You don't need to understand gradient descent to make good decisions about AI. You need the right mental models, evaluation frameworks, and leadership tools. This course gives you all three.
This is a text-first course that links out to the best supporting material on the internet instead of trying to replace it. The goal is to make this the best course on ai for managers you can find — even without producing a single minute of custom video.
No code. No math. No jargon. This course gives managers the conceptual vocabulary to lead AI initiatives without pretending to be engineers.
Every lesson answers a decision managers face: should we buy vs. build? Is this vendor's claim credible? Should we pilot this or wait?
ROI modeling, change management, risk frameworks — the business tools applied specifically to AI initiatives.
Each day is designed to finish in about an hour of focused reading plus hands-on work. No live classes, no quizzes.
Each day stands alone. Read them in order for the full picture, or jump straight to the day that answers the question you have today.
The conceptual framework every manager needs: what LLMs actually do, where they reliably excel, where they fail, and how to communicate about AI without hype or fear.
How to cut through vendor demos and evaluate AI tools honestly. The 10 questions to ask every AI vendor. Red flags that indicate unreliable systems.
ROI modeling for AI initiatives. How to quantify time savings, quality improvements, and risk reduction. Getting executive buy-in with a credible numbers-based case.
How to structure a 90-day AI pilot. Success metrics, stakeholder communication, failure modes, and how to make a go/no-go decision with data.
How to build a 12-month AI roadmap, manage the human change that AI requires, and lead a team through the uncertainty of AI adoption.
Instead of shooting our own videos, we link to the best deep-dives already on YouTube. Watch them alongside the course. All external, all free, all from builders who ship this stuff.
Clear explanations of AI concepts for managers — what LLMs are, how they work at a conceptual level, and what that means for business decisions.
How to model ROI for AI initiatives, present to executives, and build credible business cases for AI investment.
Leading organizational change through AI adoption — addressing employee concerns, managing resistance, and building buy-in.
How senior leaders at companies across industries are building AI strategy and measuring results.
Practical frameworks for scoping, running, and evaluating AI pilot projects — with examples from enterprise and mid-market deployments.
How to cut through demos and marketing to evaluate AI vendor claims rigorously — including the questions most buyers never ask.
The best way to go deeper on any topic is to read canonical open-source implementations. These repositories implement the core patterns covered in this course.
Microsoft's AI learning curriculum — provides the conceptual foundation that managers need without requiring programming knowledge.
Practical AI patterns that managers can review to understand what AI is doing in the tools their teams use.
Anthropic's Claude recipe collection — useful for managers to understand what responsible AI implementation looks like.
Production ML resources including governance, monitoring, and risk frameworks that inform responsible AI deployment decisions.
Your team is using AI tools or will be soon. You need to understand what they're using, evaluate it, and lead the adoption thoughtfully.
You are responsible for an AI roadmap. This course gives you the frameworks to make credible decisions and communicate them to both technical and non-technical stakeholders.
You are deciding whether and where to invest in AI. This course gives you the ROI modeling and vendor evaluation tools to make that decision with data.
The 2-day in-person Precision AI Academy bootcamp covers AI strategy and leadership for managers and executives — hands-on with Bo. 5 U.S. cities. $1,490. 40 seats max. June–October 2026 (Thu–Fri).
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