How data analytics and AI in government can drive greater public value

Each of these benefits is delivering greater public value through improved outcomes for the organization and citizens alike. In fact, they can be organized into six key value drivers with clear results as demonstrated in the AI use cases in government below:

1. Productivity and efficiency: Reducing costs through operational performance improvements. In a US city, AI tools that map business processes across systems and applications were used to analyze invoice processing workflows, automating the process and saving about 1,500 manual work hours annually.
2. Employee experience: Simplifying tasks to enhance employee experience and boost job satisfaction. In the UK, an AI assistant helps customer advisors quickly locate and share reliable information for citizens, improving response times by 50% and making advisors twice as confident in giving advice.
3. Citizen/end-user experience: Enabling more accessible, proactive and personalized services. A social security authority is increasing citizen access to GenAI chatbots for 24/7 inquiry resolution, with 6 million people already using the solution and an ambition to achieve 100% integration by 2027.
4. Strategic service planning: Forecasting needs and enabling smarter resource allocation. An Australian state government department piloted AI tools to improve cost and time estimates for large-scale infrastructure projects, reducing uncertainty and the financial risks associated with project overruns.
5. Financial optimization: Eliminating inefficiencies, reducing fraud and boosting revenues. A tax authority uses data analytics and AI for real-time tax return filing and analysis, generating over US$23 million in revenue through error detection and over US$38 million in cash collection.
6. Risk and resilience: Responding to threats, managing operational risk and ensuring service continuity. A French region adopted a sovereign GenAI model for IT incident resolution, reducing troubleshooting time from 1-12 hours to just 2-5 minutes.

While government organizations have acknowledged that they need to hasten their pace of adoption, implementation varies significantly. Most are taking a cautious, stepwise approach, but there is a clear progression in implementation. We found that governments are further ahead with their data and digital infrastructure deployment followed by a significantly lower adoption of AI and GenAI. This is understandable as the transformational potential of AI has only come to the fore in recent years, and lower adoption reflects legitimate concerns about the need to understand and manage inherent risks. In fact, 65% of respondents indicated that GenAI adoption is progressing too quickly, highlighting the desire for a more informed and balanced approach to deliver benefits that are realized without compromising safety.

This gradual, stepwise implementation journey makes strategic sense, as organizations build capabilities while managing risk and proving value along the way. But what governments do next to accelerate adoption is critical to driving greater public value.

Our research identified a cohort of “pioneers” that are significantly outpacing their peers both in implementation progress and strategic sophistication. What can be learned from them to help expedite the journey of others?

The pioneer formula: Building foundations for AI in government success

Our research reveals that the pioneers differentiate themselves through their strategic emphasis on building strong foundations before rushing to implement advanced AI technologies.

These pioneers successfully bridge the implementation gap by following a clear formula for success: They establish robust data infrastructure first (88% deployed vs. 58% of followers), develop comprehensive data governance, and focus on both technical foundations and organizational readiness in parallel, recognizing that technology alone isn't enough.

These organizations are pressing ahead with investment over the coming three years, which reflects this logical implementation path: They are investing differentially in building a robust digital infrastructure and data foundation, process digitalization and analytics, while setting themselves up for better deployment of AI and GenAI down the line.

This sequencing makes strategic sense by prioritizing digitization so that the data is clean, structured and well-prepared for more sophisticated applications. It also helps avoid higher costs and complexities associated with implementing AI in the public sector without first establishing robust data governance.

The initial focus has clearly paid dividends. Pioneers have developed more effective digital and data foundations than followers across several dimensions.

What truly sets pioneers apart is their comprehensive approach, addressing both technical capabilities and human dimensions simultaneously. They prioritize talent development, empower workers through strong ethical guidelines, partner externally to bridge capability gaps, and prepare citizens to accept and use AI in government services.

This balanced strategy delivers impressive results. Pioneers are 2.4 times more likely to rate the success of their AI initiatives as somewhat or significantly higher than expected compared to followers (62% vs. 26%). 

These organizations are pressing ahead with investment over the coming three years, which reflects this logical implementation path: They are investing differentially in building a robust digital infrastructure and data foundation, process digitalization and analytics, while setting themselves up for better deployment of AI and GenAI down the line.

This sequencing makes strategic sense by prioritizing digitization so that the data is clean, structured and well-prepared for more sophisticated applications. It also helps avoid higher costs and complexities associated with implementing AI in the public sector without first establishing robust data governance.

The initial focus has clearly paid dividends. Pioneers have developed more effective digital and data foundations than followers across several dimensions.

What truly sets pioneers apart is their comprehensive approach, addressing both technical capabilities and human dimensions simultaneously. They prioritize talent development, empower workers through strong ethical guidelines, partner externally to bridge capability gaps, and prepare citizens to accept and use AI in government services.

This balanced strategy delivers impressive results. Pioneers are 2.4 times more likely to rate the success of their AI initiatives as somewhat or significantly higher than expected compared to followers (62% vs. 26%). 

Implementing data analytics, AI and GenAI technologies creates a challenging paradox for governments. While they clearly recognize the transformative potential, they face persistent barriers that impede progress.

