> grep –lessons-learned ./experience/*
It’s been about nine months since I took on the AI Integration Specialist role, and I wanted to share some of the things I’ve learned along the way. These aren’t polished frameworks, they’re real observations from doing the work every day.
Accuracy is trust
When an AI bot gives a wrong answer, it doesn’t just fail to help, it actively erodes trust. Users who receive incorrect information from our Bots are less likely to engage with the bot next time. That’s why we treat accuracy as the single most important metric. Getting it right matters more than responding fast.
Review everything, assume nothing
One of the biggest shifts in my thinking has been around evaluation. You can’t improve what you don’t measure, and you can’t measure what you don’t look at. Our regular feedback reviews are the foundation of everything we do. Every conversation the bot has is an opportunity to learn.
Cross-team alignment is non-negotiable
AI work touches almost every part of our support workflow. If the AI Squad operated in isolation, we’d quickly drift out of sync with what the business actually needs. Regular check-ins and close collaboration with various stakeholders keep us grounded and focused.
The human side of AI leadership
Leading a squad isn’t just about data and strategy. It’s about people. Providing constructive feedback, supporting professional growth, and making sure everyone on the team feels valued and heard, that’s where the real impact happens. A motivated, well-supported team does better work, and that shows up in the bot’s performance.
Still learning
Nine months in, I’m more convinced than ever that the intersection of AI and customer experience is where some of the most meaningful work is happening. I don’t have all the answers, but I’m learning something new every day. And that’s exactly where I want to be.
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