Transforming Public Sector Operations: A Comprehensive Guide to Public Sector AI Adoption

Generative AI (Gen AI) represents a significant shift in how industries leverage AI. Unlike the traditional uses of AI, which often operate within discrete, isolated applications, Gen AI is fostering broader, more integrated solutions. It excels at performing semantic search and generating new content, simulating scenarios, and providing insights across various domains, thereby breaking down traditional innovation silos.

While the private sector has rapidly integrated AI to enhance services and operations, the public sector has been adopting this technology at a glacial pace. At this point, it becomes necessary to understand how Gen AI is breaking down traditional ways of isolated innovation and fostering cross-industry collaboration and how public sector entities can learn from the private sector experience to accelerate benefits and avoid the pitfalls, especially as it relates to managing and enhancing their constituent experience, resolving operational challenges, streamlining business functions, and supporting program integrity.

Private Sector Leads the Race for AI Integration

AI-powered tools have secured a prominent spot in our personal and professional lives by enhancing operational efficiency across industries. As 90% of the private sector organizations are either using or planning to invest in AI tools, AI is set to add value worth $13 trillion to the global economy by 2030.

The private sector has successfully leveraged AI to create value for the stakeholders, augment the capabilities of the existing workforce, and build confidence among regulators. A judicious and proactive use of services like Natural Language Processing (NLP), Natural Language Understanding (NLU), social listening, computer vision, anomaly detection, and personalized videos has brought substantial value to private sector operations.

Here are some of the most common AI use cases and advancements in the private sector, demonstrating the potential of AI: ^[15][16]

• Chatbots and virtual assistants streamline self-service customer services
• NLP/NLU in gen AI offers customers a conversational experience for personalized support
• Automating repetitive tasks – like documentation management, data organization, and report generation
• AI-powered analytics tools help businesses leverage data-driven insights to optimize supply chain and logistics operations, from efficient contract management and improved receivables management and deal sourcing to optimized transport plans
• AI-enabled solutions simplify financial reporting and the financial commentary process to give a real-time snapshot of the company’s financial health and performance

Government agencies are increasingly relying on public-private partnerships to incorporate AI in policymaking and offer services to the citizens. ^{[1][18][19][20]}

• In the healthcare sector, Gen AI can be used to improve patient engagement and streamline claims processing. AI-driven platforms can automate administrative tasks, allowing healthcare providers to focus more on patient care.
• One of the most impactful uses of Gen AI is in personalization. Retailers like Walmart and Instacart are utilizing Gen AI-powered chatbots and virtual assistants to offer personalized shopping experiences, making recommendations based on personal preferences. Based on this model, government agencies can leverage conversational chatbots to help citizens receive prompt and relevant information about governance and policy decisions. The same models can transform the HHS and DMV services.
• In manufacturing, Gen AI is being deployed through digital twins — virtual replicas of physical assets that allow companies to simulate, predict, and optimize performance across the entire lifecycle of a product or process. This technology is particularly useful in predictive maintenance of equipment, which helps ensure business continuity for agencies like Federal Aviation, VHA, DOT, etc.
• Gen AI’s use in e-commerce and FinTech to prevent fraud in online payments can be extrapolated to build AI systems that can analyze transaction patterns in public sector payment processors to detect and prevent fraud. Such an initiative by the US Department of the Treasury has already recovered $375 million in 2023, and similar can extended for Unemployment insurance and social service agencies.
• The Department of Human and Health Services is experimenting with the use of AI to detect counterfeits of FDA-regulated pharmaceuticals, detect and prevent potential cases of Medicare and Medicaid fraud, and more.
• The US Government Accountability Office (GAO) is exploring AI use cases for summarizing draft GAO legislative mandates, answering internal GAO policy questions, summarizing public comments from Regulations.gov, and more.

These, and many more AI use cases are in the prototype or pilot phase in the public sector. The underlying structure in each of these use cases and operational models is the ability of AI to analyze huge amounts of data to drive policy decisions that create shared services that benefit citizens and government organizations alike. Taking these use cases to the level of full-scale adoption requires organizational overhaul and adopting an AI-first mindset. Building a shared digital infrastructure, implementing change management, and fostering a partner ecosystem can help the public sector establish centers of excellence to help achieve this mammoth task.

