Organisations in Asia Pacific (APAC) have begun to capitalise on the transformative capabilities of artificial intelligence (AI) in their software development efforts, but some challenges remain. According to GitLab’s 2024 Global DevSecOps Report, 55 per cent of APAC respondents report concern about the risk of introducing AI into the software development lifecycle.

In my recent conversations with engineering leaders at some of Asia’s biggest organisations, it is evident that despite AI’s promise of heightened productivity and greater efficiency, it sits among many other conflicting priorities on their agendas. 

Home to the fastest-growing developer community globally, APAC is also the second-largest adopter of GenAI. The region’s potential is unquestionable, and local organisations are well-positioned to drive real AI innovation and all its benefits.

However, they face the daunting task of navigating the impact of AI on their teams. They also need to manage what is widely hailed as a once-in-a-generation opportunity against a persistent tech talent crisis.

Fostering a better developer experience 

To start untangling the issue, it’s important to recognise that too many organisations focus on developer productivity without considering developer experience. Most DevSecOps teams aim to achieve a short time-to-deployment for high-quality software that solves business problems and increases revenue. They have talented developers focused on time-consuming and repetitive tasks, and they perform that work under deadline pressures.

While those tasks can be counted, limiting an engineer’s productivity measurement is not the solution. DevSecOps teams should take a holistic view of the development pipeline and non-technical factors such as peer support, working environment, and job enthusiasm—and identify where process improvements can be implemented.

AI can remove friction from software delivery by taking over routine, tedious tasks. This can speed up deployment cycles, improve code security and quality, and improve developer morale. For example, AI can suggest or autocomplete code, perform various tests, or automatically document code functionality in a standard format, which would otherwise consume much of the developer’s day.

All of these opportunities equate to a better developer experience. DevSecOps has always been about automation, so why not automate the less appealing tasks?

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According to respondents of GitLab’s DevSecOps Report, this shift is underway. They report that AI and machine learning are becoming well-established in software development workflows. In fact, 96 per cent of APAC respondents said they currently use AI in software development or plan to use it. This is especially important as AI can introduce efficiencies to developers’ day-to-day responsibilities.

For example, only 14 per cent of APAC respondents report spending their time writing new code. The rest is spent on administrative tasks, improving existing code, testing, and mitigating security vulnerabilities—all of which AI can further augment.

When AI takes the strain, humans can focus on what they do best: critical thinking and creative innovation. Engineers love tackling challenging projects that test their problem-solving skills. Why not let them concentrate their time on these?

Focusing on upskilling

When organisations are intentional with their AI deployments, they can create upskilling opportunities for developers seeking career advancement.

Deloitte estimates that over 11 billion hours per week across APAC are expected to be impacted by GenAI alone, and using GenAI saves each user almost a day per week. 

Not only does AI give developers in the same cohort valuable time to spend on developing new skills, but it can also act as an outstanding coach for them. For example, AI can impart valuable lessons on optimising code, understanding how it can be better structured, and identifying and remediating vulnerabilities before code is deployed. Developers might use AI to learn or to reacquaint themselves with unfamiliar code bases, languages, and frameworks.

A 2023 report from global strategy firm McKinsey finds that developers using generative AI-based tools in their work are happier than their peers who don’t have access to these tools. According to the report’s authors, “They attributed this to the tools’ ability to automate grunt work that kept them from more satisfying tasks and to put information at their fingertips faster than a search for solutions across different online platforms.”

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Every organisation wants to hire these developers, and every engineering leader should aim to deliver the developer experience they want. The doers deserve access to the DevSecOps tools they need to get work done and enjoy that work.

In this context, AI seems to be a key ingredient in a DevSecOps solution, critical to an engineering leader’s recipe for success, and a powerful way for organisations to attract, engage and retain the best tech talent. 

Adopting AI successfully

Engineering leaders and development teams should consider the following:

• Hold your leaders accountable for responsible AI use. I asked my leaders to share how they used our AI features to do their jobs before we asked the teams to change how they work. This benefited our teams in two ways: It required the executive team to engage with the features and experience the challenging parts of incorporating AI into their work, resulting in empathy for change and a shared commitment to ensuring that AI adoption would evolve the way we work.
• Establish guidelines and workflows to realise the value of AI. Consider creating a working group to identify best practices and workflows that will change how work gets done. Having teams publish their learnings with before and after comparison data provides insights into how to measure the effectiveness of AI, and how to use these technologies with care. By fully understanding AI’s safety, security, and privacy implications, organisations can prepare for potential risks around its utilisation.
• Incentivise learning and sharing. The willingness to acknowledge that it is a journey encourages peers to support each other and problem-solve while providing a great opportunity to reward teamwork.

Implementing AI requires careful planning and consideration. By intentionally weighing current business dynamics and the complexity of current ways of working, team leaders can best determine where AI can most efficiently improve their software development workflows and, at the same time, keep their developers happy, engaged, and successful.

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