Introduction
The "Root Causes of Failure for AI Projects" report by RAND explores common challenges that lead to AI project failures. It identifies key obstacles such as poor data quality, unclear objectives, and lack of infrastructure—issues that resonate strongly with social housing providers looking to integrate AI. Understanding these pitfalls can help housing associations avoid costly missteps and ensure AI investments drive real improvements in service delivery and operational efficiency.
Key Challenges in AI Implementation
1. Data Integrity and Infrastructure
Many AI projects fail due to incomplete or poor-quality data, making it difficult to generate reliab...
