Künstliche Intelligenz im Bauprojektmanagement: Wie datenbasierte Entscheidungen die Effizienz fördern
by Prof. Dr. Lisa Lenz | January 13th, 2026
Construction projects today face a wide range of challenges. Increasing project complexity, high cost and deadline pressure, and growing regulatory requirements characterise everyday life in construction project management. Traditional methods are increasingly reaching their limits. At the same time, digitalisation in the construction industry is advancing steadily, opening up new opportunities for managing projects more transparently and with less risk. Artificial intelligence (AI) plays a key role in this transformation process. It enables the automation of administrative tasks, the intelligent use of large amounts of data, and well-founded support for decision-making processes throughout the entire building life cycle. In conjunction with Building Information Modelling (BIM) in particular, a new quality of project management is emerging that is changing construction project management forever.
AI as a driver of efficient construction project processes
Effective construction project management is the basis for construction projects that meet deadlines, budgets and quality standards. Construction project management is typically divided into different project phases based on the life cycle of a building: project initiation, planning, execution, monitoring or control, and project completion. Numerous operational, coordination and administrative processes are carried out within these phases. These include defining the construction targets, scheduling and resource planning, budgeting, quality control and risk management. In practice, these processes are often characterised by a high level of administrative effort. Documentation, reporting and coordination in particular require considerable time resources. Studies show that project managers spend a large part of their working time on administrative tasks that do not directly contribute to value creation. This situation can also be applied to construction project management. This is where the use of AI comes in.
AI-based systems enable the automation of repetitive tasks, the structured processing of information and intelligent process support. The interfaces between project phases are particularly critical here, as this is where information loss, redundancies and inefficient communication channels can occur. The use of AI can reduce these risks by ensuring that information is processed consistently and made available transparently to all project participants. The result is a significant reduction in the workload for project management and greater process efficiency.
Data-driven decision-making in construction
In modern construction practice, the availability and quality of data is a key success factor. Construction projects generate a wealth of digital information from various sources, including BIM models, project management software, sensor data, drone images and construction logs. Decisions made during the course of a project, e.g. regarding scheduling, cost control or the selection of construction methods, are increasingly based on this data.
Traditionally, decision-making processes in the construction industry have relied heavily on empirical knowledge, manual evaluations and subjective assessments. The use of AI is fundamentally changing this basis. Algorithms can analyse historical project data, recognise patterns and derive well-founded recommendations for decisions. This makes decision-making processes more objective and transparent, while also documenting them as standard.
One key area of application is predictive analytics. By comparing current project data with historical data from similar projects, potential schedule deviations or cost overruns can be predicted at an early stage. Risks become visible before they occur, enabling proactive action to be taken. In addition, natural language processing (NLP) enables the automated analysis of unstructured text data such as construction diaries, defect reports or reports. This information, which was previously difficult to use, is structured and made accessible for decision-making processes. Construction project management is thus evolving from a reactive to a data-driven, forward-looking control approach.
Symbiosis of AI and construction project management
Digitalisation in the construction industry is advancing steadily, and AI is playing an increasingly central role in this process. AI encompasses various technological approaches, including rule-based systems, machine learning, deep learning and hybrid methods.
Construction project management primarily uses data-driven AI methods that analyse large amounts of data and use it to derive forecasts or recommendations for action. One particular strength of AI lies in its close integration with existing construction project management processes. AI methods can be deployed in a targeted manner based on the functional distinction between administrative and decision-making processes. While administrative processes benefit in particular from automation technologies such as NLP or computer vision, learning-based methods such as machine learning or deep learning support complex decision-making processes.
In combination with BIM, integrated digital project environments are created in which data is used consistently and evaluated continuously. This symbiosis not only enables more efficient processes, but also a new level of transparency and traceability. Project participants gain a better overview of the project status, risks and dependencies, which significantly improves collaboration and control.
Multimodal AI architectures for integrated data usage
Construction projects are characterised by a multitude of heterogeneous data sources. Service specifications, planning documents, BIM models, expert reports and protocols are often available in different formats and systems. This fragmentation leads to media breaks, inconsistencies and increased coordination efforts. Especially in early planning phases, unrecognised errors can have a significant impact on costs and deadlines. Multimodal AI architectures offer a promising solution here. They combine various AI technologies such as natural language processing, image recognition, computer vision and structured model analyses to evaluate construction project data holistically. The aim is to bring together information from different sources, structure it and make it usable for subsequent processes.
A key element is the automated analysis of unstructured text data. With the help of NLP, construction diaries, property descriptions and emails can be evaluated and transferred to structured knowledge databases. This systematically harnesses the experience gained from previous projects and enables cross-project learning. In addition, planning documents can be checked automatically, planning delivery lists can be reconciled, and the completeness of tender documents or building applications can be ensured. BIM models can also be checked for their level of detail and compliance with standards and guidelines. The early identification of contradictions or missing information reduces risks and significantly speeds up decision-making processes.
AI in operation: automated data generation and security
In addition to planning and execution, AI also offers considerable potential in the operational phase of buildings. Automated detection, localisation and documentation of safety-relevant systems plays a particularly important role. Modern laser scanning technologies enable the capture of point clouds and 360° images, which allow for precise measurement and localisation of components and technical systems. Many buildings have safety-related systems such as ventilation systems, smoke and heat extraction systems or CO warning systems installed, which must be inspected regularly by law.
Manually locating and documenting these systems is time-consuming, error-prone and costly. These processes can be automated by using image recognition in combination with machine learning and deep learning. Safety-related systems are automatically detected and classified on the basis of training data and linked to relevant information. This includes maintenance intervals, test reports, manufacturer information and the exact location on the building floor plan. Automated data enrichment ensures that all the information required for maintenance and repair processes is efficiently available. At the same time, it supports operator responsibility and increases safety in building operations.
Conclusion
In construction project management, artificial intelligence is evolving from a pure automation tool to a strategic enabler. It supports the optimisation of administrative processes, enables data-based decision-making and creates transparency throughout the entire life cycle of a building. In combination with BIM and multimodal AI architectures, this creates an integrated, digital project environment that sustainably increases efficiency, quality and resilience in the construction industry. However, technological requirements alone are not decisive for successful implementation. A clear digitalisation strategy, structured data management and the development of interdisciplinary skills form the basis. Equally important is a shared mindset among project participants that promotes transparency, collaboration and data-driven decisions. AI can support this cultural change, but it does not replace the responsibility and cooperation of those involved.
Would you like to learn how artificial intelligence and BIM can make your construction project management more efficient, transparent and future-proof?
BIM GLW supports you in the digital transformation of your construction projects throughout the entire building life cycle. Contact us and play an active role in shaping the future of construction.
