Aspirations and Applications of AI in Social Housing (2025)

This resource, titled "Aspirations and Applications of AI in Social Housing," is published by Service Insights Ltd and authored by Dr. Simon Williams, Dr. Nicky Shaw, Dr. Emma Forsgren, and Stephen Blundell. The study investigates the integration of Artificial Intelligence (AI) in the English social housing sector, aiming to understand current applications and future aspirations for AI usage while considering core social housing values.

The research identifies that current AI applications in social housing are primarily focused on the use of Large Language Models (LLMs), such as ChatGPT. Aspirations for future AI implementations are directed towards predictive models aimed at enhancing core services. The study involved responses from 220 surveys and 50 depth interviews across nine housing providers in England, revealing a significant gap in awareness and policy development regarding AI adoption.

Findings indicate that many employees are uncertain about the AI technologies implemented in their organizations. While 22% of staff report having AI tools available for their roles, approximately 31% are actively using them. A notable 93% of those using AI find it beneficial, citing advantages like summarizing tasks and simplifying information. However, a portion of employees expresses reluctance to adopt AI due to concerns over trust, accuracy, and the potential de-skilling of human roles.

The study raises concerns that AI implementation may overlook essential values in the social housing sector, such as equality, diversity, and inclusion. There is a risk that the excitement surrounding new technology could distract from the core mission of social housing providers. Only 41.7% of respondents believe AI is being deployed in alignment with their organization’s values, indicating a need for careful consideration of ethical implications.

AI’s impact on decision-making processes is profound, with the need for human oversight emphasized. The research suggests that reliance on AI may lead to reduced critical thinking capabilities among employees, impacting service quality for tenants. Awareness of potential AI risks is high, with 75.7% of employees acknowledging these concerns, yet only 20.2% trust AI to provide consistently accurate information.

Data quality is identified as a crucial factor for successful AI implementation. The study draws connections between perceptions of data quality and the efficacy of AI deployment, highlighting a significant gap in ensuring that data is accurate and trustworthy. Only 43.2% of employees find it easy to access necessary data, pointing to a pressing need for improved data management practices in the sector.

The research underscores the importance of fostering a culture of experimentation and innovation in AI adoption, stressing that social housing providers must adapt quickly to the rapidly evolving technology landscape. The need for policies and strategic frameworks that align with AI adoption is crucial, particularly as the sector strives to maintain its core mission while leveraging AI to improve service delivery. This study contributes valuable insights into the integration of AI in social housing, highlighting both the potential benefits and the challenges that lie ahead as organizations navigate this transformative technology.