| Clarity and Intent | Defining the “why” and making deliberate choices about what should exist. The core craft when generation is cheap and intermediary artifacts are reduced. | - Can articulate the basic problem and goal before using AI to explore solutions.
- Asks “what are we trying to achieve?” to frame AI-assisted work.
- Recognises when an AI output, while plausible, doesn't serve the core purpose.
| - Uses AI as a thinking partner to sharpen and challenge the definition of the problem.
- Clearly communicates intent to stakeholders and in prompts, focusing on outcomes over assets.
- Sequences work based on clarity of intent, prioritising what needs human reasoning first.
| - Shapes meaning, coherence, and direction for projects and teams, not just outputs.
- Sets a clear “why” that guides AI system behaviour and agent workflows, ensuring alignment with human values.
- Elevates taste and discernment as a creative act, defining what good looks like for novel challenges.
|
| Critical thinking | Ability to question assumptions, evaluate evidence, and spot plausible nonsense in AI outputs before it reaches customers or stakeholders. | - Checks AI outputs against basic facts, constraints, and source material.
- Notices obvious contradictions, gaps, or overconfident answers.
- Asks follow up questions when something does not feel right rather than accepting it.
| - Applies simple tests such as source checks, comparison across models, and sanity checks.
- Identifies subtle bias, missing context, or shallow reasoning in AI answers.
- Articulates why a given output should be accepted, revised, or rejected, and captures that learning.
| - Designs review patterns that reliably catch known failure modes in AI assisted workflows.
- Coaches others in how to challenge AI outputs without losing momentum.
- Builds guardrails and evaluation loops that reduce the chance of poor decisions at scale.
|
| Systems thinking | Understanding how data, tools, people, and processes connect. Shaping system behaviour, responses, and flows to curate an intended experience. | - Can describe the basic flow of work from input to AI steps to human review and output.
- Recognises when one step depends on another or on a specific data source.
- Flags when a change to an AI workflow might affect other teams or systems.
| - Maps end to end flows, including prompts, tools, data, and decision points.
- Anticipates unintended consequences when new automation is added or an agent takes on more work.
- Identifies leverage points where a small change improves the whole system, not just one step.
| - Designs AI enabled systems that are observable, resilient, and adaptable as models evolve.
- Uses feedback loops and data exhaust to improve quality, speed, and safety over time.
- Helps the organisation think in loops and workflows, not one off tools or pilots.
|
| Judgement and Taste | Knowing what good looks like for your domain, and choosing the option that best fits context and purpose. Producing clarity of intent is a key creative act. | - Can explain in plain language why one AI output is better than another.
- Aligns choices with existing guidelines, examples, and known constraints.
- Avoids obviously off brand, generic, or low quality outputs, even when they are fast.
| - Tunes AI prompts and workflows to produce outputs that closely match the desired style and intent.
- Balances speed with quality so work ships on time without feeling commoditised.
- Actively curates examples of good work and shares them as references for others and for agents.
| - Sets and evolves the standard for what good looks like in a function or team.
- Maintains coherence and meaning across multiple agents, touchpoints, and channels.
- Uses narrative and discernment to lift the overall bar as AI takes on more production work.
|
| Creative Problem Solving | Using non linear thinking and experimentation to navigate novel or messy problems, with AI as a collaborator and thinking partner rather than a vending machine. | - Tries more than one prompt, model, or tool when the first attempt fails.
- Breaks down vague problems into smaller, clearer questions for AI and for people.
- Uses AI to quickly explore multiple directions without premature commitment.
| - Uses AI as a thought partner to explore options, combine perspectives, and bring clarity to a problem space.
- Reframes problems from different angles to uncover better paths or beachheads.
- Builds quick prototypes, drafts, or simulations to test ideas with low risk and high learning.
| - Designs structured explorations for complex, ambiguous challenges that combine humans and agents.
- Helps teams escape local optima by challenging the problem framing and suggesting new use cases.
- Uses constraints as a tool to focus creativity and explore the intent behind choices.
|
| AI Fluency and Collaboration | Working with AI as a colleague in daily, meaningful work. Knowing when to hand work to machines, when to intervene, and where deep human judgement is non negotiable. | - Uses AI several times a week in real work, beyond generic email drafting.
- Can name at least two core workflows where AI or an agent meaningfully assists their role.
- Understands the basic limits of the models they use and checks outputs before acting.
| - Works more conversationally with AI, using it daily in core workflows for acceleration and thinking.
- Maintains a small personal prompt or pattern library for recurring tasks.
- Designs simple agent or tool chains to support their role and shares them with others.
| - Re-architects workflows around agents and automation where it gives compounding advantage.
- Monitors, validates, and improves digital teammates, treating agent operations as part of the job.
- Drives deep, sticky AI adoption in their area, focusing on daily use in the work that matters most.
|