The most significant constraints cited by respondents include privacy and security concerns (62%), lack of strategic alignment (51%), inadequate infrastructure (45%), weak business cases with unclear returns (41%), and ethical considerations (42%).

Interestingly, our research revealed that these challenges often intensify rather than diminish as organizations progress in their implementation journey. Pioneers perceive these barriers more acutely than followers, suggesting a “you don’t know what you don’t know” phenomenon where deeper engagement with AI reveals greater complexity.

Despite these challenges, our research shows they aren’t insurmountable. By studying proven approaches of leading organizations, government organizations can expedite their progress through the implementation journey and start to harness the transformative potential of data and AI.

Through our analysis, we found that leading organizations succeed by mapping out a journey from strategy to value delivery through three key steps:

Step 1: Make a bold strategic commitment.

Leading organizations demonstrate bold leadership with a clear vision that aligns AI initiatives with their core mission. They establish executive-level sponsorship and dedicated investment, signaling the strategic importance of these initiatives. 

Practical actions:

• Articulate a clear vision linked to your organization’s core mission.
• Invest in executive-level AI education to build understanding of its potential.
• Establish AI governance structures (boards, centers of excellence) with executive sponsorship.
• Develop innovative funding models that allow flexibility and respond to demonstrated value.
• Secure cross-functional buy-in by demonstrating tangible mission impact.

Step 2: Build five essential foundations

Our research reveals that successful AI implementation requires a holistic approach that addresses both technological capabilities and organizational readiness simultaneously. The five foundations below represent the critical building blocks that leading organizations have put in place.

1. Data and technology

Pioneers prioritize establishing robust data infrastructure and governance before advancing to AI applications. They focus on high-quality data, modern architecture and secure platforms.

Practical actions:

• Assess current data quality, accessibility and governance while implementing cloud-based platforms for scalability and interoperability.
• Develop comprehensive data catalogs to make information discoverable.
• Create cross-departmental data sharing agreements addressing legal and cultural barriers.
• Establish clear standards and validation processes.
• Appoint dedicated leaders like Chief Data Officers and Chief AI Officers with accountability for governance and implementation.

2.  Talent and skills

Leading organizations address talent gaps through strategic workforce planning, comprehensive training programs, and external partnerships that bring technical skillsets.

Practical actions:

• Conduct skills assessments to identify critical capability needs.
• Create digital academies and learning platforms to upskill at scale.
• Design AI training programs around real operational challenges.
• Develop talent exchange partnerships with universities and technology providers.
• Establish clear career pathways for data and AI professionals

3.  Adaptive culture

Successful organizations foster environments where innovation, experimentation and comfort with emerging technologies can flourish. They create “permission structures” for calculated risk-taking.

Practical actions:

• Create innovation labs providing protected spaces for experimentation and implement comprehensive change management programs.
• Focus initial AI implementation on improving employee experience and involve employees in developing AI tools to build ownership.
• Clearly communicate how AI will complement rather than replace human work.

4. Trust and ethical governance

Building public trust is essential, with leaders developing strong ethical guidelines, transparent data practices and meaningful human oversight.

Practical actions:

• Engage with the public to address AI concerns and involve them in designing and testing AI solutions.
• Develop comprehensive AI ethics frameworks with dedicated oversight and conduct regular bias audits and impact assessments.
• Create transparent documentation of AI models and decision-making processes.
• Implement privacy-by-design principles in all applications and maintain appropriate human oversight for high-impact decisions.

5. Collaborative ecosystem

Leading organizations establish diverse partnership networks across the public, private, academic and civil sectors to accelerate implementation and extend their capabilities beyond what they could achieve independently.

Practical actions:

• Consider capability gaps that could be addressed through external partnerships.
• Engage technology partners strategically to access deep technical knowledge.
• Collaborate with academic institutions for research and talent development.
• Participate in cross-government initiatives to share best practices and resources.
• Actively support broader AI adoption across economies through innovation hubs, startup incubators and digital skills initiatives.

Step 3:  Focus on delivery excellence

Moving from isolated pilot projects to organization-wide implementation requires disciplined execution and a clear roadmap.

Practical actions:

• Begin with high-value use cases that address specific problems with direct impact.
• Implement hub-and-spoke delivery models, combining central skillsets with embedded professionals.
• Adopt incremental approaches with rapid iteration and continuous feedback, create structured evaluation processes with defined success criteria, and document successes to build organizational support.
• Plan for sustainable AI operations with flexible funding, compliance readiness and continuous strategic alignment.

Learning from these leading organizations can help to accelerate your transformation. They have been through the same journey, confronted the barriers and found ways to overcome them.

The time to act is now. The future of government depends on seizing this moment. The question for government leaders is no longer whether to adopt these technologies, but rather how quickly and effectively they can implement them to enhance their core mission: improving outcomes for all citizens. Success requires making strategic investments that strengthen all five foundations simultaneously.

Summary 

Government organizations understand the transformational power of data and AI in the public sector but deployment levels remain low. The cost of inaction is growing daily. Our research has identified a cohort of pioneers that are outpacing their peers in implementation progress and strategic sophistication. Governments can learn from these leading organizations to embed greater strategic intent and five foundations for success.