How AI ready are you?

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

The knowledge foundation — a cognitive grasp of what AI and agents can and cannot do. Measures depth of understanding only; spotting opportunities (D9) and building with AI (P5) are measured elsewhere.

How do you approach learning about AI developments?

AI evolves rapidly. A static understanding becomes obsolete within months. This dimension measures whether the professional has a sustainable learning habit — from passive consumption to active knowledge creation — which predicts long-term readiness better than any point-in-time skill measure.

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

AI will automate routine and coordination tasks while amplifying human judgement. Professionals who proactively identify which of their tasks are AI-replaceable versus irreplaceable can reposition toward higher-value work. This dimension measures strategic career thinking, not just tool adoption.

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

The defining personal mindset shift of the agent era: how willing professionals are to hand work to AI and agents and supervise the result rather than do it themselves. This measures volition and trust calibration — distinct from the skill of specifying work (D14) and the practice of verifying it (D8).

How proactively do you try AI on new problems you haven’t used it for?

How proactively professionals try AI on problems they haven't tackled with it before — drive and experimentation cadence. It measures the behaviour of trying, not the durable solutions they build (D15) or their study habits (D2).

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

Ethical AI use begins with knowing the principles exist. This dimension measures foundational awareness — from no exposure through active advocacy — because professionals cannot apply principles they have never encountered. Separated from risk identification (D7) because knowing ethics principles and being able to spot risks are distinct skills.

How well can you identify AI-related risks?

Risk identification requires domain-specific knowledge beyond general ethics awareness. This dimension measures whether professionals can name concrete risks (bias, hallucinations, privacy, agent-specific risks) and systematically assess them before adoption — a prerequisite for safe AI deployment that D6’s ethics awareness alone does not cover.

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

AI outputs (text, code, decisions) and agent actions (file changes, API calls) must be verified. This dimension measures the progression from blind trust to systematic verification workflows — the critical thinking layer that prevents AI errors from becoming real-world mistakes. Distinct from risk awareness (D7) because knowing risks exist is different from having a process to catch errors.

How well can you identify AI opportunities in your work?

Value creation starts with seeing where AI applies. This dimension measures the ability to scan one's own work for AI opportunities and progress from vague awareness to a prioritised portfolio with documented ROI — the bridge between AI literacy and AI impact.

How much has GenAI improved your personal productivity?

Identifying opportunities (D9) is necessary but insufficient — this dimension measures actual productivity impact. The progression from no use through transformative impact distinguishes between those who can talk about AI value and those who have captured it. The quantitative anchors (2–4 hours/week, 5+ hours/week) provide concrete benchmarks.

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

Working with data (D12) is different from knowing what makes data AI-ready. This dimension measures understanding of quality requirements — accuracy, completeness, timeliness — and the ability to set governance standards. The L4 anchor focuses on governance and quality standards (a data literacy skill) rather than infrastructure like vector databases (a P5 skill).

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

AI runs on data. Measures professionals’ hands-on capability with data work — preparing and shaping data for AI. It focuses on doing (clean, transform, prepare) rather than judging data fitness (D11) or the infrastructure/engineering skills that belong in P5.

How proficient are you with AI tools and AI agents?

The entry point to P5. Measures hands-on proficiency with AI tools and agents — from never having used them through architecting organisational ecosystems. The OR pattern at each level (e.g., "use multiple AI tools regularly OR delegate multi-step work tasks to AI agents") preserves backward compatibility: respondents who haven’t yet adopted agents can still accurately self-place.

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

The defining skill of the agent era. Measures the ability to decompose complex work into clear specifications that AI can execute — including success criteria, constraints, and quality checkpoints.

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

The capstone of P5. Measures the ability to produce functional outputs — not just use AI conversationally but create real work products. The progression moves from conversational Q&A through document drafting, functional outputs, tool building, to complex multi-agent orchestration. This dimension captures the "builder" mindset without requiring traditional coding skills.