A 12-minute orientation to the AI Digital Product Building skill path — restructure your build process around AI, ship features in hours not days, run agentic product pipelines, and operate at startup-scale output with AI as your team.

Restructure your entire build process around AI — from idea to ship, with AI as the primary collaborator across every stage of product development.

Ship features in hours, not days — with AI as your copilot at every step from spec to PR.

Build autonomous workflows where AI agents handle end-to-end product tasks — design, code, ship, monitor — with humans in the supervisory loop.

Operate at startup-scale output — with AI as your entire team. The playbook for solo founders and indie product builders running multi-product portfolios.

A 12-minute orientation to the Data Analysts skill path — why it exists, what you will be able to do, how the seven chapters relate, and where to begin

Map the complete AI-augmented analysis loop — from question to action — and build habits that make any LLM your daily thinking partner

Turn vague business requests into precise analytical sub-questions — use AI to decompose, challenge, and sharpen every question before you touch the data

Reclaim the 60-80% of analysis time lost to messy data — use AI to detect anomalies, generate Microsoft Excel cleaning formulas, reshape structures, and validate transformations

Let AI surface patterns, correlations, anomalies, and next questions from your data — spend less time staring at spreadsheets and more time finding what matters

Turn analytical findings into compelling narratives that drive decisions — using AI to draft chart titles, annotations, executive summaries, and slide structures

Make every analysis repeatable, auditable, and hand-off-ready — using AI to document, template, and automate your analytical workflows

Recognize the three failure modes of AI-assisted analysis — systematic bias, wrong outputs, and confident fabrication — and build verification protocols that catch them before they reach stakeholders

Six PM moments where AI accelerates your work — and three where it doesn't

From vague idea to eng-ready spec with AI

Turn interview transcripts into actionable insights

Monitor competitors and synthesize market intelligence with AI

Narrative roadmaps and updates that land with every audience

Design experiments and read results without falling into statistical traps

Build PM tools without writing code — using AI agents

A 10-minute orientation across the five-chapter beginner path — what agents are, how they work, where you already use them, when they fail, and how to work alongside them

The mental model shift from AI that answers questions to AI that does work — explained through everyday workplace scenarios, no code required

The three ingredients that turn a chatbot into an agent — tools it can use, memory it can draw on, and goals it can pursue — explained through workplace analogies

A tour of AI agents you probably already encounter at work — Microsoft Copilot, customer service bots, scheduling assistants, and more — with the exact moment each one stops being a chatbot

An honest look at agent limitations — hallucination chains, confident mistakes, and loops — framed as normal challenges with practical responses, not reasons to panic

Practical guidance for working with AI agents daily — writing good instructions, knowing when to intervene, reviewing output, and building your personal agent playbook

Your map to the 7 marketing-specific AI capabilities — pick the right tool per job, extract brand voice, plan campaigns, master Generative Engine Optimization (GEO), build small automations, recognize when AI is your partner vs your trap, and measure with judgment. Orient through the chapter, see the stack-map (hub + 6 spokes), and pick a recommended starting course based on your role and current pain

Map the 8 marketing jobs onto the 4 AI tool families (chat agents, research surfaces, embedded AI, automation), learn the 4-question routing rubric, and assemble the small multi-tool stack that actually fits a marketer's week — the foundation for the rest of the chapter

Workshop the 3 operational moves of brand-voice work with AI — extract 5-7 voice rules from 3-5 sample documents, capture them as a persistent system prompt (Custom GPT, Claude Project + Skill, or Gemini Gem) so every chat starts on-voice, and recognize the 5 drift signals that say it's time to reseed before shipping

Recognize the 4 moments where AI genuinely changes campaign planning (cited audience research, message-variant generation, pre-launch red-team, pre-ship verification) and the 3 dangers where its fluency makes things worse (positioning judgment, stakeholder politics, budget trade-offs) — operational moves with a citation discipline that keeps invented buyers out of your dossier

GEO is not better SEO. Different game, different metrics. Master the 4 operational moves — JSON-LD structured data AI engines prefer, per-engine brand-mention saturation (ChatGPT, Perplexity, AI Overviews each cite from different sources), citation-worthy content formatting (the opening 200 words rule + 5 more), and the hybrid SEO/GEO playbook — plus how to measure visibility with Otterly / Profound

Build 3 small marketing tools end-to-end with an AI coding agent (Claude Code as worked example; Cursor / Codex / Gemini CLI as escape hatches) — a competitor blog tracker from RSS feeds, a campaign data analyzer that turns HubSpot CSV exports into AI-narrated insights, and a content calendar generator that produces voice-matched multi-channel calendars. With the personal-stays-personal discipline that keeps these tools from becoming accidental products

The meta-skill that outlasts every tool churn — AI is useful for thinking, dangerous for deciding. 4 marketing cases (AI buyer personas, market sizing, competitive analysis, campaign ROI prediction) plus a 2-question decision rule (generate or adjudicate? if wrong, reversible or not?) that applies to any AI tool — including ones that don't exist yet

The chapter's analytical close — use AI to interpret analytics faster (compute outside AI, narrate with AI), design tighter A/B tests (hypothesis crispness + sample-size sanity + when to use multi-arm bandits), and recognize the 3 overclaim patterns AI is prone to (confounded variables, cherry-picked windows, vanity metrics) that turn fluent dashboards into bad decisions

Six chapters from first prompt to shipped product: the map, the sequence, and where to begin.

