Leading AI Adoption

Most AI adoption fails before any tool is opened — not because the tool could not do anything useful, but because nobody led the choices that make usefulness possible. A license is bought. An announcement is made. Enthusiasts experiment, skeptics wait, confidential material drifts toward a tool nobody governed, work is produced whose owner no one can name, and months later a leader asks for the return and receives anecdotes. This course teaches adoption as a leadership discipline: start from the work and find the friction genuinely worth removing; choose use cases on value, feasibility, and risk held in one frame; make the honest case — full costs, plain limits, no guaranteed numbers; set policy, guardrails, and data rules before pilots, not after an incident; judge what your data can actually support; pick tools without chasing them; and run a pilot small, real, and measured from day one, with a human owning every output. Then the harder half: redesign the workflow instead of bolting the tool onto the old one; treat resistance as information; be straight about what changes for people's skills and roles and what you owe them; train for adoption rather than declaring a launch; set the verification standard your team will actually meet; measure honestly against a baseline and kill what does not work; own the error when the tool gets it wrong; scale only what the evidence carries; and sign your own adoption standard. The course works on your real adoption decision throughout: bring a real team and real use cases, anonymized, and leave with a charter written and a review you can defend.