If there is one Area where most engineering teams are located do not have Making the most of AI is team management.
Finding ways to better manage engineers is often approached more like an art than a science. Over the decades, engineering management has undoubtedly become more agile and data-driven, with automated data collection improving performance. However, the evolution of his AI, especially predictive AI, in recent months has ushered the management process into a new era.
Predictive AI analyzes data to predict patterns and behaviors that may occur in the future. Automatically set goals and generate recommendations to improve your team’s performance based on real-time data, and process much more information than before.
I want to encourage all other engineering management and intelligence platforms to start using AI so we can collectively move into a new era. No company wants to lose profits or market share due to poor management.
We now have the data and technology to transform engineering management from an art to a science. This is how engineering leaders can use her AI to manage their teams and achieve more with less effort.
identify hidden patterns
Even the most effective engineering leaders have some blind spots when reviewing performance in specific areas and can miss behaviors and causal factors. One of the most important ways an engineering manager can apply her AI to their workflow is by creating complete reports on engineer performance. Typically, managers manually generate reports at the end of the month or quarter, but they often only provide superficial analysis and can easily mask hidden or early problems. there is.
Over the past few months, advances in AI, especially predictive AI, have ushered management processes into a new era.
Predictive AI automates insightful performance reporting and lets leaders know where to improve. The main advantage here is that AI is better at identifying patterns. It can process all existing data about team performance, as well as internal and external benchmarking data, to generate a level of analysis that is difficult for humans to achieve at scale.
For example, AI can better analyze the relationship between cycle time, code review time, and code churn (how often code is changed). This allows you to determine whether longer code review times are actually reducing code churn. This could mean your code is more stable and well thought out. Or you may find that longer review times simply slow down the development process without significantly reducing churn.
By analyzing multiple metrics simultaneously, AI can help organizations identify patterns and correlations that may not be immediately apparent to administrators, helping organizations make more informed decisions and improve the software development process. so that you can optimize it.