AI Training for Teams

Start here. Understand what AI really is, how it works in plain English, and what it's genuinely good (and bad) at — no coding, no hype, no jargon.

The headlines swing between 'AI will take your job' and 'AI is useless.' Both are wrong. Replace the myths with a clear, calm, realistic picture of what today's AI can and can't do.

Stop thinking 'will AI do my job' and start thinking 'what could I do with a tireless assistant.' Learn the augmentation mindset that turns AI from a threat into leverage.

The single highest-leverage AI skill isn't technical — it's asking well. Learn a simple, repeatable way to write prompts that get you great results instead of generic mush.

What Claude Chat, Desktop, Code, and CoWork each are, how to access them, and which one fits which task.

Email, reports, posts, tricky messages — writing eats hours of everyone's week. Learn to use AI to draft faster and edit sharper while keeping the result unmistakably yours.

AI can compress hours of digging into minutes — or confidently send you down a wrong path. Learn to research fast with AI while keeping a firm grip on what's real.

You don't need to be an analyst to get insight from data anymore. Learn to have AI explain, summarize, and find patterns in your numbers — and where its math absolutely cannot be trusted.

The real payoff of AI isn't grand — it's the twenty small frictions in every workday. Learn to point AI at email, meetings, notes, and planning to quietly hand yourself back hours a week.

The most important course in this track. AI is safe and powerful — if you respect a few firm rules about what you feed it. Learn exactly where the lines are so you get the benefit without the breach.

AI's most dangerous trait is that its wrong answers look exactly like its right ones. Learn to spot hallucinations, sanity-check output fast, and never ship a confident mistake with your name on it.

Scattered AI use gives scattered results. Turn the skills from this track into a repeatable personal system — the tasks you always delegate, the prompts you reuse, and the habits that make AI a permanent part of how you work.

The finish line. Prove you can put the whole track into practice — safely and effectively — across a real week of your own work, and earn your AI-Ready Employee certificate.

Before you invest a dollar or a policy, you need a clear-eyed view of where AI genuinely moves the needle for your business — and where it's a distraction. Learn to assess opportunity like a leader, not a hype-follower.

AI's upside comes bundled with real risk — data exposure, bad decisions, compliance gaps, reputational damage. Learn a practical governance framework that manages that risk without smothering the value.

A policy nobody reads protects no one. Learn to write a clear, short, human AI-use policy that people can actually remember and follow — covering the essentials without drowning them in legalese.

Every vendor now claims to be 'AI-powered.' Learn to cut through the marketing and evaluate AI tools on what actually matters — fit, security, cost, and real value — so you buy capability, not buzzwords.

The best AI strategy fails if your people fear it, ignore it, or misunderstand it. Learn to communicate AI change in a way that lowers anxiety, builds buy-in, and turns your workforce into willing participants.

AI spending is easy to justify with excitement and hard to justify with evidence. Learn to measure the real return — the costs people forget, the value that's tricky to quantify, and the metrics that actually tell you if it's working.

AI use runs straight into privacy law, IP, industry regulation, and emerging AI-specific rules. Learn the obligations every leader should understand — enough to spot the risks, ask the right questions, and know when to call a professional.

The capstone. Turn everything from this track — opportunity, governance, policy, tools, communication, ROI, compliance — into a concrete 12-month plan you can put in front of your leadership and start executing Monday. Earn your AI-Strategic Leader certificate.

Why hosting platform choice matters for speed, cost, and lock-in — decision framing for leaders, no hands-on deployment.

What AI is genuinely good at for marketing work vs. where it needs a human check, the ask-read-refine loop, and your first real exercise drafting an ICP or campaign brief.

Design a trigger-condition-action email/drip sequence on paper, draft the copy and branching logic with AI, and produce a tool-agnostic sequence spec that adapts to any ESP or CRM.

Design a points-based lead-scoring rubric with AI, handle edge cases and score decay, and build a tool-agnostic scoring and routing skeleton adaptable to any CRM.

Build a content calendar generator from one brief, auto-draft platform-specific variants from a core message, and apply brand-voice and fact-check habits before anything publishes.

Identify the marketing KPIs that actually matter, build a KPI rollup from raw exports with AI, and learn to spot a misleading metric before you act on it.

Design a valid A/B test, use AI to draft variants and calculate/interpret significance, and use a simple tracking template to avoid false-positive traps.

Pick one automation from the earlier courses, adapt it to your own org's real tool and data, and validate it works before you rely on it.

What AI is genuinely good at in sales work, where it needs a human check, and the loop that makes it useful on real deals.

