MAY 2026 COHORT
The AI Project Manager
A 12-week hands-on coaching programme built to develop AI fluency for real project delivery, guiding project managers from fundamentals to production-ready AI solutions.
Choose Your Path to Real AI Skills
Both tracks are available across all tiers—choose your path during onboarding in April based on how hands-on you want to go.
For PMs who want to get into the code. Python, APIs, LangGraph, multi-agent orchestration, Docker deployment. You build agents at the code level and understand what is happening under the hood.
- Tools you'll use
- Python 3.10+
- OpenAI SDK
- Anthropic SDK
- Jupyter Notebooks
- LangSmith
- Pydantic
- ChromaDB
- Pinecone
- LlamaIndex
- Cohere Rerank
- RAGAS
- Tavily
- E2B
- Mem0
- Redis
- LangGraph
- CrewAI
- FastAPI
- Guardrails AI
- Helicone
- Docker
- Railway / Render
- GitHub Actions
For PMs who want AI fluency without coding. Claude, ChatGPT, Copilot Studio, MCP integrations, no-code agent builders. You build agents and automate workflows using professional AI platforms.
- Tools you'll use
- Claude
- ChatGPT
- Copilot
- Gemini
- Claude Projects
- ChatGPT GPTs
- Copilot Studio
- n8n
- Relevance AI
- MCP Integrations
- Jira
- Slack
- Notion
- Confluence
- Azure DevOps
- Teams
- Google Workspace
- Vapi
- Twilio
MAY 2026 COHORT
12-Week Curriculum Overview
A detailed, week-by-week journey showing exactly what you’ll learn and build—from core fundamentals to production-ready AI solutions.
Week 1 • Self-paced
LLM Foundations and Prompt Engineering
LLM Foundations and Prompt Engineering
This week you work through the learning resources at your own pace to build your foundational understanding before the live coaching session in Week 2.
What you’ll learn
- How LLMs actually work: tokenization, context windows, temperature, and top-p
- Model differences: GPT-4o vs Claude vs Gemini vs Copilot — strengths, weaknesses, and when to use each
- Setting up your Python environment and making your first API calls
- Introduction to prompt engineering: zero-shot, few-shot, chain-of-thought prompting
- System prompts, JSON mode, and output schemas for structured data extraction
Resources
Pre-recorded video modules, reading materials, setup guides, and the Week 1 exercise pack
Exercise
Set up your Python environment, make your first API calls to OpenAI and Anthropic, and complete the prompt engineering challenge (build a structured data extractor from messy meeting notes)
Tools to set up
OpenAI SDK, Anthropic SDK, Jupyter Notebooks, LangSmith account
Week 2 • Live
LLM Foundations and Prompt Engineering
LLM Foundations and Prompt Engineering
You have worked through the resources. Now we implement together, get feedback, and refine.
Session 1: How LLMs work and direct API usage
- Concepts: Tokenization deep dive, context window management, temperature tuning for PM tasks, model selection strategy
- Hands-on: Review and improve your Week 1 API calls. Compare outputs across models for PM-specific tasks (status reports, risk analysis, stakeholder comms). Debug common API errors live.
Tools: OpenAI SDK, Anthropic SDK, Jupyter Notebooks
Session 2: Prompt engineering and structured outputs
- Concepts: Advanced prompt patterns, output validation with Pydantic, building reliable JSON extraction pipelines
- Hands-on: Build a production-quality structured data extractor: messy meeting notes to clean JSON with attendees, decisions, action items, risks, and deadlines. Facilitator reviews your code live.
Tools: LangSmith (prompt tracing), Pydantic (output validation)
Week 3 • Self-paced
RAG and Knowledge Bases
RAG and Knowledge Bases
This week you learn the theory and complete the exercises for retrieval-augmented generation and knowledge bases.
