Purpose: This paper provides a longitudinal analysis of the Stuttgart 21 (S21) megaproject, dissecting the evolution of its fiscal planning and risk management to identify the root causes of its severe cost overruns and protracted stakeholder disputes.

Design/methodology/approach: The study employs a qualitative, single-case study methodology. It synthesizes over three decades of data from official project documents, including the original feasibility study and financing contract; government and legal records, notably the 2024 court ruling on cost liability; academic literature; and chronological media archives. The analysis traces the project's financial trajectory against key risk events and governance decisions.

Findings: The project's initial €4.5 billion budget, established in a 2009 financing agreement, was founded on optimistic assumptions that failed to account for significant technical, geological, and political risks. The rigid contractual framework proved incapable of managing emergent challenges, leading to a cost escalation to over 11 billion. This triggered a legal battle where project owner Deutsche Bahn sought to compel partners to cover the deficit. This culminated in a landmark 2024 court decision that rejected Deutsche Bahn's claim, assigning the financial burden of the overruns almost entirely to the company. The core failures identified were an inadequate initial risk assessment, an inflexible governance structure, and a profound underestimation of project complexity.

Practical implications: The S21 case serves as a critical lesson for megaproject practitioners and policymakers. It underscores the necessity of independent and pessimistic forecasting, the establishment of adaptive financing agreements with clear risk-sharing clauses, and robust governance to manage uncertainty over a project’s long lifecycle.

Originality/value: This paper offers a unique start-to-finish analysis of a major European megaproject, linking its initial financial architecture directly to the ultimate legal and financial fallout. It provides a comprehensive, evidence-based cautionary tale for the management of future megaprojects globally.

Artificial intelligence has emerged as a foundational general-purpose technology reshaping economic structures, organizational strategies, and societal power relations. The rapid diffusion of machine learning, deep neural networks, and large-scale language models has fundamentally altered how data is transformed into strategic intelligence, how firms compete, and how advisory and consulting services are conceptualized and delivered. This research article develops an extensive theoretical and analytical examination of artificial intelligence–driven digital transformation, integrating perspectives from business intelligence, machine learning research, management consulting theory, innovation diffusion, and ethical governance. Drawing strictly upon the provided literature, the study synthesizes advances in algorithmic architectures such as attention-based models, reinforcement learning systems, and unsupervised multitask learners with organizational frameworks including competitive advantage theory, lean startup methodology, business model innovation, and case study research design. Particular attention is given to the ethical and democratic risks posed by algorithmic opacity, data monopolization, and automated decision-making systems, as articulated in critical scholarship on big data and inequality. Methodologically, the article adopts a qualitative, theory-building approach grounded in interpretive analysis and comparative conceptual synthesis. The results reveal that artificial intelligence operates not merely as a technological tool but as an institutional force reshaping producer–consumer relationships, redefining the boundaries of consulting expertise, and accelerating digital transformation trajectories across industries. The discussion highlights structural limitations, governance challenges, and the emerging need for hybrid human–AI consulting models that balance efficiency with accountability. The article concludes by proposing a comprehensive conceptual framework for responsible, strategy-oriented AI integration, emphasizing long-term value creation, ethical resilience, and adaptive organizational learning.