this post was submitted on 04 Nov 2023
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Technology
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This is the best summary I could come up with:
Over the decades, engineering management has undoubtedly become more agile and data-driven, with automated data gathering improving performance.
It can automatically set goals based on real-time data, generate recommendations for improving teams’ performance, and process far more information than was possible before.
Even the most capable engineering leaders have some blind spots when it comes to reviewing performance in certain areas, and may miss concerning behaviors or causal factors.
Typically, managers will manually put together reports at the end of the month or quarter, but often that gives a superficial analysis that can easily conceal hidden or incipient problems.
Or, it may find that longer review times are simply delaying the development process without any significant reduction in churn.
By analyzing multiple metrics simultaneously, AI can help identify patterns and correlations that might not be immediately apparent to managers, enabling organizations to make more informed decisions to optimize their software development processes.
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blind spots - something the AI has too?
A capable manager may make use of known unknowns. Using an AI where you can't follow the process seems risky. Asking the AI to explain itself may be elaborate hallucination.