Foundation of AIStart here
Test AI on one job you do yourself, against a standard you set before seeing any output, and write a findings note your team lead can act on.
Turn a vague prompt into one that works, then generalise it into a standing brief you install where your team can run it.
Decide, per task, how much human review an AI answer needs, and check the answer instead of trusting it.
Know what is safe to paste into an AI tool, what your company's plan changes, and what a DPA does and does not buy you.
Ask an AI vendor the security questions that matter, and know prompt injection and data leakage well enough to approve or block a tool.
AI Agents for Professionals
Build a working AI agent that uses several tools in sequence, with no code, and run it on a real process from your own team.
Sit through a vendor's agent demo and ask the questions that expose a weak one, then hold their claim to a written test plan.
Read an AI contract and find the clauses that decide who owns your data, what happens on a silent model swap, and how you get out.
Write the business case: a corrected cost baseline, three scenarios instead of one forecast, and a build-or-buy call you can defend to a CFO.
Write a governance framework that names who monitors what, at what threshold, and who can stop the process without asking permission.