Ibrahim Imam, Co-Founder and Co-CEO of PlanRadar, examines the data foundations needed for construction companies to adopt AI effectively, from standardising project information to maintaining reliable records and clear governance.
AI is rapidly becoming a priority across the construction industry. Developers and contractors are exploring how it could improve progress monitoring, identify project risks, support quality control and reduce the administrative burden placed on project teams. In the UAE, this shift is supported by an ambitious construction technology agenda. Dubai Municipality has launched a global challenge to build the world’s first residential villa constructed entirely using robotic systems. It has also announced the 70–70 Strategy, which aims to move 70% of construction to off-site manufacturing and achieve at least 70% factory automation by 2030. Meanwhile, construction technology investment globally is projected to exceed USD 30 billion by 2033, growing at an annual rate of 17.5%.
These developments demonstrate the speed at which construction technology is advancing. However, before companies ask what AI can do for their projects, they must address a more fundamental question: is their project data reliable enough for AI to use?
Construction does not lack data
Every construction project generates enormous amounts of information. Drawings, photographs, inspection records, progress updates, approvals, requests for information, safety observations and defect reports are created throughout the project lifecycle.
The problem is that this information is frequently distributed across different systems and recorded using inconsistent processes. A site photograph may remain on someone’s mobile phone, an approval may be buried in an email chain, and the same defect may be recorded differently by the contractor, consultant and developer. Important decisions may also be communicated through messaging applications without being added to the formal project record.
When this happens, the project may have plenty of data but still lack a reliable and complete source of information.
AI cannot automatically correct this underlying weakness. If project records are incomplete, outdated, duplicated or disconnected from their original location and context, the resulting analysis may also be incomplete or misleading. The technology may process information faster, but speed alone does not make the information accurate.
The gap between AI interest and readiness
The challenge is visible across the global construction sector. A 2025 report by the Royal Institution of Chartered Surveyors, based on responses from more than 2,200 construction professionals – including 14% from the Middle East and Africa – found that 45% reported no AI implementation within their organisations. A further 34% said they remained in early pilot phases.
Only just under 12% reported regularly using AI in specific processes, while fewer than 1% said AI was fully embedded across their organisations.
These findings demonstrate a clear gap between industry interest and operational readiness. Companies may be investing in trials and individual tools, but many have not yet established the data structures, governance, skills and connected workflows required to implement AI at scale.
This is particularly important in markets such as the UAE, where projects are becoming more technologically advanced and often involve large delivery teams, multiple contractors and complex supply chains. The greater the number of organisations contributing information, the more important it becomes to establish consistent rules for how that information is captured, updated and verified.
What does an AI-ready project look like?
Preparing construction data for AI does not begin with purchasing an AI solution. It begins by improving the everyday processes through which project information is created and managed.
First, teams need a standardised method for recording recurring activities such as inspections, defects, approvals and safety observations. The same type of event should be documented using consistent fields, classifications and naming conventions, regardless of the person, company or trade recording it.
Second, information must retain its project context. A photograph is significantly more useful when it is connected to a specific location, drawing, unit, inspection or issue. Similarly, an action must have a clearly assigned owner, status, deadline and closure requirement.
Third, project participants need access to current information. If teams work from different drawing versions or update separate spreadsheets, AI analysis will be based on conflicting records. Establishing a shared and traceable source of project information helps reduce this risk.
Finally, documentation workflows must be practical enough to work under real site conditions. A process that is followed only when teams have spare time will quickly create gaps. Data quality depends on making structured documentation part of everyday site activity rather than an additional administrative task.
Start with the decisions that need improvement
Construction businesses should also avoid adopting AI without a clearly defined operational objective. The starting point should be a business or project decision that needs to become faster, more consistent or better informed.
For example, a project team may want to identify recurring quality issues across multiple floors, recognise activities that are falling behind schedule or find inspection records that lack the evidence required for approval. These are specific use cases that can be tested against available project data.
This approach allows companies to assess whether their data is sufficiently complete and structured before expanding AI into other areas. It also makes it easier to measure the value of the technology against real project outcomes, rather than treating AI adoption as the objective itself.
Governance and human judgement still matter
Better data does not remove the need for professional oversight. AI-generated findings must still be reviewed by people who understand the project, the contractual requirements and the realities of the construction site. This is becoming a more formal industry expectation. The first RICS global professional standard for the responsible use of AI in surveying practice came into effect on 9 March 2026. It establishes requirements covering governance and risk management, professional judgement, transparency, client communication and responsible AI development.
Construction companies therefore need clear responsibility for both the information provided to AI systems and the decisions made using their outputs. Project professionals must be able to question results, verify evidence and recognise when important context may be missing.
AI readiness starts on the construction site
The construction industry’s AI challenge is not primarily a shortage of technology. It is the quality, consistency and accessibility of the information on which that technology depends.
For developers and contractors, the most valuable preparation may therefore be less dramatic than launching a major AI programme. It means standardising how site information is recorded, connecting evidence to its location and context, maintaining current project records and establishing clear ownership of every action.
These disciplines already help projects operate more efficiently and transparently. They also create the foundation on which AI can provide reliable and actionable insights.
The companies that benefit most from AI will not necessarily be those that adopt it first. They will be those that first make their project information worth analysing.
