As the Architecture, Engineering, Construction, and Operations (AECO) sector navigates the complexities of digital transformation, a clear shift is occurring regarding the role of Artificial Intelligence. While thousands of firms are currently trialing various tools, Hichem Troudi, Vice President of Projects Transformation at DAMAC, argues that the industry has hit a critical plateau. To bridge the gap between fragmented AI pilots and genuine, measurable project value, the responsibility must shift toward the client as the primary orchestrator of supply chain governance.
The Limitation of Isolated AI Adoption
Current industry data highlights a stark reality: despite the hype, the AECO sector remains in an “embryonic” state of AI integration. Recent reports, including findings from the RICS 2025 Artificial Intelligence in Construction study, indicate that less than 1% of organizations have achieved fully embedded, company-wide AI adoption. The vast majority of firms are either not utilizing AI at all or are limited to small-scale, internal experiments.
The core issue is that these experiments are being conducted in silos. Consultants focus on design automation, contractors test field-based operational tools, and developers look for asset management improvements. Because these initiatives are siloed within individual companies, they fail to create an integrated ecosystem. Without a central strategy to connect these disparate technological threads, the AECO sector will continue to struggle with disconnected, unscalable workflows that provide limited impact on overarching project outcomes.
Why Clients Must Take the Lead
In the traditional construction hierarchy, consultants and contractors are expected to master their own technical workflows. However, Troudi emphasizes that when it comes to the long-term success of an investment, the client occupies a unique position. As the owner of the project and the entity with the most at stake regarding final delivery, the client is best positioned to establish AI governance across the entire project lifecycle.
Rather than trying to control every technical detail or centralizing all AI operations—which would stifle innovation—the client should act as a conductor. By setting clear project objectives and defining the governance framework for data security, intellectual property, and automated decision-making, the client can provide a roadmap for the supply chain to follow. This approach ensures that when technology vendors and engineering firms bring their specialized tools to the table, those tools are aligned with the project’s specific performance metrics and business goals.
Prioritizing Business Cases Over Technology
A recurring mistake in the tech industry, including the AECO sector, is the “technology-first” approach, where firms implement sophisticated tools before identifying the specific business problems they aim to solve. This trend is backed by 2025 Autodesk research, which found that nearly half of construction leaders struggle to identify relevant, high-impact use cases for their AI investments.
To succeed, companies must invert this logic. The focus should start with a specific project challenge—such as supply chain delays or forecast inaccuracies—and then determine if AI serves as a credible solution. Potential use cases must be vetted through rigorous criteria:
- Measurable Impact: Does this solve a known, high-value problem?
- Data Readiness: Is the information behind the process clean, accessible, and structured?
- Accountability: Does the ownership of the AI outcome align with the entity responsible for the project phase?
By formalizing these initiatives as concrete business cases, clients can move beyond “pilot fatigue.” This shift allows project leaders to establish clear baselines, measure actual performance improvements, and determine with certainty which technologies should be scaled and which should be discarded. Ultimately, the future of AI in construction is not about who uses the most advanced tools, but about how effectively the client can orchestrate a collaborative environment where AI delivers consistent, tangible value.
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