AI Powered Automated Citizen Service Request Triage and Routing

Automated Citizen Service Request Triage and Routing

1. Citizen Service Request Submission

Citizens submit service requests through various channels including web portals, mobile applications, and social media platforms.

1.1 Input Channels

• Web Portal
• Mobile Application
• Social Media
• Chatbots

1.2 Data Collection

Requests are collected with essential information such as:

• Citizen Identification
• Type of Service Requested
• Description of the Issue
• Location Details

2. AI-Driven Triage Process

Upon submission, AI algorithms analyze the request to categorize and prioritize it.

2.1 Natural Language Processing (NLP)

NLP tools such as Google Cloud Natural Language or IBM Watson can be employed to understand the intent and sentiment of the citizen’s request.

2.2 Categorization and Prioritization

Requests are categorized based on predefined criteria and prioritized according to urgency using AI-driven tools like Microsoft Azure Machine Learning.

3. Automated Routing

Once triaged, the request is routed to the appropriate department or personnel.

3.1 AI-Based Decision Making

Utilizing tools like ServiceNow or Salesforce Einstein, the system automatically assigns the request to the relevant service team based on workload, expertise, and historical data.

3.2 Notification System

Automated notifications are sent to the assigned personnel and the citizen, confirming receipt and expected timelines for resolution.

4. Monitoring and Feedback Loop

The workflow includes a mechanism for tracking the progress of requests and gathering feedback from citizens.

4.1 Progress Tracking

AI tools can provide real-time updates to citizens regarding the status of their requests, utilizing platforms like Zendesk or Freshdesk.

4.2 Feedback Collection

Post-resolution surveys are sent to citizens to evaluate satisfaction, using AI sentiment analysis to gauge overall public sentiment towards the services provided.

5. Continuous Improvement

The data collected from the feedback loop is analyzed to identify trends and areas for improvement in the service delivery process.

5.1 Data Analysis Tools

Tools such as Tableau or Google Data Studio can be utilized to visualize data trends and inform decision-making for process enhancements.

5.2 AI-Driven Insights

Machine learning algorithms can be employed to predict future service demands and optimize resource allocation accordingly.