Stage 1: Ad hoc.
This stage is about raising awareness around ethical AI and educating your workforce. This is the time to get teams thinking about whether or not certain AI features should be developed, the potential unintended consequences and more. You may also experience individual advocates generating small-scale strategies and working to earn buy-in.
Stage 2: Organized and repeatable.
Whether leaders need to hire externally or promote from within, you need dedicated individuals and teams to establish and maintain ethical AI practices and execute on a strategic vision. This is the time to formalize roles and commit resources, and collaboratively develop guiding AI principles.
Stage 3: Managed and sustainable.
With the dedicated people or teams in place to help execute, this stage includes the work of building formal processes into your development lifecycle. Create meaningful rewards and consequences for launching products, and without accruing ethical debt, to have an AI practice that is viable long term and encourages responsible thinking and behaviors.
Stage 4: Optimized and innovative.
With the vision and dedicated support, your organization can now bake ethics into all levels across the business—from product development to engineering and privacy to UX—working together to build in ethics-by-design for all products and solutions launched in-market.
Figure 1: The Salesforce Ethical AI Practice Maturity Model identifies four stages.