AI Competency Framework

▉ Basic AI literacy

• Can define AI and explain, in plain language, the difference between AI, machine learning, and automation.
• Understands common AI applications in society and business, including language and image-based uses, and how AI can support typical business functions.
• Has a general understanding of what generative AI tools do, their strengths and limitations, and the basics of effective interaction such as clear prompts and verifying outputs.
• Can identify key risks and ethical issues, including bias, privacy, data security, misinformation and deepfakes, and knows when AI is not appropriate or requires human judgment.

▉ AI Innovation Skills

• Understand how AI works at a practical level and how it can be applied across functions and industries to improve decisions, workflows, and customer outcomes.
• Identify high-value use cases, redesign processes, and lead implementation with teams, including change management and measuring impact.
• Use AI to develop and test new processes, products, and services, translating business needs into requirements and working effectively with technical experts and data.

▉ Deep AI Skills

• Can distinguish AI, automation, and machine learning (ML), choose the appropriate approach (ML, deep learning, NLP, computer vision), and explain capabilities and limitations.
• Can work with complex datasets and evaluate model and architecture options for specific tasks, including how performance is assessed and monitored in real use.
• Can apply ethical frameworks and embed transparency, accountability, and data security practices into AI development and deployment.