Challenges and Opportunities for Mutual Learning

While some government agencies have begun implementing AI for specific tasks like fraud detection and citizen engagement, the overall pace of adoption lags the private sector. This gap is further widened by the public sector’s need to prioritize ethical considerations, a significant skill gap, resistance to change, and the requirement for transparency and accountability, which can delay the implementation of AI. Some of the major challenges overcome by the private sector include: ^[1][6][7]

Although the public sector can learn from private organizations to overcome these challenges, some issues are unique to government agencies. Some of the major challenges the public sector faces in AI adoption include:

• The public sector must overcome technology and data debt for successful AI adoption. The technology debt stems from a reliance on legacy systems and short-term solutions like siloed on-premise systems. This leads to a lack of uniform quality data, which poses challenges when training AI models. Cloud adoption offers a powerful solution to this problem. Migrating to cloud infrastructure allows public sector agencies to consolidate data without the high costs of maintaining hardware. It provides the scalability and flexibility needed to deliver uniform, high-quality datasets. This centralized access is crucial for driving successful AI implementations.
• Biased data can result in algorithmic bias in the AI model, leading to unfair outcomes. This is a significant challenge in the public sector, as fairness is crucial to ensuring every citizen receives the same level of care and services. This highlights the need for diverse training datasets, rigorous testing protocols, and openness to feedback to ensure equal service delivery.
• Vendor selection can also prove to be critical for AI adoption in the public sector as it has direct implications for data security, privacy, transparency, and accountability. Choosing the right vendor can ensure data security and prevent project failure.
• The public sector must also contend with budgetary constraints when it comes to legacy system modernization. This also extends to AI adoption, as integrating AI solutions with the existing systems can be a significant upfront investment.

Despite these challenges, the public sector can achieve a much bigger impact on the public ecosystem and make the technology more ubiquitous and accessible to all. Here is how the public sector has the opportunity to create a better future with AI: ^[3][8]

• Building a regulatory environment to promote and fund research, develop infrastructure, and build an expert workforce.
• Upskilling the workforce for the new human – machine paradigm starting at the education level, giving upcoming professionals the opportunity to understand the importance and implications of AI and build an AI-native workforce.
• Setting up an AI authority that can design the regulatory landscape for AI implementation across the country. This includes establishing a systematic approach to handling risks associated with AI, ensuring transparency and accountability across AI projects, and upholding the highest data security and privacy standards. This can pave the way for safe and trusted AI applications.
• Launching transformative AI projects at the national level with the right regulatory framework and technological infrastructure in place. Generative AI can be helpful in identifying the highest impact areas, brainstorming project ideas, and simulating potential policy implications to ensure the highest social and economic impact of these projects.

The AI Convergence – Public-Private Partnerships

Public-Private Partnership (PPP) offers a promising approach to overcoming the AI adoption challenges and making an AI-led future a reality. GenAI’s ability to create and analyze vast amounts of data has encouraged collaboration between previously isolated sectors. These collaborations often involve academic and industry leaders working together to address complex challenges and accelerate AI integration. They can leverage industry expertise and cutting-edge technologies from private companies while utilizing public sector data and infrastructure to drive impactful solutions.

Similarly, academia plays a critical role by providing rigorous research and development support, which complements the practical applications and strategic insights provided by industry players.

Examples of PPP include: ^[17]

• Industry self-regulation initiatives, like Partnership on AI (PAI), are where global tech companies, academic institutions, and civil organizations come together to promote responsible AI development.
• Multi-stakeholder initiatives, like the European Commission’s High-Level Expert Group on Artificial Intelligence, guide AI policy at the national level.
• Tech giants like Google and Microsoft are deploying AI ethics committees and advisory boards to promote transparent and responsible AI practices in both the public and private sectors.
• International governments are also offering grants, tax incentives, and public procurement contracts to further encourage PPP, motivating the academia and private sector to develop AI solutions that will help address pressing societal challenges.

According to the World Economic Forum, such partnerships are crucial for reskilling the workforce, ensuring that AI implementations are both effective and ethical. ^[3][4][5]

The Operational Model to Shape the Future of AI in the Public Sector

Developing policies and operational models is an inherently extensive process, fraught with red-tapism and complex decision-making, but data is at the heart of any AI initiative. AI applications take over the data analysis component of this process, identifying complex patterns in the data, recommending and evaluating policy decisions, forecasting outcomes, and improving resource allocation. Gathering public feedback with NLP, speech recognition, and sentiment analysis aids the process of building robust models for public sector AI implementation. ^[9][10]

An AI-led model uses AI technologies not as supplementary tools but as core business enablers. Each aspect of the operation is seamlessly integrated with AI tools and assists with collaboration between different operational verticals.

