From all projects Departments, Products and Engineering She spends by far the most On artificial intelligence technology. Doing this effectively generates significant value – developers can complete certain tasks up to 50% faster using generative AI. According to Mackenzie.
But this isn’t as easy as just spending money on AI and hoping for the best. Companies need to understand how much to budget for AI tools, how to weigh the benefits of AI against new employees, and how to ensure their training is on point. a Recent study Found that too from Using AI tools is a critical business decision, as less experienced developers get much more benefits from AI than experienced developers.
Not making these calculations can lead to lackluster initiatives, wasted budget, and even the loss of employees.
At Waydev, we’ve spent the past year experimenting with how best to use generative AI in our software development processes, developing AI products, and measuring the success of AI tools in software teams. Here’s what we’ve learned about how organizations can prepare to seriously invest in AI in software development.
Conduct a proof of concept
Many of today’s emerging AI tools for engineering teams are based on brand new technology, so you’ll need to do a lot of integration, onboarding, and training work in-house.
When your IT manager decides whether to spend your budget on more employees or AI development tools, you first need to conduct a proof of concept. Our enterprise customers adding AI tools to their engineering teams conduct proof-of-concept to determine if AI generates tangible value – and how much. This step is important not only in justifying the budget allocation but also in promoting buy-in across the team.
The first step is to identify what you want to improve within the engineering team. Is it code security, speed, or developer comfort? Then use an Engineering Management Platform (EMP) or Software Engineering Intelligence Platform (SEIP) to track whether your adoption of AI is moving the needle on those variables. Metrics can vary: You might track speed using cycle time, sprint time, or planned-to-executed ratio. Have the number of failures or accidents decreased? Has the developer experience been improved? Always include value tracking metrics to ensure standards do not drop.
Make sure you are evaluating results across a variety of tasks. Don’t limit proof of concept to a specific coding phase or project; Use it across various functions to see how AI tools perform best under different scenarios and with programmers with different skills and job roles.