Banking leaders across APAC are steering through a dynamic landscape. Traditional revenue streams are under pressure from digital-only players and the expansion of embedded finance. 75 percent of APAC customers have a relationship with at least one competing bank, while regulators are tightening oversight, and interest rate volatility is creating uncertainty. Against this backdrop, banks must accelerate their reinvention, rethinking how they deliver value, strengthen operations, and build resilience to stay competitive in the years ahead.

AI has the potential to transform banking by driving growth and efficiency. When applied at scale, Accenture estimates that AI can help global banks cut operating costs by 8 percent and boost revenues by 5 percent, unlocking up to $289 billion in value. However, realizing this impact requires a comprehensive, enterprise-wide approach, as current gen AI uses are predominantly self-contained such as chatbots, document summarization, or code generation.

Banks need to accelerate their efforts to scale AI, taking a holistic approach focused on functional transformation so they can achieve real gains in efficiency, growth, and resilience. While productivity may be the most visible benefit of gen AI, the greater impact comes from how it helps banks understand and serve customers in ways that strengthen satisfaction, loyalty, and share of wallet. Ultimately, this drives the bank’s bottom line. Only 10 percent of APAC banks  presently have the core AI, data and technology foundations needed to move faster and deliver consistent, scalable value across the enterprise.

The five imperatives to scale AI

Achieving scale demands more than technology adoption; it requires a clear roadmap and a strong reinvention strategy of where and how AI can create sustainable value.

1. Prioritizing strategic investments that deliver value

Framing AI purely as a cost-saver is short-sighted because efficiency alone will not drive competitiveness. Banks need to treat AI as a growth engine and place deliberate strategic bets: concentrated investments in domains where AI can solve fundamental banking problems or open new revenue streams. Globally, over 50 percent of banking IT executives are betting on areas like fraud management and Know Your Customer (KYC), where targeted use of AI can deliver industry-specific gains, including a 14% rise in productivity and a 10 percent uplift in customer satisfaction. Banks in APAC are also expecting 24–34 percent efficiency gains across the software development lifecycle, with major impact in code generation, testing, and documentation.

2. Anchoring a talent first strategy

Preparing people for AI is critical and success depends on how effectively people can work with these tools. Banks need to rethink their operating models to organize work around business outcomes, allowing AI to handle routine work while humans focus on more complex and nuanced cases. At present, less than half (40%) of employees globally say their training has prepared them for role changes. The institutions making headway are those narrowing this gap with deliberate talent strategies that build technical capabilities like AI fluency, prompt engineering, data storytelling, and human-centric skills such as empathy, ethics, and strategic thinking needed to orchestrate human-AI workflows. JPMorgan, for instance, has mandated AI literacy for all hires.

3. Building an AI-enabled, secure digital core

Banks face a complex challenge in modernizing their legacy core systems to support AI adoption, as years of patchwork updates have left these systems rigid and complex. This transformation is not just technically difficult but also strategically delicate, having to balance the need for modernization with the risk of disrupting critical operations. Leading banks are now leveraging digital twins to overcome this challenge, creating a virtual replica of their core systems to innovate and deploy AI-powered services while gradually transitioning away from legacy infrastructure, thereby creating a flexible, secure, and AI-ready digital core.

4. Operationalizing responsible AI

Responsible AI maturity requires embedding fairness, accountability, transparency, privacy, and societal responsibility into every stage of the AI lifecycle. Our research shows that while 80% of banks demonstrate strong intent around responsible AI, only 21 percent have operational maturity, or the processes to put responsible AI into daily practice. Leading banks are developing clear governance frameworks, bias testing protocols, and explainability tools to ensure their AI systems make ethical, traceable, and secure decisions. This approach not only safeguards trust but also strengthens long-term resilience and compliance.

5. Embracing continuous reinvention

Scaling AI demands agility, financial rigor, and a culture that treats change as constant. Leading banks approach AI as a cycle, setting clear value targets, tracking outcomes in real time, and shifting investment when needed. This means refining products faster, updating operating models as conditions change, and equipping people to work effectively with intelligent systems. The aim is to make adaptability a core strength, keeping competitiveness steady and forward‑looking.

Leaning in for reinvention

For banks early in the journey, four key questions precede: Why reinvent? Where are we today? How do we redesign work to unlock value? And how do we track progress? The answers may differ, but the direction is the same.

We often speak of the three Ts essential to scaling AI: technology, talent, and trust. But there’s a fourth – Timing. The banks leading the way are proving that success depends not on having the most advanced AI, but on knowing where to apply it, enabling people to use it effectively, and focusing on outcomes that strengthen growth and customer trust.

Nicole Bodack is Accenture’s Banking & Capital Markets Lead for APAC, based in Singapore. With more than two decades of experience advising leading financial institutions worldwide, she helps clients reimagine their businesses through technology, data, and AI. Nicole’s work spans Corporate & Investment Banking, Wealth Management, and Asset Management – driving innovation and transformation across the financial services ecosystem.

Vivek Luthra is Senior Managing Director and Lead, Data & AI, SEA and APAC, at Accenture.

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