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.
Training for individuals
Become an AI-native professional.
Upgrade how you work by combining your professional expertise with AI-agent capabilities.
Learn how to use AI agents across research, analysis, creation, problem-solving, and execution — while strengthening the judgment and decision-making that become more valuable as AI takes on more execution work.
This is not a prompt-engineering course. It is about learning how to work differently in an AI-native environment.
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.
Students can follow the same professional development pathway, with additional applied internship opportunities available for eligible participants.
It is especially relevant if you
- Experienced in your field but have not integrated AI deeply into your workflow
- Already use AI tools but mostly through ad-hoc prompting
- Want to redesign repetitive or time-consuming parts of your work
- Want to use AI agents for more complex, multi-step professional tasks
- Want to expand into skills or functions outside your traditional area of expertise
- Want to strengthen problem framing, judgment, system thinking, and decision-making
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 you leave with
Built around real work, not tool demonstrations.
- A completed role-relevant project
- Repeatable AI-native workflows
- Documented methods for directing and validating AI agents
- Research, analysis, product, design, automation, or software artifacts
- A professional case study demonstrating AI-native capability
- A personal roadmap for continued development
The key outcome is the ability to independently identify where AI creates value, direct AI agents effectively, apply professional judgment, and deliver better outcomes.
Insights
More on the judgement this builds.
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
Move from AI interest to AI-native capability.
Start with the business problem. We can help determine where AI fits and what the practical next step should be.
You do not need to arrive with an AI solution already defined. If we do not think AI is the right approach, we will tell you that too.