General AI & Business Productivity
This domain tests practical understanding of how generative AI and productivity tools support business work. Candidates should grasp core AI concepts - large language models, prompts, training data, hallucinations, and the difference between generative and predictive AI - plus productivity software: spreadsheets, word processing, email, and collaboration platforms. Understand data privacy, verification of AI output, and appropriate use cases such as drafting, summarising, and data analysis.
Traps to watch
- Treating AI output as automatically accurate; hallucination and bias require human review.
- Confusing generative AI (creates content) with predictive AI (forecasts from patterns).
- Ignoring confidentiality: pasting sensitive data into public tools may breach policy.
- Overlooking spreadsheet traps: relative vs absolute references, circular references, and formula errors.
Worked example
A manager wants a 10% increase applied to a column of prices in a spreadsheet. If the base price is in A2 and the factor 1.10 is stored in cell F1, the correct formula is =A2*$F1 (absolute reference to F1), copied down. Using =A2*F1 without the dollar sign shifts the reference and produces errors. Rule of thumb: anchor constants with $ so formulas copy correctly, and always verify AI-generated formulas against a known result.
Productivity principle
Use AI to accelerate drafting and analysis, but keep a human in the loop for accuracy, confidentiality, and judgement.