Is AI the Key to Greater Software Developer Productivity?
This webinar was hosted by Gartner, and covered the key intersection(s) between Generative AI (GenAI) and software development. Specifically with regards to increasing workflow speed, efficiency, and output volume, in addition to quality.
- Identify the factors that most impact developer productivity
- Keith Mann identified various aspects in which GenAI is already increasing productivity. Examples include generating repetitive code. However, it’s worth noting that simple saving time isn’t equivalent to greater productivity. The saved time needs to be used effectively to add value in order to make using generative AI worth it.
- Determine if tools or experience will drive greater productivity
- Experience seems to drive greater productivity. Keith Mann identified how the state of flow can greatly impact productivity, far more than simply using smarter tools. Flow is the single most impactful thing to do for productivity. However, flow is hard to achieve and sustain, so the tools that can help achieve flow are very useful. If generative AI can improve % of time in flow, it could be of great benefit.
- Find out if artificial intelligence helps or hurts developer productivity
- AI can both help and hurt productivity, depending on use. If AI is used to save time, and that time is reallocated to valuable tasks, then productivity can be improved. However, if GenAI merely saves time, or leads to long-term struggles (such as low understanding of the codebase) there could be a decrease in productivity in the long run.
Overall, we need to think beyond just creating code faster with AI. It’s important to assess AI’s role in every aspect of production, not just generating code. We also need to consider how this changes the role of developers, and what new skills they may need, in addition to skills that no longer have value.