Practice / The complete method
A small problem.
A real improvement.
The Painkiller Protocol: find what hurts, explore a change and build a way of working you can reuse.
Start in your own working day.
“Here is an AI tool. Find a use case.” That leaves the hardest part with the person who is still learning what the tool can do. I prefer to start with something you already recognise: the task that makes you think, “Not this again.”
Use these three conversations in sequence. You can do them on your own or bring them to a colleague or an AI-fika. Stay with one problem. The outcome should be a useful improvement and something you have learned.
- Define the painA clear problem, before a proposed solution.
- Explore the smallest useful changeA direction and a test you can actually run.
- Build, check and reuseA practical kit that can help the next time.
Step 1
What keeps getting in your way?
Look for a task that comes back, follows a recognisable pattern and consumes more effort than it deserves. Reading, writing, summarising and restructuring information are good places to explore. Choose work within your own mandate, using material you may give to the tool.
At this stage, the AI should ask questions and help you describe the problem. It should resist the temptation to design a large solution.
Act as a curious, practical colleague. Help me identify ONE small, recurring task in my own work that I could realistically improve with an AI chat tool. Ask one question at a time and wait for my answer. Be encouraging, but challenge me if the problem is vague, too large or outside my control. Look for something I do daily or weekly, with a similar pattern each time, that feels like unnecessary manual work. Start with something I can try using text or a document I am allowed to share. Do not assume new software, integrations or permissions. Do not propose solutions yet. When we have a useful problem, help me write: 1. A short task name. 2. A two- or three-sentence description: what I do, what makes it frustrating and what keeps recurring. 3. A rough estimate of the possible time or effort saved per week, using information I give you. If we cannot estimate it, say so. Do not invent a number. Ask me to confirm this description before moving on. Begin with one simple question.
A manageable example.
“After meetings I turn rough notes into a consistent follow-up. I keep searching for decisions and rewriting the same headings. I want a draft I can quickly check.”
That is a fictional example. “Fix all our meetings” is too large for the first attempt.
Step 2
Could the work become simpler?
Bring your problem description into the next conversation. First consider removing or simplifying a step. Then explore how AI could help. Sometimes a reusable prompt is enough. Sometimes the useful discovery is that the first problem needs to be made smaller.
If you do this together, share anonymised problem descriptions. Recognising a colleague's frustration can be easier than identifying your own starting point.
Help me reduce or remove this recurring frustration. Task: [name] Problem: [description from step 1] Possible benefit: [my estimate, or not yet estimated] Tools and permitted inputs available to me: [describe] First check that you understand the work. Ask one question at a time where it matters. Consider whether a step can be removed or simplified before adding automation. Suggest the smallest change I can test in an ordinary AI conversation. Describe what I supply, what you produce and what I need to check. Compare alternatives briefly if useful. Separate what we can try now from anything requiring new access or a decision from someone else. Finish with one recommended test and a clear way to judge whether it helped. Wait for my choice before building the full solution.
Step 3
Build something worth using again.
A good answer once is useful. A process that survives the next input is more useful. Ask AI to help create the instructions, examples and checks around the task. Test the result on something different from the example used to develop it.
Start with a short working guide, a reusable prompt and one test. The rest of the kit should make the method easier to use, not create a pile of documentation.
Help me turn our chosen improvement into a small, reusable working kit. Task and problem: [from step 1] Chosen approach and first test: [from step 2] Quality requirements and boundaries: [what must be true] Create: 1. A short guide explaining the purpose, required input, steps and my responsibilities. 2. One reusable prompt with clear placeholders for the next input. 3. Two short variations for common exceptions. 4. Realistic but fictional test inputs and proposed good outputs that I can review. 5. A checklist for checking completeness, accuracy and whether the result is useful. 6. A simple before/after measurement plan including setup, checking and correction time. Separate estimates from measured results. 7. A five-minute way to show a colleague what changed and how to try it. If an agent could help later, describe the next step and required access separately. Do not create or activate it now. Keep the kit proportionate to this small task. Ask me to test it on a fresh input before calling it reliable.
Keep the learning.
After the next few uses, ask: Did it help? Where did I have to correct it? What changed in my workload or attention? Could a colleague use it without me standing beside them?
Then save the useful version and show someone. That is the move from improving your own work to developing the team.
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About this companion and its sources
Prepared from the final 33-page presentation, the Circleback session transcript and selected frames from its screen-share recording. The pages follow the delivered talk and expand its methods into practical exercises. They are an edited companion, not a verbatim transcript.
Karin, Marvin and my working setup are my accounts of experience. The Kasparov framework is my teaching interpretation. The fear curve is an illustration. The 16-hour learning plan is a target-based example, not measured savings. Product snapshots and broad statistical claims have not been republished as current facts.
The recorded closing questions are paraphrased without attendee names. Meeting-chat contributions and full poll results will be added after they have been supplied and reviewed. The raw recording, private context and source transcript are not published here.
AI + JJ · 12 October 2026. No added analytics, sign-in or data collection. The appearance preference is stored only in your browser. External links have their own services and policies.