When it comes to implementing AI technology in enterprise departments, there is no doubt that the product and engineering teams invest the most. And for good reason — according to McKinsey, utilizing generative AI can increase task completion by up to 50% for developers.
But the road to reaping the benefits of AI is not a simple one. It requires careful consideration of budget allocation, weighing the advantages of AI against hiring new employees, and ensuring proper training. Furthermore, a recent study reveals the crucial role of determining who will be using AI tools, as those with less experience tend to see greater benefits compared to their more seasoned counterparts.
Fail to make these calculations and you risk lackluster initiatives, wasted budget, and even loss of employees.
At Waydev, we have spent the past year experimenting with incorporating generative AI into our own software development processes, creating AI products, and studying the effectiveness of AI tools in software teams. Through our experiences, we have learned the key factors that enterprises need to consider when embarking on a serious AI investment in software development.
To start off, it is crucial to conduct a proof of concept.
Many of the AI tools currently emerging for engineering teams utilize brand new technology, requiring a significant amount of integration, onboarding, and training conducted in-house.
Before your CIO decides whether to allocate your budget towards hiring new employees or investing in AI development tools, it is important to conduct a thorough proof of concept. Our enterprise clients who have implemented AI tools in their engineering teams have done just that, aiming to determine whether the AI is truly providing tangible value, and if so, how much. Not only does this step help justify budget allocation, but it also promotes acceptance and support within the team.
The first step in this process is to clearly outline what areas of improvement you are targeting within the engineering team. Is it code security, velocity, or developer well-being? Once that is established, you can utilize an engineering management platform (EMP) or software engineering intelligence platform (SEIP) to track the impact of AI adoption on these variables. The specific metrics you use may vary, whether it’s measuring speed through cycle time, sprint time, or planned-to-done ratio. Look at whether there has been a decrease in failures or incidents, and consider any improvements in developer experience. Be sure to include value tracking metrics as well, to ensure that standards are not lowered in the process.
It is also important to assess outcomes across a variety of tasks, rather than limiting the proof of concept to a specific coding stage or project. This allows for a more comprehensive understanding of how AI tools operate in various scenarios and with coders of different abilities and job roles.
