AI's headline-grabbing hype will ebb, but practical AI is here to stay—automating repetitive, error-prone, and time-constrained work to free people for higher-value thinking. You will learn how to separate hype from real value, map workflows for high-impact pilots, implement human-in-the-loop safeguards, measure cost and environmental trade-offs, and redeploy saved time into strategic work. Adopt small experiments, clear metrics, and policy-aware thinking to ensure AI amplifies human judgment rather than merely concentrating wealth or wasting resources.
Model Context Protocol (MCP) standardizes how LLMs discover, invoke, and consume tools, turning fragile prompt-based integrations into predictable, schema-driven contracts that decouple frontends from backends. You will learn practical patterns—define clear tool schemas, build adapter layers for auth and operations, mock responses for testing, and prioritize UI-first design—so teams can safely experiment with LLMs while reducing prompt drift and brittle parsing. The summary also explains MCP’s limits—it won’t fix bad APIs or hallucinations and may evolve as platforms introduce competing primitives—so adopt it pragmatically with strong observability and a narrow initial scope.
Claude Skills define process and behavior—instructions, templates, escalation rules—for how Claude should handle workflows, while the Model Context Protocol (MCP) standardizes what external systems the model can access. Understanding the distinction clarifies when a simple MCP connection suffices to fetch data versus when a Skill is needed for repeatable tone, decision branches, and templates; together they enable automated, consistent workflows like customer support that both reach into your systems and follow company policy. Learn practical steps to map a workflow into a Skill, when to stand up or reuse an MCP server, and how to connect the two for reliable, auditable automation.
Most AI tools promise to solve everyone's problems out of the box. But for businesses with unique processes-- that's all of them-- custom AI solutions deliver a return that generic tools never can. The more specific your situation, the more specific the solution needs to be.
