Benchmarking AI Tool Adoption in Cost Engineering : An Integrated TOE Framework for AI Tool Assessment and Prioritized Recommendations for an Automotive OEM Group

Sammanfattning

The automotive industry operates under significant cost pressure driven by global competition, supply chain disruptions, and shifting demand patterns, making the cost engineering function a critical interface between procurement and Research & Development (R&D). While Artificial Intelligence (AI) tools show strong potential to address the traditionally manual and resource-intensive nature of the cost estimation process, automotive Original Equipment Manufacturers (OEMs) face challenges due to a fragmented landscape of available solutions, often investing in AI tools that fail to sustain their intended value over time. This thesis investigates this issue through a structured evaluation of AI tools within the cost engineering function at Company X, an automotive OEM, and its four peer organizations (A, B, C, and D), all of which are undergoing active digital transformation. The study applies an integrated Technology-Organization-Environment (TOE) benchmark framework developed for comparative tool ranking and assessment to support adoption recommendations. Six measurable factors – Technical Scope, Integration, Use Cases & Value, Workflow & Culture, External Drivers, and Ecosystem Readiness – are scored across four maturity levels from L1 to L4. The research design follows a qualitative study with semi-structured interviews, a systematic literature review, and an internal observational and document study. Interview studies 1 and 2 were conducted with the Company X cost engineering team and the IT head. Triangulation of these interviews, the literature review, and management requirements at Company X yielded the six factors and their maturity-level definitions within the TOE dimensions. Interview study 3 was conducted with AI tool developers and users to capture the benchmark framework outcome across ten AI tools from Company X and four peer organizations (A, B, C, and D). The results shows that although Company X has started initiatives towards implementing AI-driven solutions, several critical areas still need to be addressed before efficiency and improvement targets can be reached. The benchmark scores ten AI tools and provides a practical recommendation order for adoption in the cost engineering function at Company X. The benchmark reveals three patterns across the studied OEM group: a shared pilot ceiling stage, data-tool mismatch where the tools score low on Ecosystem Readiness, and a strategic move as the driver for adoption. To adopt AI tools successfully, Company X must strengthen its readiness across the six factors and follow a prioritized recommendation matrix for the ten AI tools currently available. Despite the challenges, Company X has the necessary foundation for AI-driven transformation, provided the preconditions are addressed.