Step 1 of 5
Task
Is this the right kind of job for AI? What exactly am I trying to accomplish?
Not every professional task benefits from AI assistance. Some tasks are well-suited — drafting, summarising, generating options. Others carry risks that outweigh the convenience — anything involving verification of facts, high-stakes decisions with individual consequences, or tasks where the process of thinking is itself the value.
Ask yourself: If the AI gets this wrong, what happens? Is the task low-stakes enough that a quick human scan is sufficient — or does it require rigorous human judgment at every step?
Step 2 of 5
Data
What information is involved? Is any of it personal, confidential, client data, or POPIA-protected?
When you paste text into a public AI tool, that text may be used to improve the model, stored, or accessible to third parties. In South Africa, POPIA creates specific obligations around how personal information is processed — including by AI tools. Many AI tools fall outside POPIA-compliant infrastructure.
Ask yourself: Does this involve client names, employee records, patient data, financial information, or confidential business information? If yes, is the AI tool I’m using approved for this data type?
Step 3 of 5
Tool
What type of AI fits this task? Is it approved by my organisation? Do I understand its limitations?
Different AI tools are built for different purposes. A language model is strong at generating and summarising text, weak at arithmetic and factual citation. Matching the tool to the task is a basic professional judgment — the same way you wouldn’t use a spreadsheet to write a contract.
Ask yourself: Is this tool approved by my organisation? Do I understand what it’s actually doing — not just what it produces? What does it specifically struggle with that is relevant to this task?
Step 4 of 5
Trust
How reliable is AI for this specific task? What are its failure modes here? How would I detect an error?
Trust in AI output should be calibrated — higher for tasks where errors are easy to spot, lower for tasks where errors are invisible until they cause harm. The key question isn’t “is this AI reliable?” — it’s “how would I detect an error in this specific output?”
Ask yourself: Can I independently verify the most important claims in this output? What’s the specific check I would run? What would an error look like — and would I be able to see it?
Step 5 of 5
Human
Who checks the output before it matters? Who is accountable if it is wrong?
When AI output affects a real person — their job application, their loan, their medical treatment — someone at the institution that deployed that AI is accountable. “The algorithm decided” is not a legal defence under South African law.
Ask yourself: Who signs off on this before it reaches someone who could be affected by it? Is there a clear review step in place — or is the AI output going straight into action?
teaches South African professionals how to think with AI — not just how to use it. Critical judgment, real SA case studies, a certificate that actually means something.
