And honestly, I get it.
Letting go of part of your hard-earned expertise – skills you've spent years mastering – can feel unsettling. It may even spark self-doubt: If AI can write most of the code, what’s my value?
To feel safer, many developers limit AI use to tasks they already dislike, like unit tests or documentation. They accept AI-generated code in small increments – maybe a line or two at a time – while continuing to write the core code manually. But this cautious adoption limits AI’s full potential and slows down real skill development.
✅ Manage expectations – Give developers explicit permission to be less productive while learning AI tools. Growth requires adjustment.
✅ Facilitate success stories – Peer-to-peer inspiration works better than AI vendor pitches. Encourage teams to share real wins.
✅ Normalize failure – Learning new tools means making mistakes. Failures teach more than successes. Create a culture where experimenting is encouraged.
✅ Be patient – AI adoption is a journey. Encourage, support, but don’t pressure. Not everyone adapts at the same pace.
