Fluency is not evidence
Language models are designed to produce plausible outputs. A confident tone does not establish that sources are real, facts are current or reasoning fits the circumstances. High-quality work therefore begins by separating the model’s output from verified evidence.
Use AI before and after your thinking
Before a decision, use AI to organise information, expose missing questions and generate competing hypotheses. Then require the responsible person to write a short decision memo in their own words: facts, assumptions, options, risks and recommendation. Afterward, AI can challenge the memo and test scenarios.
Create comprehension checks
Ask the decision owner to explain the recommendation without the generated document. Require source links for material claims and label uncertainty. For high-impact decisions, use an independent reviewer who did not create the first analysis.
Accountability must remain visible
Responsible AI guidance emphasises human oversight, transparency and risk management. An organisation should be able to identify who approved a decision, what information they considered, how AI contributed and what monitoring follows. Efficiency is valuable only when responsibility survives.
Sources and further reading
Sources validate the general principles discussed. Conclusions and practical recommendations are the author’s synthesis.