What you’ll learn
- What embeddings are and how semantic search works under the hood
- Vector databases: ChromaDB (local) and Pinecone (production)
- The naive RAG pipeline: embed, store, retrieve, generate
- Advanced chunking strategies: fixed, semantic, and hierarchical
- Hybrid search (BM25 + semantic), re-ranking, and RAG evaluation metrics
Resources
Pre-recorded modules on embeddings and RAG, documentation guides, and the Week 3 exercise pack
Exercise
Embed a set of project documents, store in ChromaDB, build a basic RAG pipeline that answers project-specific questions from your documentation
Tools to set up
ChromaDB, LlamaIndex, Pinecone account, Cohere Rerank, RAGAS
Week 4 • Live
RAG and Knowledge Bases
RAG and Knowledge Bases
Live coaching: Implementation and feedback.
Session 3: Embeddings, vector databases and basic RAG
- Concepts: Cosine similarity deep dive, vector database selection, optimising your RAG pipeline for project documentation
- Hands-on: Review and improve your Week 3 RAG pipeline. Facilitator tests your retrieval quality with edge-case queries. Debug retrieval failures live.
Tools: text-embedding-3-small (OpenAI), ChromaDB, LlamaIndex
Session 4: Advanced RAG — chunking, hybrid search and evaluation
- Concepts: Why basic RAG fails in production, hybrid search strategies, re-ranking for relevance, measuring RAG performance
- Hands-on: Upgrade your pipeline with hybrid search and a re-ranker. Score with RAGAS and observe the improvement. Test with your own project documentation.
Tools: Pinecone, Cohere Rerank, RAGAS
Week 5 • Self-paced
Building Agents with Tools and Memory
Building Agents with Tools and Memory
Resources and preparation for building intelligent agents.
What you’ll learn
- The ReAct (Reason + Act) pattern: how agents reason, decide, and act
- Function and tool calling at the API level
- Tool design principles, error handling, and retries
- Memory types: in-context buffer, vector-based long-term, episodic
- Combining memory with RAG for persistent project context
Resources
Pre-recorded modules on agent architecture, tool calling patterns, and memory systems
Exercise
Build an agent using raw function calling that can search project data, query Jira, and generate risk assessments. Add short-term and long-term memory.
Tools to set up
Tavily, E2B, Mem0, Redis, LangGraph, FastAPI
Week 6 • Live
Building Agents with Tools and Memory
Building Agents with Tools and Memory
Live coaching: Implementation and feedback.
Session 5: The ReAct loop and tool use
- Concepts: ReAct pattern deep dive, debugging agent reasoning, optimising tool selection
- Hands-on: Review your Week 5 agent. Facilitator watches it reason step by step. Identify reasoning failures and fix them live.
Tools: OpenAI Function Calling, Anthropic Tool Use, Tavily, E2B
Session 6: Agent memory and RAG integration
- Concepts: Memory architecture for production agents, when to use each memory type, integrating RAG as an agent tool
- Hands-on: Extend your agent to decide when to retrieve from docs vs search the web vs recall past conversations. Facilitator stress-tests memory and retrieval.
Tools: Mem0, Redis, LangGraph, FastAPI
Week 7 • Self-paced • Both Tracks
AI Project Delivery and Governance
AI Project Delivery and Governance
Combined track — both Technical and Non-Technical PMs learn governance together.
What you’ll learn
- The AI project lifecycle: how it fundamentally differs from traditional and agile delivery
- CRISP-DM framework adapted for PM oversight and decision-making
- The AI Project Canvas: scoping AI projects in one page
- Experiment-driven sprints: hypothesis-driven delivery, managing uncertainty
- Data as a dependency: governance, quality, and pipelines
- Ethical AI: bias detection, fairness frameworks, transparency, explainability
- Stakeholder translation: presenting AI outcomes to non-technical leadership
- Managing hybrid teams: humans and AI agents working together
- The buy vs build decision
Resources
Pre-recorded modules on AI project delivery, AI Project Canvas template, ethics frameworks, case studies
Exercise
Complete the AI Project Canvas for a real or proposed AI initiative in your organisation. Prepare a 3-minute leadership pitch.
Tools to set up
AI Project Canvas template, Miro or FigJam
Week 8 • Live • Both Tracks
AI Project Delivery and Governance
AI Project Delivery and Governance
Combined track — both tracks together for governance and ethics.
Session 7: Leading AI projects
- Concepts: AI project lifecycle deep dive, CRISP-DM in practice, AI Project Canvas review
- Hands-on: Present your AI Project Canvas to the group. Facilitator and peer feedback. Refine based on real-world challenges.
