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The AI-Ready Manager: Lead Human-AI Teams at Work 110+ Tools
2,924 students
The AI-Ready Manager: Lead Human-AI Teams at Work 110+ Tools
ai for managers | chatgpt | claude ai | prompt engineering | ai agents | automation | ai adoption | leadership | mcp
Last updated 9/2026
English
English [Auto],
What you'll learn
- Write prompts using the seven-block architecture, and pick between RISEN, CRISPE, CREATE and RTF
- Apply Chain-of-Thought, Self-Consistency, Tree-of-Thoughts and ReAct to real decisions
- Move from prompting to context engineering with Projects, custom instructions and RAG
- Build a multi-step agent with MCP and n8n, and decide where a human must stay in the loop
- Set up a team AI stack across ChatGPT, Claude and Gemini, and transfer prompts between them
- Analyse data, build pivots and generate a board presentation without manual copying
- Plan an AI rollout: sequencing, resistance, data security and the ethical limits
- Write the policy your team needs once robots and agents do part of the work
- Learn alongside Mike's 1.6 million students from 185 countries
- Get the author's experience from Preply, Wargaming, iDeals and Alfa-Bank
Explore related topics
Course content
15 sections • 40 lectures • 20h 43m total length
Requirements
- Access to at least one AI assistant. ChatGPT, Claude or Gemini — free tiers are enough to start
- No programming background required. The agent lessons use no-code tools
- A team or a function you are responsible for, even a small one
- English at intermediate level or above, since all lessons are in English
- Willingness to hand a real task to AI and check the output honestly
Description
This course contains the use of artificial intelligence.
Your team is already using AI. You just have not decided how, which means they each decided separately, and nobody told you what they pasted into a chat window last week.
That is the actual state of most organisations right now. Not resistance. Unmanaged adoption.
The gap this course closes
Most managers use AI as a faster search engine and stop there. They type a question, get a mediocre answer, conclude the technology is overrated, and go back to doing the work manually. Meanwhile the seven-block prompt, the loaded project, the multi-step agent and the connector into their calendar all exist and take an afternoon to set up.
The gap is not enthusiasm. It is that nobody taught the difference between asking a model something and configuring one.
From prompting to context to agents
Thirty-eight lessons. Six on the foundations: what these models can and cannot do, where they fabricate, and the daily uses that actually save time rather than feeling clever.
Then four dense lessons on engineering. The seven-block prompt architecture, four frameworks and when each applies. Chain-of-Thought, Self-Consistency, Tree-of-Thoughts and ReAct for decisions with several criteria. Then context engineering — Projects, custom instructions, RAG on your own documents, and memory across sessions. Then real agents: prompt chains, MCP and tool use, n8n as a no-code builder, and the governance question of where a human has to stay in the loop.
Function by function, then the rollout
Then twelve lessons walking through where AI is already embedded across an organisation: hiring and sourcing, onboarding, performance systems, brand and marketing, training, talent movement, reward design, culture, predictive analytics without Python, and process design with financial justification attached.
Then six on a single platform end to end, from interface and data security through to Skills, connectors, spreadsheets and generated presentations. And ten on implementation: rolling out an AI project, managing the resistance that follows, security and ethics, robots in physical workspaces, the policies that coexistence requires, and a readiness checklist for every process you own.
A note on the examples. Much of the practical material runs on people-management cases, since that is the function I have run. The methods — prompt architecture, context engineering, agent design, rollout planning, governance — apply identically to finance, operations, marketing or support.
Who is teaching this
Mike, the number one HR instructor on Udemy. More than 1.6 million course enrolments, over 150,000 professionals trained, PHRi and SHRM-CP certified, HRCI representative in more than 10 countries. I built the people function of the unicorn Preply and worked at Wargaming, Alfa-Bank and iDeals.
What is included
Lifetime access to all course materials
Active instructor support in the Q&A section
Udemy Certificate of Completion
Practical assignments and real business cases
A section with additional courses, tools and resources
Do this today
Ask three people on your team what they used AI for this week. The answers will be more varied than you expect, and at least one will worry you. That conversation is the beginning of managing this rather than discovering it later. Enrol now and start the first lesson today.
Who this course is for:
- Managers whose team has started using AI without anyone deciding how
- Team leads who use ChatGPT as a search engine and suspect they are missing most of it
- Function heads asked by their executives what AI will change in their area
- Operations and process owners looking for what can actually be automated
- HR and people leaders, since much of the practical material is drawn from that function
- Consultants and analysts who need to deliver faster without dropping quality
- Anyone who has to write an AI policy for a team and has no idea where to start