From Idealism to Industrial Implementation: How Can Construction Companies Truly Harness AI?

As global digital transformation rapidly evolves into intelligent transformation, breakthroughs in artificial intelligence, especially large language models, are unlocking unprecedented opportunities across industries. As one of the largest sectors of the global economy, the construction industry is at a pivotal moment of transformation, driven by advancements in AI.

However, many construction enterprises still face common challenges in advancing AI adoption, including limited understanding, unclear implementation paths, and difficulties in delivering tangible results. To address these bottlenecks, Glodon has released the white paper "Construction Enterprises' AI Initiatives", which identifies key industry pain points and proposes a systematic and practical AI application pathway. This serves as an important roadmap for guiding construction enterprises toward high-quality development in the era of AI.

 

Confronting the Reality of AI Implementation

The white paper identifies three common misconceptions that construction enterprises globally encounter when applying AI:

• Overemphasis on Infrastructure, Underinvestment in Scenarios

Some enterprises prioritize investing in computing power infrastructure, data centers, or general-purpose large models, while neglecting the alignment with real-world scenarios and business integration. This results in poor business value realization and high investment with low return.

• Attempting Full In-House Development Increases Risk

Certain companies attempt to independently develop full-stack AI systems, overestimating their internal capabilities and underestimating the complexity of interdisciplinary collaboration. This leads to high costs, long development cycles, and a high risk of failure.

• High Expectations, Low Tolerance for Delayed Results

When early outcomes fall short of expectations, some companies halt their initiatives prematurely, missing the critical window to build AI capabilities and evolve with the technology.

Only Industrial AI Can Solve Industry Problems

The white paper emphasizes that construction enterprises cannot rely solely on off-the-shelf general-purpose AI models to generate real business value. Instead, they must adopt Industrial AI — AI that understands the business, integrates deeply with real-world scenarios, and solves specific operational problems.

In fact, across sectors like manufacturing, energy, and logistics, leading global enterprises and research institutions have long embraced Industrial AI as the core strategy for driving intelligent industrial transformation. Years of exploration have proven that only Industrial AI can truly address the unique challenges of a given industry.

Industrial AI refers to the deep integration of AI technologies with domain knowledge, business workflows, and data characteristics of a specific sector. Its essence lies in combining data, general-purpose AI, domain expertise, and specialized software to create intelligent solutions tailored to specific business scenarios.

 

The Key to Sustainable AI Implementation in Construction

The white paper proposes a framework built on three pillars—High-Quality Data, High-Value Scenarios, and High-Reliability Models—as the foundation for sustainable AI implementation in the construction industry.

• High-Quality Data

Leverage BIM (Building Information Modeling) for digital representation across the building lifecycle and combine it with IoT sensors for automated, real-time, high-quality data collection. This ensures accuracy, timeliness, and completeness, crucial for model training and intelligent decision-making.

• High-Value Scenarios

Focus on three critical areas: integrated design, refined cost management, and precise construction management. Drive systematic implementation of Industrial AI across four levels—role, project, enterprise, and industry management—forming a scenario-driven, vertically integrated closed-loop system that continuously generates high-value use cases.

• High-Reliability Models

Build reliable and adaptable models through the synergy of industry foundation models and engineering software. Foundation models cover broad capabilities across planning, design, bidding and procurement, cost, construction, operations & maintenance, and integrated project management. However, unlocking their value depends on deep integration with mature engineering software. While models excel in language understanding and creative ideation, engineering tools provide precise calculations, code compliance, and execution capabilities, ensuring professional-grade results.

This closed-loop system forms a "flywheel" that not only supports AI deployment but also strengthens over time, creating a positive feedback loop where continuous use leads to better outcomes.

Achieving Scalable, Replicable AI Implementation

The fusion of industry foundation models and engineering software provides a clear pathway for construction enterprises to move from capability building to value creation. This approach enables AI to evolve from merely facilitating intelligent conversations to delivering intelligent execution, turning large models into practical tools for solving real-world challenges in planning, cost control, and project scheduling. This model has three key value characteristics:

• Perceptibility – The impact of AI is directly observable at the role and project level

• Reusability – AI solutions can be transferred across projects and phases with minimal adaptation

• Scalability – Enterprises can rapidly scale AI adoption to drive organizational efficiency

A Deep Transformation of Cognition, Capability, and Value

AI is not a sprint — it is a profound industrial transformation involving cognitive shifts, capability reconstruction, and value system renewal. The release of " Construction Enterprises' AI Initiatives" not only dispels the fog surrounding AI but also provides a clear, actionable framework for construction enterprises to advance AI deployment.

As the construction industry enters the era of intelligent transformation, we hope more enterprises will seize the opportunity at this strategic inflection point—moving from hesitation to conviction, from observation to action. 

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