Dear Soha Zedan ,

The primary objectives include examining the potential of AI to address administrative problems, such as automating document analysis, deploying AI-driven chatbots for public engagement, and utilizing predictive analytics for resource allocation.

The digital age has brought numerous advances and technological improvements, transforming how we live, work, and interact with each other, so the importance of E-Government has also become increasingly apparent in recent years

Article Artificial Intelligence (AI) and Automation in Administrativ...

Regards,

Shafagat

AI tools can transform administrative work by automating repetitive, data-heavy tasks, freeing up human employees for more strategic work. One innovative use is to create predictive models with machine learning that can forecast future needs.

For example, a time series forecasting model could analyze historical data to predict future staffing or resource requirements. A natural language processing (NLP) model could automatically sort, route, and summarize documents. You could also use a reinforcement learning model that learns from continuous feedback to optimize complex workflows, like scheduling or supply chain logistics.

These applications not only save time and reduce errors but also provide valuable, data-driven insights to help you make better decisions.

Using Natural Language Processing (NLP) models like BERT or GPT-based systems to comprehend and process requests, artificial intelligence can make administrative jobs easier by automating operations that need to be done again and again, like scheduling, document routing, and approval workflows. Predictive analytics models, such as Random Forests or Gradient Boosting Machines, can help with resource planning by predicting when workloads will be at their highest, how many personnel will be needed, and how much money will be needed. With CNNs, computer vision can automate checking documents, matching signatures, and turning paper records into digital ones. Models for finding anomalies, like Isolation Forest or Autoencoders, might identify strange expenditure claims, buying patterns, or compliance problems. Reinforcement Learning may also dynamically optimize task allocations, which makes complex administrative operations more efficient and cuts down on turnaround times.