Introduction
Increased error rates in Ness Digital Engineering's AI-powered predictive maintenance solution for manufacturing clients pose a significant challenge to the product's effectiveness and customer satisfaction. To address this issue, I'll employ a systematic approach to identify, validate, and resolve the root cause while considering both immediate and long-term implications.
This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.
Step 1
Clarifying Questions (3 minutes)
Why it matters: This helps determine if it's a recent change or a long-term trend. Expected answer: A sudden spike in the last month. Impact on approach: A sudden spike would suggest a recent change or event as the cause.
Why it matters: AI performance is heavily dependent on its training data and models. Expected answer: No recent changes to data sources, but a model update was pushed last quarter. Impact on approach: This would focus our investigation on the recent model update and its potential effects.
Why it matters: This helps narrow down whether the issue is systemic or specific to certain use cases. Expected answer: Errors are more prevalent in newer, more complex machinery. Impact on approach: This would guide us to investigate how the solution interacts with newer, more complex systems.
Why it matters: This helps understand if it's a performance issue or a perception issue. Expected answer: A mix of both, but more client reports recently. Impact on approach: This would suggest investigating both system performance and client communication/training.
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