AI in software development: what does it mean for the tech industry?
Artificial intelligence (AI) adoption ranks high on the list of IT investment priorities across industries. Yet, businesses remain cautious about going all in. Generative AI is still a relatively new technology. Around 56% of executives view its integration in software development workflows as potentially risky. On the other hand, 46% of early AI coding assistant adopters claim a 5-10% revenue growth.
Industry leaders like Toyota, Coca-Cola, Mercedes-Benz, EY, and Capgemini have already implemented AI coding tools for in-house teams and reported significant productivity gains.
When it comes to AI in software development, it’s all shades of gray. The end value will depend greatly on the selected use cases, team skillsets, and overall engineering process maturity.
In this post, we will discuss the benefits and limitations of AI in software development and share some best practices from early adopters.
Use cases of AI in software development
Software developers are eager to adopt AI coding assistants because they can significantly accelerate and automate software delivery. These tools augment developers’ skills and remove a lot of low-value, repetitive tasks.
For example, instead of generating boilerplate code or doing manual code reviews, developers can dedicate their time to writing new code, creating original solutions to problems, and learning new skills. Yet, there are many more use cases where software engineers use Gen AI tools.
Benefits of using AI in software development
AI can potentially transform how fast and smoothly software engineering teams create, test, and deploy new software. According to McKinsey, developer productivity can increase by up to 45% when using AI coding tools. In particular, gen AI can accelerate code documentation by 45-50% and refactoring by 20-30%.
Challenges of implementing AI in software development
Although there are gains, AI adoption in a software development process is not without stumbling blocks. And it’s far from being mainstream. Only 13% of organizations implemented AI in their software engineering workflows.
Why such a precaution?
72%
of leaders, at least, worry about AI tools accessing sensitive customer information.
Source: GitLab
7 best practices for adopting AI in software development
The adoption of AI in software development requires careful planning. It’s not enough to select the most popular AI coding assistant, demonstrate it to your development teams, and then expect results in several months. You must train your engineers, implement robust security controls, and establish governance standards. This is no easy task, especially for larger IT departments.
Start with a proof of concept deployment like Duolingo and PayPal did, then work up to scaling your AI-assisted engineering program. Here’s how to do so:
What’s next?
Generative AI can add value to your software development processes, reducing routine tasks and empowering your teams to work smarter, not harder. What’s more, the technology offers almost limitless flexibility and scaling potential.
McKinsey states that market leaders use gen AI in three or more business functions while their less prominent competitors use it in two. Once you’ve seen the potential of AI based on the software development pilot project, you can consider bringing automation to other functions like marketing, sales, and HR.
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