What housing providers are actually using AI for
One of the most significant findings from the research is that practical AI activity is already taking place across much of the respondent base. While discussions about AI often focus on future possibilities, the survey shows housing providers are already applying AI to a growing range of operational, analytical and customer-facing activities.
The most common use of AI is content generation. Forty-seven respondents, 71%, reported using AI to support writing, communications, reports and document creation. Data analysis was selected by 35 respondents, 53%, followed by reporting and dashboards at 27 respondents, 41%, coding and technical work at 24 respondents, 36%, and process automation at 20 respondents, 30%. Customer service was selected by 16 respondents, 24%, while 13 respondents, 20%, reported using AI for predictive modelling.
Current uses of AI
Content generation (writing, comms, reports)
Data analysis
Reporting and dashboards
Coding / technical work
Process automation
Customer service
Predictive modelling
Decision support
These results suggest that many organisations are beginning with practical, productivity-focused applications associated with commonly reported benefits such as time savings. However, the qualitative responses show that adoption is extending far beyond simple content creation.
Productivity and reporting
Time savings were the most frequently reported benefit across the survey, selected by 45 respondents, 68%. Productivity improvements were identified by 29 respondents, 44%, making them the second most frequently cited benefit.
Many respondents described AI being used to support report writing, meeting preparation, research, performance reporting and regulatory submissions. Several identified Microsoft Copilot as one of the most valuable tools currently available to colleagues, particularly for drafting documents, summarising information and reducing administrative workload.
Reporting was another recurring theme. Respondents described using AI to support dashboard development, performance analysis and information gathering. One respondent described using AI to support board-level report writing and help ensure reports included a clear tenant outcome. Others highlighted the role of AI in supporting management reporting and operational analysis.
Data, analytics and insight
Data and insight teams feature prominently throughout the responses. Thirty-five respondents reported using AI for data analysis, while many also described using it to support dashboard creation, reporting and coding.
Several responses show analysts and practitioners using AI to support technical tasks including SQL, DAX, coding and dashboard development. This suggests AI is increasingly becoming part of the everyday toolkit used by data and insight teams.
Service delivery and customer experience
While productivity remains important, organisations are also beginning to apply AI directly to service delivery.
Examples reported by respondents included:
- Contact centre call summarisation
- Customer service support
- Complaints analysis
- Automated categorisation of customer needs
- Resident insight analysis
- Faster response handling
One organisation described using AI within its contact centre environment to automatically generate call summaries and recommended actions, reducing administration and improving records of customer interactions. Another highlighted using AI to support complaint handling by analysing issues and assisting with response preparation.
These examples demonstrate that AI is beginning to support frontline services as well as back-office functions.
Predictive analytics and operational intelligence
Some of the most advanced use cases emerging from the research involve predictive analytics and operational decision-making.
Respondents described applications including:
- Damp and mould image recognition and triage
- Damp and mould predictive analytics
- Tenancy sustainability
- Arrears prediction
- Repairs support
- Forecasting
- Operational performance monitoring
One respondent identified damp and mould image recognition and triage as the organisation's most impactful use case. Others referenced predictive models designed to support tenancy management, customer risk assessment and operational planning.
Although these examples remain less common than productivity-focused applications, they demonstrate how organisations are beginning to use AI to address more complex operational challenges.
Automation and workflow improvement
Process automation was selected by 20 respondents, making it one of the most common areas of AI use. Respondents described applications including workflow automation, reporting, regulatory activity, information retrieval and operational processes.
Several organisations described AI-assisted workflows intended to reduce manual processing, automate reporting and speed up access to information. Others highlighted the use of AI to connect data sources, support performance management and improve access to organisational knowledge.
Overall, the findings show practical applications emerging alongside continued experimentation. Only three respondents described AI as a strategic priority reflected in organisation-wide adoption. However, many are already using AI to solve specific business problems, improve productivity, support decision-making and streamline operational activity. The breadth of use cases identified across the survey suggests that AI is increasingly becoming part of everyday work across social housing rather than remaining a niche technology initiative.
Housemark expert insight:
AI is becoming part of everyday work
One of the most interesting findings from the research is that AI is increasingly being used not only by technology teams, but also by analysts, practitioners and operational colleagues.
Many of the examples identified are not large transformation projects. Instead, they involve practical applications that help colleagues analyse information, prepare reports, support customers, improve workflows and save time.
This is an important shift. As organisations become more comfortable with AI, the opportunity moves beyond individual productivity gains and towards wider improvements in service delivery, insight and organisational performance.
The challenge now is identifying which use cases deliver the greatest value and creating the conditions needed to scale them successfully.
Alison Bowles,
Research Analyst, Housemark