Here is what Cath has learned:

Examine future AI development stages for role impact: Study AI progression to anticipate changes in role competencies and deliverables.
Adapt KPI and problem-solving frameworks over time: Adjust performance metrics and challenges to align with evolving AI capabilities.
Identify cognitive biases that affect AI adoption: Analyze human biases that could hinder progression through AI stages and devise strategies to mitigate them.

Here is what Cath proved that they can do:

Projected future role competencies across AI stages: Created a roadmap detailing how role deliverables will evolve with AI, demonstrating strategic foresight.
Adapted KPI/problem-solving frameworks across AI stages: Adjusted metrics and problem-solving approaches to match AI advancements, ensuring alignment.
Analyzed cognitive biases in AI adoption: Identified and addressed key biases affecting AI progression, offering practical insights to improve acceptance.