5 benefits of AI in the public sector

Below are five benefits of AI workflow integration in the public sector with real-life use cases. These points give a first look at what level of change is possible with introducing and scaling AI in public services.

• Better accessibility to services for citizens: Google Cloud’s AI could speed up the response time to unemployment insurance claims, succeed in screening out fraudulent claims, and streamline and expedite paper applications at the Wisconsin Department of Workforce Development.

• Improved transparency of operations: AI-driven analytics can monitor public spending, pinpoint any irregular activity, and provide a look into funds being used as intended. The AI-driven bot Rosie, for instance, analyses Brazilian congresspeople’s expenses and spots suspicious spending.

• More efficiency with data-driven decision-making: In 2023, 75% of UK government employees used AI to support operational decision-making. With the help of AI, they analyzed digital images and extracted information from documents, summarised or drafted text, assessed trends and patterns, and monitored live data. 

• Increased cost saving and predictive budgeting: The NHS (National Health Service) in the UK announced its plan to start using the AI model called RETFound to identify patients with eye conditions at an early stage to prevent sight loss. It’s estimated that 15% to 25% fewer people will need treatment and NHS spending could be cut by up to £290 million per year.

Stronger citizen engagement: Go Vocal, a community engagement platform, trusted by 500+ local governments and organizations, uses AI input analysis to swiftly group and summarize input from citizens, also allowing for quicker decision-making for related parties. The city of St. Louis, for instance, successfully used Go Vocal to include 7000+ of its residents in deciding how to allocate $250 million.

3 cases of AI-driven public sector areas

Let’s look at cases of AI adoption in the public sector from three specific areas: public health, environmental sustainability, and public safety. 

• Public Health: AI can be used to encourage healthier behaviors, such as promoting vaccination, encouraging participation in early prevention, increasing physical activity, or improving diet.
• Case study: WHO’s AI-driven digital health coach, S.A.R.A.H., uses generative AI trained on trusted medical sources to help people quit smoking or improve their diet. The app also helped fight misinformation about COVID-19 during the pandemic and now Sarah shares tips that can prevent major health issues such as cancer, heart disease, lung disease, and diabetes.
• Environmental Sustainability: AI-enabled interventions can successfully motivate citizens to adopt environmentally friendly behaviors, such as reducing energy consumption, recycling, and using public transportation.
• Case study: The Enschede Fietst app promotes sustainable commuting in the city of Enschede in the Netherlands. It offers personalized rewards to citizens for each bike ride, like free coffee or a local shop discount, and even the option to request a green light more quickly at traffic lights. With its automated data collecting, the app also improves the cycling infrastructure and experience in the municipality of Enschede.
• Public Safety: AI used in behavior intervention programs can help reduce crime, improve road safety, and encourage responsible use of public spaces.

Example: The Safest Driver app uses machine learning to analyze driving habits such as speed, braking, and phone use, and rewards drivers for safe practices with weekly prizes, promoting safer driving habits while collecting data to improve traffic safety in the long run.

How can AI assist public sector projects with behavior intervention?

As mentioned, the success of public sector projects greatly depends on citizens’ behavior, therefore, actors of the sectors need to crack the code to exactly that. Along the same line, AI technology can most successfully assist public sector projects if it’s driven by behavior design strategies. 

The Fogg Behaviour Model, developed by B.J. Fogg at Stanford, suggests that every behavior results from the convergence of three elements: motivation, ability, and prompts. For any activity to happen, we need enough motivation, the ability to perform the task, and a prompt that reminds us what we need to do. 

A key insight from the model is the action line, where certain levels of motivation and ability meet to trigger a behavior. When motivation is high, even challenging actions can occur, while low motivation requires the action to be very easy. The closer motivation and ability are to the action line, the more likely the behavior will occur when prompted.

What could be the solution? AI-enabled behavior-intervention strategies.

Behavior change interventions need to address all three aspects of behavior mentioned above, from clearing up misconceptions to making processes easy to follow and creating positive feelings as a reward to build purpose and lasting habits around the given activity.

When it comes to household recycling, AI-assisted behavior change is possible, too. We have a few specific suggestions on how the right behavior intervention that utilizes AI technology could bring solutions to this problem:

How can AI be implemented in the public sector with behavior change interventions?

