Artificial intelligence is already being used to address various industry concerns.
The government’s target of 1.5 million new homes this parliament has put the planning and construction system under a spotlight. The reasons it struggles to deliver are not unfamiliar:
- a planning system that moves slowly
- fragmented land data
- infrastructure constraints
- community opposition, perhaps fuelled as much by information gaps as genuine objection
What is less well understood is how artificial intelligence is already being deployed to address each of these bottlenecks, and what that means for professionals advising clients in property and construction.
Government is already investing
This is not a future prospect. In March 2026, The Ministry of Housing, Communities & Local Government (MHCLG) awarded Google Cloud a £6.9 million contract to build an AI tool that will assist planning officers with policy research, compliance checks and report drafting, targeting a reduction in householder application processing times from eight weeks to four. The government’s Extract tool, developed with Google DeepMind’s Gemini model, can convert a single planning document from a two-hour manual task to roughly 40 seconds of automated processing, and is being rolled out to all councils in England. An estimated 250,000 hours are spent by planning officers each year manually checking documents, and a third of applications submitted annually are rejected. These are the kinds of bottlenecks that AI is well suited to address.
Separately, the £1.2 million PropTech Innovation Challenge, run by Geovation in collaboration with MHCLG, has funded twelve teams developing solutions from AI-powered land data platforms to infrastructure mapping tools and small site viability engines. Greater Cambridge Shared Planning and the University of Liverpool are stress-testing an AI tool that produces accurate consultation summaries in a fraction of the time currently required for local plan preparation.
The small sites opportunity
Research using Land Registry and Nimbus Maps data has identified almost 320,000 unused small sites owned by local authorities in England and Wales, with the potential to accommodate 1.6 million homes. These are precisely the sites that SME housebuilders could develop, but the cost of identifying, assessing and navigating them through planning is disproportionate to the scale of each project. A Lichfields study found that the average determination period for small site planning applications was around 60 weeks, more than four times the statutory 13-week period, with only one of 60 sampled permissions meeting the statutory deadline.
AI changes the economics. Property intelligence platforms can now score sites against multiple criteria simultaneously: ownership clarity, infrastructure capacity, planning history, environmental constraints and market demand. Rather than weeks of manual research across the Land Registry, local authority portals, the Environment Agency and utility providers, a developer can get a preliminary viability assessment in minutes. Six of the twelve PropTech Innovation Challenge winners are focused specifically on unlocking small site delivery.
Construction and beyond
The applications extend well past planning. On construction sites, computer vision systems are being deployed for real-time safety monitoring, detecting hazards and PPE non-compliance from camera feeds. Digital twins are moving from concept to production, with AI-driven models simulating building performance, predicting maintenance requirements and optimising energy efficiency across property portfolios.
For existing stock, AI is being applied to green retrofit planning, modelling decarbonisation pathways using EPC data and thermal performance analysis to identify optimal intervention sequences. With only around 3% of UK housing stock rated EPC A or B and an estimated £250 billion investment needed to bring homes to net zero by 2050, the scale of the analytical challenge is immense. AI’s ability to process building-level data across millions of properties and recommend cost-effective retrofit strategies will be critical to meeting that challenge.
What this means for the profession
A Heriot-Watt University study published in March 2026 found that while some councils are moving quickly to deploy AI, many remain at the earliest stages of building basic digital and data foundations. That unevenness creates both risk and opportunity for professionals advising property and construction clients.
Three areas deserve attention. First, feasibility analysis is being transformed. Clients adopting AI-augmented site assessment will make faster, better-informed investment decisions. Advisors need to understand these tools well enough to assess whether clients are using them effectively or being left behind.
Second, when material business decisions rest on AI-generated analysis, questions about model risk, algorithmic bias and data governance become central. Who audits an automated valuation? What assurance framework applies when a planning viability assessment is AI-generated? These are emerging areas where the profession’s expertise in risk, governance and assurance is directly relevant.
Third, the regulatory landscape is evolving rapidly. The government’s planning reform agenda, the emerging AI governance framework and sector-specific requirements from the FCA and PRA are converging. Understanding how AI tools work, where their limitations lie and how to assess their outputs critically is becoming a core professional competency, not a niche specialism.
The homes Britain needs will not be built by AI. They will be built by developers, contractors and tradespeople. But the intelligence that identifies where to build, what to build and how to navigate the process efficiently is being transformed at pace. The profession has a role to play in ensuring that transformation is well governed, effectively deployed and genuinely serves the public interest.
*the views expressed are the author’s and not ICAEW’s