Banks scaling artificial intelligence are hitting a new barrier and it is not the technology itself. At the Asian Banking & Finance and Insurance Asia Summit in Singapore on 1 July, senior executives from ING, Maybank Singapore, Green Link Digital Bank, and former Prudential chief strategy and transformation officer Sherwin Siregar said governance and leadership fluency will determine which institutions succeed with AI - and which ones burn significant capital on tools that deliver no business outcome.
Siregar pointed to a study from the Massachusetts Institute of Technology indicating that 95% of generative AI spend produces no business result. He said organisations remain in early stages of generative AI adoption and leaders must first understand how the technology works before expecting meaningful returns.
"AI gives us speed, but speed needs direction, and that direction has to come from humans and leaders. Only speed times direction gives you velocity. Otherwise, AI will amplify broken processes and mistakes," Siregar said. He added that leaders need to become "AI fluent" themselves so they can decide what should be delegated to AI and what should remain under human judgment. AI for Executives & Strategy programmes are one way leadership teams are building that fluency, but the shift must go deeper than a course - it requires embedding AI understanding into decision-making routines.
Where banks are already seeing value
Anand Sachdev, country manager for Singapore and head of South & Southeast Asia at ING, said AI is already at work in anti-money laundering, know-your-customer processes, transaction monitoring, sustainability assessments, and financial markets. But he cautioned against using AI to automate inefficient processes.
"Applying AI to inefficient or broken processes won't fix them - it can actually make things worse. You risk significant spend without seeing the return on investment," Sachdev said. He said banks have a responsibility to ensure AI models remain ethical, explainable, traceable, and free from bias because financial institutions operate in a highly regulated environment.
The shift from assistant to enterprise tool
Mohammed Meraj Khan, group head of digital banking delivery & operations and tech transformation director at Maybank Singapore, said many organisations still use generative AI primarily as a productivity assistant. Larger business benefits will likely come only after AI is embedded into enterprise workflows rather than being used as a standalone tool. That requires tighter integration with core banking systems and a governance framework that treats AI outputs as auditable business decisions, not just suggestions.
Governance does not stop at deployment
David Song, head of digital business unit at Green Link Digital Bank, said governance must continue long after AI systems go live because models keep learning from new customer data. "After launching, it's not like traditional software development, where you complete a checklist and deploy. You need continuous tracking and guardrails to monitor the AI solution, especially the models, because once real customer data flows in, they keep learning and the model may drift," Song said.
The panel also addressed AI's impact on jobs. Sachdev said history suggests technology creates new roles over time. Rather than replacing workers, AI will require organisations to invest in retraining and reskilling employees so they can work alongside the technology. For finance professionals, that means developing the skills to evaluate model outputs, spot drift, and understand the regulatory implications of automated decisions. This is where targeted AI for Finance training can help teams move from passive users to informed supervisors of AI systems.
Why this matters for finance professionals
The message from the summit is clear: AI spending without governance is wasted money. For professionals in compliance, risk, operations, and leadership, the immediate priority is not chasing the newest model but building the internal frameworks to ensure AI is explainable, auditable, and aligned with business outcomes. The institutions that treat governance as an enabler rather than a blocker will be the ones that turn AI pilots into measurable returns.
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