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Intelligent Automation in Government: Top Examples for 2024

According to KPMG, 61% of government decision-makers say AI is at least “moderately” functional in their organizations (Figure 1), and 79% of them believe AI will improve efficiency in the public sector. 

Figure 1. Rate of AI adoption across different sectors.

Intelligent automation, also called cognitive automation or hyperautomation, is the combined use of RPA, with AI technologies such as machine learning, NLP, computer vision, and more. 

In the public sector, it can help governmental agencies improve decision-making and service delivery to citizens. Deloitte, for instance, reports that automation technologies can potentially free up 1.3 billion hours in the US government.

In this research, we’ll explore various use cases and case studies of intelligent automation in the public sector.

Use cases

Legacy system integration

U.S. government IT systems contain legacy technologies that are between 8 and 51 years old and cost nearly $340 million annually to operate and maintain. These legacy systems are a barrier to digital transformation because they are incompatible with modern technologies and rely on old programming languages for which there is a shortage of skilled professionals.

However, replacing these legacy systems can be difficult in the short to medium term. Intelligent automation can help public sector agencies integrate these systems with modern systems such as cloud platforms by:

  • Reading screens of old systems through computer vision with an understanding of the interface,
  • Extracting and migrating structured or unstructured data to new systems on demand or at regular intervals at a specified time.
  • Cross-checking data across different systems to verify data accuracy and quality.

Feel free to check our article on the use of RPA and intelligent automation for legacy system integration for more on the topic.

Citizen services

Intelligent automation can help government agencies improve services to citizens across different channels. Bots can:

  • Handle citizen queries, verify citizen information, and forward them to the appropriate office,
  • Extract data from any document provided by the citizen,
  • Update government systems during and after the correspondence,
  • Bring data from different systems together when a contact center agent communicates with a citizen.

These can enable government agencies to provide faster and better services to citizens.


Public sector agencies regularly produce reports on their budgets, expenditures, operations, etc. Report generation is a time-consuming process that requires staff to:

  • Log into various legacy and modern systems where data is stored,
  • Extract structured and unstructured data from these systems,
  • Create a data file for the reports.

Using NLP, OCR, and computer vision technologies, intelligent bots can:

  • Read the screens of different systems,
  • Extract relevant data including texts in images and documents,
  • Generate the desired reports with minimal human intervention.

If you are interested in learning more about reporting automation, read our research on RPA for reporting

Public sentiment analysis

Government agencies can use intelligent automation to: 

  • Extract public data from news, social media sites, blogs, surveys, or through interactions with citizens,
  • Categorize text as an expression of positive, neutral, or negative attitude using NLP and machine learning models,
  • Capture the most used keywords to understand the nature and context of the complaints voiced by the general public.

Public sentiment analysis with intelligent automation can help government agencies better understand and address citizens’ problems.

For more, check our comprehensive article on sentiment analysis.

Case studies

Feel free to read our article on intelligent automation case studies. Some example case studies in government organizations include:

The City of Seattle

Problem: The City of Seattle provides a wide range of public services from utilities and transportation to parks, police, fire, and emergency management. They provide a Utility Discount Program which helps low-income residents with utility bills. The program was impacted during COVID-19, where a self-service online form collected  data in disconnected Excel files, resulting in a backlog of 6200 applicants.

Solution: The automation team deployed an intelligent automation solution that works in the front-end of the process, and enters data collected from applicants into relevant systems.

Result: Unattended bots reduced the time to research, review, and approval for applications. They helped the city eliminate the backlog of applications and extend the utility credits.1


Problem: viadonau is a subsidiary of the Austrian Ministry of Transport which takes care of the preservation and development of the Danube River. The Finance and Controlling department wanted to relieve the staff from generating and provisioning of ERP evaluations, which is a time-consuming and repetitive task.

Solution: The department implemented an intelligent automation solution that helped deploy digital workers in 10 days. Digital workers can log in to the ERP system and complete the end-to-end reporting process.

Result: Digital workers enabled the department to generate reports 15 times faster than before.2

For more on intelligent automation

You can read our article on the use cases of intelligent automation in different business functions and industries.

If you are ready to implement intelligent automation in your organization, feel free to check our data-driven list of intelligent automation solutions. If you need additional help, feel free to reach out:

Find the Right Vendors


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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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