One pattern I’ve observed across many organisations experimenting with GenAI: Local experimentation works well at first — but quickly becomes a structural problem at scale.
While experimentation is valuable for learning, several issues typically emerge:
• Tool sprawl — multiple models, vendors and APIs used across teams
• Duplicated effort — prompts, pipelines and integrations rebuilt repeatedly
• Limited governance — unclear ownership, monitoring and model lifecycle management
• Invisible costs — token usage and infrastructure growing without central visibility
• Increasing risk exposure —…