Learning path for applying AI at work

Choose a real task, define the value you want to create, and prepare for a controlled AI trial.

See what AI can use, try it on one bounded task, check the draft, and prepare a note for a manager to review.

Turn a task into clear steps, select one bounded AI test, and keep decisions, checks and data within the right boundaries.

Choose permitted sources, decide how to provide them, check what AI read and cited, and identify what may be shared.

Write and try an instruction using supplied data, design a reviewable output, add examples where useful and revise specific failures. Compare quality, time and cost to prepare for building a Skill.

Find errors in a supplied case, check sources and recalculate figures. Build a checklist, identify required approvals and prepare criteria for testing a Skill.

Define a reusable Skill for one task, test ordinary and exception cases, and track versions. Compare quality, total handling time, rework and cost before deciding whether to continue a limited trial, share it, or compose it into a workflow.

Design a multi-step workflow with clear inputs, outputs, owners, exception paths, and review points.

Choose permitted sources, check what the available MasterOS Knowledge feature retrieves, verify important answers, and keep source material accurate when it changes.

Choose needed data, define tool permissions, check results and handle failures. Understand MCP's role and assemble evidence for a pilot decision.

Compare AI-assisted work, fixed automation, workflows and agents on the same task. Define the assignment, permissions, limits and evidence before choosing what to keep, revise or test.

Build tests, compare the agent with the workflow within approved scope, and rehearse stopping and takeover. Record quality, time and cost before deciding what comes next.

Check whether AI improves your own work and decide what to continue or adjust. Learn what else to check before sharing with colleagues or expanding across the organization.