Beyond prompt engineering
Complete workflows, not isolated instructions.
Training for individuals
Become an AI-native professional.
Use AI agents to extend your expertise and improve the work you deliver.
Practice on a role-relevant project: frame the problem, direct agents, verify the work, and present a credible result.
For working professionals and students.
Why it matters
Why AI-native capability matters.
AI is becoming capable of performing more professional execution work: research, analysis, documentation, coding, design, reporting, and operational tasks. At the same time, AI allows professionals to work beyond their previous skill boundaries.
The goal is not simply to use AI to work faster. It is to become a more capable professional by combining your expertise with AI.
Where professional value shifts
- Define the right problem
- Provide the right context
- Evaluate AI-generated work
- Make strong decisions
- Connect information across functions
- Design effective human + AI workflows
- Take ownership of the final outcome
Who this is for
For professionals who want to increase their leverage as AI changes how work is done.
- You use AI tools but want a repeatable workflow
- You want to improve a real task in your role
- You want to direct and verify multi-step agent work
- You want to extend into adjacent skills with AI support
Students can follow the same project-based learning pathway.
Curriculum
What you learn.
01
Identify where AI creates leverage
Understand what can be delegated, accelerated, improved, or kept human-led.
02
Design AI-native workflows
Move beyond isolated prompts and structure complete workflows around AI agents.
03
Work effectively with AI agents
Learn how to provide goals, context, source material, constraints, examples, evaluation criteria, and feedback.
04
Strengthen human judgment
Practise problem framing, critical thinking, prioritization, verification, decision-making, and communication.
05
Expand beyond your existing skill set
Use AI to bridge gaps in research, data analysis, automation, prototyping, design, coding, financial analysis, or other adjacent capabilities.
A workflow, not a prompt: goal → context → AI execution → human review → validation → decision.
fig. 03 · workflow
The project
Learn through real professional work.
Participants apply AI agents to a real or realistic professional problem relevant to their work or role.
defineresearchplanexecutereviewvalidateimprovepresent
Success is measured by whether you can use AI to produce a credible professional outcome and explain the judgment behind it.
Role-based practice areas
Product managementSoftware engineeringData science and analyticsBusiness analysisFinance and investment analysisUI/UX designOperations and other business functions
These are professional contexts for applying AI-native workflows — not junior job tracks.
What makes it different
Built around real work, not tool demonstrations.
Realistic professional work
The programme is built around the work you actually own.
Human judgment stays central
Framing, verification and decisions remain yours.
Build and validate real outputs
You learn by producing work that has to hold up.
Expand your professional range
Reach into adjacent capabilities with AI support.
Reusable workflows
Methods that continue to work as tools evolve.
Outcome
What you leave with.
Depending on your role and project, you may develop the following.
The key outcome is the ability to independently identify where AI creates value, direct AI agents effectively, apply professional judgment, and deliver better outcomes.
- A role-relevant project
- A repeatable AI workflow
- A method for directing and checking agent work
- A case study you can explain and defend
Insights
More on the judgement this builds.
How to choose your first AI workflow
The usual selection method produces forty ideas scored on two axes nobody can estimate before building. Four questions predict a first workflow better, and every one of them is answerable in a week.
How to measure AI value without misleading the organization
Most AI metrics are true and useless: activity counted as outcome, projection reported as result, a pilot cohort extrapolated to a department. Five parts fix all three.
Why AI is harder in a business that already works
The constraints an established business arrives with — the process, the systems, the exceptions nobody wrote down — are not obstacles between you and the AI. They are the specification for it.
Next step
Start with one problem worth solving.
Tell us about a workflow or product you want to improve. We will help you find a useful next step.
No AI solution needs to be defined before we speak.