From “we should use AI” to a task worth starting with
Pick a recurring task, such as preparing a customer proposal. Define what a useful result looks like, how long the task takes today and what would make the pilot worth continuing.
AI3 · AI Transformation for Business Leaders
AI Masters
You bought a premium ChatGPT subscription. You ask about your business and still get generic answers. Make AI produce work your team can actually use.
- Answers from your documents. Drafts based on your business.
- Build on a fictional company. Reuse it in yours.
Your business has work AI could help with. Learn where to start, what company information to give it and how to connect the steps. Build a knowledge base, a useful application and an agent workflow on a fictional company, then adapt them to yours.
6 practical sessions × 3 hours. 18 hours in total.Strategy. Data. Applications. Agents. Governance. Adoption.
Cohort dates and enrollment details to be announced.
@@ Not for @@
- 1−If you are still learning to give AI useful tasks and check its output, start with AI1.
- 2−If your main challenge is getting several teams to use AI consistently, AI2 focuses on that rollout.
- 3−If you want an advanced coding course or a peer discussion circle without a curriculum, this program has a different format: guided workshops with concrete outputs.
@@ Who it is for @@
- 1+CTOs, heads of AI and business leaders who need to connect AI initiatives across data, technology and operations.
- 2+Managers who already use AI and want hands-on experience turning a business problem into an integrated solution.
- 3+Leaders who can bring a process, its constraints and a clear business goal into the room, and want to make better decisions about what to build and how to run it.
The shared case
One company.
Six connected decisions.
The same fictional B2B company runs through the whole program. You work with its business priorities, documents and processes as you move from strategy and data to applications, agents, operating controls and adoption.
Everything you build is designed to be reused in your own company. You take the methods, templates, knowledge base, application and agent workflow with you, then adapt them to your own data, systems and processes. The fictional company is a shared starting point; your own company is where you put the results to work.
{ The program }
Six sessions.
One transformation.
01 — 06
3 hours per session
- 01
AI-Ready Company
Pick a business problem worth solving.
Assess how ready the case company is for AI. Find opportunities, compare their expected value and delivery effort, and identify the dependencies that could stop them. Define the baseline, assumptions and success measure before choosing what to build.
You leave with: a prioritized opportunity map, a first business case with explicit ROI assumptions, and the first version of your AI transformation roadmap.
- 02
AI Data Foundation
Give AI the business context it needs.
Map the data, documents and systems behind the chosen process. Explore how data architecture, a semantic layer and retrieval-augmented generation help AI work with company information. Build a small knowledge base from the case documents and check its answers against the source material.
You leave with: a working knowledge-base exercise, an initial answer-quality check, and a map of the data sources, access requirements and gaps your use case depends on.
- 03
AI Builders
Turn the problem into a small working application.
Map the process and decide where an application would help. Compare building with adapting an existing solution, then create a small prototype. Explore how skills, plugins, MCP and CLI tools connect AI to useful actions, and identify the integrations and deployment work your company would need.
You leave with: a functional prototype for a bounded business task and a list of the integration and deployment requirements for taking it further.
- 04
Agentic Enterprise
Make the steps work together.
Extend the case into a multi-step workflow. Decide which steps should follow fixed rules, where an AI agent adds value and where a person must approve an action. Test a failure case, inspect the execution log and examine the tradeoffs between quality, speed and cost.
You leave with: a working agent-assisted workflow for the case, explicit human checkpoints, and an initial evaluation and monitoring checklist.
- 05
AI Governance
Define the conditions for putting it into use.
Review the prototype's data access, permissions, failure modes and ownership. Map the questions raised by GDPR and the EU AI Act that need review in your organization. Define approval, logging, monitoring and escalation requirements, including when the system should stop and hand work to a person.
You leave with: a deployment-readiness plan, a risk register and named responsibility areas to resolve with your security, legal and operational owners.
- 06
AI Adoption & Transformation
Turn the solution into a plan the company can execute.
