I’ve gone through a similar journey, not in big tech, but in practical business work. We started with quick experiments: generative prompts in internal tooling, a couple of proof-of-concept bots, and integration of recommendations in mobile apps.

What shifted for us was when we stopped experimenting for novelty and started embedding AI where routine work slowed people down. For example, we built an intake assistant for hospitals: guided questions that organize structured history before a doctor sees the patient. At first it felt promising, but adoption only happened when clinic staff saw that it saved them time and didn’t replace their judgment. That forced us to rethink how we framed the feature. It became about support, not replacement.

The real adoption turning point came when non-technical team members began using the tools without hesitation. That’s when it stopped being AI and just became part of workflow.