23 Singapore financial institutions commit to train 80,000 staff in AI by 2028
The IBF AI Workforce Co-Lab pairs a sector-wide training pledge with job redesign pathways for leaders, wealth managers and operations teams.
Most enterprise AI training starts with the people closest to the technology: data teams, engineers, a handful of enthusiasts in the business. Singapore's finance sector has decided that sequence is backwards. When a sector's biggest employers agree to train everyone, from the branch to the boardroom, they are making a bet that AI will touch every job, not just the quant desks, and that a workforce watching the transformation from the outside is the bigger risk.
On 24 September 2026 Deputy Prime Minister Gan Kim Yong, also Chairman of the Monetary Authority of Singapore, launched the IBF AI Workforce Co-Lab at the Institute of Banking and Finance Distinction Evening. Twenty-three pioneer institutions across banking, insurance and asset management – including DBS, OCBC, UOB, AIA Singapore and Prudential – committed to train their entire Singapore workforce, totalling over 80,000 employees, in critical AI skills by 2028 through IBF-recognised programmes. More than half of those employees have already been trained. Carolyn Neo, Chief Executive Officer of IBF, said the pioneer institutions employ around 40 per cent of the financial services workforce.
The Co-Lab will assess how jobs are changing, test practical approaches to training and job redesign, and share findings with the wider sector. That structure matters. Most enterprise AI programmes run inside a single company, so the lessons stay inside the building and the occasional conference keynote. A sector-wide laboratory with government backing can publish what works, which means smaller institutions and adjacent industries can borrow the playbook without paying for the same mistakes twice.
What the three pathways do
The Co-Lab launched three pathways. Co-Lab@Leaders helps executives identify valuable applications of AI, establish sound governance and lead workforce transformation. Co-Lab@Wealth Managers has ten private banking employers committed to upskill relationship managers, working with providers such as the Wealth Management Institute. Co-Lab@Operations redesigns operational jobs around capabilities including process improvement, exception handling, risk oversight and validating AI output.
That third pathway is the one most L&D teams will recognise. Operations roles – compliance analysts, back-office processors, customer service agents – are where generative AI arrives first and where the redesign question is sharpest. If the model drafts the email, what does the human own? If the model flags the anomaly, does the analyst still need to understand the regulation well enough to argue with the machine? The Co-Lab@Operations track answers by naming the capabilities: exception handling, risk oversight, validation. Those are judgement tasks. They require context the model does not have and accountability the model cannot carry.
Gan framed the challenge plainly. "Financial institutions must plan for their people even as they plan for AI – identify how jobs will change, prepare employees early, and help them benefit from these new opportunities." He told leaders developing AI strategies to "consider which jobs will change, where new opportunities will emerge, and how to prepare employees for them." The strategy has three Ups: uplift the workforce with critical AI skills, upskill professionals for specific AI-enabled roles, and upbuild a pipeline of AI- and job-ready young talent.
The precedent and the playbook
Singapore has run this pattern before. Since the Sustainable Finance Jobs Transformation Map launched in 2024, there have been over 21,000 training completions across nearly 220 IBF-recognised courses. That programme gave the sector a shared curriculum, a recognised credential and a way to compare progress across institutions. The AI Co-Lab borrows the same architecture: a shared standard, a public commitment and a timeline that forces the conversation out of the innovation lab and into workforce planning.
IBF also launched a Job Redesign Playbook for Financial Services with the Skills and Workforce Development Agency and the Institute for Human Resource Professionals, giving business leaders and HR practitioners practical guidance on job redesign. An enhanced tripartite MOU between IBF, the NTUC Financial and Professional Services Cluster of Unions and seven industry associations was signed; the associations represent over 800 financial institutions and FinTech firms and more than 70 per cent of the sector's workforce. That union involvement is not decorative. If job redesign means some roles disappear and others split into new shapes, the people in those roles need a voice in the room when the new org chart gets drawn.
Gan cited UOB's Jenny Chan, who after 37 years at the bank moved from wholesale banking into a retail anti-money laundering role through UOB's Better U Pivot programme in 2025 and now uses generative AI to automate checks. That example does two things. It shows that a mid-career pivot into an AI-enabled role is possible, and it shows that the AI changed what the role does rather than replacing it. Chan still owns the judgement. The model handles the repetitive checks.
What this means for L&D
The Co-Lab model is exportable. Any sector with a regulator, a training body and a handful of large employers can run the same play: agree on a shared skills framework, commit to train the entire workforce by a public deadline, and publish the job redesign lessons so smaller players can follow. The financial services precedent matters because the sector has enough scale to test what works before the rest of the economy catches up.
For L&D and innovation leaders outside Singapore, the lesson is not to wait for a regulator to build the coalition. If your industry has a trade body, a skills council or a cluster of employers who already share a talent pipeline, you can propose the same structure: a shared curriculum, a public commitment and a timeline that turns workforce AI readiness from a talking point into a measurable programme. The alternative is that every company builds its own AI training from scratch, which means most will build it badly and none will share what they learn. The Co-Lab model assumes that some problems are too large and too urgent for competitive advantage to matter. Workforce transformation in the age of AI is one of them.
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