We start with one task your team does every day, show how the way you ask changes what you get back, and end with a hands-on exercise so it sticks the next morning.
Ask about trainingSee a real lesson before you spend a dollar.
Your people see the plan, do the work with their own tasks, and walk away with something they can use.
See a full lesson first, so you can match the training to where your team actually needs help.
Who the training helps.
Individuals
For professionals who need a practical baseline around tools, prompting, risk, and day-to-day AI usage habits.
Teams
For groups with uneven fluency, inconsistent usage, or unclear expectations about how AI should support the work.
Organizations
For leaders who need a clearer read on readiness, training priorities, and safer adoption judgment across the business.
What your team will learn.
1
Foundations
What AI Is, and How to Work With It Responsibly
For individuals and teams who need to get on the same page before anything else.
Five modules
- What AI Actually Is (and Isn't)
- Why AI Gets Things Wrong
- What AI Can Do for Your Business
- How to Work With AI (hands-on exercise, with or without tool access)
- Your Responsibility When AI Is Involved
Ends with feedback that tells each person where they stand.
You walk away with
- A clear read on where each person stands
- Shared language for talking about AI at work
- Practical habits for using it day to day
- A completion certificate
2
AI in Your Business
Applying AI to Your Own Operations
For business owners and teams who have Course 1 fluency and are ready to put AI to work in their own business.
Four modules
- Mapping Your Business for AI
- Selecting and Evaluating Tools
- Building Your First AI Workflow
- Setting the Rules for Your Team
Ends with the 12-question knowledge check, covering all of Course 2.
You walk away with
- A scored opportunity map
- A tool evaluation scorecard
- A working first AI workflow
- A one-page team AI policy
- A completion certificate
The full path
1
Foundations
Live now
2
AI in Your Business
Live now
3
AI Leadership
Opens after Course 2
4
Org-Wide Rollout
Opens after Course 3
Is your team grounded in the basics?
LLM
A large language model — the kind of AI behind tools like ChatGPT or Claude. It learns patterns in language from huge amounts of text, then predicts the most likely next words for whatever you ask it. It is not looking things up; it is generating a plausible answer.
API
A way for one piece of software to talk to another directly, without a person clicking through a website. When a tool "connects to AI," it is usually calling an API behind the scenes — the same AI model, without the chat window around it.
Context window / tokens
Tokens are the small chunks of text an AI model reads and writes — roughly pieces of words. The context window is how much of that text the model can hold in mind at once during a conversation. Once you exceed it, the model starts forgetting the earliest parts of what you told it.
Prompting
Writing instructions an AI can act on. A good prompt describes the task, the context, and the result you want, in plain language — the same way you would brief a colleague. The clearer the instruction, the more usable the response.
Drift
What happens when an AI tool's behavior changes over time — because the underlying model was updated, the data it sees shifted, or a workflow that worked last quarter quietly stops working. Drift is why tool references and prompts need periodic review, not a one-time setup.
Try a few Course 1-level questions.
A business owner says "I'm not technical enough to use AI." What's the most accurate response?
Answer: AI tools today are designed to be used in plain language, no technical background required. Modern tools like ChatGPT, Claude, and Gemini work through plain conversation — you describe what you want, the same way you would to a person. Technical expertise matters for building AI systems; using them is a different skill.
An AI draft includes a specific statistic about your industry that sounds impressive. What should you do?
Answer: Verify the statistic against a reliable source before using it. AI can generate statistics that are plausible, correctly formatted, and attributed to real-sounding sources — all of which may be fabricated. The fact fits a statistical pattern; that doesn't make it accurate.
An AI hiring tool gives lower scores to candidates from a certain background. What's the most likely cause?
Answer: The tool learned from historical hiring data that reflected those patterns. AI bias is inherited, not intentional — the tool is functioning exactly as designed on data that already carried the bias. That's why the fix requires human oversight, not a technical patch.
Your name is on a proposal. AI wrote the first draft. If the proposal contains an error, who is responsible?
Answer: You — everything that goes out with your name on it is your responsibility. No client, professional body, or legal standard currently treats AI involvement as a defense against an error. That's why a review step before sending is standard practice, not optional overhead.
A free AI tool's privacy policy says inputs may be used to improve the model. What's the practical implication?
Answer: Only enter information you'd be comfortable becoming public. If inputs may be used for training, they may be seen by the provider and could surface in outputs for other users. The specific data-handling agreement matters more than whether the tool is free or paid.
What makes the training credible.
People can see the module shape before they buy.
Every module lists its agenda, its exercise, the 12-question knowledge check, and what people finish with.
The learning stays tied to recognizable work.
That keeps the program grounded in practical use, safer habits, and better implementation judgment.
People leave with something that holds.
The 12-question knowledge check, a completion certificate, and the work they produced all travel back with them. The goal is usable capability.