Catalogue AI Training.

Objectives

• Reconstruct missing documentation for existing code, a process or specs using AI
• Tell apart what AI reconstructs reliably from what must be checked by hand
• Produce documentation that is usable and shareable across the team

Programme

1. Scope and limits: where AI speeds up reverse documentation, where it gets it wrong
2. Reverse-engineering a piece of code or an existing data schema
3. Reconstructing a business process from scattered elements (tickets, emails, screenshots)
4. Formatting: structure clear, sourced and maintainable documentation
5. In-depth workshop: document a complete scope end to end
6. Workshop on a real case brought by participants

Objectives

• Generate covering test cases from a spec or a user story
• Build representative, anonymised test datasets
• Speed up acceptance execution and documentation

Programme

1. From requirement to test plan: derive cases with AI and challenge coverage
2. Generation of realistic datasets, edge cases and anonymised data
3. Assisted execution and writing of acceptance reports
4. Quality reflexes: what you never delegate to AI
5. In-depth workshop: build a complete acceptance plan on a real case
6. Workshop on a real case brought by participants

Objectives

• Turn a vague requirement into clear, prioritised framing
• Formalise a complete, coherent specification faster
• Use AI as a sparring partner to challenge the requirement

Programme

1. Turn a vague request into a well-framed problem
2. Structure a specification: objectives, scope, constraints, criteria
3. AI detection of blind spots and inconsistencies
4. Formalisation and presentation to stakeholders
5. In-depth workshop: run a full framing through to the specification
6. Workshop on a real case brought by participants

Objectives

• Ideate, prioritise and validate product hypotheses faster
• Synthesise user feedback and verbatims at scale
• Structure personas and opportunities from real data

Programme

1. Frame an AI-assisted discovery without losing rigour
2. Synthesis and categorisation of verbatims, tickets and interviews
3. Building personas and mapping opportunities
4. Reasoned prioritisation of the problems to address
5. Workshop on participants' own product topics

Objectives

• Produce quality user stories in half the time
• Maintain a clear, broken-down and prioritised backlog with AI
• Build a reusable assistant for your stories

Programme

1. From initiative to story breakdown with AI
2. Writing stories: value, criteria, INVEST breakdown
3. Cleaning and prioritising an existing backlog
4. Creating an AI assistant configured for your backlog
5. Workshop on participants' real backlog

Objectives

• Produce robust, complete specs faster
• Derive testable acceptance criteria
• Secure consistency between requirement, spec and test

Programme

1. Structure a functional spec usable by AI
2. Generation and challenging of acceptance criteria
3. Detection of inconsistencies and uncovered cases
4. Traceability spec / acceptance / dev
5. Workshop on participants' real spec

Objectives

• Write complex queries in minutes
• Prepare and clean a dataset faster
• Check and harden the generated SQL

Programme

1. From business need to query: prompt the right SQL
2. Joins, windowing and complex queries, assisted
3. Data preparation, cleaning and consistency checks
4. Critical review: pitfalls and errors in generated SQL
5. Workshop on participants' own data

Objectives

• Produce clearer, more convincing deliverables
• Build a decision-oriented data narrative
• Speed up the formatting of visualisations

Programme

1. Choose the right visualisation for the right message
2. Data storytelling: structure a decision-oriented narrative
3. Assisted generation and improvement of visuals
4. Adapt the delivery to the audience (business, ExCom)
5. Workshop on participants' real report

Objectives

• Spot weak signals in a dataset faster
• Formulate hypotheses and test them with AI
• Turn an analysis into actionable insights

Programme

1. Frame an assisted exploratory analysis
2. Detection of patterns, anomalies and correlations
3. Formulating and testing hypotheses
4. From analysis to actionable recommendation
5. Workshop on a participants' dataset

Objectives

• Understand a legacy IS faster
• Map flows, applications and dependencies with AI
• Identify areas of risk and debt

Programme

1. Reconstruct the mapping of an IS from the existing landscape
2. Analysis of flows, interfaces and dependencies
3. Spotting areas of risk and technical debt
4. Delivery of a clear, shareable map
5. Workshop on participants' real IS

Objectives

• Produce complete, consistent specifications
• Detect gaps and inconsistencies automatically
• Align functional spec and technical requirements

Programme

1. Structure a usable functional spec
2. Generation, challenging and completeness of requirements
3. Detection of inconsistencies and functional gaps
4. Traceability requirement / specification / test
5. Workshop on participants' real spec

Objectives

• Produce BPMN diagrams from interviews
• Speed up modelling and its updates
• Strengthen the consistency of modelled processes

Programme

1. From interview to diagram: generate a first BPMN
2. Refine and challenge a modelled process
3. Detection of gaps between target and actual process
4. Documentation and sharing of models
5. Workshop on participants' real process

Objectives

• Build robust schedules faster
• Produce well-argued estimates
• Anticipate dependencies and critical milestones

Programme

1. Structure a schedule from a project scope
2. Assisted estimation: workload, assumptions, margins
3. Identifying dependencies and the critical path
4. Formatting and communicating the schedule
5. Workshop on participants' real project

Objectives

• Anticipate, document and prioritise risks
• Prepare better-substantiated trade-offs
• Decide faster with AI support

Programme

1. Build and challenge a risk matrix
2. Mitigation plans and warning signals
3. Preparing trade-offs: options, impacts, recommendation
4. Documenting decisions
5. Workshop on participants' real project

Objectives

• Produce usable reports in ten minutes
• Prepare a clear, decision-oriented steering committee
• Adapt the delivery to each audience

Programme

1. From project tracking to automated summary reporting
2. Preparing a decision-oriented steering committee
3. Adapting the message to the audience
4. Setting up a steering routine
5. Workshop on participants' real reporting

Objectives

• Generate and optimise Spark, Python and SQL scripts
• Speed up the building of reliable pipelines
• Keep control of the quality and maintainability of generated code

Programme

1. Frame a pipeline and generate the code skeleton
2. Assisted ETL / ELT transformations (Spark, Python, SQL)
3. Optimisation and refactoring of generated code
4. Best practices for review and security
5. Workshop on participants' real pipeline

Objectives

• Design data schemas and models faster
• Formalise robust data contracts
• Connect modelling with MDM requirements

Programme

1. Assisted schema design (relational, dimensional)
2. Writing and validating data contracts
3. Consistency with governance and MDM
4. Documenting models
5. Workshop on participants' real model

Objectives

• Automate data tests
• Set up augmented monitoring
• Detect and diagnose anomalies faster

Programme

1. Assisted data-testing strategy
2. Generation of quality checks and assertions
3. Observability: monitoring, alerting, diagnosis
4. Augmented incident handling
5. Workshop on participants' real pipeline

Objectives

• Transform your productivity and code quality
• Master the main coding assistants
• Keep control of architecture and security

Programme

1. Overview and the right stance: Copilot, Cursor, Claude Code
2. Generation, completion and explanation of code in context
3. Steering the assistant: context, constraints, iterations
4. Guardrails: review, security, debt
5. Workshop on participants' codebase

Objectives

• Generalise test coverage without friction
• Practise AI-assisted TDD
• Improve the reliability of delivered code

Programme

1. Generation of relevant, maintainable unit tests
2. Assisted TDD: from test to implementation
3. Coverage, edge cases and regression tests
4. Integration into CI
5. Workshop on participants' codebase

Objectives

• Take on legacy code with method and confidence
• Speed up debugging and refactoring
• Secure a technical migration

Programme

1. Understand and document legacy code with AI
2. Guided, safe refactoring
3. Assisted debugging: from symptom to cause
4. Progressive migration strategy
5. Workshop on participants' codebase