📅 Why 2026 Is the Right Time
There is a void huge gap between wanting AI and knowing how to implement AI. Boards expect AI strategy from their teams; the teams don't know where to start. Whoever brings clarity at this moment wins the market—and demand proves the size of the wave.
📊 The demand numbers
- •28.974 enrolled only in Wharton's "AI Strategy & Governance" course (Coursera).
- •2.5 million people in Andrew Ng's "AI For Everyone" — hunger for AI on a global scale.
- •Executive AI strategy programs cost from US$1.000 a US$28.000.
- •88% of companies already use AI, but only 6% extract real value (McKinsey, 2025) — the gap you fill.
- •AI consulting market: ~US$22 bi → US$72,8 bi até 2030 (+31.6%/year). And there aren’t enough people: 44% of executives are held back by a lack of expertise.
intent > execution
millions studying
strategy "for 2026"
of who delivers
🧩 What AI consulting looks like in practice
Forget the glamour. AI consulting is a simple, repeatable process: discover where the company is, find where AI creates real value, and deliver a plan the client can execute. That's exactly what your Factory will automate.
Diagnosis
Maps the stack, department pain points, and AI maturity (scale of 1–5). Where the company really is.
Opportunity
Identifies quick wins (low effort, high impact) and use cases by department. Where AI pays off.
Plan + delivery
30/60/90 roadmap, ROI, governance, and the deck/SOW. Something the client can open on Monday and start executing.
where it stands
where AI pays off
actionable roadmap
deck + SOW
⚡ The thesis: minimal input, maximum output
This is the idea that guides the entire course. You won't sell hours. You'll build a machine once and deliver packages endlessly—with almost zero effort per delivery.
🎯 The leverage principle
Two text boxes (name + description) go in. Nineteen deliverables come out. The intellectual work is already built into the machine; each new deliverable just takes the push of a button.
- •Freelancers trade time for money—a low ceiling.
- •A product replaces one-off work with endless deliveries — high ceiling.
💡 Practical tip
Whenever you come across a repetitive task in the course, ask: “Could this become part of the machine?” If so, turn it into a reusable skill or prompt—you’re building your asset, not doing a side gig.
1 build, N deliverables
almost zero
machine > hour
high ceiling
💰 From tool to income
The same engine opens three monetization paths. You choose — and can combine them. The important thing is to understand what to promise (and what no promise) in each one.
🏢 At your company
Internal diagnosis that prioritizes AI projects and justifies investment. Immediate value, zero risk.
🤝 As a project
Sell the package to a client. Deliver in days what would take weeks to do manually.
🔁 Productized
A recurring offer: diagnosis + follow-up. Predictable revenue, high margins.
✓ What to promise
- ✓A professional, fast package based on real frameworks
- ✓Clear priorities and an actionable roadmap
- ✓A solid starting point for the AI strategy
✗ What NOT to promise
- ✗Absolute truth — AI researches, but you review and validate
- ✗Execution-ready—the plan needs people to run it
- ✗Replace the client’s judgment about their business
💡 Proof that it makes money
Jason Liu (AI consultant) scaled from US$170/hora para US$100.000/mês em um ano — by no longer charging for time and productizing the offer. The engine you’re going to build is exactly this kind of lever.
zero risk
high-ticket
predictable
always review
🚫 The myth of "I need to be a programmer"
This is the belief that holds most people back—and it's wrong. Claude Code writes, installs, and runs the code. Your role is to direct: describe the goal, review the result, and request adjustments.
✓ What YOU do
- ✓Describe what you want in Portuguese
- ✓Reviews, tests, and gives feedback
- ✓Decides what goes into the package
✗ What CLAUDE CODE does
- →Write and fix the Python code
- →Install dependencies and configure .env
- →Runs the Factory and fixes the errors
// example: what you type to Claude Code
Clone the repo https://github.com/inematds/ai-strategy-factory, help me configure the API keys and run a quick analysis of the company "Stripe".
direct > type
you in control
by conversation
errors are part of it
🗺️ How the course works
Five tracks, five levels. You move up one at a time and never get lost: each module wraps up with a Mission a hands-on exercise that gives you a reusable resource.
💡 How to make the most of it
Complete each Mission before moving on. The course builds on itself: the resource you create in one module becomes an input for the next. By the end, the entire arsenal is yours.
clear progression
concrete payoff
accumulated arsenal
real client
✅ Module summary
🎯 Mission 1.1 — Your consultant thesis
Write 3 sentences (the foundation of your positioning, which we'll refine in Track 5):
- Niche: what kind of company do you want to serve?
- Pain point: what AI problem do they have today?
- Offer: what package will you deliver to solve it?
Success: 3 sentences written. What you gained: the seed of your offer—and clarity on who you’re building the Factory for.
Next module:
1.2 — Anatomy of the Factory (the 19 deliverables and the 3-phase pipeline)