For some industries, the use of AI and machine learning models is novel, but several industries--consumer finance and insurance in particular--have been building, using and governing models for decades. These industries have well-developed governance practices built largely around algorithmic, rule-based and other model technologies and regulations that predate AI models. Many of the enterprises I talk to are revisiting their model operationalization and governance processes and strengthening them with new capabilities to accommodate the increased use of AI/ML technologies. You can't govern what you can't see, so every model risk management (MRM) program must start with a centralized model inventory that includes all the metadata associated with every model throughout its life cycle, from development to deployment, modification and retirement. This model metadata, which documents the model's complete history and lineage, captures a broad range of elements including the specific software and libraries used in its development, the data used to train the model, the people involved in the model's development and maintenance and what they created or changed, the model's intended business use and KPIs, an explanation of the key influencing factors behind the model's decision-making, etc.

How does technology challenge WA businesses, and how can governance professionals proactively manage the risks? I work predominantly in the technology industry, and I can't afford to stay still. This is an industry that is continuously reinventing itself, and I've had to constantly re-educate myself to stay up to speed with every new development. Widening this scope and reflecting on WA's dominating industries, there has been a shift in from back-office technology (traditional IT) to more operational technology (OT). And the integration of IT and OT means technology is becoming an enterprise asset.