this year that you might have missed on your own? Because reflection isn't about looking back. It's about learning how to move forward with intention. So as I like to say, stay curious, stay intentional, and keep building what's next.