Tools: AI Project Canvas template, Miro or FigJam
Session 8: Ethics, stakeholders, and hybrid teams
- Concepts: Ethical AI in practice, stakeholder translation techniques, hybrid team management, security and compliance
- Hands-on: Prepare and deliver a leadership-ready presentation of your AI initiative as if pitching to your CTO. Peer review and facilitator feedback.
Tools: Presentation tools, AI Project Canvas
Week 9 • Self-paced
Advanced Implementation — Scaling and Deploying
Advanced Implementation — Scaling and Deploying
Resources and preparation for production deployment.
What you’ll learn
- Multi-agent orchestration: supervisor, hierarchical, and parallel patterns
- Agent-to-agent communication and shared state
- Input and output guardrails for production safety
- Full-run observability and LLM cost tracking
- Containerisation and CI/CD for agent pipelines
Resources
Pre-recorded modules on multi-agent systems, guardrails, and deployment
Exercise
Break your agent into specialised sub-agents orchestrated by a supervisor. Add guardrails and structured output validation. Prepare for deployment.
Tools to set up
LangGraph, CrewAI, AutoGen, Guardrails AI, Helicone, Docker, Railway or Render, GitHub Actions
Week 10 • Live
Advanced Implementation — Scaling and Deploying
Advanced Implementation — Scaling and Deploying
Live coaching: Implementation and feedback.
Session 9: Multi-agent orchestration
- Concepts: Orchestration pattern selection, avoiding infinite loops, task decomposition for reliability
- Hands-on: Review your multi-agent system. Facilitator tests orchestration, finds failure points. Refine and optimise live.
Tools: LangGraph, CrewAI, AutoGen
Session 10: Guardrails, observability and deployment
- Concepts: Production guardrails, end-to-end tracing, cost management, containerisation
- Hands-on: Add guardrails to your system. Trace a full run in LangSmith. Deploy via Docker to a cloud host. Facilitator validates the deployment.
Tools: Guardrails AI, LangSmith, Helicone, Docker, Railway or Render, GitHub Actions
Week 11 • Self-paced • Both Tracks
Showcase Preparation
Showcase Preparation
Combined track — prepare your final showcase.
What you’ll learn
- Finalise your AI solution and fix any remaining issues
- Prepare your 5-minute showcase presentation
- Record your portfolio demo video (final version)
- Complete your AI fluency self-assessment (compare with April baseline)
- Review peer showcase guidelines and prepare feedback
- Gather all documentation: AI Project Canvas, agent architecture, test results
Resources
Showcase presentation template, portfolio demo guide, peer review framework, AI fluency self-assessment
Exercise
A polished 5-minute presentation of your AI solution ready for live demo in Week 12.
Tools to set up
Presentation tools, screen recording tools
Week 12 • Live • Both Tracks
Showcase Presentations and Rigorous Q&A
Showcase Presentations and Rigorous Q&A
Combined track — final session and demo day.
Project presentation and demo day
- Concepts: Present your full AI solution (5 minutes + Q&A per participant). Live demo and peer reviews.
- Hands-on: Facilitators stress-test your solution in front of the group. Showcase: recognition for most creative, most useful, and best designed solution.
Tools: AI fluency score comparison, certificate of completion
Week 1 • Self-paced
LLM Foundations and Prompt Engineering
LLM Foundations and Prompt Engineering
This week you work through the learning resources at your own pace to build your foundational understanding.
What you’ll learn
- What AI agents are: autonomous vs rule-based vs cognitive agents
- Anatomy of an AI agent: input, reasoning, action, memory, output
- Overview of Claude, ChatGPT, and Copilot — how they work and when to use each
- Introduction to prompt engineering: how to write prompts that get reliable, useful outputs
- Prompt chaining: connecting multiple prompts into workflows
Resources
Pre-recorded video modules, reading materials, prompt template starter pack, and the Week 1 exercise pack
Exercise
Set up Claude, ChatGPT, and Copilot. Build your first prompt library for your top 5 PM tasks. Test across all three platforms and compare outputs.
