Which practice ensures metrics align with business outcomes when evaluating AI interventions?

Which practice ensures metrics align with business outcomes when evaluating AI interventions?

• A. Metrics aligned to business outcomes
• B. Randomly selected metrics
• C. Focus on training time
• D. Ignore performance metrics

Answer: (A)

When evaluating AI interventions, the key is to choose metrics that reflect the business outcomes you actually want to influence. This means picking measures tied to value the AI delivers, such as revenue impact, cost savings, faster cycle times, reduced risk, or improved customer satisfaction. With metrics aligned to business goals, you can clearly see whether the AI initiative moves the needle, set meaningful targets, and compare different approaches in terms of real-world impact. For example, if the objective is faster customer support, you would track time-to-resolution or customer effort rather than just model accuracy, because those measures show true value to the business. Metrics that are randomly selected don’t provide a coherent view of impact, and focusing only on training time analyzes efficiency of development rather than business results. Ignoring performance metrics leaves you without evidence of how well the AI system actually functions, which is essential before judging value.

When evaluating AI interventions, the key is to choose metrics that reflect the business outcomes you actually want to influence. This means picking measures tied to value the AI delivers, such as revenue impact, cost savings, faster cycle times, reduced risk, or improved customer satisfaction. With metrics aligned to business goals, you can clearly see whether the AI initiative moves the needle, set meaningful targets, and compare different approaches in terms of real-world impact. For example, if the objective is faster customer support, you would track time-to-resolution or customer effort rather than just model accuracy, because those measures show true value to the business.

Metrics that are randomly selected don’t provide a coherent view of impact, and focusing only on training time analyzes efficiency of development rather than business results. Ignoring performance metrics leaves you without evidence of how well the AI system actually functions, which is essential before judging value.