AI solutions can be implemented in the public sector via behavior design strategies and behavior change interventions by unlocking the following key AI capabilities: 

1. ​​Detecting behavioral patterns and identifying solutions

Every behavior design intervention should begin with detecting behavioral patterns and forming insights. This often requires the analysis and processing of large datasets where AI algorithms excel. 

• To improve road safety, we can utilize telematics data from car sensors to examine the actions of drivers that lead to potential crashes or statistical data on road accidents to detect dangerous hot spots in a city. 
• With public street cameras, we can detect issues like public littering, while also identifying potentially dangerous behavioral patterns in public transportation systems.

2. Targeting and planning

To maximize the effectiveness of our behavioral interventions, we must ensure they are well-timed and carefully tailored to the specific circumstances of each target group. This includes behavioral research to identify high-priority groups and make predictions on their future behavior, before delivering interventions.

• In public health, machine learning models can predict the likelihood of individuals skipping their vaccinations based on their demographics, location, socio-economic background, or history of health records.
• Social media text analysis can also reveal common fears, misconceptions or emotional barriers of people related to vaccinations. These predictions can inform us on who the priority groups to target with a pro-vaccination campaign, and what is the right timing and content of messages.

3. Delivering personalized interventions

AI technologies can be used as part of behavior interventions to:

• Simplify the actions we aim to promote (reducing the difficulty of desired actions)
• Motivate people through targeted messages (constantly fueling motivation)
• Deliver timely nudges to guide them in the right direction (providing prompts at the right moments) 

With generative AI, these personalized interventions can scale effectively, offering ongoing, tailored coaching and support to help shift habits sustainably over time.

• Solutions like the Center Health system leverage data from wearable devices and Continuous Glucose Monitoring (CGM) sensors to offer users tailored nudges based on personal glucose values and recommendations on which foods to avoid and which types of physical activity are best for them to improve health. 

Cheat sheet for AI-assisted projects in the public sector

Here’s your cheat sheet for implementing AI technology in public sector projects.

1. Know exactly what you want to change

Driving behavioral and social change is extremely complex, therefore it’s essential to clearly define the problem you want to address. Start by identifying the target behaviors and the specific outcomes you aim to achieve. What would you like people to do differently? Who is affected, when, and in what context? This helps you focus your efforts, and define measurable objectives. 

2. Research and understand behavioral root causes 

AI can analyze vast amounts of data—like accident statistics or social media attitudes on health—but this alone doesn’t capture the full picture. It is critical to complement this with understanding the root causes, and the human factors that influence behavior. Qualitative research, such as interviews and observations, helps uncover the motivations, abilities and prompts that drive or hinder behavior change.

3. Consider AI as a potential building block of the solution, but not the solution itself

While simple, non-technical interventions and nudges can be highly effective, AI enables us to deliver these more rapidly, affordably, broadly, and with unprecedented personalization. Use AI where it provides unique advantages, such as scaling personalized communication or identifying complex patterns beyond human capacity.

4. Think of AI as a toolbox with widely different technologies

When considering AI, match the different AI tools to your specific needs: some models excel at detecting anomalies, others at processing text, images, or personalizing communication. Decide if you’ll use AI for research, targeting interventions, or scaling personalized coaching, and identify where it uniquely enhances your approach.

5. Behaviour change is a highly iterative process

Before scaling an AI-assisted project, begin with micro tests, such as A/B testing messages or piloting AI nudges with a small group. These tests provide valuable feedback, helping refine approaches that genuinely work.

6. Use AI thoughtfully and transparently

Public sector projects require public trust, and behavior-influencing initiatives can often raise inherent concerns. Ensure AI algorithms are transparent and serve the public good. Using AI isn’t an all-or-nothing decision, but it does carry ethical responsibilities—be mindful of data privacy, potential biases, and transparency in interventions.

AI in the public sector: What’s possible in the next three years? 

Investing heavily in AI will boost a country's economic competitiveness in the private sector while reducing costs and improving the quality of public services. In fact, according to the Alan Turing Institute's most recent estimates, AI could automate 84% of the UK Government's complex repetitive transactions to some degree.

This data shows the huge potential of AI to enhance productivity if the opportunity is fully realized. But how could the public sector actually leverage AI for these meaningful efficiency gains?

Join our upcoming webinar, AI Plans in Practice: Driving Large-scale Public Sector Efficiency Gains in the Next Three Years, where we’ll discuss concrete use cases that are viable today, the practical big wins that could be realized in the next three years, and the ways to achieve them.