Scaling Without Losing Control

Scaling is about maturity, not activity — value comes from repeatable workflows, not more tools or experiments.

Common traps to avoid: Letting everyone try AI without structure leads to inconsistent results. Tool bloat from chasing new "shiny" apps instead of fixing workflows. Centers of excellence become political bottlenecks where ideas stall. Adding governance too late undermines trust and consistency.

Move from a tools mindset (features, hype, volume) to a systems mindset (workflows, triggers, friction points).

Training should focus on prompting, output review, and iteration, not just tool demos.

True scaling means repeating what works with tighter workflows, governance, and integration — not just "more."