Future AI Director
AI Director Training: Lead AI Strategy at Scale
As an AI Director, you're expected to set the technical direction, defend budgets in front of the board, and translate model capabilities into business outcomes. This platform gives you the full path: dozens of lessons organized into blocks that cover data strategy, model selection, governance, team structure and ROI measurement. You move through it at your own pace, building the vocabulary and judgment that let you challenge vendors and engineering leads on equal footing.
Beyond the lessons, you get interactive tools to model scenarios, a glossary to settle terminology debates in meetings, and an action playbook you can apply directly to your first 90 days in the role. Start with the level assessment: it maps what you already know and points you to the blocks that will close your gaps fastest.
Lessons for this profile
- What is AI, machine learning, and a large language model?
- What LLMs are great at (and what they are not)
- Tokens, training, and inference: what happens when you hit enter
- Hallucinations: why confident answers can be wrong
- The context window: the model's working memory
- What a model actually does: prediction, not understanding
- Thinking before building: framing an AI problem
- The AI tools landscape: APIs, no-code, and vector databases
- Your first API call: Python and the basics
- Choosing the right approach: prompt, RAG, fine-tune, or agent
- Retrieval-augmented generation (RAG): giving models your data
- Evaluating outputs: how do you know it works?
- What an AI agent really is: the perceive, plan, act, observe loop
- Tools and function calling: giving your agent hands
- Evaluating and debugging agents: traces, evals, and failure modes
- Agents vs workflows vs automations: choosing the right level of autonomy
- Memory and state: short-term context and long-term recall
- Guardrails, permissions, and human-in-the-loop
- The OpenAI model family: GPT, reasoning models, and when to use each
- What custom gpts are and the GPT store
- GPT actions: letting ChatGPT call your apis
- Advanced data analysis: files, charts, and spreadsheets
- ChatGPT for coding and canvas
- The ChatGPT agent: browsing and taking actions
- The Claude model family: opus, sonnet, haiku, and when to use each
- Connectors and the marketplace: plugging Claude into your apps
- What skills are and why they matter
- MCP explained: the USB-c for AI tools
- Claude code: an agentic coder in your terminal
- Connecting Claude to GitHub: repos, issues, and prs
- The Gemini model family: pro, flash, and when to use each
- Gemini in docs, gmail, sheets, and slides
- Building a gem: your own reusable assistant
- Google AI studio: prototyping prompts
- Gemini CLI: agentic coding in your terminal
- Agents on Google AI: the agent development kit
More resources
Frequently asked questions
Who is the AI Director training path for?
It targets people who lead or are about to lead an AI function: setting technical direction, defending budgets in front of a board, and translating model capabilities into business outcomes. It also fits executives moving into the role from data, engineering or strategy positions. The content assumes you will be accountable for results, not for writing code.
Is access free, and does it lead to a certification?
Reading is free and open, and there is no diploma or state-recognized certification at the end. An account only serves to save your progress across lessons and blocks. What you take away is the vocabulary and judgment to challenge vendors and engineering leads, not a credential.
Do I need a technical background to follow it?
No coding background is required. The lessons on model selection, data strategy and governance are written so a non-engineer can arbitrate technical choices and understand the trade-offs behind them. If you already come from a data or engineering role, the level assessment will steer you toward the blocks on budget, team structure and ROI measurement instead.
What subjects does the curriculum actually cover?
Five areas structure the path: data strategy, model selection, governance, team structure and ROI measurement. They are spread across dozens of lessons grouped into blocks, so you can go deep on one area without reading everything. Each block is designed to be usable in decisions you already face, such as choosing between a vendor and an in-house build.
Where should I start if I don't want to read everything?
Start with the level assessment. It maps what you already know and points you to the blocks that close your gaps fastest, which matters if your time is limited. From there you follow only the blocks flagged for you rather than going through the lessons in order.
What is the 90-day playbook, beyond the lessons?
It is an action playbook you apply to your first 90 days as AI Director: what to audit, what to decide, what to postpone. It comes alongside interactive tools for modeling scenarios and a glossary you can use to settle terminology arguments in meetings. The lessons build the reasoning; the playbook turns it into a sequence of moves.