AI LITERACY • WORKING PROFESSIONALS

AI Literacy Self-Assessment for Working Professionals

Assess how effectively you use AI in your day-to-day work—across understanding, workflows, impact, and responsible usage.

Progress0 / 16 questions answered

Pillar 1: Conceptual Understanding

How well you understand core AI concepts, limits, and what tools can and cannot do.

Q1.1

Understanding AI vs. Traditional Automation

How clearly can you explain the difference between simple automation (macros, scripts) and AI/ML-based systems to a non-technical colleague?

1 — No real understanding / not confident at all2 — Basic awareness, but cannot explain clearly3 — Working understanding; can discuss at high level4 — Strong understanding; can challenge and guide decisions5 — Expert; can lead strategy and advise others

Q1.2

Knowing When to Use Which Tool

When facing a new task, how confident are you in deciding whether to use search, Excel, BI tools, classic ML, or GenAI (ChatGPT/Copilot etc.)?

Q1.3

Limits of AI Outputs

How well do you understand the limitations of AI tools (hallucinations, bias, missing context) and when to double-check results?

Q1.4

Basic Data & Model Intuition

How comfortable are you with basic ideas like training data, prompts, tokens, and why better inputs usually lead to better AI outputs?

Pillar 2: Workflow & Productivity

How effectively you use AI tools to speed up, improve, and structure your daily work.

Q2.1

Using AI in Daily Workflows

On a typical week, how consistently do you use AI tools (ChatGPT, Copilot, Gemini, etc.) to draft, summarise, analyse, or brainstorm?

Q2.2

Prompting & Iteration Skills

How confident are you in iterating on prompts (giving examples, constraints, context) to significantly improve the quality of AI outputs?

Q2.3

Designing Reusable AI Workflows

How often do you turn successful AI experiments into reusable workflows, templates, or SOPs that you can repeat and refine?

Q2.4

Collaborating with AI in Teams

How effectively do you integrate AI into team rituals (project planning, documentation, client decks, code reviews, analysis) rather than using it alone?

Pillar 3: Impact & Career Readiness

How well you translate AI usage into visible outcomes, skills, and portfolio signals.

Q3.1

Measuring Impact of AI Usage

How often do you quantify or showcase the time saved, quality improved, or new capabilities unlocked through your AI-assisted work?

Q3.2

Building an AI-Fluent Portfolio

How confident are you that your CV, LinkedIn, or internal profile clearly demonstrates your AI skills and examples of work?

Q3.3

Learning Plan for AI Skills

How structured is your learning plan for AI (courses, practice projects, reading, experiments) over the next 6–12 months?

Q3.4

Cross-Functional Collaboration

How comfortable are you working with data/engineering/ops/legal colleagues on AI ideas, even if you’re not the technical expert?

Pillar 4: Responsible & Secure Use

How safely and ethically you use AI with data, clients, and colleagues.

Q4.1

Handling Sensitive Data with AI

How confident are you in knowing what data can/cannot be pasted into AI tools (client data, internal docs, code, financials) according to policy?

Q4.2

Checking for Bias & Fairness

When AI outputs touch customers, hiring, credit, or recommendations, how often do you check for biased or unfair suggestions?

Q4.3

Explaining AI-Assisted Work

How comfortable are you being transparent with managers/clients about where you used AI and how you validated its output?

Q4.4

Following Org AI Guidelines

How familiar are you with your organisation’s AI policies or guidelines—and how consistently do you follow them?