among all companies Departments, Products and Engineering spend much more About AI technology. Doing this effectively can create tremendous value. Developers can use generative AI to complete certain tasks up to 50% faster. According to McKinsey.
But it’s not as simple as throwing money at AI and hoping for the best. Companies need to understand how much to spend on AI tools, how to weigh the benefits of AI versus new hires, and how to ensure they’re getting the training right.a recent research I also found that who Deciding whether to use an AI tool is an important business decision because junior developers can benefit much more from AI than experienced developers.
Not making these calculations can lead to lackluster efforts, wasted budget, and even loss of staff.
At Waydev, we’ve spent the past year experimenting with the best ways to use generative AI in our software development processes, developing AI products, and measuring the success of AI tools on our software teams. Here’s what we learned about how companies need to prepare for serious AI investments in software development.
Conduct a proof of concept
Many of the AI tools emerging for engineering teams today are based on entirely new technologies, requiring much of the integration, onboarding, and training work to be done in-house.
When CIOs decide whether to spend their budget on hiring increases or AI development tools, they must first perform a proof of concept. Our enterprise customers who are adding AI tools to their engineering teams are conducting proofs of concept to establish whether and how much AI is creating measurable value. This step is important not only to justify the budget allocation but also to foster buy-in across the team.
The first step is to specify what you want to improve within your engineering team. Is it code security, speed, or developer happiness? Then use an Engineering Management Platform (EMP) or Software Engineering Intelligence Platform (SEIP) to determine how these variables change with the introduction of AI. Track whether you are doing so. Metrics vary. You might track velocity using cycle time, sprint time, or planned percentage complete. Have the number of failures and incidents decreased? Has the developer experience improved? Always include value tracking metrics to ensure standards are not declining.
Be sure to evaluate the results of various tasks. Don’t limit your proof of concept to a specific coding stage or project. Using it across different functions allows programmers with different skills and job functions to improve the performance of AI tools under different scenarios.