Why CRM data rot quietly kills forecast accuracy, and how to design a rules-based hygiene check that flags stale deals, missing fields, and duplicates — adaptable to any CRM.

Designing follow-up cadences keyed to deal stage, drafting stage-specific variants with AI, and a tool-agnostic trigger/action spec for any sequence tool.

Building a fields-to-document template that auto-fills from deal data, using AI for persuasive framing while keeping pricing verified, and a pre-send review checklist.

The handful of pipeline metrics that actually predict revenue, using AI to build a rollup dashboard from a CRM export, and how to spot a pipeline that looks healthy but isn't.

Turning call notes and transcripts into structured CRM updates, using AI to summarize and extract action items, and the verification step you can never skip.

Pick one automation from this track, adapt it to your own CRM and data, and validate it actually works before you rely on it.

What AI is genuinely good at in finance work, where it must never be trusted blind, and the read-only boundary this entire track operates under.

Design a categorization ruleset with AI's help, flag ambiguous transactions for human review, and build a tool-agnostic read-only workflow that never auto-posts to the books.

Design a variance-flagging workflow that compares two sources of truth, surfaces mismatches for a human to resolve, and never auto-corrects anything.

Build a rollup dashboard of the finance KPIs leadership actually needs, stay tool-agnostic across accounting platforms, and sanity-check every number before it reaches a board deck.

Auto-draft a monthly or board report narrative from verified exports and KPI data, using a repeatable structure and a non-negotiable human-verification step before anything ships.

A clear, explicit framework for what never to automate in finance, how to design human-in-the-loop checkpoints, and how to maintain an audit trail for anything AI assisted with.

Adapt one real, read-only automation from this track to your own org's accounting export, then validate it actually works before you rely on it.

A decision framework for picking the right AI tool by task, not by hype -- read this before diving into any single tool's Field Guide.

What ChatGPT actually is, its real capabilities, and how its privacy defaults work by tier.

What makes Gemini genuinely different (context window, Workspace integration) and its account-type-dependent privacy story.

The AI assistant inside Word, Excel, PowerPoint, and Teams — not to be confused with GitHub Copilot.

A practical field guide to Perplexity -- what makes it different from a chatbot (citation-first, research-native), its capabilities including the Comet agentic browser, how to prompt it well, and its privacy/security posture.

xAI's assistant, its real-time X strength, and its documented data-sharing and content-reliability history.

The AI assistant built into WhatsApp, Instagram, and Facebook — and its region-dependent training data practices.

An honest field guide to DeepSeek -- its genuine cost/capability story, how to use it, and the specific, independently documented data-exposure findings and government bans that should shape whether and how you use it.

Running Claude Code and the Claude CLI inside Visual Studio's integrated terminal for real, agentic development work.

What Visual Studio actually is, choosing an edition, installing it, and creating your first solution.

Windows, panels, Solution Explorer, the editor, and fast navigation in Visual Studio.

Structuring multi-project solutions with Project References and NuGet packages in Visual Studio.

Quick Actions, solution-wide refactoring, snippets, and multi-caret editing in Visual Studio.

Breakpoints, stepping, inspecting state, and exception debugging in Visual Studio.

Cloning, committing, branching, and resolving merge conflicts using Visual Studio's built-in Git tools.

Live code analysis, Roslyn analyzers, .editorconfig, and disciplined warning suppression in Visual Studio.

Test Explorer, writing meaningful unit tests, and reading code coverage as a discovery tool.

Finding, evaluating, and managing Visual Studio extensions safely across machines.

Build configurations, Publish Profiles, and container-based deployment from Visual Studio.

Working productively across C#, TypeScript/JavaScript, and Python in one Visual Studio solution.

Directing Claude Code and the Claude CLI effectively inside Visual Studio's integrated terminal.

Remote debugging, Hot Reload, and memory/performance diagnostic tools in Visual Studio.

Authoring GitHub Actions workflows and monitoring pipeline results directly from Visual Studio.

Keybindings, workspace customization, and navigation habits that compound over time in Visual Studio.

Version control fundamentals: init, add, commit, diffs, .gitignore, and undoing mistakes safely.

Creating branches, merging them back, and resolving real merge conflicts by hand.

Connecting local Git repositories to GitHub: remotes, clone/push/pull, and forking.

Opening PRs, giving effective code review feedback, and choosing the right merge strategy.

Tracking work with Issues and Project boards, and automating repository events with GitHub Actions.

Interactive rebase, cherry-pick, stash, bisect, and recovering lost work with reflog.

