I’ve learned this the hard way. If you wait until Monday to get focused, you’re already behind. Here’s how I set up my week before it starts: 1. I choose ONE win that mattersNot a to-do list. Not busy work. One outcome that actually moves my life or business forward. That goes on the calendar first. 2. I remove friction ahead of time I look at my week and ask,“What’s going to trip me up?” Too many meetings, distractions, low-energy days. I fix it now so I’m not relying on willpower later. 3. I reset my environment Desk clear. Calendar clean. Priorities visible. When Monday hits, I don’t want to think... I want to execute. This isn’t about discipline. It’s about design. Winning weeks are built before they begin. What about you? What’s the ONE thing you do to set yourself up to win the week ahead? Drop it below 👇

One of the most persistent myths about AI fluency is that it requires big changes. New systems, redesigned workflows, or dramatic shifts in how we work. This belief quietly stalls progress because it makes adoption feel heavier than it needs to be. In reality, long-term fluency with AI is almost always built through small, consistent adjustments rather than sweeping transformations. ------------- Context: Why We Overestimate the Size of Change ------------- When people think about becoming “good” with AI, they often imagine a future version of themselves who works completely differently. Their days look restructured. Their tools look unfamiliar. Their thinking feels more advanced. That imagined gap can feel intimidating enough to delay action altogether. In organizations, this shows up as waiting for perfect systems. Teams postpone experimentation until tools are approved, policies are finalized, or training programs are complete. While these steps matter, they often create the impression that meaningful progress only happens after a major rollout. At an individual level, the same pattern appears. We wait for uninterrupted time, for clarity, for confidence. We assume that if we cannot change everything, it is not worth changing anything. As a result, adoption stalls before it begins. Micro-adaptations challenge this assumption. They suggest that fluency does not come from overhaul. It comes from accumulation. ------------- Insight 1: Fluency Is Built Through Repetition, Not Intensity ------------- Fluency with AI looks impressive from the outside, but its foundations are remarkably ordinary. It is built through repeated exposure to similar tasks, similar decisions, and similar patterns of interaction. Small, repeated uses allow us to notice how AI responds to our inputs over time. We begin to see what stays consistent and what varies. This pattern recognition is what turns novelty into intuition. Intense bursts of experimentation can feel productive, but they often fade quickly. Without repetition, learning remains shallow. Micro-adaptations, by contrast, embed learning into everyday work where it has a chance to stick.