The modern constituent experience

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Generative AI is transforming public service by automating tasks and enabling professionals to focus on strategic service aspects. However, AI adoption comes with challenges, including declining customer satisfaction due to an overreliance on automated systems. Mitigating complex challenges such as algorithmic bias, user privacy, and content accuracy also pose difficulties.

To help chart a path forward, KPMG has explored how agencies can create an AI-augmented experience. To successfully harness the power of AI and build trust with the public, governments should concentrate on the following strategic areas:

Prioritize the "Service" in civil service
Governments should help equip their workforce with AI tools that handle low-value, repetitive work. This allows civil servants to dedicate more time to complex casework, community engagement, and providing empathetic, high-quality service to constituents.
Create streamlined and accessible experiences
Invest in developing user-friendly digital platforms. By leveraging AI, agencies can offer personalized customer journeys, automated application processes, and intelligent self-service options that help users navigate government benefits and systems with ease.
Enhance agency research, data, and decision-making
Utilize AI to improve how agencies gather and analyze information. This includes using AI assistants to synthesize laws, policies, and reports, as well as tools to analyze public comments and feedback, ensuring that decisions are data-driven and responsive to constituent needs.

Trust is paramount. Agencies must develop and implement a robust governance framework to mitigate risks. This includes creating a centralized review process for AI tools, ensuring the security of sensitive data, and establishing clear protocols to address potential algorithmic bias and ensure ethical use.

Create a centralized process for AI and machine learning models and tools
Enhance security operations, such as limiting training model access to sensitive data
Develop clear protocols for threat identification and escalation processes
Address systemic, human, and statistical biases through evaluation, training, and education
Apply controls in response to how employees use AI technology to make decisions