Tools to set up
Claude, ChatGPT, Copilot, Notion or Confluence (for template storage)
Week 2 • Live
LLM Foundations and Prompt Engineering
LLM Foundations and Prompt Engineering
You have worked through the resources. Now we implement together, get feedback, and refine.
Session 1: Understanding AI and your first PM workflows
- Concepts: Agent types and when to use them, connecting AI to PM work, evaluating output quality
- Hands-on: Review your Week 1 exercises. Build your first end-to-end AI automation: meeting notes to structured minutes with action items. Get live feedback on your prompts.
Tools: Claude, ChatGPT, Copilot, MCP integrations
Session 2: Advanced prompt engineering for PM work
- Concepts: Conditional logic in prompts, building reusable frameworks, designing prompts for status reports, risk logs, and stakeholder updates
- Hands-on: Refine your prompt library based on facilitator feedback. Build a complete automated workflow for one recurring PM task. Test with real work data.
Tools: Claude, ChatGPT, Copilot, Notion or Confluence
Week 3 • Self-paced
RAG and Knowledge Bases
RAG and Knowledge Bases
This week you learn about knowledge bases and connecting AI to your PM tools.
What you’ll learn
- What makes an AI agent smart: memory, context, and knowledge bases
- Introduction to Claude Projects, ChatGPT GPTs, and Copilot Studio knowledge bases
- How to upload documents and build domain-specific agents
- MCP (Model Context Protocol): connecting AI to Jira, Slack, Notion, Confluence, Azure DevOps
- Webhook triggers and automated status reporting
Resources
Pre-recorded modules on knowledge bases and MCP, integration guides, and the Week 3 exercise pack
Exercise
Build a Knowledge Retrieval Agent that answers questions from your project documents. Connect your AI tool to one PM platform (Jira, Slack, or Notion).
Tools to set up
Claude Projects, ChatGPT GPTs, Copilot Studio, Claude MCP
Week 4 • Live
RAG and Knowledge Bases
RAG and Knowledge Bases
Live coaching: Implementation and feedback.
Session 3: Building knowledge-powered AI agents
- Concepts: Knowledge base design, document organisation, testing agent responses for accuracy
- Hands-on: Review your Week 3 agent. Facilitator tests with edge-case questions. Refine knowledge base and improve response quality.
Tools: Claude Projects, ChatGPT GPTs, Copilot Studio
Session 4: Connecting AI to your PM tools
- Concepts: MCP integration patterns, automated pipeline design, proactive alerting
- Hands-on: Build the full automated pipeline: meeting notes to Jira tickets. Set up a weekly status report generator. Create proactive risk alerts and deadline reminders.
Tools: Claude MCP, Jira, Slack, Notion, Confluence, Teams
Week 5 • Self-paced
Building Agents with Tools and Memory
Building Agents with Tools and Memory
Resources and preparation for industry-specific AI solutions.
What you’ll learn
- Industry-specific AI use cases: supply chain, finance, healthcare, IT, construction, government, consulting
- Designing agents for your specific industry workflow
- Knowledge base creation for your domain
- Multi-step agent workflows and conditional logic
- Preparing your 3-minute use case pitch
Resources
Industry use case guides, example agents by sector, and the Week 5 exercise pack
Exercise
Design and build your industry-specific AI agent. Test with real data from your work. Prepare a 3-minute pitch for the live session.
Tools to set up
Claude Projects, ChatGPT GPTs, Copilot Studio, Relevance AI, n8n
Week 6 • Live
Building Agents with Tools and Memory
Building Agents with Tools and Memory
Live coaching: Implementation and feedback.
Session 5: Industry-specific AI solutions (Part 1)
- Concepts: Industry deep dive, refining your use case with expert coaching
- Hands-on: Present your 3-minute pitch. Build your industry agent live with personalised coaching. Not watching a demo — building your own solution.
Tools: Claude Projects, ChatGPT GPTs, Copilot Studio, Relevance AI
Session 6: Industry-specific AI solutions (Part 2)
- Concepts: Multi-step workflows, connecting to existing tools, edge case identification
- Hands-on: Refine your agent. Stress-test with facilitators. Test with real data and collect feedback.
