How AI can transform public services

Insights

• Implementing an AI incubator offers government agencies a controlled environment to test new technologies without impacting live systems.
• It enables fast, low-risk deployment and market readiness of identified AI solutions.
• It uses structured frameworks – deployment pipelines, playbooks, compliance checks, and change management – to rapidly scale use cases.

Public-sector organizations want to implement AI to drive faster, more accurate decision-making, and service delivery. AI’s influence on public service design and delivery has grown significantly, and today 67% of the Organization for Economic Co-operation and Development (OECD) countries rely on it to improve these functions. Targeted use of new AI technologies such as in case processing, which is organized, workflow-driven processing of individual cases in service delivery, is likely to help governments cut budget costs in affected areas by as much as 35% over the next decade. But for AI to succeed, organizations must evaluate the business impact of potential use cases, prioritize those with the greatest scalability, and expand them while also addressing resistance to change. Establishing an AI incubator can help organizations address these challenges and drive implementation, provided they can get its frameworks running successfully.

How AI helps the public sector

Several countries have established dedicated public bodies to oversee AI, such as the UAE’s Ministry of State for Artificial Intelligence, Digital Economy and Remote Work Applications, and the UK’s AI Council, while others like China and Japan have tasked existing ministries with implementing AI within their respective sectors.

Integrating AI into functions such as public safety, healthcare, infrastructure management, and citizen services, can help governments process vast amounts of data in real time, identify patterns, and make proactive decisions. This shift enhances transparency by making government operations more visible and traceable. It reduces administrative bottlenecks by using automation to speed up workflows, such as document verification or eligibility checks, and ensures that resources are allocated more effectively by showing where current resources are being underused or overused, so it can be adjusted immediately.

AI-powered predictive analytics can help forecast demand for public services, prevent crises before they escalate, and optimize budget planning. Over time, these innovations can redefine public trust by delivering services that are not only faster and more accurate but also fairer, more accessible, and personalized.

A public-services entity — a high-income central government in the Middle East and a client of Infosys — was looking to implement AI in services like financial reporting, consumer complaints processing, and e-commerce start-up services. It also aimed to help citizens gain access to data on entities like merchants and commodities, and consumer trends related to products and brands, thus helping them make informed decisions about who they buy from.

AI in financial reporting, as an example, can introduce digital workflows to reduce dependencies on manual audit filings and make it cost-effective, faster, and less error-prone for micro and small business setups. It does this by pulling data directly from enterprise resource planning systems, ledgers, invoices, bank feeds, and transaction logs, eliminating the need for auditors or finance teams to manually gather and compile documents. Another example of AI-led productivity is of document digitization, where PDF and scanned archives on company records are converted to database formats using techniques like computer vision and optical character recognition, for use by the public entity employees in the Ministry of Finance. This helps private-sector officials gain easy access to the documents quickly, as needed.

However, implementing AI in the public sector is more complex than it appears and demands strong foundational elements to ensure success and deliver meaningful results.

Conditions required for AI

Infosys research shows that successful AI implementation requires a comprehensive AI strategy. It is important to assess the business value of AI use. That is, to select use cases with measurable benefits, identify scalable ones, and then scale them. This is in addition to tackling change resistance – which organizations struggle with. The Infosys client in public services also faced similar issues.

Organizations also face challenges around data sovereignty and calculating return on investment in a public services scenario where profit is not the motive. Some public-sector organizations use sovereign AI systems that are designed, governed, and operated under the control of a specific nation or public authority with the goal of ensuring national autonomy over data, infrastructure, and AI capabilities. They require a vast amount of citizen information, often collected passively through digital devices and online activity, sometimes without individuals’ full awareness or explicit consent. Because AI systems are often complex, it can be challenging for citizens and their government entities to understand how their data is being used, where it is stored, where it is computed, and where it is ultimately exposed for trials and training of models. Compliance mechanisms like General Data Protection Regulation lag the innovation and growth of AI models and applications. Furthermore, AI data centers are not always within the sovereign boundaries.

Organizations should introduce an AI incubator to tackle these issues and implement AI successfully. An AI incubator is a program or initiative designed to support the development, testing, and scaling of AI solutions, especially for startups, government teams, or corporate innovation groups. It provides the resources, infrastructure, and guidance needed for teams to build and experiment with AI technologies. It acts as a supportive environment where early-stage ideas can grow into mature AI applications. It provides access to AI development tools and model libraries, and sandboxed environments for safe experimentation. It also brings together domain experts and advisors whose subject-matter knowledge helps ensure that AI systems are accurate, relevant, and usable in real-world contexts. Incubators bridge the gap between technical AI teams and the operational or policy environment in which the solution will be deployed.

How an AI incubator can help

An incubator uses structured frameworks that include the design of deployment pipelines, use of deployment playbooks, and ensuring compliance checkpoints, and change management measures to quickly deploy and scale the identified use cases and take them to market.

