Artificial intelligence (AI) adoption has surged globally, with 72 percent of businesses actively using the technology. In APAC, AI spending is expected to reach US$90.7 billion by 2027, with nearly three-quarters (70%) of organizations citing generative AI as a significant or dominant factor driving IT spending.
As organizations push towards faster, more efficient software delivery cycles, DevOps teams have become key beneficiaries of AI technologies. A recent Tricentis report found that mature DevOps teams that have adopted AI are nearly a third (30%) more likely to rate their teams as either extremely or very effective. Indeed, AI is already being used to address major DevOps challenges, from team efficiency and skills gaps to cost reduction or enhancing software quality, saving teams over 40 hours per month. Generative AI is now the most widely adopted type of AI used by DevOps practitioners, with AI copilots also on the rise, offering use cases in planning, code development, and software testing.
Unlocking AI’s potential in DevOps
As AI continues to evolve in its applications for business, its ability to streamline and optimize processes is reshaping how DevOps teams operate. Beyond simply automating routine tasks, AI is speeding up innovation, improving performance, and delivering measurable value in key aspects of the software development lifecycle (SDLC).
The area perceived to be delivering the most ROI from AI across the SDLC is testing, with nearly two-thirds (60%) of DevOps practitioners naming it the most valuable area for AI investment in the survey. Teams use AI to augment a wide range of testing tasks, including test planning, test case generation, analyzing test results, and conducting risk analysis of code changes, helping quality assurance (QA) teams focus on code areas with the greatest risk of errors.
Coding and security were the second and third most valuable areas for AI application. AI’s significant impact on security, following closely behind coding and testing, is notable. AI-powered tools can proactively detect and fix vulnerabilities, enhance threat detection, and automate responses to emerging security threats. However, there are still key opportunities for AI investment in release, deployment, platform engineering, and planning. These phases, essential for software stability and scalability, could benefit greatly from AI’s ability to predict failures, optimize resources, and streamline maintenance, operations, and management processes.
Accelerating AI adoption: Skills and trust as critical enablers
While generative AI and AI copilots emerged as key drivers of AI adoption in the report, one of the most significant barriers to AI integration identified in DevOps was a lack of AI skills. This is crucial considering that humans are still very much “in the loop” when it comes to AI, with over two-thirds of survey respondents checking AI outputs at least half of the time.
In Singapore, where the government is actively promoting digital transformation, organizations are presented with a range of available programs to help them upskill their teams in AI. Government-led initiatives like AI Singapore are designed to enhance AI research, innovation, and talent development, creating a supportive environment for organizations to build AI expertise. These should be embraced to stay ahead of the curve.
Organizations can complement these initiatives by developing customized training programs for their DevOps teams, teaching users how to leverage AI tools effectively. In addition, encouraging participation in certifications such as those offered by AI Singapore, industry-recognized courses, or even internal certification can help deepen technical expertise within teams.
Upskilling teams to ensure human oversight will be critical to building trust and ensuring AI’s success in DevOps. While AI excels at processing large datasets and identifying patterns, it still requires human intervention for strategic thinking and contextual understanding.
Establishing clear governance frameworks to ensure compliance with regulatory standards will also be an essential trust constituent to create confidence in AI’s contribution to software development. This includes focusing on data privacy, security, and continuous testing. Singapore’s proactive approach to developing comprehensive AI frameworks provides a foundation for organizations to innovate safely and responsibly. By aligning with these frameworks, businesses can ensure their AI initiatives meet industry best practices and regulatory standards.
Empowering teams and building trust for lasting impact
In APAC, organizations have immense opportunities for AI-augmented DevOps practices. However, for AI adoption to succeed, it is crucial to train development and testing teams with the skills needed to work effectively alongside AI. Building trust through clear governance and regulation will also be essential in gaining confidence in AI’s capabilities.
Those who equip their teams with the right skills and foster trust in AI outputs will be well-positioned to balance AI use with human oversight. By doing so, they can ensure that AI becomes a valuable asset in driving efficiency, accelerating time to market, and maintaining high standards of software quality.
Damien Wong is Senior Vice President for Asia Pacific and Japan at Tricentis.
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