Bridging AI readiness and application: Prototyping a strategy-aligned language model for quality insights at Skanska. A comprehensive study of organizational AI maturity, applied NLP development, and scalable implementation in construction quality management

Sammanfattning

The construction industry is under increasing pressure to improve efficiency, reducecosts, and enhance sustainability. While other sectors have advanced in AI adop tion, construction remains comparatively behind. This thesis explores how artificialintelligence (AI) can support decision-making in construction, with a focus on Qual ity Management at Skanska Sweden AB.First, organizational AI Readiness was assessed through interviews and workshopsusing established organizational frameworks. This reveals both strategic interest andpractical challenges in applying AI. Second, an operational use case was explored bydeveloping an AI prototype that processes historical quality deviation texts. Theprototype was developed with the purpose of creating value for the Quality Depart ment by providing insight. Using natural language processing (NLP), the prototypeexplored a weakly supervised classification approach combining unsupervised clus tering, pseudo-labelling via zero-shot learning, and a fine-tuned transformer classi fier (XLM-R and SBERT). Two promising category types, incident type and affectedbuilding component, were identified and co-developed with domain experts to struc ture the data.The results show that while AI readiness is moderate, initiatives often remain siloeddue to limited infrastructure, resources, and unclear ownership. Skanska shows agrowing awareness and curiosity around AI and there is potential to learn frominternational practices within the company. However, although large volumes ofdata available, barriers remain particularly in terms of the availability of structuredand labelled data. There is also a need for further AI-specific expertise, and it re mains challenging to integrate new tools into established workflows. The prototypedemonstrates practical value by visualizing patterns in text data, enabling the Qual ity Department to adopt a more data-driven and preventive approach. While weaksupervision proved challenging due to limited label quality and model sensitivity,the final classifier achieved approximately 67% accuracy through fine-tuning with amanually labelled dataset, accounting of 6‰. Despite this, the approach successfullyenabled structured insights into issue frequency, duration, and distribution acrossprojects. The prototype also serves as a scalable proof of concept, illustrating howtailored AI solutions can accelerate digital transformation in construction.