Three paths. Hundreds of careers.

Build foundational AI literacy and safe, confident first-use habits.

A conceptual walkthrough of large language models, generative AI, and why outputs vary. Builds the mental model every AI user needs before going further.

A curated overview of the key AI tools in your professional context — what each does well, its limitations, and how to choose the right one for a given task.

Role-tailored introduction to the highest-value AI use cases in your specific function. Move from theory to your first real productivity wins with hands-on task walkthroughs.

Explore why emotional intelligence, human judgment, and interpersonal skills matter more than ever as AI handles routine tasks. Build confidence in the skills AI cannot replicate.

Learn to write clear, structured prompts that get useful outputs first time. Covers basic prompt anatomy, role-setting, and common failure modes to avoid.

Understand AI hallucinations, data privacy boundaries, bias risks, and when not to use AI. A practical ethics framework for everyday users.

Move from occasional use to systematic, high-quality AI integration.

Develop a rigorous AI strategy: prioritise use cases, evaluate build vs. buy decisions, assess ROI, and build the governance structures that make AI investment defensible to boards and investors.

Apply AI to talent acquisition, workforce planning, learning design, and people analytics. Covers bias mitigation, skills-based hiring, attrition modelling, and personalised development at scale.

Develop the AI skills employers are hiring for right now — from AI-assisted research and project work to industry-specific applications and ethical AI practice that demonstrates professional judgment.

Move beyond basic prompts to sophisticated multi-step techniques — system-level context, persona injection, chain-of-thought, and structured output formats.

Practical frameworks for modelling AI use, addressing displacement fears, setting team expectations, and measuring adoption impact on output quality.

Build resilience and psychological safety in an AI-augmented workplace. Covers managing uncertainty, supporting peers through change, and maintaining professional identity as AI reshapes roles.

Build a reliable review process for AI-generated content. Covers fact-checking techniques, bias detection, and when to trust — or reject — AI output.

Deep-dive into AI use cases mapped to your function — with worked examples, prompt libraries, and peer discussion on what actually works in practice.

Hands-on introduction to working with AI APIs, evaluating model outputs programmatically, and building lightweight automations that connect AI tools to your existing technical stack.

Map your existing workflows, identify AI augmentation points, and build repeatable AI-enhanced processes that save time without sacrificing quality.

Design AI-native processes, automations, and systems at scale.

Build the governance structures your organisation needs — acceptable use policies, ethics oversight, risk frameworks, and board-level AI reporting.

Redesign core processes from the ground up with AI as a first-class participant. Includes change management, stakeholder communication, and adoption measurement.

Design and lead an end-to-end AI reskilling and people transformation programme. Covers diagnostics, learning pathway design, skills measurement, stakeholder buy-in, and sustaining AI capability long-term across the organisation.

Design sophisticated prompt architectures for production use — including system prompts, retrieval-augmented generation, multi-agent workflows, and evaluation frameworks for consistent, high-quality AI output.

Design, build, and deploy AI-integrated automation pipelines. Covers n8n/Zapier orchestration, API chaining, and building reliable multi-step AI workflows.

Develop a standout portfolio of AI-augmented projects that demonstrates your skills to employers. Covers AI-assisted work at a professional level, transparent attribution, and showcasing AI judgment alongside technical capability.

Lead AI transformation at the executive level — translating AI capability into board-level strategy, communicating ROI and risk to investors, sponsoring cultural change, and building long-term organisational AI capability.