To address these challenges, the article suggests three key steps: educating governance teams on AI risks and maturity models, integrating governance into the model development process, and using a system of record for model revalidation. By continuously training governance teams, integrating governance with development, and employing a system of record, companies can streamline their processes, reduce delays, and ensure compliance without sacrificing innovation. The article argues that slow governance is reckless in the AI era, and companies that modernize their governance frameworks will lead in AI-driven advancements that positively impact society.
Key takeaways:
- AI models face significant delays in deployment due to outdated governance frameworks, which can lead to model drift and decay, rendering them ineffective before they go live.
- Regulated sectors, such as healthcare, finance, and defense, experience pronounced delays due to rigorous compliance and safety standards, hindering innovation and responsiveness.
- Many businesses acknowledge the need to modernize governance frameworks to keep up with AI's rapid evolution, but struggle due to budget constraints, organizational inertia, or uncertainty.
- To streamline governance, companies should educate governance teams on AI risks, integrate governance into model development, and use a system of record for model revalidation.