What might I practise and display?
Which assumption about the work, the machine or my own role deserves a test? What would someone see me do differently?
AI and human / Learning into practice
Make the learning useful in your next decision.
Bring one real priority. Use AI and what you experienced in your session to explore what deserves to change, choose a small experiment and learn from what happens.
For executive leaders, their direct reports and people leading a significant area of work. The same depth of thinking applies whether you lead the enterprise, a function or a team. Your responsibility and situation shape the practice.
01 / Bring the learning to your work
Start where you are.
Choose the session you attended. These questions help you consider your own practice, the conditions around it and what to explore with others. Choose what matters to your work.
My judgement in real work
Use a live decision, document or conversation to explore what AI changes and what still needs your judgement.
What might we need from each other?
Whose feedback would help me judge the result? Team involvement can wait if this is a personal experiment.
Bring technical and adaptive learning together
Try one method from your session on a live priority. Produce something useful, check a pivotal claim against its source and explain what you would retain or change.
02 / Choose something worth trying
Give the practice a place to happen.
A real occasion
Which decision, conversation or recurring task is coming up? What should improve for the customer or next recipient? Write your provisional view before asking AI for suggestions.
The obstacle that matters
Do you need skill or confidence, time or access, a different expectation or permission to act? Explore what makes the current way attractive or sensible. Consider one condition you can influence.
A small experiment
Choose a practical AI action, useful output and independent check, alongside a habit or assumption to test. Agree any team involvement and what support you will provide. Decide what to stop or defer.
The next real meeting or decision is your cue. Day, week and month can help you review progress; adapt the timing to the work. If you miss an attempt, choose a manageable restart.
What could this look like for a head of FP&A?
Illustrative scenario, not a client result or a prescribed action.
A head of FP&A in a large organisation is preparing an investment recommendation. They use AI to compare their preferred interpretation with a credible alternative, then check the pivotal assumptions against the original evidence.
They also rehearse responding when a colleague challenges their preferred option. In the real review, they choose to make room for that alternative before announcing their view. They agree what evidence the team will bring and protect the time needed to examine it.
Afterwards, they review the work and a colleague’s observations. The original recommendation may still stand. What matters is whether the reasoning, interaction and eventual decision were better informed, and what they should try next.
03 / Think it through with AI
Use the conversation that helps now.
Copy the text into your organisation’s approved AI tool. Add your own context there, with permitted session materials, notes or transcript extracts. A transcript is optional. This page does not collect your work or session records.
Choose your next experiment
AI should ask and wait, offer options and let you choose. You can ask for an example, more challenge or less guidance.
04 / Return with evidence
Notice what changes in the work.
Next day: choose
Identify the next real opportunity. Choose what to practise, what may prevent it and the support or agreement you need.
Next week: learn from an attempt
Bring the output, source checks and any agreed feedback. What happened? If you did not try it, what got in the way? Keep, revise or stop the experiment.
Next month: examine transfer
What have you repeated? Can you explain the reasoning and limits, and use the learning in a different situation? Check effects on colleagues, customers and downstream work.
One good attempt can build confidence. Further practice and evidence help you decide what deserves to become part of how you work. A month is a review point, not proof of a lasting habit.
Keep a short experiment card
- The real situation and outcome that matters.
- The action, useful output and independent check.
- The assumption or visible behaviour you want to test.
- The obstacle, leadership support and any agreement needed.
- The next occasion and evidence you will bring back.
Write it in your own words. Consider what the experience changes in your AI philosophy and which future decision that principle should inform.
Use AI’s interpretation with care
Check important claims against original sources or another appropriate independent method. Another AI answer can help you explore, but does not by itself verify a claim.
A transcript is a partial record. Ask for actual passages, distinguish observation from interpretation, and check meaning with the people involved. Avoid inferring motives or scoring colleagues. Choose material you have permission to use in the approved environment.
Confidence, AI usage and a completed plan are useful observations, but the evidence of learning is in your understanding, practice and consequences.
The thinking behind this guide
The design draws on research into capability, opportunity and motivation, practice and feedback in leadership development, and linking intentions to real situations.
A 2025 study of knowledge workers also highlights the importance of examining thinking when working with AI. It reports associations and self-reported effort, rather than proving loss of skill.
These sources inform the approach. They do not establish the effectiveness of this particular guide for leaders. Use the evidence from your own practice to refine it.