As organizations begin using artificial intelligence more and more, there are certain technical skills that will help teams stay on top of any changes.

When it comes to these technical skills, it's about being able to understand, work with, and supervise AI-enabled workflows. That means workforces need a basic level of data literacy, AI literacy, and a baseline understanding of responsible AI principles.

For data literacy, people need to understand what data is used, how it's collected, and how data quality affects how systems perform. That way, they can interpret and rely on AI outputs more confidently, as well as spot gaps and flag potential issues.

AI literacy includes understanding how AI models, like machine learning models, generate predictions or recommendations. It's also about being able to recognize things like the difference between a normal or abnormal output. People should be able to tell when a system is performing as expected, when to intervene, and how to document failures.

Finally, responsible AI principles cover areas like fairness, transparency, accountability, and safety. In fact, how to use AI responsibly is becoming a core part of AI literacy training.

That's because it means people can recognize when AI outputs might cause potential harm, as well as how to apply good governance processes. And it helps them ensure that AI solutions are aligned with their organization’s values and regulatory expectations.

Preparing and supporting the workforce through AI change

About