Developers' guide: 8 ways to fast-track AI integration

AI empowers developers to co-create the software that powers our world with greater efficiency and improved security. That’s why businesses are already making significant investments in AI. According to GitLab’s 2024 DevSecOps report, 98 percent of UK respondents said they are currently using AI in software development or plan to use it. It’s therefore no surprise that today, many companies are shipping software at least twice as fast as last year.

Here are eight ways developers can tap into AI’s potential:

1. Add AI assistants and AI and data platforms to your toolbelt.

Invest in scalable and flexible AI platforms, cloud infrastructure, and tools to support applications and AI models' development, deployment, and management.

2. Identify AI adoption goals and objectives and create a governance model.

Identifying a leader to manage AI strategy and implementation is critical. This 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. It can also be a chief AI officer (CAIO).

The main goal is identifying and prioritizing high-impact AI use cases that directly support business outcomes and focus on areas where AI can create the most value, such as automation, personalization, or data-driven decision-making. Also, it’s essential to remember that AI success is impossible without first addressing the privacy, security, and legal requirements your organization might face and how AI adoption plays into continued compliance.

3. Ensure that AI solutions can integrate seamlessly with existing business applications, IT systems, and data lakes.

Companies using AI-driven code development tools report faster release cycles and fewer bugs in production. Gartner says that by 2028, systematic adoption of AI code assistants will result in at least 36 percent compounded developer productivity growth. The benefits of code assistants include faster security cycles, enhanced productivity, and resource optimization.

4. Establish an AI structure driven by AI.

AI success relies on high-quality, relevant data. Enterprises must:

• Ensure solid data collection, storage, cleaning, and processing mechanisms.
• Establish transparent data access, usage, security, and privacy governance, especially to ensure compliance with regulations like GDPR or CCPA.
• Remove 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 work together on using data warehouses and data lakes to facilitate access to training models and application usage.

5. Create workflows and guardrails for AI.

Establish guidelines to ensure AI is used responsibly and effectively. Set up automated testing, including using a security analyzer, to create a gating mechanism that ensures 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 IP leakage.

6. Transform talent and culture.

Consistent upskilling is critical to safely, securely and responsibly unlock AI’s potential. Build a team of data scientists, AI engineers, and other experts to design, develop, and implement AI solutions. Embracing AI is a journey, and it will require some cultural shifts. To succeed, fostering a culture that embraces AI and data-driven decision-making is critical. Encourage experimentation and innovation while addressing fears around automation and job displacement.

7. Look beyond lines of code to measure success

It is essential to move beyond traditional productivity metrics and focus on KPIs that demonstrate measurable business value. Success must also be measured by how quickly software can be delivered, improved developer satisfaction, and higher customer satisfaction scores. Effective software development is not about increasing the lines of code produced; it is about solving problems efficiently and improving application quality.

8. Focus on iteration.

Implementing AI is an iterative 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.

Even if an organization has not fully embraced AI, now is the time to start. According to Gartner, by 2028, 75 percent of enterprise software engineers will use AI code assistants, up from less than 10 percent in early 2023.

The adoption curve is steep, but we are still relatively early in the AI hype cycle. If an organization’s developers haven’t fully adopted an AI code assistant, they may be well-positioned to avoid some of the growing pains early adopters have experienced.

By integrating AI into the entire software development process, developers can spend more time on high-value tasks, reduce cognitive load, and deliver greater value to organizations and end users.

As AI transforms the workplace, we should all ask how businesses can harness its power across the software development life cycle to accelerate innovation and drive tangible business impact for customers.

Michel Isnard is VP of EMEA at GitLab.