Keep ownership of decisions when AI offers advice
You ask AI which option to choose. It provides a clear recommendation, reasons and an implementation plan. The next step can easily become polishing a few details and accepting. Yet a well-explained option still needs to be compared with your own objectives.
This article proposes a compact decision log for work choices such as selecting a support process or designing a feature.
🔬 1. Read the research within its scope
On October 27, 2026, Anthropic described a study with 50 analysts using Claude Code. Agents received randomized preferences; analysts' analytical choices matched them 86% of the time. That is not an AI accuracy rate. The study used one task and deliberately strengthened agent preferences, limiting generalization. Anthropic research.
For everyday work, ask: “Which criteria justify my choice, and what evidence would change my mind?” Record the answer in the project document.
📝 2. Set criteria before requesting advice
Imagine a software team choosing between a dedicated support form and its existing shared inbox. This is an illustrative scenario.
Before asking AI, record three criteria: intake time, traceability and operating effort. State practical constraints: one support person per shift, customers accustomed to email and no new management tool.
Avoid changing the weights merely to make the recommended option win. If priorities change, record why and who confirmed the change.
⚖️ 3. Request comparable alternatives
“A form looks more professional” is not enough. Ask what additional steps each option requires, what information remains unknown and how to test it.
Include continuing the current process. Sometimes the missing piece is a response template or a clear owner, rather than another tool.
Apply the same criteria to every alternative. Do not compare an ideal version of option A against a broken version of option B.
💬 4. A prompt for expanding the options
I need a comparison, not a decision made for me. Decision: [describe] Criteria, in priority order: [...] Confirmed constraints: [...] Available evidence: [...] Offer feasible options, including the current approach. For each option, state: - Fit and trade-offs against each criterion. - Known facts, assumptions and missing information. - A small test that could disprove its suitability. - Conditions under which it would be unsuitable. Do not redefine my priorities. Ask about decisive missing facts instead of concluding.
Another answer from the same AI is not independent evidence. Use it to identify checks involving data, documentation or the responsible people.
🧪 5. Run a reversible trial
In the support example, try a few simulated internal requests through the form. Record submission time, missing fields and how the support person retrieves the history.
Those tests address operational questions; they do not establish that real customers prefer the form. Gather feedback from a relevant customer group before a broader change when needed.
Set stopping conditions in advance: lost requests, confusing required fields or excessive re-entry. This is a practical suggestion, not an intervention validated by the study above.
✅ 6. Save the rationale and review date
A short record can include decision, criteria, evidence, trade-offs, owner and review date. Separate reasons you verified from suggestions that merely sounded plausible.
For example: “Keep the inbox this month; add request identifiers because the trial revealed slow retrieval. Review after collecting feedback.” That decision has a specific basis and leaves room to adapt.
Ownership means being able to explain the choice, identify its limits and revise it when evidence changes. The confirmation button is simply the final step.