Corporate AI training
AI skills for every role in your company
Six role-based tracks, from the boardroom to the engineering team, about 150 hours in all: three for business roles and three levels of technical training, from people who have never coded to engineers building AI systems. Every chapter stands alone, so you can buy any set, and each ends in something your people can use at work on Monday.
Executive
CXOs, directors, board members. No code.
Manager
Managers, product owners, team leads. No code.
Analyst
Business and data analysts. Excel, Power BI, SQL and AI tools, no Python.
Technical Basic
Technical staff who cannot code yet. Analogy-first, with spreadsheets, Orange, Colab forms and AI assistants.
Technical Moderate
Staff with some coding. The technical syllabus with fill-in-the-blank code, hints and extension tasks.
Technical Advanced
Engineers and IT staff moving into AI. Python notebooks on Colab, from data prep to LLMs and MLOps.
Chapter by chapter
Open a track to see every chapter.
EXExecutive track
| Chapter | Title | Time | Leaders will be able to | Activity |
|---|---|---|---|---|
| EX01 | AI in 2026 | 60 min | Describe what AI can and cannot do today; question an AI claim | One decision to improve |
| EX02 | Data to Decisions | 75 min | Follow data to decision; judge a model against a baseline; price errors | Map one decision |
| EX03 | GenAI and Agents | 75 min | Explain LLMs simply; choose copilot, RAG, agent or customisation | Design one assistant |
| EX04 | Use Cases and Value | 75 min | Score use cases, size value and full cost, design a pilot | Score three use cases |
| EX05 | Risk and Governance | 60 min | Place systems in risk tiers; pick frameworks (EU AI Act, NIST, ISO 42001) | Classify six AI systems |
| EX06 | AI-ready Organisation | 60 min | Choose an operating model, roles and skills; drive adoption | Your first 100 days |
MGManager track
| Chapter | Title | Time | Managers will be able to | Activity |
|---|---|---|---|---|
| MG01 | Data Literacy | 2.5 h | Spot data-quality problems; read trends correctly | Data audit; dashboard check |
| MG02 | Analytics Brief | 2 h | Turn a vague request into a sharp question | Sharpen requests; a brief |
| MG03 | Predictive Models | 2.5 h | Read a confusion matrix; set a threshold | Threshold; model review |
| MG04 | Forecast and Optimise | 2.5 h | Judge a forecast; plan for a range | Forecast check; plan framing |
| MG05 | GenAI at Work | 2.5 h | Prompt well, keep data safe, check outputs | Prompt clinic; team playbook |
| MG06 | RAG, Agents, Automation | 2 h | Pick the simplest build: RAG, agent or rules | Scope one assistant |
| MG07 | Running AI Projects | 2 h | Plan in stages, staff the team, test vendors | A 90-day pilot plan |
| MG08 | Responsible AI | 2 h | Check unfair errors, privacy, human oversight | Risk review of a use case |
ANAnalyst track
| Chapter | Title | Time | Hands-on work |
|---|---|---|---|
| AN01 | The modern analyst's toolkit | 2 h | Tool tour and a data-quality checklist |
| AN02 | Cleaning data with Power Query | 2.5 h | Excel Power Query on a messy orders export |
| AN03 | Analysis in Excel | 2.5 h | Pivots, lookups, what-if and forecast in one workbook |
| AN04 | SQL for analysts | 2.5 h | Browser SQL or DuckDB UI |
| AN05 | Dashboards in Power BI | 3 h | Power BI Desktop, step by step |
| AN06 | Data storytelling | 2 h | Rebuild one dashboard into a five-slide story |
| AN07 | AI assistants for analysis | 2.5 h | AI data analysis with a verification checklist |
| AN08 | No-code forecasting and ML | 2.5 h | Forecast Sheet, key influencers, decomposition tree |
TBTechnical Basic
| Chapter | Title | Time | Hands-on work |
|---|---|---|---|
| TB01 | What data and AI really are | 3 h | Machine failures in a spreadsheet |
| TB02 | Reading a notebook without writing code | 2.5 h | Run a notebook with Colab forms and an AI assistant |
| TB03 | Getting data ready | 3.5 h | Clean appointment data in Sheets and Orange |
| TB04 | Asking a database questions | 3 h | Orders and stores in DB Browser with AI help |
| TB05 | Summaries and charts that tell the truth | 2.5 h | Honest summaries and charts |
| TB06 | Forecasting: what happens next | 3.5 h | Daily power demand forecast |
| TB07 | Predicting yes or no | 3.5 h | Loan default model in Orange |
| TB08 | Grouping customers and recommending | 3 h | Segments and recommendations |
| TB09 | Making sense of text | 3 h | Themes in reviews and tickets |
