Use AI to Build a Transparent Decision Matrix

AI can organise a decision without making the decision for you. A decision matrix lists options, criteria and scores so you can see why one choice appears stronger. Its value comes from transparent assumptions, not from a final number that looks scientific.

Practice time: About 15 minutes. Use any text-based AI assistant you already have access to. Interface names and available tools vary. The examples below are fictional, and you do not need a paid plan or private information to practise.

Start with a small, clear example

Compare three fictional venues for a study group. Venue A costs 20, fits 10 people and is a 5-minute walk away. B costs 10, fits 8 and is 20 minutes away. C costs 25, fits 15 and is 10 minutes away. You expect 10 people and have a maximum budget of 25.

Try this prompt

Copy the following into your assistant. Keep the sample input attached to the request so the task can be understood without guessing. Treat the response as a draft to inspect, not as a finished answer simply because it sounds confident.

Help compare these venues for a 10-person study group. First separate must-have requirements from preferences. Exclude options that fail a must-have before scoring. Suggest a simple scorecard for cost and travel convenience, but ask me to choose the weights. Show the raw facts beside the scores. Do not invent venue features.

Work through the result

Capacity is a requirement, so B should not win simply because it is cheapest. Excluding it first prevents a weighted score from disguising an unsuitable option. A and C remain within budget and capacity.

Choose a transparent scoring rule, such as lower cost and shorter walking time being better within the remaining options. State the scale and weights. Different scales can produce different winners, so the assistant should not present its first scoring scheme as objective truth.

Test sensitivity by changing a weight. If a tiny change reverses the ranking, describe the decision as close. Then consider factors not in the data, such as accessibility or opening hours, as unanswered questions rather than scored facts.

Catch the common mistakes

Do not let the assistant supply factual ratings for real products without current sources. Also avoid scoring a must-have as merely a low-priority preference. A venue that cannot fit the group is not rescued by a pleasant walk.

Check before you reuse it

Compare the response with your original input. Look for a missing condition, a changed number and any information the assistant added without support. Read names, dates and instructions particularly carefully. If the task involves an action, distinguish a suggested action from one you have actually completed.

Revise one specific weakness at a time. A follow-up such as “keep the uncertain date marked unconfirmed” gives a clearer correction than “make it better.” Keep the useful parts and check the revised passage again. An improved format does not guarantee improved accuracy.

Try it yourself

Add a confirmed requirement that the venue must be step-free. A is not step-free; C is. What should happen to the ranking, and what information is still missing?

Keep exploring: AI learning path.