This post builds on my last one describing the risk anatomy of an AI use case. It takes that way of describing the risk of a use case and builds a scalable governance model and standards around it to balance that mitigation of risk with enablement of people to achieve value!

How might we describe the risk associated with a use case that uses a single, or multiple, AI ‘units’. I started by trying to break down different parts of an AI Use Case. This post starts to explore that challenge!

Not all solutions are the same complexity, criticality, size so we can’t, or more importantly shouldn’t, look to govern them all in a single way. Small solutions should be governed lightly, more critical or sensitive solutions should be governed robustly.

In v2 of this scalable governance model this model gives an improved method of defining a common language and a standard way of assessing and defining action around different scales of ideas, use cases, and solutions, whilst giving flexibility to apply it to any organisation, in any industry, at any scale.

Once size doesn’t fit all with governance! What might be right for one ‘Size’ of solution may be drastically over bureaucratic for another.

Using a scalable governance model, that can be understood and followed by anyone in an organisation!