AI at Work Quiz: Are You Getting the Most From AI Productivity Tools?

An employee's organization has not published any formal AI use policy. What is the safest assumption about AI use at work?

Treat sensitive client data, confidential business information, and employee data with the same protection you'd apply to any third-party tool — assume AI APIs are external services No policy means AI cannot be used at work under any circumstances No policy means the employee is personally liable for any AI-related incidents No policy means all AI tools are approved for any use

What is the primary benefit of marking variables in a prompt template using brackets like [CLIENT_NAME]?

It makes the changeable elements visually obvious, so any team member can fill them in without needing to understand prompt engineering It allows the template to work across different AI provider APIs The brackets are required syntax for AI models to recognize variable values Brackets encrypt the variable so the AI doesn't see the actual client name

A marketing team asks AI to generate 10 example customer personas for their advertising campaign. After reviewing the output, all 10 personas have similar demographic characteristics. What should the team do?

Replace AI with manual persona creation to avoid bias entirely Add a disclaimer to the campaign noting AI was used Accept the default output — AI generates representative samples of actual customer data Explicitly prompt the AI to generate diverse personas across age, gender, background, and geography to ensure more inclusive representation

A strategy team wants to use AI to stress-test a business plan. Which prompt is most effective for this goal?

'Act as a skeptical board member. What are the three riskiest assumptions in this plan, and what evidence would change your view?' 'Summarize this business plan in three paragraphs' 'What improvements can you make to this plan?' 'Tell me if this business plan will succeed'

Which of these professional tasks is LEAST reliably handled by current AI tools?

Translating a document into another language Generating a first draft of a project status update Making final strategic judgment calls that require understanding organizational politics and relationships Summarizing a long email thread

What habit best protects a professional from automation complacency when using AI regularly?

Forming an independent take on a problem before asking AI, then comparing and critically evaluating the AI's perspective Never using AI for tasks that could affect clients Using only the most expensive AI model to ensure maximum accuracy Limiting AI use to one tool at a time

A financial analyst uses an AI tool to draft a client investment report. The report contains an inaccurate statement. The analyst submits it without review. Who bears professional responsibility?

The client — they requested the report The AI tool provider — they provided the drafting capability The analyst — they are accountable for work they submit, regardless of how it was generated No one — inaccuracies in AI-generated content are treated as errors outside human control

Which of these is the most effective prompt for generating a useful competitive analysis?

'What do you think about these two products?' 'You are a market analyst. Compare the pricing, integrations, and enterprise support of ProductA and ProductB in a two-column table. Target audience: VP of Sales.' 'Compare these products somehow' 'Tell me about ProductA and ProductB'

A small business wants to use AI for drafting responses to customer complaints that include customer names and order details. What is the simplest way to reduce privacy risk while still using AI?

Use AI only on Tuesdays when server load is lower and data is more secure Anonymize the data — replace the customer name with 'Customer' and describe the order generically — before sending to the AI tool Ask the AI not to store the customer's name after the session Never use AI for any customer-related communications

An AI customer service chatbot performs measurably worse for customers who use non-standard English syntax. This is an example of which type of AI bias?

Performance disparity bias — the model performs less accurately on language patterns underrepresented in its training data Training data bias caused by outdated information Representation bias in AI-generated images Recency bias from the model's training cutoff date