In the automotive industry’s transformational path towards the electrification era,
several aspects of the industry’s existing functions have been redefined. A critically
affected domain that has emerged as a key driver of this development is automotive
project management. Currently, in the industry, traditionally rigid project management
methodologies are being replaced by more agile approaches due to their inherent
limitations, such as lack of adaptability and reduced operational efficiency. These
limitations have become apparent as large multinational automotive companies strive
to transition to this new electrification era, characterized by disruptive technological
innovations and significant sustainability goals.
This dissertation explores the automotive industry’s paradigm shift towards
applying Agile Project Management (APM) to introduce flexibility and adaptability in
managing automotive projects, focusing on an important disruptive factor: the advent
of Artificial Intelligence (AI), and its integration into the existing APM methodologies.
The core principles of APM perfectly complement the modern, complex product
development processes in the automotive industry. Key characteristics of the agile
framework, such as iterative testing and development, continuous integration and
feedback loops, and cross-functional collaboration help automotive companies adapt
to their customer's constantly changing and diverse demands.
This endeavor is significantly enhanced when AI’s innovative techniques are
embedded into agile practices. The ability of AI algorithms to process and analyze vast
amounts of data augments the APM process by introducing unique capabilities such
as data-driven decision-making, predictive analytics, and process automation.
To better contextualize this significant merge, a concise review of the existing
literature is complemented by a systematic analysis of industry experts’ interviews that
have real-world experience managing complex automotive projects, to highlight the
key synergy areas of APM and AI, recognize important integration challenges and
evaluate this integration’s impact on the future of the automotive project management.