AI isn’t just knocking on the doors of Singapore’s financial institutions, it’s already inside, rewriting how the sector thinks, operates, and competes. From tightening scam detection to reimagining risk models and personalizing customer experiences, AI is quietly becoming the engine behind smarter, faster, and more secure financial services. For decision-makers across banks, insurers, asset managers, and fintechs, the question is no longer “Should we adopt AI?” but “How do we lead with it—responsibly and effectively?”  

AI is no longer an experiment reserved for innovation labs. It has moved from hype to boardroom priority, from curiosity to competitive advantage. But while investment is accelerating, outcomes aren’t keeping pace. Recent industry data shows that nearly 88% of AI pilots never reach production. The gap between ambition and execution is widening. 

Client Overview 

The client is a healthcare and research organization operating in the oncology and cancer care sector. The organization focuses on improving long-term health outcomes for young adult survivors of childhood cancer by leveraging digital tools to support research-driven care models. The client aimed to design a mobile-based solution to support a research study centered on online self-management and peer mentor intervention. 

Client Overview 

The client is a research-driven healthcare and academic institution operating in the public health and behavioral health sector. Supported by a combination of federal research funding and private contributions, the organization focuses on advancing evidence-based approaches to address eating disorders, overeating, obesity, and overall well-being across both childhood and adult populations. 

Client Overview 

The client is a public sector organization operating in the healthcare and public health research sector. The initiative focused on addressing gaps in sexual health support by developing a digital intervention designed to be effective, accessible, and sustainable. 

The project aimed to support individuals through different stages of a preventive healthcare journey using a mobile application, with a focus on education, engagement before in-person testing, and continued support after testing. 

Client Overview 

The client is a healthcare and cancer research organization operating in the oncology sector. The initiative focused on supporting individuals diagnosed with lymphoma who had completed their active treatment phase. The objective was to collaboratively design a survivorship-focused digital intervention with inputs from both healthcare professionals and patients, ensuring clinical relevance and patient-centered care.  

Client Overview 

The client is a leading organization from the healthcare and academic research sector, focused on improving public awareness and education around critical health conditions. The client’s objective was to leverage mobile technology to educate individuals about a preventable health condition and empower them with actionable knowledge through a user-friendly digital platform. 

Client Overview 

The client is a public health and environmental policy research organization focused on addressing environmental pollution caused by commercial tobacco product waste. The organization works closely with academic institutions and community partners to support policy-driven research initiatives aimed at reducing the environmental and public health impact of tobacco-related pollution. Its efforts emphasize data collection, community engagement, and policy advocacy to drive meaningful environmental change.  

So many companies are swimming in AI pilots that look amazing in slides… but end up feeling like expensive paperweights when it comes to production. Sound familiar? You’re not alone.

By 2026, most enterprises will stop asking whether AI is important. That debate is already over. The real question now is more uncomfortable: why does so much AI still fail to scale?

Despite massive investment, research from MIT, Gartner, and IDC consistently shows that 70–90% of enterprise AI initiatives never make it into sustained production. Not because models don’t work, but because the enterprise stack underneath them wasn’t designed for AI in the first place.