Mediations #23: How to Train Yourself Under Work Deadlines
You finish one piece of work and look what’s next and see there are thousands of things waiting for you. It’s a never-ending story. Features pile up, deadlines look at you, and the pressure to go faster never lets up. Simultaneously, the need to train yourself and upskill (especially as AI transforms how we work) has never been more urgent. Your manager asks you to spend time training, but never clarifies if that’s more important than the feature you have to deliver. So, you default back to delivering the feature because if you don’t do that, you might find yourself in trouble.
Meanwhile, if you ask any manager, they will tell you up-skilling is key to survival, especially for knowledge workers, as AI reshapes careers. Even with this awareness, they still don’t give any time block for training. When you’re sprinting from one release to the next between tight deadlines, who has hours to spare for training modules? If your manager is laser-focused on output, training feels like a “nice-to-have,” not a “must-do.” It’s easy to put off learning until “things calm down,” which is never.
The key is making learning work within your existing workflow rather than against it. Integration is everything. Apply what you’re learning directly to your current projects.
Let’s say you’re a software engineer juggling code reviews, feature development, and meetings while trying to stay up-to-date with testing best practices and push yourself to do more with AI. Instead of blocking out a whole afternoon for training (which never happens), use an AI-assistant to explain to you testing best practices and experiment with the assistant’s suggestions for additional test scenarios for your new feature, learning about boundary testing and mocking strategies in the process. While coding with AI, ask AI to explain to you the concepts, and mix practice with theory. This way, you gain both theoretical and practical knowledge while still advancing your work. And you shouldn’t stop there.
Form an unofficial “learning pod” with your teammates where everyone shares insights, holds each other accountable, and swaps tips on what you learned. Don’t wait for your manager to organize this. Learning pods turn individual learning into a team advantage. These meetings don’t have to be big. A thirty-minute one-on-one sessions also work. You can share these testing patterns you’ve discovered with another engineer on your team and brainstorm what else you can do, turning the session into a mutual learning opportunity.
Once you’re ready, show your approach to your manager and ask for support. Research shows that middle managers make or break training engagement. Teams with proactive managers—those who champion learning and can actually connect it to career growth—see much higher participation.
Whether your manager is one of these types or not, you can take the first step without consulting your manager and surprise them with the result. They will see you still delivering while learning and contributing to others’ growth, too. This approach will help you get buy-in for dedicated learning time (when needed) and connect the training to team objectives. Frame it as an investment in better delivery, not a distraction from it.
There’s no perfect formula for balancing delivery and learning. The reality is messy, and the pressure to ship isn’t going away. But integrating learning directly into existing work will help you adapt to industry changes, while the cost of standing still is rising with AI. The organizations and individuals who thrive will be those who make learning a habit, not a luxury.
Good to Great
Good: I will look, understand, act on the survey results from Lenny: How tech workers really feel about work right now + many things resonate a lot.
Better: Is Netflix dumbing down the TV Series so you can watch it easier when you scroll through your Instagram/TikTok? I rarely watch Netflix, but for other reasons.
Great: I found nothing great this time. Great things should be rare. Finding one is a blessing and requires a bit of luck.
Until next time,
Candost
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