In all organizations Divisions, production and engineering spend the most on AI technology. According to McKinsey, doing it effectively generates huge value – developers can complete certain tasks up to 50% faster with productive AI.
But it's not as simple as throwing money at AI and hoping for the best. How much should enterprises budget for AI tools, how to weigh the benefits of AI against new recruits, and how to ensure their training is on point. A recent study also concluded that WHO Using AI tools is a critical business decision, as developers with less experience will benefit far more from AI than experienced ones.
Failure to do these calculations can lead to poor programs, wasted budget, and lost staff.
At Vedev, we spent the last year experimenting with the best way to use generative AI in our own software development processes, develop AI products, and measure the success of AI tools in software teams. Here's what we learned about how enterprises should prepare for serious AI investment in software development.
Conduct a proof of concept
Many of the AI tools emerging today for engineering teams are based on entirely new technology, so you'll have to do a lot of the integration, onboarding, and training work.
When your CIO is deciding whether to spend your budget on more hires or AI development tools, you need to conduct a proof of concept first. Our enterprise customers who are adding AI tools to their engineering teams are doing proofs of concept to determine if AI is generating tangible value – and how much. This step is important not only in justifying the budget allocation but also in encouraging buy-in across the team.
The first step is to specify what you want to improve on the engineering team. Is it code security, speed or developer welfare? Use an Engineering Management Platform (EMP) or a Software Engineering Intelligence Platform (SEIP) to determine if your adoption of AI is moving the needle on those variables. Metrics can vary: you can track speed using cycle time, sprint time, or the ratio of completion to plan. Have the number of failures or incidents decreased? Improved developer experience? Always include value tracking metrics to ensure standards don't drop.
Make sure you measure results across a variety of tasks. Don't limit the proof of concept to a specific coding phase or project; Use it in different functions to see if AI tools work better in different situations and with coders of different skills and job roles.