BMC starts from the AI workflow blockage in front of you, not from a tool name. Pick a path, start with the first mini class, then add the checklists for cost, permissions, or acceptance.

General reader

Data, consent, everyday AI boundaries

What should I ask before an AI service reads my data, sees my environment, or decides for me?

  • You will learn: how to break “this is convenient” into data source, consent scope, retention, withdrawal, and high-risk actions.
  • After this path: you can pause before using AI scam-message flows, fake-voice calls, home services, sensitive documents, or personalization memory, and ask the right boundary questions first.
  • Switch paths when: you are setting team rules, buying tools, or putting AI into development workflows; use Team lead / operator, Buyer / small team, or Engineering / tool user instead.
  1. When a text looks real, don’t let an AI scam push you to the next step
  2. Familiar caller, fake voice? Use three steps for AI impersonation calls
  3. Free cleaning wants to film your home? Use a consent table first
  4. Before always-on AI assistants, write a permissions table
  5. Before turning on ChatGPT Lockdown Mode, decide which data really needs it
  6. Before stronger Windows AI PCs, ask whether they save waiting time
  7. Want to see less AI slop? Do not wait for one perfect platform toggle
  8. Before using design AI, turn “make it look good” into a brief
  9. Before You Let AI Build Apple Shortcuts, Read the Workflow Step by Step
  10. Before Real-Time Voice Translation Joins Your Meeting, Decide What Cannot Rely on AI Alone
  11. Before AI remembers you, decide which preferences should not shape its judgment

Team lead / operator

Permissions, review, handoff formats

How should a team set permissions, review loops, and handoffs before adding AI assistants or automation?

  • You will learn: how to split AI adoption into permission tables, human confirmation, handoff formats, cost stop-loss rules, data boundaries, and third-party integration governance.
  • After this path: you can write minimum viable rules for what AI may read, what it may change, when it must stop, and who owns the decision.
  • Switch paths when: the issue is mostly personal data safety, use General reader; when it has become coding-agent, logging, or deployment work, use Engineering / tool user.
  1. Before connecting an always-on AI assistant, write a permission table
  2. When your AI tool suddenly goes offline, check whether your workflow depends on one model provider
  3. Before You Let AI Build Apple Shortcuts, Read the Workflow Step by Step
  4. Before Real-Time Voice Translation Joins Your Meeting, Decide What Cannot Rely on AI Alone
  5. Before AI remembers you, decide which preferences should not shape its judgment
  6. Before departments build AI automation, decide what can be released
  7. A clean AI summary does not mean a teammate can take over
  8. An AI workbench is not a chat box: check whether the work can be rerun, audited, and handed off
  9. After AI writes a report, check whether the citations hold up
  10. Before turning on ChatGPT Lockdown Mode, separate which data truly needs it
  11. Before AI model bills run away, split tasks into three cost tiers
  12. Before long-running AI tasks run too long, set first-pass cost and stop controls
  13. When AI tools promise savings, first calculate what it really replaces
  14. When AI Search Can Read Your Inbox, Leaks Do Not Require Hackers
  15. Claude Can Be Enabled in Microsoft Foundry, But Can It Handle Real Data Yet?
  16. When CRM data is pulled by integrations, check three permission gaps first
  17. Before AI Judges Age, Ask Who Can Overrule It
  18. Before AI Answers Company Numbers, Fix the Source of Truth
  19. Can You Trust an AI Research Report Just Because It Has Citations?
  20. Figma Put Code on the Canvas. How Should Teams Review the Handoff?
  21. When an Automation Fails Halfway, Who Cleans It Up?
  22. Give AI the Vulnerability List, Keep Release Approval Human

Engineering / tool user

Coding agents, logs, cost, uncertainty

When coding agents, logs, model uncertainty, and usage cost enter development, what should humans still check?

  • You will learn: how to put AI coding and technical tools into reviewable workflows: task scope, log evidence, tests, cost, model uncertainty, and rollback.
  • After this path: before handing work to an agent or accepting an AI fix, you can list the checkpoints, acceptance criteria, and areas that cannot be auto-approved.
  • Switch paths when: you are still deciding whether to buy a tool, use Buyer / small team; for non-technical team operations, use Team lead / operator.
  1. Before handing code to AI, put checkpoints into the task
  2. When things break, can your logs save you?
  3. Before Copilot usage becomes a bill, set high-cost task rules
  4. Before putting AI into workflows, define when it must stop
  5. When a Docker scanner dumps a pile of vulnerabilities, first decide which fixes need a human
  6. Give AI the Vulnerability List, Keep Release Approval Human
  7. When an Automation Fails Halfway, Who Cleans It Up?

Buyer / small team

AI PCs, cost, adoption criteria

Will this AI tool, AI PC, or paid service really save time, cost, or risk?

  • You will learn: how to split a new tool promise into waiting time, replaceable cost, data boundaries, compatibility, usage rules, and acceptance criteria.
  • After this path: before a trial, purchase, or upgrade, you can define success conditions, stop-loss lines, and when not to expand or prepay annually.
  • Switch paths when: you already bought the tool and need team rollout rules, use Team lead / operator; if it is entering development workflows, use Engineering / tool user.
  1. Before stronger Windows AI PCs, ask whether they save waiting time
  2. When AI Makes Hardware More Expensive, Decide Whether to Buy, Wait, or Change the Workflow
  3. When AI tools promise savings, calculate what they really replace
  4. Before long-running AI tasks run too long, set first-pass cost and stop controls
  5. Before the AI model bill surprises you, sort tasks into three cost tiers
  6. AWS FinOps Agent turns cost alerts into an ownership routing table
  7. Before Copilot usage becomes a bill, set high-cost task rules
  8. Before using design AI, turn “make it look good” into a brief