AI for Operations & Process Improvement

Most process improvement with AI fails before any tool is opened — not because the tool could do nothing useful, but because nobody looked at the process first. A step is automated because it looked automatable in a demonstration. The procedure manual is trusted instead of the work. The queue nobody mapped grows in the dark. The inputs are a mess, so the outputs are a faster mess, now produced with confidence. Exceptions have nowhere designed to go, so they go to whoever is nearest, at whatever hour, by whatever private method. And when quality slips, there is no baseline to slip from and no way back. This course teaches improvement as a process discipline: watch the work as it actually runs; map steps, handoffs, and queues; find the friction, waiting, and rework; take a simple honest baseline; decide step by step where a tool genuinely fits and where a step must stay human; redesign with the tool inside the workflow; place human checkpoints where errors are caught cheapest; design exceptions for the bad day; hold quality by sampling real output; test small before you roll out; write the standard work; teach the people who run it; watch for drift; and refuse the steps that should not be automated. The course works on one real process you own throughout: bring a process you run, anonymized, and leave with its redesign written and its operating standard ready.