It is particularly useful for public-sector organizations as government agencies can face high risks when implementing new technologies. An AI incubator provides a controlled, low-risk environment for them to test ideas without affecting live systems, create prototype solutions using synthetic or restricted datasets, and identify risks related to privacy issues or security gaps early, which can prevent expensive failures.

For the public-services client, Infosys built an incubator, which includes tools to test use cases for business value and identify scalable ones. For instance, the incubator helped evaluate one large language model (LLM) over another, comparing costs, token size, annual investment outlay, data sovereignty, infrastructure and network requirements, deployment and maintenance complexity, training data security, multimodality, and inferencing accuracy on local languages. Local language LLMs are often more effective than English-centric models, as they avoid the pitfalls of translation artifacts and English accents present in multilingual models. Furthermore, they show better performance in lexical simplification, information retrieval, and information accuracy.

An incubator model is also useful to understand if an AI use case has the potential to scale based on accuracy and adoption. In one instance of financial auditing and reporting, Infosys realized that from an annual data consumption cost perspective, there is more value in building a heuristic-based digital workflow compared to a full-blown generative AI solution. If you look at how much data a solution needs each year, and how expensive that data is to store, process, and use, then a simpler, rule-based digital workflow by means of a heuristic solution – which is an earlier type of AI – is cheaper and more cost-effective than building a large generative AI system. The total cost of ownership for a digital database and workflow in terms of maintenance and run costs is significantly lower when compared to AI token costs.

The project took six weeks to build a financial auditing AI pilot from the ground up, which enabled the team to model the future run costs over a 12-month rolling period. The realistic assessment of data and run costs are only possible through a proof of concept or pilot, and should be used to objectively assess the future total cost of ownership.

How public-sector organizations can establish a successful incubator

Launching a successful AI incubator begins with a clear plan. These steps help organizations get the most value, and speed up adoption.

• Bridge capability gaps through strategic partnerships: Public-sector organizations often lack the specialized AI expertise needed to build an incubator from the ground up. They should partner with AI-experienced tech companies who possess the know-how to build the incubator, including understanding the choices of open source versus proprietary or custom stack versus platform dependency. For example, Infosys helps companies establish AI incubation hubs through its blend of strategic partnerships with NVIDIA and hyperscalers like Google, AWS, and Oracle, supported by its internal culture of innovation and start-up ecosystem. These partnerships ensure access to the latest AI tools, scalable cloud environments, and domain specific knowledge.
  
  Partnering with experienced tech companies shortens the learning curve for public-sector organizations, and speeds up design, development, and deployment, allowing them to realize benefits sooner. As public-sector entities operate under stringent regulatory, privacy, and ethical obligations, partnering with mature AI providers ensures access to battle-tested security practices, responsible AI frameworks, model risk management structures, and governance standards.
• Embed users at the center of AI testing and design: AI use cases must be tested by business teams and users, and not by the data science team alone. This ensures that the tools are user-focused and necessary improvements can be made to them depending on errors identified. Public-sector AI must meet high standards of accuracy, fairness, and transparency. When users validate the system, trust increases, risks of bias or incorrect decisions drop, and compliance with policy and ethical standards is strengthened.
  
  Getting the user experience (UX) and user interface (UI) right – so that it is easy and intuitive for people to use – is a key success criterion for AI adoption. Especially in the context of countries with high digital penetration and a young population that is digital savvy, user-centric design has transformed tasks like renewing licenses, accessing healthcare records, and registering for government programs compared to lengthy waiting times. Two great examples are Saudi Arabia’s Tawakkalna platform and Amsterdam’s Accessible Route Planner, both showing how inclusive UX design has helped adoption across digital divides on a citizen-scale implementation.
• Lay the data groundwork for reliable and responsible AI: Public-sector organizations must provide high-quality test data to use in the AI models for better results accuracy and embed local languages and laws to add relevant context. Public-sector operations are governed by specific laws, regulations, policies, and administrative practices. Embedding this context into AI models ensures that their outputs are legally compliant, aligned with government procedures, and tailored to the realities of public-sector decision-making, reducing operational and compliance risks.
  
  However, sometimes this step is the hardest part. As the Infosys team found out in its implementation for the public-sector client, this can also be the longest part. The answer to this challenge is often creation of synthetic data to mirror the real data that cannot be used for model training and testing. This is a multiphase project, where the Infosys team conducted a thorough evaluation of the original dataset, mapping sensitive attributes, assessing compliance requirements, and defining privacy risks and intended utility for use. Next was ensuring privacy mechanisms, including anonymization and identifiability metrics that are integrated into the generation process to minimize risk of re-identification. Finally, synthetic datasets were generated and subjected to privacy evaluations against real data to assess accuracy, and statistical resemblance, and all traces of personally identifiable information were removed.

These best practices can help public-sector entities set up a successful incubator and address the challenges associated with AI implementations. It is important to consider that not all AI use cases that work in the private sector will make a difference in the public domain, hence public-sector entities should also be prepared to pull the plug on use cases that don’t deliver actual service value.