SlowTech Education Program

The first module lays the foundations the whole program rests on. We map artificial intelligence, a territory that stretches well beyond chatbots and Gen-AI, and open up the box of language models to understand how they work: how they are trained, how they generate answers, why they get things wrong with such confidence. We analyze the model market and its pricing mechanisms, so you can tell frontier, open source and local options apart and know what you are paying for. We close with the state of the art shown live: what frontier models can do today, what to expect tomorrow and how to be ready.

Objectives

By the end of the module, participants will:

• Navigate the AI landscape and tell families and fields of application apart
• Understand why language models behave the way they do
• Compare models and vendors by capability, parameters and pricing
• Know the state of the art and stand ready for what comes next

Lessons

1. 1.1 The AI landscape: from classical AI to GenAI
2. 1.2 2026 State of AI
3. 1.3 Inside the box: how a language model works
4. 1.4 Models, their parameters and their limits
5. 1.5 Economics of AI 1 — The model market: frontier, open source, local
6. 1.6 Economics of AI 2 — Tokens, credits and pricing models
7. 1.7 A practical look at inference architectures
8. 1.8 The frontier and what's next in AI

The second module turns everyday GenAI use into a method. We start by dissecting the typical interaction with a language model: where quality comes from and where the pitfalls hide. We learn to architect effective instructions: context first, a brief instead of a prompt, text structured from the start so the model can read it. We then explore the modes of GenAI and the vocabulary of the trade, and close with the interaction patterns and methods that yield the most.

Objectives

By the end of the module, participants will:

• Understand how an interaction with a language model unfolds and where it is most likely to fail
• Architect effective instructions and content: context, brief, AI-friendly structured writing
• Choose the right mode for every kind of task
• Master the vocabulary of the trade and the main interaction patterns
• Make sense of any information about AI they come across

Lessons

1. 2.1 Anatomy of an interaction: mindset, mental models and failure points
2. 2.2 Context Engineering
3. 2.3 Writing for machines (plus hands-on session)
4. 2.4 The modes of GenAI
5. 2.5 The vocabulary of the trade
6. 2.6 Instructing AI agents
7. 2.7 Interaction patterns and high-yield methodologies

The third module shifts the focus from personal use to business value. We start from process analysis and from the question everything begins with: what are we trying to solve? We learn to read a process as a system: inputs, decisions, bottlenecks. We then explore the AI toolkit (RAG, workflows, agents, evals) and understand what to reach for in each situation. We follow a process as it evolves from classical to agentic and apply the method to a real process in your company, mapping it together to find where AI creates value. We close with a framework to spot and prioritize opportunities, build adoption roadmaps that make sense and understand the non-negotiable starting conditions.

Objectives

By the end of the module, participants will:

• Read a process as a system and identify where AI can create value
• Tell the components of an AI system apart and know which approach makes sense when
• Turn a vague idea into a concrete, assessable use case
• Know the real prerequisites of an AI project
• Understand how an enterprise AI system is born and structured

Lessons

1. 3.1 System thinking: what are we really trying to solve?
2. 3.2 Inside an AI solution: systems, not just models
3. 3.3 AI Toolkit
4. 3.4 Agentic AI
5. 3.5 Evolution of a process: agentification (plus live workshop)
6. 3.6 Use Case discovery

The fourth module treats artificial intelligence as an organizational choice. We start by aligning team and organization: a framework for enterprise AI and the rules to experiment and learn safely. We continue with the basics of risk, governance and regulation. We then analyze the anatomy of an AI project, understanding why the prototype, today, is only the tip of the iceberg, and the build vs. buy crossroads: when to develop, when to buy and how to see what you are actually buying. We close by analyzing the main cognitive mechanisms triggered by interacting with language models, so you can recognize and defuse them.

Objectives

By the end of the module, participants will:

• Align team and organization with clear rules to experiment and learn safely
• Know the essentials of risk, governance and regulation needed to decide
• Understand what an AI project is made of
• Assess an AI offer with judgment: build vs. buy, the right questions, the red flags
• Recognize the cognitive mechanisms at play with language models and know how to defuse them

Lessons

1. 4.1 Enterprise AI
2. 4.2 Experimenting & Learning: the rules of the game
3. 4.3 Risks and governance
4. 4.4 Anatomy of an AI project
5. 4.5 Regulations bits & bytes
6. 4.6 Build vs. Buy: when to develop, when to buy
7. 4.7 Pitfalls in interacting with language models

The first module gives the program its frame: AI transformation as a discipline. We start from the anatomy of real projects, what sets apart those that generate value from those that stop at the pilot, and define the operating model: the roles of the internal AI core team and the services it offers the organization. We learn to tell GenAI and non-GenAI apart and methodically build your company's portfolio of opportunities. We close by choosing the real case that will run through the whole program and opening its project canvas.

Objectives

By the end of the module, participants will:

• Read an AI transformation and recognize its success and failure factors
• Define the roles and services of the internal AI core team in their organization
• Tell the families of AI apart and map opportunities onto their own processes
• Have selected and framed the real case the whole program will work on

Lessons

1. 1.1 Anatomy of an AI Transformation
2. 1.2 AI Adoption, Operating Model & Service Design
3. 1.3 Evolutionary Design
4. 1.4 GenAI vs. non-GenAI
5. 1.5 AI Opportunity & Use-Case Strategy
6. 1.6 AI Transformation Canvas

Artifacts

• Opportunity portfolio
• Use Case
• AI Transformation Canvas (v0)

The second module brings AI down to numbers and rules. We learn to build a solid business case: usage costs, expected returns, the metrics that matter. We learn to see where value is created and where it leaks away. We then move to governance: risks assessed per use case, responsible AI, the rules to experiment safely and the current regulatory framework translated into operational practice. We close by getting our hands on the two tools every organization should have: your business case and your AI policy.

