Simulation, Modeling & Organizational Design
Complex systems demand robust analytical tools. OrgLab utilizes advanced computational modeling and simulation to study organizational dynamics, optimize decision-making, and refine structural design. This pillar integrates empirical evidence and scenario-based experimentation to capture the interplay between human behavior, technology, and governance. The resulting insights support data-driven transformations that help organizations succeed in unpredictable, rapidly evolving environments.
Our Research on Simulation, Modeling & Organizational Design
AI Adoption Playbook: a Decision Model for Aligning AI with Organizational Models and Task Complexity
Abstract. Integrating Artificial Intelligence (AI) into organizations requires more than simply installing new technologies. This paper introduces a decision-support framework to help leaders evaluate whether and how to implement AI, drawing on each organization’s unique structure, tasks, and goals. By linking organizational design theory with practical adoption strategies, we show how different task types—routine, engineering/craft, and non-routine—pair with three potential impacts of AI on organization dynamics: replace, reinforce, or reveal. Rather than prescribe a universal solution, our model emphasizes that effective AI implementation depends on adopting the right approach for each context. In this way, it guides decision makers in choosing the most suitable AI solution and in anticipating corresponding changes in roles, processes and culture. This clear, structured method helps organizations avoid misalignment, reduce costly experimentation, and unlock AI’s potential as a genuine driver of efficiency, innovation, and strategic value.
Reference. Bolici, Varone and DIana (2025). AI Adoption Playbook: a Decision Model for Aligning AI with Organizational Models and Task Complexity. In Theorizing AI and Data Workshop. Amsterdam, Netherlands.