Why rollouts fail, how to make AI stick, and the operational playbook from pilot to scale

Stage-by-stage operational model from pilot to scale — the five adoption stages, resistance patterns, and the grassroots tactics that make AI stick

Find, enable, and sustain the grassroots layer — champion profiles, toolkits, burnout prevention, and rotation models

Leading indicators, behavior-driving metrics, and when to declare success — the four metrics that matter and the four vanity metrics to kill

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

The five decisions that determine whether you control your AI destiny—or vendors, competitors, and inertia control it for you

Four strategic postures, ROI math for CFOs, bets that compound, and when to go all-in vs hedge

The strategic framework that determines whether you control your AI destiny—or vendors control yours

Where to place AI teams, what roles to create, how to allocate budget for reskilling

The five board-level risks, disclosure obligations, and governance KPIs that separate leaders from laggards

Orientation across the AI Foundations skill path — vocabulary, mechanism, judgment, daily-practice habits, and prompt persistence, with judgment-depth chapters on reading and building AI benchmarks, plus five paths through based on where you're starting from

A decoder ring for every AI concept — what it means, which shelf it goes on, and the next question to ask

The machine under the magic — tokens, embeddings, attention, and prediction, explained without math

Five patterns, four surfaces, and a 7-day plan that turns ChatGPT or Claude into a daily co-worker by the end of week one

Spot the three failure modes, run a 30-second verify, and build the daily habit that keeps AI useful instead of dangerous

Stop retyping the same context every chat — configure ChatGPT Custom Instructions, Claude Projects, and Gemini Gems as your persistent prompt library, and recognize when a long chat has stopped serving you

Decode the percentages every vendor cites — what they measure, what they hide, and how to read a model report card without an engineering degree

Design and run a mini-benchmark on your own data to decide whether AI is really ready for a real task — no code required

A 12-minute orientation to reading the open-weight model market like a strategist — what each chapter gives you, how they connect, and where to begin.

The word "open" hides two very different deals — and the one that decides what you can legally ship is the license, not the label.

A working map of who leads the open-weight field right now — and the bigger story of where the lead has moved.

The leaderboards that rank open models, the self-reported numbers that flatter them, and how to read an open-vs-open comparison without being fooled.

Two cost worlds — renting by the token and running it yourself — and why "free weights" lands you in the more expensive one more often than people expect.

The providers that serve open models for you, the aggregator that hands you all of them through one door, and how to pick — by price, speed, and where your data may go.

A durability framework for picking an open-model vendor that will still be strong and supported next year — and how to hedge so no single bet can sink you.

Orientation across the five-chapter beginner path — why 2026 is the inflection point, why your soft skills are the moat, why you don't have to wait for technical specialists, what real companies are actually doing, and what to do this week

Why 2026 is the year AI quietly stopped being a side project for technical teams and became a daily tool for everyone else — and what doing nothing now actually costs

Judgment, communication, taste, domain expertise — the soft skills you've spent your career building are the moat. Walk through each one with concrete examples of it in action with AI

AI closes the gap between non-tech workers and the specialists you used to depend on — data analysts, designers, translators, legal reviewers. Concrete moves you can make without becoming a developer

Six to eight named companies, public numbers, and role-level stories — what Klarna, Shopify, Anthropic itself, and a marketer, salesperson, HR partner, and finance analyst actually did with AI in 2025–26

Pick one tool, three tasks, and a 4-question filter for when AI is the right call — the concrete plan you can start Monday morning, and the explicit handoff to AI Foundations as the next path

A 12-minute orientation to the AI Governance & Compliance skill path — the rules, the evidence, and the response that turn "we should be compliant" into a program regulators accept

Ship compliant general-purpose AI before 2 Aug 2026 — scope, tech docs, copyright, systemic risk, and the Code of Practice shortcut

Produce audit-ready model cards and signed, immutable logs that tie every output to its source, model version, and policy

Handle the 3am page — detect AI failures, contain blast radius, file Article 62 reports on time, and turn post-mortems into hardening

Navigate the fragmented US AI regulatory landscape — NIST AI RMF, state-level laws, post-EO federal posture, and enforcement actions that set precedent

Map the compliance terrain beyond the EU and US — UK AISI, OECD AI Principles, ISO/IEC 42001 certification, and the frameworks shaping AI governance worldwide

Comply with sector regulators — HIPAA for healthcare AI, FDA for medical devices, FINRA for financial services, and PCI-DSS for payment systems

A 12-minute orientation across the three-rung Copilot Foundations skill path — License (what you have) → Graph (what it sees) → Labels (what it's allowed to use). The mental model that explains every Copilot answer you'll ever read

Your org just turned Copilot on. Figure out which one you have, what it can actually do for you, and what to ask IT to fix the gap between 'I have a license' and 'it works for my job'

How Copilot actually grounds its answers in your org's data — what it sees, what it can't see, and why answers sometimes miss. The same physics as ChatGPT Enterprise's knowledge feature or Claude Projects, but private to your tenant

Of everything in your Graph, what is Copilot actually allowed to use? Sensitivity labels, the EXTRACT usage right, label inheritance into generated content, and the seven-question checklist for your compliance team