AI: The missing puzzle piece for decarbonising UK housing

In recent years, housing decarbonisation has moved firmly into the spotlight of climate policy.  

The UK has a legally binding commitment to reach net-zero emissions by 2050, with housing playing a major part in the national effort. Homes account for around 17% of the country’s greenhouse gas emissions, and despite strong progress in some areas, much of the nation’s housing stock remains old, energy-inefficient and expensive to heat. This means that decarbonising these homes - through works like retrofitting - will be essential for achieving government targets, while also protecting households from rising energy costs. 

Artificial intelligence (AI) is emerging as a powerful enabler to drive the changes needed within UK housing stock, offering new tools to make sense of complex data and prioritise investment. As momentum picks up, we must make the most of this rare opportunity to reshape the future of UK housing. 

Momentum in government 

The UK government has been deepening its commitment to housing decarbonisation through a mix of regulation, funding, and planning reform. 

A good example of this is the Warm Homes Plan, which seeks to expand funding for insulation, energy-efficiency upgrades and low-carbon heating, particularly for low-income households. Policies are also tightening in the private rented sector, with reforms to Energy Performance Certificate (EPC) requirements and planning reforms to streamline the installation of heat pumps. The reforms also seek to improve data management, transparency, and quality control in the EPC system. 

Together, these initiatives among many others signal a growing expectation that all parts of the housing system must accelerate retrofit activity. AI is uniquely positioned to support this transition. 

AI as an enabler of decarbonisation 

Decarbonising the UK’s 29 million homes represents a huge challenge. Each property is different; retrofit measures are multi-layered, supply chains are stretched, and funding is complex.  

From EPC records to local infrastructure, AI-powered analytics can help councils assess vast datasets to forecast where interventions will have the greatest impact. Machine-learning (ML) models can predict which homes are best suited for heat pumps, where insulation deficits are highest, and how local energy networks will respond as low-carbon technologies scale. For national bodies, these tools support long-term planning; for local authorities, they enable better prioritisation and budget allocation. 

AI can ensure funding reaches the homes with the greatest need, aligns retrofit strategies with regulatory expectations, and helps delivery partners meet tightening EPC and heat-pump deployment targets. 

Tools to support the government’s goals

There are already AI tools that can be deployed in local and central government, including highly contextualised data-layer platforms such as Customer and Citizen Data Layers (CCDLs), which can support the delivery of retrofit programmes. 

Additionally, tools that enable the segmentation of households can help public sector bodies identify groups most likely to benefit from specific upgrades or financial support. This ensures that outreach campaigns target those who are most in need. In an era of tight funding, ensuring that the support goes to the right places is more important than ever.  

AI can also help increase grant uptake. Predictive models can identify households that may be eligible for schemes but have not yet applied, and chatbots or guided digital pathways can simplify the grant-application journey, offering tailored advice and real-time clarification of eligibility criteria. This simplifies the process for government, as well as for the residents themselves. 

Mastering data management 

​With numerous tools available, the question remains of how to manage data effectively.  

Effective decarbonisation relies on the ability to manage and draw insights from large, fragmented datasets that can be spread across councils, housing associations, utilities and central government departments. 

AI can play a transformative role in integrating, cleaning and analysing these data sources, allowing for faster rollout of support and more accurate reporting. Without that, the process could lag and accuracy could suffer. 

In delivery, automation can support tasks such as verifying documentation, scheduling assessments, and flagging missing information. While this increases efficiency, we must remain cautious about automating decision-making processes due to concerns surrounding bias. Human oversight should remain central to all decisions, supported by AI as an enabler instead of a replacement.  

Equally important is the need for secure data-sharing frameworks across the housing ecosystem. AI systems must operate within stringent data governance and cybersecurity standards to protect citizens’ information and maintain public trust.  

Providers of digital and network infrastructure will play a crucial role in helping councils and housing associations meet these standards, manage the data, and ultimately deliver the programmes the sector needs to meet its goals. 

Managing the risks 

Although AI offers substantial benefits, it also introduces risks. AI itself has a carbon footprint, especially when using large models. Public sector organisations must balance the carbon cost of running AI systems against the emissions savings they enable. Deploying efficient models and using renewable, cloud-powered infrastructure can help ensure that the net impact remains positive, but it’s important to prioritise this, considering the goal being decarbonisation. 

Data governance risks also arise when integrating multiple datasets, particularly in relation to privacy, consent, and accuracy. Clear accountability and transparent data handling processes are essential here.  

Finally, there is also a risk of over-reliance on algorithmic outputs. AI can guide decision-making; final judgements must also remain human-led, particularly when allocating public funds or setting priorities that affect vulnerable households. 

Partnerships, transparency and the path to net-zero 

AI is rapidly becoming a key enabler of the UK’s housing decarbonisation agenda, supporting everything from planning and segmentation to citizen engagement and data management. While its applications spread far and wide, its adoption must be accompanied by responsible governance and transparency.  

Central government and ambitious local authorities can certainly use it responsibly to support the drive for net-zero. Alongside robust governance and transparency, there is no doubt that the public sector and technology providers can work in partnership, using AI to provide residents with warmer, higher-quality housing, and ultimately propel the UK towards the end goal - decarbonisation.