Change management is the most underestimated part of every AI deployment. The technology is often the easy part. The hard part is getting the people who should benefit from AI to actually use it consistently, trust it appropriately, and incorporate it into their real workflow rather than treating it as a demo toy.
The patterns that work: involve end users in the POC (they need to feel like they shaped the tool, not had it imposed on them), start with the most enthusiastic team as a lighthouse (visible success creates pull), make the AI output easy to edit rather than approve/reject (lower friction), and measure adoption as a first-class metric alongside output quality.
The patterns that fail: top-down mandate without training, deployment without success metrics, or treating AI as a cost-cutting signal (which immediately activates self-preservation instincts). Frame AI as capability expansion, not headcount reduction.
Bring this to your business
Knowing the term is one thing. Shipping it is another.
We do two-week AI Sprints — one term, one workflow, into production by Day 10.