The Future of Software Development: How Enterprises Can Harness AI Technology
Of all enterprise departments, product and engineering spend by far the most on AI technology. Doing so effectively stands to generate huge value — developers can complete certain tasks up to 50% faster with generative AI, according to McKinsey.
However, that’s not as easy as just throwing money at AI and hoping for the best. Enterprises need to understand how much to budget into AI tools, how to weigh the benefits of AI versus new recruits, and how to ensure their training is on point. A recent study also found that who is using AI tools is a critical business decision, as less experienced developers get far more benefits out of AI than experienced ones.
Not making these calculations could lead to lackluster initiatives, a wasted budget, and even a loss of staff. At Waydev, we’ve spent the past year experimenting on the best way to use generative AI in our own software development processes, developing AI products, and measuring the success of AI tools in software teams.
Carry Out a Thorough Assessment Before Implementing AI Technology
Before diving into the world of AI, it’s essential to carry out a thorough assessment of your organization’s needs. This involves identifying areas where AI can be applied, understanding the potential benefits, and determining how to integrate AI technology seamlessly into your existing workflows.
At Waydev, we’ve found that the key to successful AI implementation lies in:
- Defining clear goals: Identify what you want to achieve with AI, whether it’s improving developer productivity or enhancing code quality.
- Assessing current processes: Evaluate your existing development workflows and identify areas where AI can be integrated to streamline tasks.
- Selecting the right tools: Choose AI solutions that align with your goals and fit seamlessly into your existing tech stack.
Best Practices for Implementing AI Technology
Implementing AI technology is not a one-time task; it’s an ongoing process that requires careful planning, execution, and monitoring. Here are some best practices to keep in mind:
- Start small: Begin with a pilot project or a small-scale implementation to test the waters and refine your approach.
- Monitor performance: Track key metrics to measure the effectiveness of AI technology and make adjustments as needed.
- Continuously educate teams: Provide training and support to ensure that developers understand how to use AI tools effectively.
Take Inspiration from Others
Especially when it comes to generative (or predictive) AI, this is a whole new world for enterprises, so it helps to consider what success looks like in other businesses. Follow similar companies that are talking openly about how they are leveraging AI in software development, from integrated developer environment (IDE) tools to general chatbots.
Some notable examples include:
- GitHub: Utilizes AI-powered tools to facilitate code review and improve collaboration among developers.
- Backstage: Built an open-source chatbot into its local version of the developer portal.
- Joe Welch’s examples: Used AI to create summaries of subsystems and modules to facilitate onboarding for new developers.
Every Step Will Require Careful Planning
Integrating AI into software development is not a 0-to-1 process. Every step will require careful planning to ensure that your time and money are going toward better overall developer experience and performance, not wasted.
By following these best practices and taking inspiration from others, you can harness the power of AI technology to drive innovation and improve your organization’s competitiveness in the market.
