Data Governance Maturity versus AI Governance Maturity
A few years ago the state of data governance was like the wild west. Since then it has seemingly matured a bit so there is general acceptance in business circles that it is a good idea, though people still seem to struggle with how to implement it, how to define it, and how to prioritize and fund it. In my observation there is a recent move to accept that data governance needs to be in service to business priorities and problems and not just data governance for the sake of data governance. If data governance was the wild west, it seems that AI governance is the wild, wild west. They hype around AI seems to be creating a feeding frenzy to implement it even when there are not clear use cases. It is in everything from Excel and Word to Internet searches to project management and ticketing software such as Jira, Smartsheet, and ServiceNow. The fear is that if you don't implement AI you'll get left behind by the populace that is clamoring for it, whether that differentiator is valid or not. What do you all think? Is data governance reaching some semblance of maturity, or at least adolescence? Is AI governance different enough that it should be handled by different people? Is the demand for AI governance outpacing the ability to understand and implement it? Will they eventually converge? I welcome your thoughts?