How Is AI Expanding the Future of Strategic War-Gaming?

Strategic simulations, including wargaming and scenario planning, are rapidly becoming indispensable tools for governments and enterprises worldwide. Traditional war games, while valuable, are constrained by time, data, and human imagination. Increasingly, organisations are turning to AI to rigorously test strategies on a larger scale, gaining insights that would be difficult to achieve through conventional methods alone. 

In defence and national security, the stakes are particularly high. Failing to consider a plausible scenario today could lead to strategic failures tomorrow. In fiscal year 2025, the U.S. Department of Defence allocated over US$1.8 billion to AI research and development, marking a significant effort to modernise defence capabilities through technology-driven strategic exercises.  

These simulations empower decision-makers to anticipate complex scenarios, test responses and gain data-driven insights for uncertain futures. 

At AgileIntel, we view AI-enhanced wargaming as the next frontier for decision superiority. By integrating adversarial agents, scenario generation, and comprehensive simulation architectures, we can uncover hidden risks, expedite planning cycles, and refine decision-making. 

Core components of an AI-driven war game 

AI-driven strategic simulations merge artificial intelligence with scenario planning and wargaming methodologies. These simulations enable businesses to evaluate various strategies under realistic and complex conditions, explore potential outcomes, and assess risks before they materialise. By incorporating AI into strategy development, organisations can enhance foresight, strengthen resilience, and achieve a measurable competitive edge. 

AI-driven wargaming combines three key capabilities: 

• Large-scale data fusion and model orchestration facilitate simulations that encompass everything from orders of battle to logistics and cyber events. 

  
  

• Machine learning agents are creative adversaries, revealing non-obvious courses of action and vulnerabilities. 

  
  

• Generative techniques produce realistic narratives, red team inputs, and metrics, allowing planners to compare tradeoffs across many more scenarios than previously possible. 

These capabilities enable planners to stress-test assumptions, quantify risks, and explore low-probability, high-impact outcomes with greater scale and speed than manual methods. By embedding AI into scenario planning, companies can shift their strategic decision-making from reactive to predictive. 

AI-Enhanced Scenario Planning Beyond Defence 

While defence has been an early adopter of AI-driven strategic simulations, the business sector is equally enthusiastic. Technology, energy, and finance organisations use AI to develop complex scenario models accounting for geopolitical shifts, regulatory changes, market disruptions, and technological innovations. 

AI-powered scenario planning tools allow businesses to stress-test strategies against potential futures, continuously refining assumptions and predictions. This agility enables firms to anticipate risks better and capitalise on emerging opportunities, enhancing resilience in volatile markets. 

Examples of AI-Driven Strategic Simulations 

Here are specific instances of industry and government utilising AI-enabled wargaming and simulations: 

• DARPA Gamebreaker: The Defence Advanced Research Projects Agency has funded Gamebreaker. This program applies AI in open-world game environments to assess balance and uncover destabilising tactics that human designers might overlook. This initiative illustrates how adversarial AI can systematically probe rules and environments to identify weaknesses.  

• Palantir Technologies: Palantir, an American software company, develops government and enterprise data integration and decision platforms. Its toolset, which includes large language model integration, has been effectively used in operational settings to help analysts query integrated data and rapidly prototype plans. Palantir's platform approach demonstrates how data fusion and human-machine interfaces make AI-assisted wargaming actionable for operational users.  

• Anduril Industries: Anduril, a U.S. defence technology company, specialises in autonomous systems and networked command and control software. Companies like Anduril integrate physically connected systems and their digital twins into simulations, enabling more realistic testing of autonomous behaviours, sensor fusion, and distributed decision loops. This convergence of hardware and simulated AI agents helps bridge the gap between virtual wargaming and real-world systems. 

• RAND Corporation: RAND, a U.S.-based research organisation, has been a leading voice on AI's methodological and ethical limits in wargaming. Their studies emphasise that while AI can enhance modelling, simulation, and wargaming, it cannot replace the need for rigorous human judgment, structured validation, and scenario design, which consider cognitive and organisational dynamics. RAND's research provides practical guidance on integrating AI without amplifying hidden biases. 

How organisations should approach implementation 

Integrating AI into wargaming is not merely a technological choice; it necessitates five practical shifts: 

• Define the question before the model: Start with the decision you need to inform. Models should be crafted to illuminate tradeoffs rather than generate answers in isolation. 

  
  

• Invest in realistic data and model validation: The fidelity of simulations relies on the assumptions and data that inform models. This includes rigorous red teaming, historical back-testing, and sensitivity analysis. 

  
  

• Keep humans in the loop: AI should enhance human creativity and judgment, not replace it. Design interfaces and control mechanisms that allow planners to scrutinise AI outputs and override agents when necessary. 

  
  

• Establish governance and safety protocols: Scenario outputs can be persuasive. It is crucial to maintain audit trails, ensure the provenance of inputs, and establish rules for responsible use to prevent misuse or overreliance. 

  
  

• Integrate across the enterprise: Wargaming is most effective when insights are shared across acquisition, doctrine, training, and logistics. Develop pipelines that translate simulation findings into policy and material decisions. 

Ethical and Strategic Considerations 

While AI can accelerate insights, it can also amplify errors. Simulations are abstractions, and proficient agents may discover unrealistic tactics if the environment is poorly defined. Additionally, there are geopolitical and legal risks when simulations involve sensitive data or influence kinetic decisions. Empirical validation, normative review, and multi-stakeholder oversight are essential to managing these risks. 

Conclusion 

AI-driven wargaming and scenario planning provide robust tools for defence and security planners. When grounded in thoughtful question framing, validated data, human oversight, and strong governance, AI can broaden the range of plausible futures that leaders explore and enhance the quality of strategic decisions.

The early programs and commercial platforms discussed illustrate both potential and cautionary lessons. Organisations that view simulation as a mission capability rather than merely an analytic exercise will find AI an indispensable component of strategic preparedness.