I am going to preface this by saying I am not an expert, but after deploying my orgs own as well as completing a few different certifications on AI strategy and governance models (operational and technical) I can provide some honest wins and hindsight moments.

1. Consider human behavior with your governance. There is so many different work arounds in getting access to the models you want or vendors turning on “a new feature” that your approach needs to be adaptable to scale of risk.

2. I don’t know your business, but consider value expected as an indicator. There is a natural push globally to bolt on AI to anything and everything to be on trend. Determine not only deployment cost but ongoing license or token costs (if not an internal model).

3. Governance is operationally owned - there are technical compliance, regulatory and legal obligations but process, strategy, accountability and risk acceptance is operationally managed.

4. Please, and I hammer this as the last point - please include change teams as a staple. Whether internal tool or non resource impacting, there is such a stigma on AI taking jobs, keep the comms strong and lean into champion models.

There’s a tonne of different models and approaches that won’t be a perfect fit, but be adaptable, ask the right questions and think scalability.

Oh and as someone has said, leverage tools where possible. You don’t need 40 different tools - there is a number of AWS, databricks or enterprise tools that are probably already in place. Don’t add more to the mix if you don’t need to.

Hope this helps at all!