Accelerate AI Adoption: 8 Strategies for Developers

The modern world is powered by software, and AI will strengthen developers’ roles and importance. It will enable developers to build new software experiences faster and more securely.

The good news is that businesses are already making significant investments in AI. According to GitLab’s 2024 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. So it’s no surprise that today, many companies are shipping software at least twice as fast as last year.

Here are eight tips to equip leaders to embrace AI:

1. Create a governance model and identify the goals and objectives of AI adoption.
Identifying a leader to oversee 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 primary goal is identifying and prioritizing high-impact AI use cases that directly support business outcomes and 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.
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.

3. Take advantage of AI and data platforms and AI assistants.
Invest in scalable and flexible AI platforms, cloud infrastructure and tools to support the development, deployment and management of applications and AI models.

4. Plan for seamlessly integrating AI solutions with existing IT systems, data lakes and business applications.
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% compounded developer productivity growth. The benefits of code assistants include faster security cycles, enhanced productivity and resource optimization.

5. Build a data-driven AI structure.
AI success relies on high-quality, relevant data. 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.

6. 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.

7. 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.

8. Measure success beyond lines of code.
Moving beyond traditional productivity metrics and focusing on KPIs that demonstrate measurable business value is essential. 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.

Even if an 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 code assistants, up from less than 10% in early 2023.

Even though the adoption curve is steep, we are still relatively early in the AI hype cycle. In fact, 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 strategic 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 the power of AI across the software development life cycle to accelerate innovation and drive tangible business impact for customers.