Straight answers on AI at work.
Short, sourced, and written to be useful. Capability, upskilling, adoption, learning, measurement, and what the rules actually say.
Capability is whether people can actually get better results from AI in real work, and keep doing it as the models change. These answers define what that means and how it is built and kept on record.
The workforce is being asked to change how it works faster than any course cycle can keep up. These answers cover upskilling and reskilling for AI: what to teach, how long it takes, and how to make it stick.
Most AI budgets are lost between the pilot and the daily work. These answers cover why adoption stalls, how to move from experiments to everyday use, and how to measure it honestly.
AI does not sit still, so a course captured today ages within months. These answers cover how learning and development teams can build durable AI capability instead of shipping content that dates.
You cannot manage what you cannot see. These answers cover how to measure, benchmark and prove AI capability with evidence from real work rather than quizzes or self-ratings.
Since February 2025 the EU AI Act expects organisations to keep their people AI literate. These answers explain the duty in plain terms and how to meet it with practice you can evidence.