• Services: AI tools automate tasks such as data analysis and prototyping, lowering costs. For instance, AI can identify design flaws, optimize workflows, and personalize user experiences based on customer behavior. This results in more efficient service delivery and better customization.
• Design: AI facilitates data-driven decisions that lead to better design – and, by extension – better user experiences. AI tools allow for prototyping, testing, and user feedback analysis, allowing iterative improvements and faster development cycles.
• Data: Organizations must have robust data collection, analysis, and governance frameworks to harness AI’s full potential. The insights that form the basis of service delivery can only be refined from this data.
• Talent: AI can enhance human capabilities. However, an innovative and smooth synergy between the two is contingent on successful adaptation and upskilling. Organizations must invest in training their workforce to leverage this collaboration.
• Engineering: AI-driven automation helps streamline technical workflows, allowing organizations to build more scalable, flexible, and resilient systems. AI augmentation improves the technological collaboration between partners and the core technical foundations of the enterprise.

Policymakers cannot rely on computer scientists and AI experts alone to drive the policies and operating models that will help the public sector. A collaboration between legal experts, economists, public representatives, psychologists, and ethicists is crucial to ensure inclusive and mutually beneficial shared services. ^[3][11][21]

Key Dos and Don’ts for Cross-Sector Innovation

Implementing AI-powered solutions in the public sector and achieving cross-sector innovation can seem challenging at first glance. However, being mindful of a few factors can help ensure successful AI implementation and better serve citizens.

Here are the dos and don’ts for effective cross-sector innovation and public-sector AI implementation:

Dos: ^[7][12]

• Identify important result areas and set clear and measurable targets for AI adoption
• Evaluate data quality and address data gaps
• Break down data silos and enhance data-sharing capabilities
• Help the affected workers through reskilling and upskilling and focus on AI literacy
• Augment existing data governance capabilities for responsible AI use
• Create a dynamic regulatory framework for the rapidly developing technology
• Start with a pilot project, assess feedback, and scale successful projects
• Ensure ethical and accountable AI use
• Continuously monitor and track AI’s impact, gather feedback, and evaluate project performance

Don’ts: ^[13][14]

• Do not try to reinvent the wheel; build on successful use cases in other sectors and modify them to meet the use-case-specific needs
• Do not underestimate the importance of data security and privacy protocols at any stage of AI implementation
• Do not neglect stakeholder engagement and feedback during the planning, pilot, implementation, or evaluation stages
• Do not overlook data quality or algorithm bias concerns
• Avoid “black box” AI implementation to ensure transparency and explainability
• Do not disregard the potential for job displacement or redefinition

With our AI-first approach, Infosys Public Services is platforming several perspectives and creative thinking to help our public sector clients with increased efficiencies and improved time to value, as well as ROI in this emerging technology category. We have assessed the needs and already commissioned a collection of use cases examining the potential of AI applications on various functional domains, e.g., customer services, document processing, risk management, fraud detection, predictive analytics, automated front and back-office processes, efficient data handling, and more.

Our objective is to bring cross-industry knowledge & learnings to create real-world change and help improve the lives of millions by unlocking the potential of AI in a responsible, sustainable, collaborative, and inclusive way that will profoundly shape the future landscape. We remain committed to helping government organizations bring collaborative innovations towards improved public service delivery and driving AI adoption at scale in the public sector.

Author Details

Dr. Suman De, Principal Consultant and Head, Government Healthcare Analytics Solutions, Infosys Public Services

Dr. Suman is head of government healthcare analytics for Infosys Public Services. He has extensive experience in the public healthcare sector and previously worked for the World Health Organization, UNICEF and the Indian Public Health Association.

At Infosys, Dr. Suman leads the areas of vaccine management, advanced data science and artificial intelligence-enabled population health, social determinants of health analytics, opioid management, care management, and value-based care. He is a frequent public speaker at various healthcare conferences, forums and at major universities, including the Massachusetts Institute of Technology.

Dr. Suman is based in Hartford, Connecticut. He holds a medical degree from the University of Calcutta and master’s degree in healthcare administration from the Tata Institute of Social Sciences in Mumbai, India.