March 12, 2024 at 11:32PM
HUD used RPA and AI/ML to address operational challenges during US government shutdowns and legacy system rigidity. They automated housing contract monitoring with RPA, overcoming legacy system constraints, and utilized AI/ML to understand federal regulation impacts on procurement and HR, identifying and addressing bottlenecks in these processes. This digital transformation aimed to improve operational resilience and efficiency at HUD.
Based on the meeting notes provided, the key takeaways are as follows:
1. HUD faced significant operational challenges compounded by the rigidity of its legacy systems and the impacts of the longest US government shutdown.
2. Challenges included major citizen impacts from the shutdown and legacy technology, such as disruptions to housing stability for elderly and disabled residents.
3. HUD innovatively deployed Robotic Process Automation (RPA) to automate monitoring and notification of housing contract expirations, showcasing a significant leap towards operational resilience and efficiency.
4. Federal regulation created significant challenges in procurement and HR, hindering efforts to modernize legacy systems and recruit skilled personnel.
5. HUD leveraged Artificial Intelligence/Machine Learning (AI/ML) and big data analysis to understand operating constraints, federal regulation impacts, and identify bottlenecks in acquisition and hiring workflows.
6. Leveraging AI/ML and big data analysis allowed HUD to demonstrate performance gaps, make scenario recommendations, and take corrective action steps in recruitment and procurement processes.
These takeaways highlight HUD’s proactive approach to addressing operational challenges through the innovative use of RPA and AI/ML to modernize its systems and improve efficiency in the face of complex federal regulations and system constraints.