Digital transformation in the public sector goes beyond simply digitalising citizen services and day-to-day operations.
At the recent AWS Public Sector Day 2025 in September, Singapore public and private sector speakers highlighted that successful transformation hinges on embracing three key lessons that redefine digital leadership, trust and strategic investment.
The opening plenary keynote speakers included Central Provident Fund Board (CPFB)’s Deputy CEO of ICT & Digital Services, Ng Hock Keong; Nanyang Technological University (NTU)’s Senior Principal Research Scientist at Digital Trust Centre (DTC), Dr Chi Hung Chi; as well as ST Engineering’s VP and Head of DeepBrain, Data Science & Information, Tan Boon Leong.
To deliver real, sustainable value to citizens in the era of artificial intelligence (AI), the three organisations share more about their commitment for continuous improvement, an awareness of the rising cost of trust, and a strategy for efficient AI implementation.
1. Even if it ain’t broke, improve it anyway
The first takeaway was for public sector agencies to adopt a mindset of continuous improvement instead of waiting for a crisis.
True public sector transformation hinges on an organisational culture that’s willing to change and constantly do better, even when existing systems are not failing.
In the case of CPFB, the agency decided to improve their call centre operations despite a high satisfaction index of 98 per cent and no “burning platform”.
According to Ng, CPFB became the “first agency in Singapore to do four things” by leveraging AI and other tech systems to improve services and operations.
These innovations include connecting real-time call transcripts with 360-degree customer information stored in its existing customer relationship management (CRM) system, using SingPass for four-factor authentication, and utilising AI for automated evaluation and real-time feedback to call centre agents.
Ng’s sharing pointed to how digital leadership in the public service isn’t about championing the latest tech, but about discovering business use cases that leverage technology to provide real value to citizens.
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2. Prioritise digital trust and security in the AI era
“The future is good, okay, but everything comes with a price,” said NTU’s Dr Chi.
The promise of AI and collaborative intelligence comes with the cost of trust, and the immediate challenge is for agencies to secure identity, privacy and accountability across the digital data flow.
NTU’s Digital Trust Centre (DTC) is funded by IMDA and the National Research Fund (NRF) that leads Singapore’s research and development (R&D) efforts in trust technologies, including AI safety.
He highlighted three challenges for agencies in the flow of collecting, sharing, analysing and subsequently making decisions with AI and data.
These included verifying who is communicating and if the data is real, balancing data sharing with privacy and accountability, and enabling collaborative analysis across these data sets.
These challenges are exacerbated with agentic AI, where machines interact with other machines, he added.
Reiterating CPFB Ng’s takeaway, Dr Chi highlighted the value of simple and practical tools to tackle advanced threats.
To address the problem of identity scams and financial loss, the centre developed a simple mobile application tapping on AI scanning to help people determine the authenticity of the person they are talking to.
Once again, the tool’s value isn’t derived from expensive, high-performance tech, but from its ability to be reliable and easily adopted by users to ensure.
3. Don’t reinvent the wheel, focus on unique strengths
By strategically leveraging commercial cloud services, public agencies could focus internal resources on developing and fine-tuning proprietary algorithms, said ST Engineering’s Tan.
ST Engineering was originally focused on supporting Singapore’s national defence when it was founded in 1967, but had evolved to create enterprise solutions in software, AI and cloud.
Tan shared that the firm’s current focus is on creating high-quality decision support systems that integrate multiple data sources for decision-making.
He elaborated how AWS services have helped accelerate ST Engineering’s development of these solutions.
ST Engineering leveraged AWS’s SageMaker and Bedrock to efficiently power its multi-modal AI detection platform hosted on the cloud that is focused on misinformation and deepfakes.
This move allowed the team to dedicate its development efforts to enhance its unique, proprietary algorithms and models for this purpose.
The platform has since been used for key use cases, including tackling online scams, deepfakes during election season, and hate speech.
According to Tan, the engineering efficiency gains amounted to 50 per cent of savings, but these saved resources were instead strategically channeled to improve detection capabilities, enhance algorithms, and accelerate R&D.
In short, the key takeaway for public agencies is to adopt a strategic cloud approach that prioritises proprietary innovation over rebuilding common infrastructure.
