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

• 64% of respondents recognize the importance of AI adoption
• 26% have integrated AI across their organization
• Nearly two-thirds (62%) cite data privacy and security issues as barriers to adopting digital solutions 

Government and public sector organizations recognize the vital role of data and artificial intelligence (AI), yet a substantial implementation gap remains, according to a recent EY survey of nearly 500 senior government executives. While 64% of respondents acknowledge that AI adoption could lead to significant cost savings and 63% see its potential to enhance service delivery, the reality is stark – only 26% have integrated AI across their organization and just 12% of those surveyed have adopted generative AI (Gen AI) solutions. These findings highlight the need for governments to take decisive actions to bridge this gap, with 58% of respondents advocating for an accelerated pace of data and AI adoption within public sector organizations. To achieve this, the report shows that government organizations must have a dual focus on technology and the human dimension.

Government organizations already deploying data analytics and AI are experiencing widespread benefits across multiple areas, including enhanced citizen experiences through improved access and personalized services; better monitoring and efficiency savings; stronger security with reduced fraud and errors; improved workforce productivity and satisfaction; and more informed, data-driven decision-making. To demonstrate this in action, the report provides real-world proof points and use cases to show how pioneering government organizations are solving implementation challenges and offers replicable models for others to follow.

Pioneers are setting the pace for change

The research identified a cohort of pioneers – organizations that are significantly outpacing their peers not just in implementation progress but in their strategic approach. The top 20% of survey respondents – classified as pioneers – further advanced in deploying digital solutions. The remaining 80% of respondents are classified as followers.

Pioneers distinguish themselves through their strategic emphasis on building strong foundations, such as high-quality data and digital infrastructure, before rushing to implement advanced AI technologies. Eighty-eight percent of pioneers vs. 58% of followers have deployed data and digital infrastructure. Seventy-six percent of pioneers vs. 33% of followers have digitized or automated existing processes and services and 58% of pioneers have deployed data analytics capabilities compared with a third (33%) of followers.  

Learning from the pioneers, the EY organization sets out a holistic five-foundation framework for successful data and AI implementation to help government organizations approach their own transformation journeys with confidence. The steps are tried and tested and proven to work for government and public sector organizations.

In addition, the report includes data from EY organization’s AI Sentiment Index, which captures the views of 15,000 citizens across 15 countries. The insights can help governments understand citizens’ appetite and concerns about using AI to deliver government services.  

Permenthri Pillay, EY Global Government & Public Sector Digital Modernization Leader, says:

“The initial focus has paid off for pioneers who have developed a more effective digital and data foundation, and in some cases data platforms that embrace cloud technologies. They have made faster progress in embedding data capabilities organization-wide, rather than just in specific teams and departments. This helps maintain high standards of data quality and consistency, breaks down organizational silos and provides a unified approach to data governance and regulatory compliance. This allows for scalable and flexible data management and ultimately leads to more cohesive and aligned strategies that benefit the entire organization.”

AI implementation challenges

Implementing data analytics, AI and Gen AI technologies poses a dilemma for government organizations. They recognize the potential benefits but face significant challenges that hamper implementation. Nearly two-thirds (62%) of respondents say that data privacy and security concerns constrain its organization’s current ability to adopt data and digital solutions. Fifty-one percent of respondents cite a lack of data and digital transformation strategy and 45% say its organization had inadequate data infrastructure.

Unlike the private sector, governments hold vast amounts of legally protected data. Privacy laws and legislative barriers originally intended to protect citizens make data sharing a challenge as they restrict public employees from acting beyond what is explicitly permitted without the necessary frameworks and protections. The pioneers identified in the report address these challenges by establishing clear data governance frameworks that specify permissions, access controls and usage limitations; and create transparent data usage policies and techniques to protect personal data while enabling analytics.

Seizing the AI moment

The convergence of unprecedented challenges facing government today – resource constraints, 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.

Catherine Friday, EY Global and EY Asia-Pacific Government & Infrastructure Industry Leader, says:

“The stakes are high. People increasingly expect a seamless customer experience when they buy goods and services from companies. Governments are already behind the private sector when it comes to harnessing data and technology to provide a great consumer experience. Those that fail to act decisively risk their citizens experiencing a wider gap between the tech-enabled services they receive from companies and those they receive from governments. In this space, skepticism grows about the public sector’s ability to meet citizens’ expectations and serve their interests – and confidence erodes in the effectiveness of government itself. 

“Conversely, those who successfully implement these technologies stand to realize substantial benefits– enhanced productivity; improved employee experience; transformed citizen services; enhanced planning; strengthened financial stewardship; and greater resilience. This results in better outcomes for citizens, which helps to restore trust in the public sector as an effective provider of services and programs – with clear benefits for governments that must demonstrate positive change to impatient electorates.”

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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.