When going Claude Design to Claude Code beats the Figma-and-handoff pipeline, and when it doesn't.

Run the full core loop once, then master all three refinement modes without burning usage.

Connect your existing design tokens, components, and brand guidelines so Claude Design enforces them automatically.

Turn a Claude Design project into interactive prototypes, presentation decks, and shareable exports without rebuilding anything.

Hand a Claude Design project to Claude Code, implement it against your real component library, and ship it — the payoff of the whole pipeline.

Concrete Monday-morning recipes for PMs, designers, and IT teams — pick your role and run it.

A 12-minute orientation across the six-chapter BA skill path — from requirements elicitation to enterprise governance, the complete Copilot toolkit for advanced business analysis

Turn messy stakeholder inputs into structured BRDs, user stories, and acceptance criteria using M365 Copilot in Word, Loop, and Teams — with enterprise-scale context priming.

Use M365 Copilot in Visio, Whiteboard, and Excel to accelerate process documentation, build as-is/to-be comparison matrices, and surface cross-departmental inefficiencies.

Translate technical BA findings into audience-adapted communications using PowerPoint, Outlook, Loop, and Teams — managing competing interests and preserving precision across enterprise stakeholder levels.

Use Excel Copilot, Python in Excel, and Power BI Copilot to pivot, clean, analyze, and visualize enterprise data exports — building stakeholder-ready insights from messy CSVs without writing SQL or Python manually.

Trace how proposed changes ripple through systems, teams, and processes — building impact matrices, risk registers, and readiness assessments that synthesize technical and organizational dependencies for complex enterprise change programs.

Navigate sensitivity labels, DLP policies, data residency constraints, and audit requirements to remain productive with Copilot inside heavily regulated enterprise environments — from financial services Chinese walls to cross-border data boundaries.

Your orientation to creative AI foundations — landscape, inputs, collaboration, rights, and compound systems

Collaboration patterns that make human-AI creative work better than either alone

Build a creative asset system that compounds — every project makes the next one faster and better

Navigate ownership, Content Credentials, and the C2PA standard for AI-generated creative work

A clear-eyed map of AI creative tools in 2026 — what works, what is hype, and how to think about a stack that changes every quarter

How to prepare the inputs that determine whether AI creative tools produce magic or mediocrity — structured briefs, curated references, and living moodboards

A 12-minute orientation across the five-chapter Daily AI Toolkit — name the four AI tool families that the foundation chapter teaches, see how the three daily-surface chapters (docs/decks, email/meetings, research) apply that framework, recognize the ecosystem-routing chapter as the meta-decision overlay, and pick the right starting chapter based on which surface is biting hardest

Map the four AI tool families, build a default stack of three or four tools that fits your week, and learn the meta-skill of evaluating any new tool that lands

From a pile of messy inputs to a polished document and a stakeholder-ready deck — one repeatable 4-step pattern, executed across M365, Workspace, or hybrid stacks

Triage your inbox in minutes, capture meetings cleanly with the right notetaker, and run the post-meeting chain (transcript → summary → actions → per-person follow-up) so nothing falls out — with the privacy and consent layer that keeps it professional

Replace 12 browser tabs with cited answers in minutes — name the four research surfaces, brief deep-research runs that compress 4 hours of manual work into 25 minutes of yours, decide when a browser agent earns the click delegation, and apply the 3-source rule that keeps fabricated facts out of your work

Diagnose whether you are M365-native, Workspace-native, or hybrid — then route every AI task with rules that fit the ecosystem you actually live in, and know when to leave Chapter 2.1 for the deep dives (Copilot Mastery, future Workspace AI, AI Coding for Beginners)

Turn a single approved image into a complete multi-format, multi-channel asset library using AI-powered adaptation, outpainting, and batch generation workflows.

Turn brand briefs into production-ready campaign visuals using AI image generators — hero images, social assets, and ad creative at the speed of thought.

A practitioner's map of the Images with AI skill path — from your first marketing visual to a production asset pipeline.

Generate studio-quality product shots, lifestyle scenes, and packaging mockups without a physical studio — master the tools and workflows that turn a single product photo into an entire e-commerce vis

Make AI image generators produce illustration that looks like YOUR brand, not generic AI art.

Transform existing photographs with AI-powered removal, replacement, enhancement, and correction — the other half of the creative workflow.

Use AI image generation to create narrative-driven visuals that support articles, reports, and content series with emotional coherence and editorial intent.