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 — sensitive data, hallucinations and compliance concerns
The paradox is simple: experimentation accelerates innovation, but without structure it slows scaling.
It also makes it harder to demonstrate clear business value — which ultimately limits leadership investment.
At some point GenAI can no longer remain a collection of experiments. It needs to become a managed enterprise capability.
In practice this usually means introducing:
• controlled model access
• shared retrieval and orchestration infrastructure
• monitoring and evaluation pipelines
• clear governance and risk frameworks
This transition — from experimentation to industrialisation — is where many organisations are currently struggling.
Curious to hear how others are approaching this shift.
In the next post I’ll share a framework I often use to categorise enterprise GenAI use cases (assistive → workflow-embedded → agentic), which helps clarify both value and risk.