EY survey reveals large gap between government organizations’ AI ambitions and reality

• 71% cite public wariness over data collection and processing as a significant hurdle in AI adoption
• Close to three-quarters (74%) say their organizations are effective in managing data ethics as well as data quality and reliability
• 74% are prioritizing foundational data and digital infrastructure over the next three years

SINGAPORE, 7 NOVEMBER 2025. Government and public sector organizations recognize the vital role of data and artificial intelligence (AI), yet an implementation gap remains, according to an EY survey of nearly 500 senior government executives globally, including more than 30 from Singapore. 

Across Singapore respondents, 74% (global 64%) acknowledge that AI adoption could lead to significant cost savings and 68% (global 63%) see its potential to enhance service delivery.  

However, only 26% have deployed AI in their organizations, mirroring global numbers. Underlying factors like challenges in justifying AI investments due to unclear returns on investment and uncertain outcomes (Singapore 81%, global 59%), as well as public wariness over the collection and processing of their data through AI-powered models (Singapore 71%, global 62%), may be holding back AI integration in the government and public sector. 

Samir Bedi, Singapore Government and Public Sector Leader at EY, says:  

"In Singapore, there is already a whole-of-government effort to upskill public officers and catalyze cross-agency collaboration. By harnessing data and AI, governments can deliver more accessible, personalized and secure services. To overcome public wariness, they can start by building transparency and accountability into how data is used. In addition, demonstrating measurable outcomes like faster services, optimized costs and better citizen experiences can show clear returns from AI.” 

Data remains a roadblock to scalable AI

Findings from the survey suggest that implementing data and digital solutions poses a dilemma for government organizations. Sixty-one percent (global 45%) point to inadequate data infrastructure including legacy systems as internal barriers and 52% (global 62%) cite concerns around data privacy and security. These issues continue to undermine organizations’ ability to scale AI with confidence. 

Despite these barriers, government organizations are reporting relatively strong performance in specific areas of their data infrastructure. According to the survey, 74% of Singapore respondents say their organizations are effective in managing data ethics frameworks (global 71%), internal data interoperability (global 65%), and data quality and reliability (global 70%). Fifty-five percent (global 64%) have rolled out data security training to improve workforce preparedness. This suggests that while foundational capabilities may be in place, there is still work to be done to address deeper structural challenges and ensure these strengths are scaled consistently across the enterprise. 

Manik Bhandari, EY Asean Data and Artificial Intelligence Leader, says:  

“To bridge the gap between ambition and reality in AI adoption, governments must first invest in a strong digital and data foundation. Unlike the private sector, governments hold vast amounts of legally protected data. Those that have established clear data governance frameworks and have embedded data capabilities across the organization rather than limit them to isolated teams are seeing faster, broader progress. Unified data platforms that are AI-ready accelerate the build out and the adoption of AI to deliver better citizen services in a more cost-effective way. 

Recognizing the need to extend AI capabilities across the wider economy and local enterprises, EY has launched an Agentic AI Center of Excellence (CoE) in Singapore. The CoE will develop differentiated agentic capabilities that are scalable and repeatable to serve the needs of enterprises.”  

The EY Agentic AI CoE seeks to help drive higher levels of productivity, create new intellectual property and innovation, and contribute to Singapore’s AI talent pool and AI-enabled workforce. With the CoE, EY AI professionals will work with government agencies as well as corporates to address real-world agentic AI opportunities and challenges and develop solutions and knowledge can serve the wider Southeast Asia region, strengthening Singapore’s bid to be a trusted AI hub. 

Seizing the AI future

The EY survey also highlights that the convergence of unprecedented challenges facing governments today, from resource constraints to demographic shifts, complex societal problems and rising citizen expectations, demands a transformative response. The survey demonstrates that data and AI technologies offer precisely the capabilities needed to address these challenges, but only when implemented thoughtfully and systematically. 

Encouragingly, many organizations recognize the urgency to act. The survey finds that 84% of Singapore respondents (global 78%) plan to invest in digitizing and automating existing services in the next three years, and 74% (global 59%) are prioritizing foundational data and digital infrastructure, laying the groundwork for more advanced analytics in the future. Over half also intend to enhance data privacy practices (Singapore 58%, global 52%) and improve data sharing within the organization (Singapore 55%, global 37%). These planned investments signal a growing commitment to close the gap between ambition and execution, turning the promise of data and AI into tangible outcomes for citizens. 

Samir concludes:  

“The stakes are high. Citizens now expect government services to be as seamless as those from the private sector. Meeting these expectations is important to maintain public trust and confidence.” 

-ends- 

Notes to editors

About the survey

Research Methodology: Capturing Global Insights  
 
In August and September 2024, in collaboration with Oxford Economics, the global EY organization conducted research to explore the question: How can governments leverage data and AI to maximize public value? 

We surveyed 492 government leaders across 14 countries with significant familiarity or involvement in their organization’s Data and AI programs. This encompassed roles such as chief executive officer, chief Information officer, chief data officer, chief Strategy officer, chief AI officer, chief innovation officer, chief operating officer/director of operations, chief technology officer, director general, and commissioner. The respondent profile was also carefully designed to provide comprehensive coverage:  

Levels of government: National/federal (40%), state/regional (25%), local (25%), and public/state-owned entities (10%) 

Diverse functions: Executive offices, digital agencies, home affairs, economic affairs, treasury/tax, health, social services, education, energy, regulation, defense and intelligence, infrastructure and transport. 

To complement the quantitative findings and provide deeper context, 46 in-depth qualitative interviews were conducted, including 38 with public officials, four with academic and policy professionals and four with private sector leaders in deploying AI. These interviews provided further insights on the maturity of data and AI adoption, challenges encountered, and leading practices in addressing these challenges. 

The following definitions were used in the survey to provide consistency across diverse government contexts: 

Data and digital infrastructure: The foundational data, digital systems and services that support data storage, processing, and communication. Example: a government cloud computing platform that hosts various public service applications, enabling efficient data sharing between administration departments. 

Advanced data analytics: The use of sophisticated techniques and tools to analyze large and complex data sets to uncover insights, predict outcomes and inform decision-making. Example: Using predictive analytics to forecast traffic patterns and enhance public transportation schedules. 

Artificial intelligence (AI): The branch of computer science that focuses on creating systems capable of performing tasks such as understanding natural language, recognizing patterns, solving problems and learning from experience. Example: An intelligent chatbot used by a city's administration to handle inquiries, provide information on services and assist residents with requests that helps improve citizen engagement and service efficiency. 

Generative AI: A subset of AI that creates new content — including text, images, code and other media — based on patterns learned from training data. It possesses the capacity to learn, reason and apply knowledge contextually across diverse domains. Example: A system that seamlessly integrates with various government departments, such as health care, education and transportation, to autonomously make informed decisions, allocate resources optimally and adapt to new legislation or societal needs without explicit human instruction. 

Methodological note: The survey relies on self-assessment from participating organizations, which is recognized as a potential source of bias. To mitigate this, we cross-validated key findings through the interview process and comparative analysis across regions. 

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