Objectives

By the end of the module, participants will:

• Build an AI business case with realistic costs, returns and measurement criteria
• Assess the risks of a use case and design its countermeasures
• Know the regulatory obligations that apply to their organization
• Have set up the AI policy and the register of company use cases

Lessons

1. 2.1 Economics of AI 1 — Defend an AI Investment: building the business case
2. 2.2 Economics of AI 2 — Usage economics
3. 2.3 Governance, Risk & Responsible AI
4. 2.4 Experimenting safely
5. 2.5 Advanced Regulations — the current framework, from the AI Act to practice

Artifacts

• Business case (v0)
• AI Policy (where applicable)

The third module goes to where projects are won or lost: data and knowledge. We learn to read modern data architectures and to treat company knowledge as an asset, because the competitive advantage lies in the meaning of your data. With a diagnostic framework we take stock of the real state of your information assets and define the work needed to bring them where the case requires. We close with the frontier: synthetic data, simulations and scenarios to get projects started even when real data is missing.

Objectives

By the end of the module, participants will:

• Read and discuss a modern data architecture
• Diagnose the state of their information assets against a use case
• Specify and commission the activities to build the data layer
• Know the synthetic options to prototype in the absence of ready data

Lessons

1. 3.1 Data Engineering & Warehousing
2. 3.2 Data & Knowledge Architectures
3. 3.3 Data Diagnostic Framework
4. 3.4 Synthetic Data, Simulation & Scenarios

Artifacts

• Knowledge domain map
• Data readiness diagnostic template
• Data layer activity plan for the case study (where applicable)

The fourth module builds architectural competence: choosing the right pieces and composing them into a stack. We compare models, providers and deployment options; we learn to read benchmarks in relation to the task and to master the economics of inference: routing, caching, what you pay when AI runs. We analyze the AI supply chain: vendors, tools, open standards, lock-in to spot in time, with an evaluation exercise on a realistic offer. We close by drawing the architecture of your case, complete with infrastructure and supply chain.

Objectives

By the end of the module, participants will:

• Critically assess model and deployment in relation to the task and the constraints
• Critically assess the sizing of an architecture
• Question vendors and offers with structured criteria
• Have composed the architecture, infrastructure and supply chain of their case

Lessons

1. 4.1 Models, Providers & Deployment
2. 4.2 Benchmarks: making task-driven decisions
3. 4.3 Model Serving, Infrastructure & Inference Economics
4. 4.4 AI Supply Chain Fundamentals
5. 4.5 The AI Stack in the Organization

Artifacts

• Case architecture and infrastructure (v0)
• Case supply chain
• Vendor evaluation checklist

The fifth module gets into the behavior of systems, starting from what you already master: prompting, reread as AI engineering in miniature. Every move of a good interaction is the seed of a system pattern. From there we scale up: advanced RAG paradigms, agents and orchestration patterns, harness and context engineering, the design of human-AI collaboration with its points of oversight. We close with the skill that sets a mature buyer apart: evals, meaning acceptance criteria, test cases and kill criteria. And we consolidate everything in the technical dossier of your case.

Objectives

By the end of the module, participants will:

• Sketch the behavior of an AI system: patterns, harness, checkpoints
• Critically assess human-AI collaboration in relation to risk and reversibility
• Draft a first set of acceptance criteria, test cases and kill criteria for a system
• Have consolidated the technical dossier that will guide the building of the prototype

Lessons

1. 5.1 From Prompt to System — prompting as the archetype of AI engineering
2. 5.2 Advanced RAG Paradigms
3. 5.3 Advanced AI Agents
4. 5.4 Harness & Context Engineering
5. 5.5 Human-AI Orchestration Design
6. 5.6 Enterprise Evaluation Framework
7. 5.7 A Snapshot on the Frontier

Artifacts

• Human-AI orchestration map for the case study (v0)
• Eval set with kill criteria (v0)
• Case technical dossier (v0)

The last module is a fully hands-on day: the build begins. We dissect a GenAI application live (for example Claude or Codex) to recognize in real use everything the program has taught. We write the spec of your case in dialogue with the AI, which interviews you on the artifacts of the previous three days. Then we build: the prototype takes shape, gets tested against the evals written the day before, then presented. We close by attacking it with a guided red team and turning everything into the roadmap for the next 90 days. The project is ready to start.

Objectives

By the end of the module, participants will:

• Outline a project spec
• Have started building a prototype with agentic tools
• Have tested and challenged their own system, knowing its limits
• Have planned the following 90 days with explicit progress criteria

Lessons

1. 6.1 Inside a Desktop GenAI App
2. 6.2 Vibe Coding Essentials
3. 6.3 Build & Iterate
4. 6.4 Demo & Red Team
5. 6.5 90-day roadmap

Artifacts

• Case spec (v0)
• Tested prototype (v0)
• 90-day roadmap