21 programs across 7 categories.
Designed for adult learners with limited or no technical or coding background, and they work just as well for people who have coded and built before. Executive programs serve senior leadership teams.
Delivered in class or online: talks and 90-minute sessions · 3-hour and half-day workshops · multi-session cohort courses · multi-day certified programs · executive forums with follow-on advisory. Every program is adapted to your context.
A two-day forum where a leadership team works out, together, what AI demands of their business. A 90-day execution program follows, with a plan the board can hold the team to.
Key topics: Where AI changes the business, division by division; which measures prove progress (and which get gamed); a clear decision on every initiative: build it, watch it, or stop it; which decisions stay human and which go to AI; who personally clears each blocker
Executive education for leaders who direct AI work without doing the engineering: what AI changes about decisions, measurement, and the design of work.
Key topics: Ways of working with AI, from delegating to collaborating; writing effective instructions for AI; deciding which AI investments deserve resources; aligning stakeholders before building
A workshop that moves professionals from avoiding or dabbling with AI to engaging it deliberately. The session ends with something built.
Key topics: Why capable professionals avoid AI, and what gets them past it; when to delegate to AI and when to collaborate with it; from a personal goal to a working app in one session
Working fluency in generative AI: what these systems are, what they're for, and how to get reliable results from them.
Key topics: What AI models can and can't do; using AI as a thinking partner rather than a search box; knowing what to hand over and how to judge what comes back; responsible habits of use
For people who already use AI assistants and want more: teaching the assistant your way of working through reusable instructions, and building good habits for working alongside AI on technical tasks.
Key topics: Turning repeated requests into reusable instructions the assistant follows every time; reviewing and directing AI work rather than redoing it; good practices for coding with AI
A 45-hour certified program: find where AI genuinely pays off in a business, write the requirement precisely, then build and deploy a working solution, not slides.
Key topics: Choosing the right problem before choosing the technology; writing requirements a builder can build from; giving AI the context it needs to perform; building automated workflows; AI that answers from company documents; handling the complications real deployments hit
Case-based postgraduate teaching on AI in smart-city and urban-systems contexts.
Key topics: Where AI changes urban operations; what real deployments got right and wrong
Designing AI education programs for institutions: module structure, session plans, and assessments aligned to stated learning outcomes.
Key topics: Sequencing AI learning for non-engineers; aligning sessions and assessments to outcomes
Most AI failures are information failures. This program teaches how to design everything an AI system sees (instructions, examples, documents, memory) so its behaviour is reliable and auditable, not lucky.
Key topics: How AI models actually read what you give them; design patterns that make behaviour predictable; giving AI memory and access to documents; measuring quality and catching failures
Practical instruction-writing for non-engineers: from one-off prompts to a personal library of instructions that work every time.
Key topics: What makes an instruction work; testing and improving your prompts; building a reusable library; treating a good prompt as a specification
What changes when AI stops answering questions and starts doing work: how AI agents operate, where they create value, and where they fail.
Key topics: How an AI agent actually works, step by step; systems where several agents work together; why agent systems fail in ways chatbots don't; where the technology is heading
Build a working business solution where several AI agents research, verify, and report together, with the quality controls that make it dependable enough to use.
Key topics: Dividing work between AI agents; keeping agent outputs consistent and checkable; grounding research in live web sources; testing the system before trusting it
How an AI agent fits inside a production application: the full path from a working prototype to something an organisation can run.
Key topics: The machinery around an agent that makes it safe and useful; connecting agents to existing systems; taking a build from demo to dependable
Hands-on builds: AI that answers from your organisation's own documents and data, wired into automated workflows, using the same approach as the certified modules.
Key topics: Connecting AI to your documents so answers come from your data, not the internet; keeping that data trustworthy; automating multi-step work end to end
From an idea to a working app using AI as the builder. No coding background needed. The discipline is in the thinking: define it clearly, and AI can build it.
Key topics: Turning a goal into a clear product definition; writing the document a builder, human or AI, can build from; keeping scope under control
A structured method for building products people want: make it simple, make it lovable, then make it complete, in that order. Applied end to end with AI build tools.
Key topics: Why 'minimum viable' products often fail and what to build instead; deciding what makes the first version, and what waits; mapping how users actually move through your product; building in the right order
An eight-module course that turns a basic app into a deployed AI product, one capability at a time, so learners always have something working.
Key topics: Adding AI capabilities to an existing app; building the user-facing side; putting it live on the internet; reading and extending code you didn't write
Enough design literacy to build products people will use, for builders who aren't designers and don't need to become one.
Key topics: The difference between how it looks and how it works; why design pays commercially; the handful of rules behind usable products
Ethical AI treated as a management practice: what accountability, fairness, and privacy require of the managers deploying AI. Not a philosophy seminar.
Key topics: Who answers when an AI decision goes wrong; spotting unfair outcomes before they ship; explaining AI decisions to the people affected; the trap of trusting the machine too much; making ethics operational, not aspirational
AI applications fail in ways traditional software doesn't. This program covers the ways they get attacked and the defence principles that hold, grounded in the industry-standard OWASP risk list for AI applications.
Key topics: Tricking AI into ignoring its instructions; coaxing out data it shouldn't reveal; poisoning the documents an AI trusts; what goes wrong when AI agents trust each other; what to defend first
What AI governance frameworks mean for working managers, taught as something you run, not something you file. Adapted to your regulatory context.
Key topics: How AI governance rules are evolving; what managers are obliged to do; measures that keep AI use honest; practical controls that survive contact with daily work
