As AI becomes more pervasive, every business will require governance frameworks to integrate AI tools into their operations.
In the process, businesses in the private sector may bypass certain AI governance principles, if it means it will hinder product development. I argue this is less likely to occur the public sector, with its greater acceptance of bureaucracy.Industries with a hunger to innovate, like the medical/health tech space, would see regulatory frameworks as unnecessary red-tape; shooting off the hip and not placing strong efforts to document their product development. This becomes a reliance on lawyers to swoop-in late in the process of creating products, rather than having them present from ideation to Go-To-Market.
Implementing an AI governance framework is not one-size-fits-all. More established businesses need extensive AI governance frameworks, given the sheer scale of their business activities. For start-ups, rigorous governance frameworks wouldn’t make sense as a more flexible approach would.
Legal teams dealing with AI need to assess what approach allows compliance to be achieved for their company’s size, speed of working, and culture. The challenge is to do so whilst preventing it from losing gravitas or convoluted & lengthy parameters that aren’t relevant.
For start-ups, it is the responsibility of the product manager to work with AI governance frameworks from the idea stage to the rest of the product life cycle. It shouldn’t be seen as a an overbearing dictator on innovation, but rather helpful information for them to make the final call; a product decision that may seem really small can change how a it is seen by a regulator, adding extra compliance work and documentation for a team.
AI governance frameworks work best when they are a cultural directive from the top-down and all relevant stakeholders are part of every step in the process.
