AI & machine learning strategy
Build AI, GenAI, NLP, recommendation systems and responsible AI strategies
AI is the line that separates CDOs who set the agenda from the ones who wait for it. This block puts you on the right side of that line. You will move past the demos and the hype into the decisions that actually matter: where to invest, what to build versus buy, and how to turn AI ambition into a value chain your CFO respects.
Module 5.1 lays the foundation. You will craft an AI strategy that prioritizes ruthlessly, weigh build versus buy without kidding yourself, and stand up generative AI in the enterprise the right way, with RAG architectures, risk controls, and governance that hold up under scrutiny. Then you will draw a roadmap that maps maturity, teams, and investment so your board sees a plan, not a science project.
Module 5.2 takes you into applied AI, where reputations are made or broken. You will run NLP at scale to mine Voice of Customer signals, sentiment, and text at a volume no human team could touch. You will design recommendation systems that personalize without creeping people out. And you will master responsible AI, from bias and fairness to the EU AI Act and the ethical review processes that keep you off the front page for the wrong reasons.
This is not a tour of buzzwords. It is the operating manual for a CDO who intends to own AI as a business capability, defend the spend, and ship systems that create measurable value. By the end, you will speak fluently to your data scientists, challenge your vendors, and answer to your board with numbers and conviction. AI leadership is a decision. This block is how you make it.
What you'll master
- Build an AI strategy that prioritizes use cases by value and feasibility
- Decide build versus buy with a clear view of cost, risk, and control
- Deploy generative AI using RAG with governance and risk guardrails in place
- Draft an AI roadmap aligned to maturity, team capacity, and investment
- Run NLP at scale to extract Voice of Customer, sentiment, and text insights
- Design recommendation systems that personalize ethically and effectively
- Operationalize responsible AI to meet EU AI Act and fairness requirements
Modules
Set AI's strategic foundations: prioritization, generative AI in the enterprise and a maturity roadmap
Implement applied AI through NLP, recommendation and responsible AI practices
Shows how to identify high-value enterprise use cases, choose between RAG and fine-tuning, and run and evaluate LLMs in production.
Covers AI risk classification under the EU AI Act, bias and explainability practices, and how to structure human oversight and incident response.
Shows how to move AI projects past pilots, build the operating model that sustains them, and prove business return.
Frequently asked questions
What does the AI & machine learning strategy block actually cover?
It covers five modules, 15 lessons in total, spanning AI strategy and prioritization, generative AI and LLMs in the enterprise, applied AI (NLP, recommendation systems), AI governance under the EU AI Act, and scaling from POC to production. The angle is decision-making rather than coding: where to invest, what to build versus buy, and how to prove return.
Do I need a technical background to follow it?
No. The AI & machine learning strategy block is written for a CDO or an executive who arbitrates AI investments, not for someone who trains models. Terms like RAG, fine-tuning, embedding or MLOps are explained so you can challenge vendors and talk credibly with your data scientists.
What is the difference between RAG and fine-tuning, and how do I choose?
RAG connects a language model to your own documents at query time, while fine-tuning adjusts the model's weights on your data. The lesson "Build vs buy: RAG vs fine-tuning" frames the choice around cost, control, maintenance and how often your knowledge base changes, rather than treating one as universally better.
Where should I start if my company is still at the pilot stage?
Start with the AI strategy module, then go directly to "Scaling AI: from POC to production". That module addresses the POC-to-production gap, the operating model and platform needed to sustain AI projects, and how to measure AI ROI, which is usually what blocks pilots from becoming systems.
Does the block deal with the EU AI Act?
Yes, in two places. The module "AI governance & responsible AI" covers risk classification under the EU AI Act, bias and explainability with model cards, and how to structure human oversight and incident response. The applied AI module also treats fairness and ethical review processes alongside NLP and recommendation systems.
Is reading free, and do I need an account?
Reading the AI & machine learning strategy lessons is free and open. An account only stores your progress across the modules. There is no diploma, no state-recognized certification and no university affiliation attached to it.