When should you measure success for an AI initiative that is being built from scratch?

Question

BlackTom AI · Accepted Answer

When evaluating when to measure success for an AI initiative built from scratch, consider the project lifecycle and how feedback informs improvement.
Option 1: 'After full deployment' suggests waiting until the entire system is live to assess success. This delay can miss early issues, validation gaps, and opportunities to iterate, making it less effective for guiding development.
Option 2: 'During development and post-deployment' aligns with iterative, evidence-based practice. Measuring during development allows for monitoring progress, validating assumptions, and adjusting design or data collection as needed, while post-deployment measurements confirm real-world impact and sustainment.
Option 3: 'Before development starts' would ignore any baseline, metrics, or goals, which are essential for knowing what success even looks like and for shaping the development plan.
Option 4: 'Only after deployment' is similar to option 1 in delaying insights, and it neglects the value of continuous monitoring, control over quality, and early detection of misalignment with business or user needs.
In summary, a practice of evaluating success throughout the build (during development) and again after deployment provides continuous learning, better risk management, and clearer alignment with desired outcomes, whereas the other options imply inert or delayed measurement that reduces the ability to steer the project effectively.

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