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

An analyst needs to quickly understand the key findings of a 40-page industry report. Which AI use case is most appropriate?

Use AI to generate a new industry report based on training data Document summarization — paste or upload the report and ask the AI for a structured summary of key findings Search for the report's conclusions using a general web search Ask AI to write an email about the report without reading it

A company uses AI to screen resumes and notice that the tool systematically ranks candidates from certain universities higher. What is the most likely cause?

There is no cause — AI resume screening is required to be unbiased by design The university ranking reflects the AI's access to university prestige databases The AI is correctly identifying the objectively better universities The AI was trained on historical hiring data where those universities were over-represented among successful hires — encoding past selection bias

You ask an AI to write a project risk assessment and receive a generic, shallow response. What is the best next step?

Iterate with a more detailed follow-up: provide the specific project context, the type of risks to focus on, and the format you need Ask the AI the same prompt a second time without changes Accept the generic output and edit it heavily yourself Switch to a different AI tool — this one is too weak for business tasks

Which AI tool is purpose-built to automatically transcribe and summarize meetings, extracting action items?

Meeting transcription tools like Otter.ai, Fireflies.ai, or Microsoft Copilot in Teams ChatGPT — by manually typing what was said in the meeting Adobe Acrobat AI — designed for PDF document review Perplexity AI — which is designed for web research

A sales team writes a different prompt every time they need to generate a follow-up email after a meeting. What is the highest-leverage improvement?

Switch to a more powerful AI model Each salesperson should develop their own unique prompting style Only the most senior team members should use AI — it causes quality variance Create a reusable prompt template with clearly marked variables for meeting date, client name, key discussion points, and next steps

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

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

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?

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

A company builds a prompt library in Notion with 30 useful templates. After 3 months, some templates produce worse results than they used to. What is the most likely cause and solution?

All templates expire after 3 months due to API policy AI model updates changed behavior — review and update templates when models are upgraded, and record a version history The team is using too many templates — reduce to fewer than 10 Notion is not a suitable storage tool for prompt templates

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

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

A doctor uses AI diagnostic assistance for every patient case. Over two years, which professional risk should they actively manage?

Automation complacency — over-reliance may atrophy their independent diagnostic reasoning skills The AI will eventually replace them entirely within two years AI diagnostic tools will create legal liability because they are not FDA approved Other physicians will distrust the doctor for using AI assistance