AI has become a vital component in digital transformation within the architecture, engineering, construction and operations (AECO) domain. Developers, consultants, contractors, technology providers and specialist service providers have been testing AI in various aspects such as design, project planning, project controls, construction delivery and asset operations.
Nevertheless, despite the increased number of experiments, widespread use of AI within the construction industry is still low. According to the RICS Artificial Intelligence in Construction Report 2025, which is based on a global survey of over 2,200 participants, 45% of companies had not deployed AI, whereas 34% had just started with their pilots. Only less than 1% of companies had reached full-scale deployment of AI.
Such statistics reveal a problem facing the industry as a whole – how to move from AI experiments to scaling the technology and generating value through projects.
Moving From Company Adoption to Project Value
At present, most organisations are using AI in the areas of their own responsibility. For example, consultants can use AI in design and engineering; contractors in construction and fieldwork; and developers in development, delivery and asset operations.
Such approaches can bring value to individual organisations, but they cannot deliver an integrated AI system throughout the whole construction project.
A construction project usually consists of a number of organisations with different processes, information, knowledge and technology. If AI is used only within organisational boundaries, then there may be missed some opportunities to solve joint problems of the project.
It does not imply that the client would have to direct how the consultancy, the contractor and any other partners build their internal AI capabilities. That is for the relevant organizations to take charge of.
When we talk about individual projects, this is where the client finds themselves on a different perch. Normally, the client owns the financial resource, set the project goals, and has an interest in its performance over a period.
Instead of centralizing all applications of AI, the client would be able to play the role of orchestrating various initiatives of different parties with regards to the same project objectives.
Start With the Use Case, Not the Technology
A challenge with the adoption of new technology is starting with the technology and then trying to find applications for it. This can be better accomplished by starting with a business problem and seeing if AI can solve the problem.
One piece of research from Autodesk in 2025 demonstrates this challenge. In the research, 47% of construction leaders believed finding the right AI use cases was a big or moderate issue, while only 32% felt that they had reached or were close to their AI goals.
The AI application possibilities need to be assessed based on practical criteria, such as:
- Expected business impact and measurable benefits
- Implementation complexity and scalability
- Availability, quality and readiness of data
- Risk, governance and human oversight requirements
After that comes the task of converting these promising use cases into business cases. Every business case needs to be formulated based on a baseline of the current situation along with expected benefits and success criteria.
This transforms the fundamental question from “Where can we apply AI?” to “Where can AI add measurable value to our projects?”
Bringing the Construction Supply Chain Together
Client leadership does not make the role of contractors, consultants, technology firms, or any other supply chain participant irrelevant. Their participation is vital to AI deployment success.
Applications in design may require the technical know-how and project information from consultants. Applications in construction may require the processes, knowledge, and data from contractors. Technology and solution providers may be able to provide the platform, integration, and implementation expertise.
The challenge is to harness all these resources around clearly selected use cases at the project level instead of running several disconnected AI pilots.
This brings us to an important principle: AI ownership should follow accountability.
For every AI use case, it must be clear who is accountable for the business impact, technology, data, implementation, validation, human oversight, and adoption.
A proper governance model must also consider issues like data security and AI data sovereignty, intellectual property rights, relevant regulations, and automation limits.
From AI Deployment to Measurable Value
Starting a pilot AI project is not the guarantee of success for transformation of a project.
In case when the AI business case will be focused on decreasing the time of reviews, increasing the forecast accuracy, minimising rework, increasing quality or productivity, it should be compared to the initial baseline.
The client can set the general framework for measuring value, while the supply chain parties will help to implement it.
It can consist of four steps:
- Assess the use case and define the business case
- Establish ownership and deploy collaboratively
- Measure realised value against the baseline
- Scale successful applications while adjusting or discontinuing those that do not deliver the expected results
This way will aid in bridging the gap between implementing AI and proving that the technology has actually created project value.
What the Next Stage of AI Maturity Could Look Like
AI experimentation is growing within the AECO industry, but the next hurdle for the industry will be to make the experimentation lead to tangible results.
Maturity in terms of AI does not always have to mean the use of more AI by each individual organization. Rather, it could be that clients set the proper governance framework at the project level, thereby allowing consultants, contractors, technology vendors and solution providers to work together towards a common goal.
The capabilities of AI could still be spread out through the construction supply chain, but project level accountability for results is essential.
Conclusion
There is great scope in the field of construction in terms of application of AI ranging from designing and planning to project controls, field management and asset management. But simply having technology is not enough to improve project performance.
There should be a move away from the current AI pilots to use cases, business cases, implementation and governance. The clients have a key role to play in this respect since they set project objectives and are responsible for the entire investment and its result.
By governing the process without taking control of technological decisions, the clients can create an atmosphere where each stakeholder in the construction chain brings in his expertise while achieving common results.
FAQs
Clients typically establish project objectives and oversee the overall investment, giving them a strong position to coordinate AI governance at the project level. Their role can focus on orchestration rather than controlling how individual organisations use AI internally.
AI use cases should be evaluated based on factors such as potential business impact, measurable benefits, implementation complexity, scalability, data availability and quality, risk, governance and human oversight requirements.
Projects can establish a baseline before implementing an AI solution and then measure outcomes against defined success criteria. Metrics may include reduced review time, improved forecast accuracy, lower rework, increased quality or improved productivity.