Exploring AI opportunities within industrial engineering : A business process redesign and readiness assessment for AI integration in cost engineering

Sammanfattning

This thesis aims to investigate how artificial intelligence (AI) can optimize the cost engineering business process within industrial industries, with Company X as an empirical case. The purpose is to analyze what a technological transformation like this would entail in practice, both in terms of redesigning workflows and in terms of organizational readiness. The study applies two central theoretical frameworks; Business process reengineering (BPR), to analysis and map out a feasible AI-driven cost engineering process at Company X, and a developed readiness framework to analyze if Company X has the essential preconditions in place to adopt to an AI-driven cost engineering process. The research design follows a qualitative case study with a combination of data collection methods through systematic reviews, interview studies and observational study. Interview study 1 and 2 are conducted with both cost engineers and purchasers to understand the current cost engineering business process, to then identify gaps and opportunities within the process. Furthermore, a secondary data analysis is carried out to analyze similar cases to get insight into what preconditions are required for an organization to have for a successful AI-implementation. The identified preconditions serve as the foundation for the readiness framework, combined with two already existing frameworks; Intel´s AI readiness model and MITRE AI maturity model. The readiness framework is then measured through interview study 3, which is conducted with 11 representees from the procurement department. The results show that Company X has slowly started taking steps towards implementing AI-driven solutions, however there are several critical areas that need improvement to enable a successful implementation. It has been identified that there are gaps in the current business process, including inefficient process initiation, manual and time-consuming data gathering, challenges in data processing and storage, limitations in cost calculation tools, inefficiencies in validation and supplier comparison, storage and final handover inefficiencies, and late involvement of cost engineers. Additionally, it is clear that further investments in both technological and organizational change are required to ensure an effective AI integration. To successfully implement a data-driven cost engineering process, the company must work on improving its readiness within the defined preconditions, including data quality, data infrastructure, education, change management & organizational adaptability, strong leadership and cross-functional collaboration. Despite these challenges, the results show that Company X has the necessary foundation for an AI-driven transformation, provided that these preconditions are addressed.