Tools: Claude, ChatGPT, Copilot, n8n, Relevance AI
Week 7 • Self-paced • Both Tracks
AI Project Delivery and Governance
AI Project Delivery and Governance
Combined track — both Technical and Non-Technical PMs learn governance together.
What you’ll learn
- The AI project lifecycle: how it fundamentally differs from traditional and agile delivery
- CRISP-DM framework adapted for PM oversight and decision-making
- The AI Project Canvas: scoping AI projects in one page
- Experiment-driven sprints: hypothesis-driven delivery, managing uncertainty
- Data as a dependency: governance, quality, and pipelines
- Ethical AI: bias detection, fairness frameworks, transparency, explainability
- Stakeholder translation: presenting AI outcomes to non-technical leadership
- Managing hybrid teams: humans and AI agents working together
- The buy vs build decision
Resources
Pre-recorded modules on AI project delivery, AI Project Canvas template, ethics frameworks, case studies
Exercise
Complete the AI Project Canvas for a real or proposed AI initiative in your organisation. Prepare a 3-minute leadership pitch.
Tools to set up
AI Project Canvas template, Miro or FigJam
Week 8 • Live • Both Tracks
AI Project Delivery and Governance
AI Project Delivery and Governance
Combined track — both tracks together for governance and ethics.
Session 7: Leading AI projects
- Concepts: AI project lifecycle deep dive, CRISP-DM in practice, AI Project Canvas review
- Hands-on: Present your AI Project Canvas to the group. Facilitator and peer feedback. Refine based on real-world challenges.
Tools: AI Project Canvas template, Miro or FigJam
Session 8: Ethics, stakeholders, and hybrid teams
- Concepts: Ethical AI in practice, stakeholder translation techniques, hybrid team management, security and compliance
- Hands-on: Prepare and deliver a leadership-ready presentation of your AI initiative as if pitching to your CTO. Peer review and facilitator feedback.
Tools: Presentation tools, AI Project Canvas
Week 9 • Self-paced
Advanced Implementation — Scaling and Deploying
Advanced Implementation — Scaling and Deploying
Resources and preparation for deployment and packaging.
What you’ll learn
- Hosting and managing AI workflows for your team
- Connecting agents with Notion, Slack, Jira, CRMs
- Monitoring and scaling agent performance
- Security, limits, and responsible AI use
- Packaging your solution: portfolio-ready demonstrations, the consulting angle, monetisation
Resources
Pre-recorded modules on deployment, integration, and packaging
Exercise
Deploy your AI solution so your team can access it. Package it into a portfolio demonstration. Record a 5-minute video walkthrough.
Tools to set up
n8n, Relevance AI, Claude Projects, Copilot Studio, screen recording tools
Week 10 • Live
Advanced Implementation — Scaling and Deploying
Advanced Implementation — Scaling and Deploying
Live coaching: Implementation and feedback.
Session 9: Deployment and real-world integrations
- Concepts: Production deployment, team access, monitoring, security
- Hands-on: Review your deployment. Facilitator tests accessibility and reliability. Connect to your real project tools. Set up monitoring.
Tools: n8n, Relevance AI, Claude Projects, Copilot Studio
Session 10: Packaging and ownership
- Concepts: Intellectual property, portfolio presentation, consulting positioning, monetisation
- Hands-on: Review your portfolio demonstration. Facilitator gives feedback on presentation quality. Refine and finalise.
Tools: Screen recording tools, presentation tools
Week 11 • Self-paced • Both Tracks
Showcase Preparation
Showcase Preparation
Combined track — prepare your final showcase.
What you’ll learn
- Finalise your AI solution and fix any remaining issues
- Prepare your 5-minute showcase presentation
- Record your portfolio demo video (final version)
- Complete your AI fluency self-assessment (compare with April baseline)
- Review peer showcase guidelines and prepare feedback
- Gather all documentation: AI Project Canvas, agent architecture, test results
Resources
Showcase presentation template, portfolio demo guide, peer review framework, AI fluency self-assessment
Exercise
A polished 5-minute presentation of your AI solution ready for live demo in Week 12.
