Meeting the new standard for citizen services
By 2026, 30% of new applications will use artificial intelligence (AI) to deliver personalized, adaptive user experiences (Gartner). This rapid shift reflects how AI is reshaping user expectations. Citizens now demand digital services that learn and respond to their needs—not just basic websites and forms that treat every user the same way. So, how can AI transform modern government services?
For government leaders, intelligent applications (IA) offer a clear path forward. These systems combine machine learning (ML) for pattern recognition, natural language processing (NLP) for human-like interactions, and predictive analytics to transform basic services into smart, responsive ones. By embedding IA into their core systems, governments can process documents in minutes instead of days, predict service demands before they arise, and deliver the personalized experience citizens expect.
What are intelligent applications?
Citizens today expect government services to match the responsiveness and personalization of private-sector platforms. Intelligent applications leverage advanced AI technologies to deliver adaptive, user-focused services that evolve with changing needs.
Core technologies
- Machine Learning (ML): Automates processes and predicts outcomes for faster decision-making
- Predictive Analytics: Anticipates citizen needs and enables proactive service delivery
- Natural Language Processing (NLP): Powers conversational, multi-lingual interfaces for intuitive user interactions
- Large Language Models (LLMs): Generates contextually relevant, human-like responses at scale
Key capabilities in action
- Contextual awareness: Adapts to individual environments to deliver personalized service recommendations, like childcare subsidies tailored to user profiles.
- Proactive notifications: Uses predictive analytics to alert citizens about benefit renewals or expiring deadlines, reducing missed opportunities.
Seamless interactions: NLP enables 24/7 AI-powered support for tasks like permit applications or document verification, with multilingual and accessible interfaces.
Learn more about identity verification in our article Establishing digital trust with verifiable credentials.
Benefits for government agencies
For government agencies, the adoption of intelligent applications offers substantial benefits, driving improvements across efficiency, decision-making, cost management, and citizen satisfaction.
Intelligent applications automate routine tasks, freeing staff to focus on complex challenges.
Example: The National Institute of General Medical Sciences (NIGMS) reduced grant referral times from 2–3 weeks to less than one day by implementing an AI-powered natural language processing (NLP) and machine learning (ML) system.
Intelligent applications analyze complex data in real time, enabling faster, more accurate responses.
Example: US Customs and Border Protection (CBP) uses AI to identify anomalies in cargo screening and imagery, enhancing border security and stopping illegal goods more efficiently.
By enabling faster, data-driven decision-making, governments can better prepare for emergencies and reduce costs.
Example: Using AI-powered early warning systems, Jammerbugt Municipality in Denmark can predict local flooding up to 48 hours in advance, enabling faster citizen response and better emergency preparedness.
Personalized, adaptive services cater to individual citizen needs, leading to higher satisfaction and more accessible public services.
Example: We used AI-powered tagging and semantic search to help park visitors easily find and add relevant accessibility information with our hackathon prototype for BC Parks.
Challenges and solutions
While intelligent applications offer a wide range of benefits, governments face some critical challenges when implementing these advanced technologies. From data security to ethical concerns, these challenges must be carefully navigated with the appropriate solutions to ensure successful AI adoption in the public sector.
Data privacy and security
Challenge: Governments manage sensitive citizen data, and improper implementation of AI—particularly large language models (LLMs)—can risk breaches or misuse. Without strict safeguards, AI systems may inadvertently expose personal data or violate privacy laws.
Solution: Adopt strict data governance frameworks and processing safeguards. For example, CBP’s AI systems for cargo screening ensure secure, real-time anomaly detection without compromising identity validation processes, balancing security with privacy.
Addressing bias and fairness
Challenge: AI models can inherit biases from their training data, leading to inaccurate or unfair outcomes. This issue is compounded when datasets are incomplete or fail to reflect real-world diversity.
Solution: Continuously refine models using diverse, representative datasets and validate outputs through testing. For instance, CBP’s AI anomaly detection required iterative improvement to ensure accuracy in identifying threats from streaming video data, reducing false positives and enhancing trust in automated decisions.
Ethical and legal implications
Challenge: AI raises critical ethical questions about accountability: who is responsible when automated systems make decisions that affect citizens’ lives? Without oversight, AI tools risk eroding public trust.
Solution: Introduce clear accountability frameworks and maintain human oversight for critical processes. For example, DHS combines AI-driven decision-making with real-time operator validation, ensuring AI remains a tool for support, not an unchecked authority in border security operations.
High implementation costs
Challenge: Deploying AI solutions requires significant investments in infrastructure, workforce training, and technology integration. These costs can be prohibitive, especially for resource-limited municipalities or agencies.
Solution: Start with targeted pilot projects that demonstrate ROI before scaling. NIGMS began by piloting an AI system to automate grant referrals, testing it on three funding programs (K99, R35, and R01). After proving the system’s accuracy and reducing processing times from 2–3 weeks to less than one day, the solution was expanded to handle all grant applications, showcasing measurable efficiency gains and justifying further investments.
Citizen trust and adoption
Challenge: Citizens may distrust AI-based systems due to fears of unfair decisions, data misuse, or lack of transparency. Without public confidence, adoption of AI tools may falter.
Solution: Build trust through transparency and validation. For instance, Jammerbugt Municipality paired its AI flood-warning tool with drone validation, ensuring predictions were reliable and encouraging citizen buy-in through visible proof of accuracy
Achieving equity
Challenge: AI systems risk deepening the digital divide by disproportionately benefiting tech-savvy or higher-income users while leaving marginalized groups behind.
Solution: Ensure inclusive design and equitable access to AI-powered services. For example, Revenue New South Wales uses AI to identify financially vulnerable citizens and provide tailored support, preventing debt escalation and ensuring those most in need receive timely assistance.
The future includes using AI to transform modern government services
Intelligent applications present a transformative opportunity for governments to exceed citizen expectations by delivering services that are personalized, efficient, and accessible. By embracing AI thoughtfully, agencies can elevate service delivery and cultivate deeper citizen trust.
Realizing this potential requires addressing critical challenges—from data privacy and ethical considerations to ensuring inclusivity—so that AI-powered solutions benefit all citizens equitably.
The path forward demands strategic investment in responsible AI approaches that prioritize human needs and build a resilient, adaptive foundation for the future of public service.
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