What can AI do for me?

The shared vocabulary the rest of the course depends on. Software, the cloud, APIs, databases, frontend vs. backend, and what it means for a piece of software to go live.

What people actually mean when they say machine learning, model, LLM, multi-modal, and so on. The honest cost and accuracy story. The two privacy rules that matter most.

Three short lessons to figure out which business-size module to read next, what a typical AI project actually looks like from kickoff to rollout, and when you should buy off-the-shelf vs. commission something custom.

Two narrative case studies for one-person businesses: a landscaping contractor drowning in lead emails, and an Etsy seller losing two hours a day to product descriptions. Each story sketches a realistic, modest AI solution.

Two narrative case studies for businesses with roughly five to fifty people: a family honey company buried in repetitive customer-service email, and a veterinary clinic whose patient intake is scattered across paper and voicemail.

Two narrative case studies for organizations with roughly fifty to five hundred people. You're an individual contributor here - a marketer, an ops analyst - bringing a pitch to the team. Outdoor-gear search, and freight-dispatcher voice agents.

Two narrative case studies in regulated, large-org environments. You're an individual contributor - a claims specialist, a clinical informaticist - who sees an obvious win. Insurance-claim summaries, and a private clinical-records assistant. Privacy and human-in-the-loop are central.

Wrap-up. The four red flags that show up in nearly every bad pitch, the small set of questions worth asking before signing, and a short note on staying current.