# Learning-by-Twinning: Leveraging Organizational Digital Twins for Strategic Search Generative AI and digital trace data enable virtual experimentation for complex corporate decision-making. - **Source:** [Strategic Entrepreneurship Journal](https://onlinelibrary.wiley.com/journal/1932443x) - **Published:** 2026-11-04 - **Topic:** [technology](https://brifing.si/c/technology) - **Original article:** - **On Brifing:** https://brifing.si/s/4b02b451-7eca-48ec-a5a9-4a6f044cdf98 ## The Evolution of Search Traditional offline search methods like mental models and analogical reasoning are constrained by individual cognitive limits. Managers often struggle to recall or imagine complex outcomes before committing resources. The proposed learning-by-twinning framework utilizes generative AI and firm-specific digital trace data to overcome these inherent human limitations during strategic evaluation processes. ## Defining Organizational Digital Twins An organizational digital twin acts as a sophisticated infrastructure for virtual experimentation. It integrates firm-specific data with multi-agent AI models to replicate internal routines, interactions, and decision-making processes. This evolving simulation environment allows leaders to observe how specific strategic choices unfold within a controlled, digital replica of their firm. ## Mechanisms of Value Creation Learning-by-twinning generates value through three primary mechanisms: expanded search capabilities, safer experimentation, and richer system-level feedback. By simulating alternatives in a virtual space, managers can explore a wider array of options. This process effectively reduces the risks associated with real-world implementation by highlighting potential failures before they occur. ## Simulating Strategic Consequences The digital twin approach reveals second-order organizational consequences that are often invisible to human planners. By modeling complex interactions, the system provides insights into how a change in one department might ripple across the entire organization. This systemic feedback is crucial for refining opportunities under conditions of high uncertainty. ## Data-Driven Behavioral Modeling The efficacy of this model relies heavily on the quality of firm-specific digital trace data. These data points feed the multi-agent AI models, ensuring the simulation reflects actual organizational behavior rather than abstract assumptions. As the firm evolves, the digital twin must also evolve to maintain its predictive accuracy. ## Risk Mitigation in Strategy Strategic entrepreneurship requires testing ideas without exhausting scarce resources. Learning-by-twinning offers a low-risk environment where managers can stress-test incentive plans or market entry strategies. This systematic virtual experimentation allows for the refinement of ideas, ensuring that only the most viable strategies are eventually deployed in the real world. ## Limitations and Attenuation While the benefits are significant, the researchers note specific conditions under which these advantages may attenuate. The accuracy of the digital twin is dependent on the granularity of the underlying data and the fidelity of the AI models. If the simulation fails to capture critical human nuances, the results may mislead. ## Future of Decision Making This technological shift represents a fundamental change in how organizations approach strategic commitments. By moving from intuition-based planning to simulation-based discovery, firms can navigate complex environments with greater precision. Future adoption will likely depend on the integration of these tools into standard corporate governance and strategic planning workflows. ## Sources covering this story - https://sms.onlinelibrary.wiley.com/doi/10.1002/sej.70061?af=R — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._