The hardest part of deploying AI in a traditional company isn't the technology. It's the trust.

I deployed a demand forecasting model at a retail company. The model was 30% more accurate than the existing spreadsheet-based approach. But the procurement team refused to use it for 4 months.

"We've been doing this for 20 years. Why should we trust a black box?"

Fair question. Here's what finally built trust:

We ran the model in shadow mode alongside the existing process. No decisions made by AI — just predictions logged and compared. After 3 months of data showing the model was consistently better, skepticism softened.

We added explainability. For every forecast, the system showed: "Forecast is higher because of holiday proximity (40% weight), marketing campaign (30% weight), and weather forecast (30% weight)." People could see the reasoning.

We let domain experts override the model. And we tracked when overrides were right vs when the model was right. After a while, the team started trusting the model on most predictions and only overriding when their domain knowledge added something the model couldn't see.

Trust is earned through transparency, parallel operation, and time. There's no shortcut.

If you deploy AI and expect immediate adoption, you'll be disappointed. Plan for a 3-6 month trust-building period. It's not wasted time — it's the foundation for real adoption.