Presentation

[Audio] Priya has several reasonable scheduling options in front of her. She is not asking AI to choose for her. She is using a structured MJA Academy resource to organize the decision, examine the tradeoffs, and keep human judgment where it belongs—at the center. [Sources] - MJA Academy: 03_Priya_Decision_Making_Matrix.xlsx (approved learner resource) - MJA Academy: Priya Shah approved learner identity reference - MJA Academy: Learning-in-Action and current visual production authorities [/Sources].

[Audio] The decision is not simply which option has the highest score. Priya must compare timing, workload, implementation demands, available evidence, and the risks attached to each choice. Every option may solve part of the problem, so she needs a transparent way to see what each one requires. [Sources] - MJA Academy: 03_Priya_Decision_Making_Matrix.xlsx (approved learner resource) - MJA Academy: Priya Shah approved learner identity reference - MJA Academy: Learning-in-Action and current visual production authorities [/Sources].

[Audio] The MJA Decision-Making Matrix provides the structure. Priya names the decision, defines Options A, B, and C, selects the criteria, assigns weights, adds ratings supported by evidence, and reviews scheduling impacts and risks. The calculated totals support the decision; they do not replace it. [Sources] - MJA Academy: 03_Priya_Decision_Making_Matrix.xlsx (approved learner resource) - MJA Academy: Priya Shah approved learner identity reference - MJA Academy: Learning-in-Action and current visual production authorities [/Sources].

[Audio] Before AI can organize anything, Priya must define the decision. She identifies the three options, chooses the criteria that reflect the real purpose of the decision, assigns weights that total one hundred percent, and identifies the evidence available for each rating. The values come from Priya—not from AI. [Sources] - MJA Academy: 03_Priya_Decision_Making_Matrix.xlsx (approved learner resource) - MJA Academy: Priya Shah approved learner identity reference - MJA Academy: Learning-in-Action and current visual production authorities [/Sources].

[Audio] AI can organize Priya's supplied criteria, weights, ratings, and evidence. It can point out missing information, conflicting constraints, assumptions, and tradeoffs that deserve attention. But this is only an organized draft. AI does not know which values should lead, and it does not select the option. [Sources] - MJA Academy: 03_Priya_Decision_Making_Matrix.xlsx (approved learner resource) - MJA Academy: Priya Shah approved learner identity reference - MJA Academy: Learning-in-Action and current visual production authorities [/Sources].

[Audio] Priya reviews the organized comparison instead of accepting it. She notices missing evidence, questions whether one criterion is weighted too heavily, corrects information, and identifies a scheduling conflict. She also adds context that was never part of the original input. This review changes the quality of the decision record. [Sources] - MJA Academy: 03_Priya_Decision_Making_Matrix.xlsx (approved learner resource) - MJA Academy: Priya Shah approved learner identity reference - MJA Academy: Learning-in-Action and current visual production authorities [/Sources].

[Audio] A responsible leadership decision includes more than the numbers in a table. Priya considers workload, change readiness, communication needs, timing, and the people who will live with the result. She listens, applies context, and decides what actually matters. Human responsibility remains visible. [Sources] - MJA Academy: 03_Priya_Decision_Making_Matrix.xlsx (approved learner resource) - MJA Academy: Priya Shah approved learner identity reference - MJA Academy: Learning-in-Action and current visual production authorities [/Sources].

[Audio] The completed matrix creates a transparent decision record. In this illustrative example, the revised comparison supports Option B. Priya records the evidence, tradeoffs, scheduling impact, remaining risk, and her rationale. The score helped her see the pattern. The final decision still belongs to Priya. [Sources] - MJA Academy: 03_Priya_Decision_Making_Matrix.xlsx (approved learner resource) - MJA Academy: Priya Shah approved learner identity reference - MJA Academy: Learning-in-Action and current visual production authorities [/Sources].

[Audio] Now try the same thinking yourself. Choose a low-risk decision with three reasonable options. Define the criteria, decide how much each criterion should count, and add the evidence you already have. AI may help organize and compare. You review the assumptions and make the decision. [Sources] - MJA Academy: 03_Priya_Decision_Making_Matrix.xlsx (approved learner resource) - MJA Academy: Priya Shah approved learner identity reference - MJA Academy: Learning-in-Action and current visual production authorities [/Sources].

[Audio] AI can assist with organization, comparison, and questions. Human judgment leads. Compare three low-risk options using criteria you define, review what the analysis leaves out, and record the reason for your final choice. Try the MJA Decision-Making Matrix when you are ready. [Sources] - MJA Academy: 03_Priya_Decision_Making_Matrix.xlsx (approved learner resource) - MJA Academy: Priya Shah approved learner identity reference - MJA Academy: Learning-in-Action and current visual production authorities [/Sources].