Both, Lean Construction (LC) techniques and Artificial Intelligence (AI) methods strive for the continuous improvement of production systems in projects and organizations. A combined implementation of both approaches is an ongoing research area. Therefore, the question arises as to whether the added value generated by implementing both approaches jointly is greater than the added value generated by implementing them independently and what is the significance of people in their combined use.This paper explores theoretically the potential of synergies between LC and AI in the AEC sector with exemplary use cases as well as their resulting effects. Humans play a crucial role as interface between a combined use of both of them. As a result, a framework containing LC, AI and people is formed as basis for further combined developments. Therefore, change management, an area in which Lean has spent several years developing, can help both approaches gain traction. With the results, targeted applications can be developed, and practice can be supported.

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In lean construction projects, much information is collected during the process analysis with the trades. This data is increasingly documented as a reference for use in future construction projects. By doing this, efficient methods are required to use this data. Often, the unstructured naming of data is a challenge for a rule-based allocation of information, and manual work is required to identify the needed data. Therefore, the aim is to develop an automatic mapping of historical performance factors to the tender specifications of a new construction project. To support the process analysis with historical project data, a case study is executed using Natural Language Processing (NLP). With a NLP model, the process descriptions from the tender specifications of the new construction project can be compared with a master database, to filter the right performance factor and calculate the duration for a process. This procedure can be used to support the further process analysis together with the trades to generate a validated construction schedule. The case study shows promising results in the prediction results. First, the mapping quality and second, the prediction accuracy are evaluated. With the developed mapping concept, last planners can validate their estimations of durations in lean construction process planning with a target to support stability in a project. Still, a more detailed description of the processes could increase the prediction results.