Teams, permissions, branch protection, and enterprise security features (SSO, audit logs, Dependabot).

Monorepos vs. polyrepos, submodules, Git LFS, and assessing large-repository health.

Deploying from GitHub, and understanding Railway's Project/Service/Environment model.

Managing configuration properly on Railway: variables, reference variables, and per-environment config.

Provisioning, connecting, migrating, and backing up managed databases on Railway.

Attaching custom domains with HTTPS, scaling vertically/horizontally, and monitoring services on Railway.

Explicit build configuration, rollbacks, health checks, and multi-service orchestration on Railway.

Cost management, team access control, and a genuine production-readiness standard for Railway projects.

Real Postgres plus tooling: creating a project and connecting a real application to it.

Designing well-structured Postgres tables, relationships, and query performance on Supabase.

Sign-up, sign-in, sign-out, session handling, and OAuth providers with Supabase Auth.

File uploads, server-side Edge Functions, and live data subscriptions with Supabase.

Understanding, writing, testing, and auditing Postgres RLS policies on Supabase.

Migrations, connection pooling, performance optimization, and production readiness on Supabase.

Deploying from Git on Vercel, and how it differs from Railway's always-running services model.

Vercel's per-branch live deployments, environment variable scoping, and safe promotion to production.

Custom domains with automatic HTTPS, the edge network, and Vercel Functions as a serverless backend.

Building unified, observable AI features on Vercel with the AI Gateway and AI SDK.

Structuring monorepos on Vercel, Team access control, and integrating with broader CI/CD.

Cost management, monitoring, and a genuine production-readiness standard for Vercel projects.

Deliverability, domain authentication (SPF/DKIM/DMARC), and sending real transactional email with Resend.

Avatars, voices, templates, and repeatable production with the tool behind this platform's own videos.

What Claude Pro costs, who it's actually for, and the real steps to sign up and install it -- including when to skip it for Claude Team instead.

What Claude Team costs, who it's for, and the real steps to buy and set it up for 2 to 150 seats.

What Claude Enterprise actually costs, the self-serve vs sales-assisted paths, and the real steps to get started.

What ChatGPT Plus costs, who it's actually for, and the real steps to sign up and install it.

What ChatGPT Business costs, who it's actually for, and the real steps to set up a company workspace.

What ChatGPT Enterprise actually includes, who it's for, and how the sales-gated setup process really works.

GitHub Free is not recommended for corporate use -- what a shared Free account actually risks, and how to tell if your company needs Team or Enterprise instead.

What GitHub Team costs and includes -- real Organizations, Teams-based access control, and branch protection -- plus real steps to sign up.

What GitHub Enterprise actually buys you -- SSO, audit trails, and SLA support -- and the one question that should decide whether you need it.

What GitHub Copilot Pro costs under 2026's usage-based AI Credits billing, who it's for, and how to sign up and install it.

What GitHub Copilot Business costs, how per-seat admin control and pooled AI Credits work, and how to roll it out to a team.

What GitHub Copilot Enterprise costs stacked on top of GitHub Enterprise Cloud, the SSO and compliance it adds over Business, and how to get started.

What Perplexity Pro costs, who it's actually for, and the real steps to sign up — including why there's no Team tier to grow into.

Enterprise Pro vs. Enterprise Max — real pricing, what actually differs between them, and how to get started.

What VS Code actually is, installing it, and the Command Palette habit that makes everything else faster.

The Activity Bar, panels, split editing, and making VS Code's layout genuinely yours.

Folders vs. saved Workspaces, multi-root setups, and Workspace Trust for unfamiliar code.

IntelliSense, multi-cursor editing, and snippets for typing less and editing faster in VS Code.

Breakpoints, launch.json configuration, conditional breakpoints, and Logpoints in VS Code.

Staging, committing, diffs, branching, and merge conflicts using VS Code's built-in Git tools.

Finding, evaluating, and organizing VS Code extensions with Profiles and Settings Sync.

Running commands and automating repeated workflows with VS Code's terminal and Tasks.

Test Explorer, inline coverage, and test-driven workflows across any testing framework.

Full editor fidelity against remote machines (SSH), Dev Containers, and WSL.

The settings hierarchy, committing team-shared configuration, and portable personal setups with Sync and Profiles.

Directing Claude Code effectively in VS Code, via terminal and the official extension.

Conditional breakpoints, logpoints, compound multi-process debugging, and CPU/memory profiling.

Language servers, per-language settings, and multi-root workspaces for genuinely polyglot projects.

Keyboard-first navigation, custom keybindings, multi-cursor editing, snippets, and this track's capstone workflow.