The AI technology landscape in social housing
The rapid growth of artificial intelligence has created a crowded technology market, with new platforms, tools and suppliers emerging at pace.
One of the objectives of this research was to understand which technologies are actually being used across social housing and whether any clear patterns are emerging among housing providers.
Choice of AI tools
The findings show a strong concentration around Microsoft's AI ecosystem. When respondents were asked which suppliers or platforms provide most of their organisation's AI capability, 54 respondents, 82%, selected Microsoft, including Copilot and Azure AI. By comparison, only five selected analytics platforms such as Tableau or Power BI, two selected Google Gemini and one selected OpenAI.
Microsoft Copilot, Azure AI
Analytics tools (Tableau, Power BI)
Google Gemini
Open AI
This dominance is also reflected throughout the qualitative responses. Copilot was repeatedly referenced as the primary AI tool being used across organisations, with respondents describing applications including drafting reports, summarising meetings, generating content, conducting research, analysing data, writing code and supporting day-to-day productivity. Several respondents identified Copilot as the AI application delivering the greatest impact within their organisation.
One respondent described Copilot as helping colleagues save time, improve productivity and reduce administrative effort through AI-assisted drafting, summarisation, research and content creation. Another respondent highlighted AI’s role in developing dashboards and supporting SQL and DAX work. Others referenced its use in complaints analysis, board reporting, meeting preparation and data analysis.
Although Microsoft dominates as the primary provider, the findings show organisations are using a range of AI technologies. Sixty-four respondents reported using general AI tools such as Copilot, ChatGPT or Gemini. Twenty-eight reported using AI features embedded within supplier systems, while 17 reported using AI agents, 13 were using workflow automation platforms and 10 had implemented custom-built AI solutions.
Reported using AI agents
Uses and applications
The survey also demonstrates that AI activity is extending beyond specialist technology teams. Organisations reported applying AI through housing systems, CRM platforms, contact centre solutions, reporting tools and analytics environments. In many cases, respondents described using AI alongside existing systems and workflows rather than introducing entirely new platforms.
A significant proportion of respondents reported AI being adopted through multiple routes. Twenty-three said AI was delivered through a mixture of approaches within their systems, indicating that many of the organisations represented are not relying on a single route to adoption.
The research also identifies interest in more advanced applications. Seventeen respondents reported using AI agents, with qualitative examples including policy and knowledge retrieval assistants, repairs support tools and other defined operational assistants. Respondents separately reported contact centre AI, predictive analytics and workflow automation. Although adoption remains relatively early, the findings suggest some organisations are moving beyond basic productivity use cases to explore more specialised applications.
At the same time, technology itself does not appear to be the sector's most pressing concern. Only eight respondents selected investment in new tools as a priority for the next 12 months. By contrast, 39 prioritised improving data quality and 39 prioritised upskilling staff. For many respondents, improving organisational capability appears to be a more immediate priority than expanding their portfolio of AI technologies.
What are your top priorities for AI in the next 12 months?
Improving data quality
Upskilling staff
Explore new use cases
Strengthen governance
Scale use cases
Invest in new tools
Overall, the findings show a clear concentration around Microsoft's AI ecosystem within the respondent base, alongside supplier-embedded AI, automation platforms, agents and custom-built solutions. Forty-eight respondents, 73%, said their core business systems provided either limited support for AI or no support at all. This reinforces a wider theme in the research: for many respondents, the immediate challenge is less about finding another tool and more about strengthening the systems, skills, data and governance needed to use AI effectively.