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When my daughter was in kindergarten many moons ago, she came home every day in pink shoes with Velcro straps and a pink barrette to match. I worked from home, and when she walked through the door, I would ask about her day.
One day, the conversation went something like this:
"Today was great. We had fairy bread."
"Fairy bread? Tell me more."
"It was my classmate's birthday, and my teacher gave us plain bread and sprinkled hundreds and thousands on it and turned it into fairy bread."
Her teacher was Australian and called the pink, purple, blue, red, and yellow sugary sprinkles "hundreds and thousands" and the transformed slice of bread "fairy bread."
AI is the plain bread, and the ideas we bring from our human experience are the "hundreds and thousands." Together, they become fairy bread. But AI in isolation, without our human sprinkles (our intuition and judgment), is just plain bread.
I am getting better at figuring out how to keep my intuition and judgment while using AI. While I am not an AI researcher, my technical roots are in math, especially the kind of math underpinning today's language models. Because of my math roots (or maybe despite my math roots), I initially vacillated between not using AI at work because I was intellectually stubborn and guiltily offloading every cognitive task to AI for a couple of weeks.
Neither felt right. The first didn't feel right because I was leaving productivity on the table, and the second felt off because I was losing my ability to think deeply and originally (yes, in just a few weeks).
At HRCI, I use AI to build product proofs of concept, internal tools, and data analysis; and to research and sharpen plans. Whatever I'm using AI for, I keep my human judgment and intuition through a first-middle-last mile framework. I begin with my own thinking, use AI to help me work through the problem, and return to my own judgment to evaluate the result. I am intentional about what I contribute and what I ask AI to do.
Before I start any task with AI, I write a memo to myself. This is my first mile. I think best when I write, so I get all my thoughts on paper. These memos often end up as streams of consciousness with windy sentences, but the important thing is they are my windy sentences, created by my brain using all my experience, context, and judgment.
In the middle mile, I interact with an AI—sometimes chat, sometimes coding agents—to solve whatever goal I need to achieve that session. AI gives me an output, but it is output anchored in my original thinking from the first mile.
In the last mile, I review the output critically. If it's text output from research, I synthesize it in my own words. If it's a product prototype or data analysis, I go through multiple iterations to align it with what I wrote in the first mile. So, I keep AI in the loop during the middle mile and my original thinking in the first and last miles.
I also wrap my most important AI philosophical principle around this first-middle-last mile framework: I write all documents and communications that I share with others, without AI. The only exceptions are when I use AI to write code or create markdown files, which are essentially instructions for an AI.
The benefits for me in not surrendering all my cognitive tasks to AI are: first, my work product retains my unique intuition and spark. If I exclusively use AI, then my work output will look like everyone else's work output who only uses AI. I do not want to create things that look like everyone else's; and second, I will become worse at my job over time. If I do not practice hard thinking and applying my human judgment, then I will get worse at it and I will not improve.
My day-to-day work probably looks different from yours as HR professionals. I spend a lot of time experimenting with technology and building tools. You're often working through decisions that directly affect people and organizations. So I wouldn't presume that my approach to AI is necessarily the right one for you.
But I do think the underlying principle applies across professions. We all bring experience, intuition, and judgment to our work, and those are worth preserving. My hope is that you'll consider how you might preserve your own original thinking and professional judgment as AI becomes a bigger part of your work.
I will end with this: Early in my career someone encouraged me to read Sources of Power: How People Make Decisions by Gary Klein. I recently read it again through an AI lens. I had forgotten his framing of expert judgment: intuition (recognizing patterns and subtle cues based on accumulated experience, often without consciously analyzing them); mental simulation (imagining how a situation might unfold to evaluate possible actions and outcomes); storytelling and metaphor (drawing on past experiences and familiar situations to make sense of new problems). His framing of expert judgment is now one of my favorite summaries of what it means to think like a human and to be the hundreds and thousands in a world of AI.
Chris Scandlen is HRCI’s chief innovation and information officer, driving AI, analytics, and technology strategy to transform customer and community experiences. A former global leader at Laureate Education and PwC, he has built groundbreaking AI and data products, led high-performing tech teams, and co-founded a successful startup.
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