All businesses parts, products and engineering they spend by far the most in AI technology. Doing this effectively creates enormous value — developers can complete certain tasks up to 50% faster by building AI, according to McKinsey.
But that’s not as easy as just throwing money at AI and hoping for the best. Businesses need to understand how much to budget for AI tools, how to weigh the benefits of AI against new hires, and how to ensure their training is right. ONE recent study also found that WHERE The use of AI tools is a critical business decision, as less experienced developers get much more benefit from AI than experienced developers.
Failure to make these calculations could lead to unsustainable initiatives, wasted budget and even lost staff.
At Waydev, we’ve spent the past year experimenting with how best to use genetic AI in our own software development processes, developing AI products, and measuring the success of AI tools on software teams. Here’s what we learned about how businesses should prepare for a serious AI investment in software development.
Run a proof of concept
Many AI tools emerging today for engineering teams are based on entirely new technology, so you’ll need to do much of the onboarding, onboarding, and training work in-house.
When your CIO is deciding whether to spend your budget on more hiring or AI development tools, you need to do a proof of concept first. Our enterprise customers who are adding AI tools to their engineering teams do a proof of concept to see if AI is creating tangible value — and how much. This step is important not only to justify the budget allocation but also to promote buy-in across the team.
The first step is to identify what you want to improve on the engineering team. Is it code safety, speed, or developer welfare? Then use an engineering management platform (EMP) or a software engineering intelligence platform (SEIP) to track whether your AI adoption is moving the needle on these variables. Metrics may vary: You may track speed using cycle time, sprint time, or scheduled run ratio. Did the number of failures or incidents decrease? Has the developer experience improved? Always include value tracking metrics to ensure standards don’t fall.
Make sure you evaluate results on a variety of tasks. Don’t limit your proof of concept to a specific stage or coding project. use it in different modes to see how AI tools perform best in different scenarios and with coders of different skills and job roles.