Plan how people will learn, use and improve the new process. Define the role of AI champions, internal training and knowledge sharing. Connect adoption measures to the business baseline, set criteria for expanding the pilot, and complete the transformation roadmap with owners and dependencies.
You leave with: an AI transformation roadmap for your organization, including the first pilot, responsibilities, success measures and conditions for scaling.
From the Engineering Leaders community
Before → After
What changes by the end.
From generic answers to answers based on company information
Ask a question about the case company’s services or internal procedures. Get an answer based on its documents, with sources you can check, and test what happens when the information is missing.
From copying between chats to a repeatable workflow
Use a customer brief to retrieve relevant company information and prepare a proposal draft. Check the draft before anything is sent, and reuse the workflow on the next brief.
From “can we trust this?” to checks and clear responsibilities
Define who checks facts and pricing, which information AI can access and when a person must approve the next step. Know what to do when the output is wrong.
From a promising exercise to a pilot in your own company
Take the workflow back to your company with a plan for your data, systems and team. Name the owner and decide how you will compare the result with the way the work is done today.
The takeaways
What you take
back to work.
- A prioritized AI opportunity map and a business case with assumptions you can challenge.
- A data and knowledge-source map for your chosen process.
- A knowledge-base exercise, a small application prototype and an agent-assisted workflow built on the shared case.
- Initial evaluation criteria, human checkpoints and monitoring requirements.
- A deployment-readiness plan covering risks, access and responsibilities.
- Your own AI transformation roadmap connecting the pilot to organizational adoption and measurable business value.
You keep everything you create during the program. The methods, templates and working prototypes are reusable foundations for your own company. Your roadmap identifies how to adapt them to your data, connect them to your systems and put them into use.
Why this format
Learn the whole path.
Make better decisions.
01
The business decision stays connected to the build.
The problem you prioritize in session one is the problem you carry through data preparation, prototyping, automation and operating requirements.
02
You make the decisions yourself.
You build and inspect working examples so you can ask better questions, assess proposals and understand the tradeoffs behind an AI initiative.
03
Data and governance are part of the same process.
Information access, answer quality, permissions and human responsibility shape the solution as it develops.
04
Each session produces something you can review.
The final roadmap draws on your business case, practical exercises and operating decisions. It shows what is ready, what remains uncertain and what needs to happen next.
Format
18 hours.
Built around practice.
- Six practical sessions, three hours each: 18 hours in total.
- One shared fictional B2B company throughout the program, with methods, templates and solutions you can adapt and reuse in your own organization.
- Guided exercises covering strategy, company knowledge, application building, agent workflows, governance and adoption.
- A transformation roadmap adapted to your own organization.
Questions
Before you join.
How does AI Masters differ from AI1 and AI2?
AI1 develops your own ability to get useful work done with AI. AI2 focuses on adoption across teams. This proposed AI3 program connects business priorities, company data, applications and agents into a process you can evaluate and plan to operate.
Do I need to complete the earlier courses?
The proposed entry point is equivalent practical experience: you already use AI for real work and can assess its output. The program builds on that foundation rather than teaching basic prompting.
Will we work on my company's live systems?
We build on the shared fictional company, but everything is designed for reuse in your own organization. You leave with the methods, templates and working prototypes, plus a roadmap for adapting them to your context. Connecting them to live company systems requires the relevant access and approvals.
Will I leave with a production-ready application?
You leave with working workshop prototypes and an explicit plan for what deployment requires. Production readiness depends on your company's integrations, security controls, validation and operational ownership.
How much can we build in 18 hours?
The scope is a small knowledge base, a bounded application and an agent-assisted workflow within one case. The aim is to understand the whole path and make informed decisions about the next implementation.
AI3 · AI Masters
What do you keep asking AI to do — and still end up doing yourself?
Bring that task. Learn how to give AI the information and workflow it needs to help, then leave with reusable examples and a plan for trying it in your company.
Dates, instructors and pricing will be confirmed before enrollment.