This thesis investigates the integration of artificial intelligence (AI) to augment critical decision-making in military targeting processes, with the intention to make a significant empirical contribution to applied research in War Studies. In the context of contemporary warfare, rapid and informed decision-making is imperative. Grounded in Boyd's OODA loop theory (Observe, Orient, Decide, Act) which emphasizes adaptability and timely action in complex, dynamic situations, this research aims to enhance the speed, precision, and consistency of decision-making within joint targeting by incorporating AI as an intelligent agent capable of perceiving and acting within these conditioned environments. By constructing and applying two AI models designed to augment the dynamic targeting method, the study addresses two distinct problems in contemporary joint targeting, showcasing practical applications of AI in this context. Model 1 enhances decision-making by improving precision and efficiency in sensor allocation. It identifies optimal locations for deploying target engagement radars (TERs) of medium-range surface-to-air missile systems (MSAMS) and enables decision-makers to achieve more efficient sensor deployment as well as more precise intelligence collection tasks. Model 1 can be utilized for predictive analysis of an adversary's missile system disposition in specific geographical areas and supports the "Observe" and "Orient" stages of Boyd's OODA loop. If validated as an independent intelligence source, Model 1 could initiate direct target engagements. Model 2 addresses a multi-criteria optimization problem involving multiple targets under given constraints. The results suggest that optimization models can incorporate a commander's targeting guidance to effectively integrate a commander's decision policy as a multi-criteria input into the decision-making calculus mathematically. Model 2 supports all four stages of Boyd's OODA loop and assists in synchronizing feasible attack options to achieve desired effects under time and resource limitations. The findings demonstrate that AI augmentation can significantly expand the decision space for military commanders and offers more opportunities to rapidly exploit, adapt and take the initiative with a greater variety of options. The integration of AI facilitates the transition from hierarchical and linear targeting structures to more dynamic and non-linear concepts and enhances organizational adaptability and effectiveness under dynamic targeting conditions. This research underscores the transformative potential of AI in military decision-making and challenges the current human-centric paradigm by introducing AI as an intelligent agent and actor capable of solving problems beyond human limits. The project has important implications for both practitioners and researchers. For practitioners, it offers insights into how AI applications can augment joint targeting practices by improving efficiency and effectiveness in military operations. For researchers, it provides perspectives on the role of AI in military decision-making and how this integration affects command and control arrangements as well as joint warfare concepts. The study suggests that defense organizations should prioritize AI integration to maintain strategic advantages in modern warfare and recommends that future research explore the ethical considerations and long-term impacts of AI-augmented warfare, particularly with regard to command authority and the mechanisms by which forces and assets are directed. In conclusion, this thesis provides empirical evidence of AI's potential to augment critical military decision-making and proposes two applications for integrating AI into joint targeting processes. By addressing the necessity for military organizations to adopt AI technologies, it contributes to the broader discourse on the future of warfare and the evolving relationship between humans and intelligent machines.