How AI ready are you?

How would you describe your understanding of AI’s potential in your work?

This is the knowledge foundation: a working grasp of what AI and AI agents can and cannot do. We measure understanding only here. Spotting where AI applies to your work is covered later, under Use Case Identification (question 9), and hands-on building under the Tools & Technical Skills pillar (questions 13–15).

How do you approach learning about AI developments?

AI moves fast. What you understand today can be out of date within months, so a one-off grasp is not enough. This question measures whether you have a lasting learning habit; a sustained habit predicts long-term readiness better than any skill you happen to hold today.

How well have you positioned your role for the AI era?

AI takes over routine and coordination work while amplifying human judgement. If you can see which parts of your role AI can take over and which it cannot, you can deliberately shift your time toward the higher-value work. This measures strategic thinking about your career, not how many tools you have adopted.

How willing are you to delegate work to AI and agents and supervise the result?

The defining mindset shift of the agent era: are you willing to hand work to AI and agents and supervise the result, rather than do everything yourself? We measure willingness and trust here. Instructing AI well is Specification Thinking (question 14); checking its work is GenAI Critical Thinking (question 8).

How proactively do you try AI on new problems you have never used it for?

This is about drive: do you try AI on problems you have never used it for, on your own initiative? We measure the act of trying here. The working solutions you eventually build are covered by AI-Assisted Creation & Orchestration (question 15), and keeping your knowledge current by Learning Agility (question 2).

How familiar are you with AI ethics and responsible AI principles?

Responsible AI use starts with knowing the principles exist; you cannot apply what you have never encountered. We measure awareness here, from no exposure through to actively advocating responsible practice. Spotting concrete risks is a separate skill, covered by Risk Awareness (question 7).

How well can you identify AI-related risks?

Knowing ethics principles is one thing; naming the concrete risks in your own work is another. This question measures whether you can call out specific risks, such as bias, made-up answers, privacy leaks, or agents acting beyond what you intended, and weigh them before adopting a tool. Ethics Awareness (question 6) alone does not cover this.

How well can you evaluate AI outputs and actions for accuracy and reliability?

Everything AI produces, from text and code to decisions and the actions agents take on your behalf, needs checking before you rely on it. We measure how you verify, from taking output on trust through to a systematic review habit that stops AI errors from becoming real mistakes. Risk Awareness (question 7) is knowing the risks; this is catching them.

How well can you identify AI opportunities in your work?

Value starts with seeing where AI applies. This question measures whether you can scan your own work for AI opportunities, from a vague sense that AI might help through to a prioritised list with recorded benefits. The question that follows checks whether those opportunities have turned into time saved.

How much has GenAI improved your personal productivity?

Spotting opportunities is necessary but not sufficient; Use Case Identification (question 9) asked whether you can see them, and this question asks whether AI has saved you time. An honest answer here separates people who can talk about AI value from people who have captured it. The time benchmarks in the options give you a concrete scale to place yourself on.

How well can you evaluate whether data meets AI project requirements?

Working with data is not the same as knowing what makes data good enough for AI; the hands-on side is Data Proficiency (question 12). We measure judgement here: whether you understand accuracy, completeness and timeliness well enough to tell AI-ready data from data that is not, and to set standards for it. The technical infrastructure side belongs to the Tools & Technical Skills pillar.

What is your proficiency in working with data for AI applications?

AI runs on data, so readiness includes hands-on data work: cleaning, transforming and preparing data for AI to use. We measure doing, not judgement. Judging whether data is good enough is Data Evaluation (question 11), and the technical infrastructure side belongs to the Tools & Technical Skills pillar.

How proficient are you with AI tools and AI agents?

The entry point to the Tools & Technical Skills pillar: hands-on use of AI tools and AI agents. Each option counts either regular use of AI tools or handing multi-step tasks to agents, whichever you do today, so you can place yourself accurately whether or not you use agents yet. The two questions that follow build on this.

How well can you specify tasks and outcomes for AI tools and agents?

The defining skill of the agent era: taking a complex piece of work apart and writing it down so AI can execute it, including what a good result looks like, the constraints, and the checkpoints along the way. The more capable AI becomes, the more value moves to the people who can specify work clearly.

How proficient are you at creating solutions with AI assistance or orchestrating AI agents?

The capstone of the Tools & Technical Skills pillar: producing real work outputs with AI, not just chatting with it. The options run from asking questions, through drafting documents and producing finished work, to building tools and setting several AI agents to work together, with no traditional coding required. Where Tool & Agent Proficiency (question 13) measured using AI, this measures building with it.