| TB10 | How large language models work, and how to prompt them | 3.5 h | Prompting on customer-care tickets |
| TB11 | AI assistants that use your documents (RAG) and agents | 3 h | A help-centre assistant |
| TB12 | Checking AI and using it responsibly | 2.5 h | Test an assistant and a model for errors and fairness |
| TB13 | From pilot to everyday use | 1.5 h | Plan the roll-out of a chat assistant |
| TBP | Final project showcase | 2 h | Your choice of the six industries |
TETechnical Moderate (TM) and Advanced (TE)
| Chapter | Title | Time | Hands-on work |
|---|---|---|---|
| TE01 | Python for data work | 3 h | Run, read and change working notebooks |
| TE02 | pandas and data preparation | 3 h | Clean, join and reshape real business tables |
| TE03 | SQL and databases for engineers | 3 h | Queries, joins and a server database |
| TE04 | Regression and forecasting | 3 h | Demand and load forecasts with backtests |
| TE05 | Classification and model evaluation | 3 h | Risk models, thresholds and honest evaluation |
| TE06 | Segmentation and recommendation | 3 h | Customer segments and recommendations |
| TE07 | Text analytics and NLP | 3 h | Classify and theme support tickets |
| TE08 | Working with LLMs | 3 h | Prompting and structured output with open models |
| TE09 | RAG and agents | 3 h | Search a help centre and answer with sources |
| TE10 | Customising and evaluating LLMs | 3 h | Fine-tuning choices and LLM evaluation |
| TE11 | Deployment, MLOps and LLMOps | 3 h | Ship, monitor and retrain a model |
Technical Moderate (TM01-TM11) and Technical Advanced (TE01-TE11) share the chapters above. Moderate gives fill-in-the-blank code with hints and an extension task; Advanced has learners write and change full notebooks.
Six growing industries, one case company
Every track meets the same six industry cases at its own depth: leaders weigh value and risk, managers run the projects, analysts build dashboards, engineers build the models. Final projects can use one of the six or the learner's own organisation.
| Industry | Predict | Decide (prescriptive) | Generative AI |
|---|---|---|---|
| Retail and e-commerce | Weekly demand, churn | Price, stock, promotions | Customer-care assistant |
| Banking, fintech and insurance | Default risk, fraud, claims | Credit limits, fraud review queue | Document extraction, advisor copilot |
| Healthcare: clinics and diagnostics | Appointment no-shows, demand | Overbooking and staffing | Clinical-note summaries with human review |
| Manufacturing, logistics and supply chain | Machine failure, lead times | Maintenance schedule, inventory, routing | Maintenance-manual assistant |
| Energy: renewables and utilities | Load and solar output | Battery dispatch, crew scheduling | Field-report summaries |
| Telecom and SaaS | Churn, network faults | Retention offers, capacity | Support agent, code assistant |
Quizzes per chapter, one workplace project per track
| Track | Final workplace project | What learners hand in |
|---|---|---|
| Executive | AI opportunity portfolio and board memo | Three scored use cases and a one-page investment memo, presented in 10 minutes |
| Manager | AI use-case brief and 90-day pilot plan | A 4 to 6 page brief: problem, data, metrics, approach, risks, team, plan |
| Analyst | Decision dashboard and story | An Excel or Power BI dashboard on an industry dataset, plus a five-slide story |
| Technical | Working prototype | A Colab notebook (model or RAG prototype) with an evaluation and a deployment note |
Each chapter has a 5 to 8 question quiz in the deck and the learner workbook. Each project brief has a 100-point rubric.
How corporate training works
Instructor-led or self-paced
Each chapter has a deck with trainer notes and a learner workbook for self-paced study. Run chapters as workshops at your office or online.
Modular
Pick single chapters or full tracks. Combine tracks so leaders, managers and teams share one language.
Hands-on files
Analyst and technical chapters add Excel workbooks, Power BI files and Colab notebooks with industry data.
Industry case studies
Deeper industry assignments drawn from our 16 case studies.
Free tools
Excel, Power BI Desktop, Google Colab and free open AI models. No licences to buy before you start.
Setup support
Step-by-step setup tracks get every laptop ready before day one.
Plan training for your team
Tell us the roles, team size and goals, and we will propose a programme.