Artificial intelligence (AI) adoption is surging globally, with a vast majority of banks, financial services and other businesses now actively using the technology.
In APAC, AI spending is expected to reach $90 billion by 2027, with nearly three-quarters of finance organisations 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 more likely to rate their teams as either extremely or very effective.
Discussing the findings, Damien Wong, senior vice president for Asia Pacific and Japan at Tricentis, said that “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.”
He added: “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.”
The area perceived to be delivering the most ROI from AI across the SDLC is testing, with nearly two-thirds of DevOps practitioners saying it is the most valuable area for AI investment, according to Wong.
He stressed teams use AI to augment a wide range of testing tasks, including test planning, test case generation, analysing 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.”
– Damien Wong
“AI-powered tools can proactively detect and fix vulnerabilities, enhance threat detection, and automate responses to emerging security threats,” Wong said.
However, there are still key opportunities for AI investment in release, deployment, platform engineering, and planning, he continued.
“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,” Wong stressed.
Skills and trust
While generative AI and AI copilots emerged as key drivers of AI adoption, Wong said 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,” he explained.
In Singapore, where Wong is based, 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,” Wong elaborated. “These should be embraced to stay ahead of the curve.”
Financial institutions 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,” he continued.
“Humans are still very much ‘in the loop’ when it comes to AI.”
– Damien Wong
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,” Wong noted.
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.
Wong said this includes focusing on data privacy, security, and continuous testing.
“Singapore’s approach to developing comprehensive AI frameworks provides a foundation for organizations to innovate safely and responsibly,” he stressed.
“By aligning with these frameworks, businesses can ensure their AI initiatives meet industry best practices and regulatory standards.”
In APAC, organisations have immense opportunities for AI-augmented DevOps practices, Wong argued.
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,” he warned.
NEXT MONTH IN SINGAPORE
REGISTRATION IS NOW OPEN FOR THE QA FINANCIAL FORUM SINGAPORE 2024
Test automation, data and software risk management in the era of AI
The QA Financial Forum launches in Singapore on November 6th, 2024, at the Tanglin club.
An invited audience of DevOps, testing and quality engineering leaders from financial firms will hear presentations from expert speakers.
Delegate places are free for employees of banks, insurance companies, capital market firms and trading venues.
QA FINANCIAL FORUM LONDON: RECAP
Last month, on September 11, QA Financial held the London conference of the QA Financial Forum, a global series of conference and networking meetings for software risk managers.
The agenda was designed to meet the needs of software testers working for banks and other financial firms working in regulated, complex markets.
Please check our special post-conference flipbook by clicking here.
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