Tools to set up
Presentation tools, screen recording tools
Week 12 • Live • Both Tracks
Showcase Presentations and Rigorous Q&A
Showcase Presentations and Rigorous Q&A
Combined track — final session and demo day.
Project presentation and demo day
- Concepts: Present your full AI solution (5 minutes + Q&A per participant). Live demo and peer reviews.
- Hands-on: Facilitators stress-test your solution in front of the group. Showcase: recognition for most creative, most useful, and best designed solution.
Tools: AI fluency score comparison, certificate of completion
Programme Goals & Outcomes
By the end of this 12-week journey, you won’t just understand AI—you’ll know how to apply it confidently within real project environments, build and deploy working solutions, and lead AI-driven initiatives with clarity, control, and measurable impact.
- Understand how AI and LLMs actually work
- Write sharp, effective prompts for real workflows
- Build secure, production-ready AI agents
- Connect AI with tools like Jira, Slack, Notion, and Teams
- Deploy and manage AI workflows in practice
- Lead AI projects using structured frameworks
- Create your own industry-ready AI solution (yours to keep)
Why Choose Us
This isn’t just another course—it’s where you move beyond theory to apply AI in real projects, building and refining systems so you leave with solutions that actually work.
The Usual Route
- $1,500+ for generic AI certificates
- Heavy on theory, light on real execution
- Pre-recorded lessons, little to no support
- No link to your industry or actual workflow
- No real feedback, no pressure testing
- If you get stuck, you’re on your own
The Project School Way
- 100% hands-on, built around real implementation
- Live coaching with experienced practitioners
- Tailored to your industry and tools
- Everything you build is tested and refined
- Led by professionals with real-world experience (Amazon, Apple, Microsoft, Consulting)
- Two clear paths: Technical or Non-Technical
Meet Your Instructors
Learn from practitioners with real-world experience at top tech companies
Kemi Gabriel, MBA, PMP
Programme Lead
Project manager with experience across Amazon, Apple, and Microsoft. 4+ years integrating AI into project delivery. Fluent in Copilot and enterprise AI solutions. Covers the Non-Technical Track and programme strategy.
Liz Herrera
AI and Data Practitioner
Consultant working across Copilot, Claude, and Gemini at depth. Fluent across multiple AI tech stacks. Builds frameworks, open resources, and research that bridge the gap between technology and the people who use it. Covers the Technical Track and advanced implementation.
Your Investment
Choose the level that matches your goals. All tiers include both track options.
- Technical or Non-Technical track
- Full April onboarding
- Full self-paced resources
- Recordings only (no live sessions)
- General industry templates
- Self-guided agent building
- 6 months community access
- Certificate of completion
- Technical or Non-Technical track
- Full April onboarding
- Full self-paced resources
- Live sessions + 1:1 with Kemi
- Tailored to your industry
- Coached + stress-tested agents
- 6 months community access
- 3 months WhatsApp priority support
- Certificate of completion
- Technical or Non-Technical track
- Full April onboarding
- Full self-paced resources
- Live sessions + 1:1 with Kemi & Liz
- Tailored + transformation
- Two experts + stress-tested agents
- 6 months community access
- 6 months WhatsApp priority support
- Certificate of completion
Tier Benefits & Comparison
See what each tier includes and compare features across all three options to choose what fits you best.
| Feature | Kickstarter | Accelerator | Transformation |
|---|---|---|---|
| Track choice | Technical or Non-Technical | Technical or Non-Technical | Technical or Non-Technical |
| April onboarding | Full access | Full access | Full access |
| Self-paced resources | Full access | Full access | Full access |
| Live sessions | Recordings only | Live + 1:1 (Kemi) | Live + 1:1 (Kemi + Liz) |
| Industry use case | General templates | Tailored to your industry | Tailored + transformation |
| Agent building | Self-guided | Coached + stress-tested | Two experts + stress-tested |
| Community access | 6 months | 6 months | 6 months |
| WhatsApp Priority Support | — | 3 Months | 6 Months |
| Certificate | Yes | Yes | Yes |
Secure Your Spot Today!
Ready to shape your AI future? Join our next cohort starting May 4th, 2026 and gain production-ready AI skills with expert coaching. Limited to 10 participants – act now to reserve your place!