AI adoption | change management | AI transformation | automation | prompt engineering | upskilling | Kotter | ROI
What You Will Learn:
- Choose which processes to automate first, using an assessment of readiness rather than enthusiasm
- Describe a process in BPMN before automating it, so the tool does not accelerate the confusion
- Structure a prompt from seven blocks and apply chain-of-thought and tree-of-thoughts where they earn their time
- Build context with Projects, RAG and connectors so the assistant answers from your documents rather than the internet
- Design an agentic workflow with MCP and no-code tooling, and decide where a human must still approve
- Train people on a new tool so the knowledge survives the week after the session
- Evaluate adoption honestly: what to measure, what cannot be measured, and how to report it
- Run the rollout through Kotter’s eight steps, including the resistance that is really about status
- Learn alongside Mike’s 1.6 million students from 185 countries
- Get the author’s experience from Preply, Wargaming, iDeals and Alfa-Bank
Overview: Real Talk on Moving Beyond the AI Hype
Let’s be honest: most “AI transformation” initiatives are currently stuck in a cycle of endless PoCs and flashy demos that never actually move the needle on the P&L. I’ve seen enough “AI-powered” projects fizzle out because leadership mistook employee enthusiasm for operational readiness. That’s why this course, AI Adoption, Change Management: From Pilot to Scale, caught my eye. It isn’t just another tutorial on how to generate an image or write an email; it’s a blueprint for anyone who has been tasked with actually making AI work in a corporate environment without breaking the existing culture.
The instructor, Mike—who brings heavy-hitting experience from Preply, Wargaming, iDeals, and Alfa-Bank—takes a Refreshingly pragmatic approach. He treats AI as a management challenge first and a technical one second. We’ve all seen the “shiny object syndrome” where a department head wants an AI bot just because the competitors have one. This course forces you to take a step back and look at BPMN (Business Process Model and Notation) before you even touch an LLM. The logic is simple: if you automate a mess, you just get a faster mess. By mapping out processes first, you ensure the tool doesn’t just accelerate the confusion but actually resolves a bottleneck.
What I appreciated most was the shift from “prompting as a hobby” to prompt engineering as a structured discipline. We move from simple queries to complex agentic workflows using MCP (Model Context Protocol) and no-code tools. This is where the career growth happens—moving from a user to an architect who understands where a human-in-the-loop is non-negotiable for ROI and risk management.
Prerequisites
- A foundational understanding of what LLMs are (no Python coding required, but a logical mindset is a must).
- Experience working within a team or leading a project—this is a beginner to advanced transition course for professionals.
- Familiarity with basic business processes; if you’ve ever felt frustrated by a slow workflow, you’re ready.
- Access to a modern LLM (ChatGPT Plus, Claude Pro, or Gemini Advanced) to get the most out of the hands-on labs.
Skills & Tools You’ll Master
- Strategic Automation: Assessing process readiness vs. mere “AI excitement.”
- Process Mapping: Using BPMN to visualize and fix workflows before applying automation.
- Advanced Prompt Engineering: Using the seven-block structure, Chain-of-Thought (CoT), and Tree-of-Thoughts (ToT).
- Knowledge Management: Building RAG (Retrieval-Augmented Generation) systems with Projects and connectors to keep AI grounded in your proprietary data.
- Agentic Systems: Designing agentic workflows with MCP and no-code tooling for autonomous task execution.
- Change Management: Applying Kotter’s eight steps to handle the “status anxiety” that often fuels AI resistance.
- Honest Analytics: Defining what to measure (and what’s just vanity) when reporting ROI to stakeholders.
Career Benefits & Job Roles
Completing this course provides the kind of job-ready skills that separate a casual user from an AI Transformation Lead. In an era where companies are desperate to bridge the gap between “buying licenses” and “seeing results,” these skills are gold. This is excellent certification prep for those eyeing roles like AI Project Manager, Operations Lead, or Head of Digital Transformation.
By working through real-world projects, you gain the confidence to lead a rollout that doesn’t just evaporate once the initial training week is over. You aren’t just learning a tool; you’re learning how to build an AI-capable culture. This is the path to career growth in the next decade of industry-standard tech implementation.
Pros
- Pragmatic Over Political: I loved the focus on “readiness over enthusiasm.” It’s an honest take on why some departments should wait while others lead.
- Deep Dive into Context: The section on RAG and connectors is crucial. It teaches you how to stop the AI from hallucinating by feeding it your own Projects and documentation.
- The Human Element: Using Kotter’s framework to address resistance—specifically the “fear of losing status”—is a masterclass in change management that most technical courses ignore.
- Global Perspective: Learning alongside a community of 1.6 million students adds a layer of shared experience that is hard to find elsewhere.
Cons
- The Pace of No-Code: While the no-code tooling and MCP sections are cutting-edge, the AI tool landscape moves so fast that you’ll need to stay active in the community to keep your hands-on labs knowledge perfectly up to date.