By integrating artificial intelligence (AI) into the coding process, software developers can spend more time on strategic tasks, reduce cognitive load, and deliver greater value.
Organizations are already making significant investments in AI. According to GitLab’s 2024 Global DevSecOps Report, 78% of respondents said they are currently using AI in software development or plan to in the next two years, up from 64% in 2023. And organizations adopting AI are already seeing benefits, such as improved developer productivity, better code quality, and more secure code. Embracing AI enables development teams to devote more time to creative problem-solving and innovation rather than time-consuming and repetitive tasks such as manually writing boilerplate code.
Despite AI's clear benefits, teams may struggle to integrate AI tools successfully into their day-to-day processes. This challenge can be attributed to various factors, such as a lack of knowledge or resources, difficulty adapting existing workflows and tools, and the fear of losing jobs to automation. Nearly half (49%) of our survey respondents voiced concern that AI will replace their roles in the next five years.
Understanding where your team is today is necessary to set them up for success when integrating AI. Our research shows that the majority (56%) of organizations are in the Evaluation and Exploration stage — meaning most teams have started to set achievable targets for AI adoption but haven’t actually started using it in their software development lifecycle.
Whether you’re an early adopter or you’re still exploring the idea of AI, here are six strategies you can use to set your team up for success:
1. Clarify the goals and objectives of AI adoption
Your first step should be to create an AI governance model for your organization. What are the goals and objectives of AI adoption? How will it fit into your existing processes and workflows?
Identifying a leader to oversee AI strategy and implementation is critical. While some companies are beginning to hire a chief AI officer (CAIO), the role doesn’t have to be an immediate addition to the C-suite; it can be a transitional title that a VP assumes to coordinate AI usage across teams.
The primary goal is to identify and prioritize high-impact AI use cases that directly support business outcomes, focusing on areas where AI can create significant value, such as automation, personalization, or data-driven decision-making. It’s important to remember that AI success isn’t possible without first addressing the privacy, security, and legal requirements your organization might face and how AI adoption plays into continued compliance.
2. Establish AI guardrails and workflows
Before incorporating AI into your development environment, you'll need to establish guidelines to ensure it is used responsibly and effectively. Set up automated testing, including using a security analyzer, to create a gating mechanism that ensures all AI-generated code is reviewed before being promoted to production. And beware of shadow AI — the latest variation of shadow IT — where workers adopt their own AI assistants while working on your code base, which can lead to the leakage of sensitive information and intellectual property.
You'll also want to think now about how your teams will use different machine learning models for different types of tasks. One size does not fit all. Large language models (LLMs) are often tuned for specific tasks, meaning teams that are using the same AI models across multiple use cases may not be getting optimal results. As you shop around for AI tools, look for vendors that allow you to use a variety of models tailored to specific use cases — this will save you from having to rip and replace later.
3. Build a data-driven AI structure
The results that AI can drive for organizations are only as good as the data that AI systems have access to. Feeding data into your AI systems will allow you to tailor the results to your organization’s needs and improve efficiency and productivity across your software development lifecycle. However, long-term success requires a structured approach that allows data to be used across the organization to inform prompts and enhance generative AI outputs.
To that end, enterprises must:
- Ensure robust data collection, storage, cleaning, and processing mechanisms.
- Establish clear governance around data access, usage, security, and privacy, especially to ensure compliance with regulations like GDPR or CCPA.
- Break down data silos to facilitate cross-department collaboration and leverage data across various parts of the organization. Now is the time for developers and data scientists to collaborate on using data warehouses and data lakes to facilitate access to training models and application usage.
4. Focus on talent and culture transformation
Continuous upskilling is critical to safely, securely, and responsibly unlocking AI’s potential. Build a team of data scientists, AI engineers, and other experts to design, develop, and implement AI solutions. Upskilling employees to ensure they can use and maintain AI systems effectively is critical. Finally, embracing AI is a journey, and it will require some cultural shifts. To succeed, it is critical to foster a culture that embraces AI and data-driven decision-making. Encourage experimentation and innovation while addressing fears around automation and job displacement.
5. Embrace iteration
Implementing AI is an ongoing process. Adopt a continuous learning approach, where AI solutions are constantly refined and improved based on feedback, new data, and technological advances. Developers must be given an experimentation period to assess how AI fits into their individual workflows. It’s also important to note that there might be a short-term dip in productivity before the organization benefits from long-term gains. Managers must anticipate this by emphasizing transparency and accountability throughout the implementation and iteration cycles.
6. Measure success beyond lines of code
It's true that metrics such as the number of tasks completed or lines of code written can be good proxies to help you identify areas where AI is having the biggest impact on your team. However, AI is more than just code generation. What really matters is how AI is driving metrics that are important to the business, such as how quickly teams are able to deliver value to customers, or the code quality of the final product.
Knowing how many lines of code a team produced won’t tell you the full story here. Measuring success in AI adoption requires moving beyond traditional productivity metrics and focusing on KPIs that demonstrate measurable business value, such as faster software delivery, improved developer satisfaction, and higher customer satisfaction scores.
Conclusion: Empowering developers through AI adoption
Even if your organization has not fully embraced AI, the time to start is now. According to Gartner®, by 2028, 75% of enterprise software engineers will use AI coding assistants, up from less than 10% in early 2023 [1].
The adoption curve is steep, but we are still relatively early in the AI hype cycle. In fact, if your team is only just starting to look into adopting an AI code assistant, they may be well-positioned to avoid some of the growing pains early adopters have experienced.
In addition to the strategies above, adopting an AI solution integrated into an end-to-end DevSecOps platform can jumpstart success by supporting developers at every stage of their workflow.
As AI transforms the workplace, business leaders should ask how they can harness the power of AI across the software development lifecycle to accelerate innovation and drive tangible benefits for customers.
[1] Source: Gartner, Top 5 Strategic Technology Trends in Software Engineering for 2024, Joachim Herschmann, Manjunath Bhat, Frank O'Connor, Arun Batchu, Bill Blosen, May 2024. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
How to think about developer productivity in the age of AI
Hear from Google and GitLab experts about how you can measure the impacts of AI and deliver results across your software development teams.
Watch the webcast