I Planned Military Ops. AI Just Made It My Most Valuable Skill

Newsweek | Jon Licht

Artificial intelligence integration within enterprise technology environments requires constraint-first military planning methodologies to prevent operational drift and system failures. Former Marine Corps infantry officer Jon Licht argues that the disciplined, contingency-focused planning processes drilled into military personnel are highly scarce, critical skills needed to successfully guide AI systems.

This methodology prioritises defining operational boundaries, mapping data models, and challenging assumptions before executing technical builds. Quality instructions depend on human judgment. As AI increasingly transitions from digital platforms into physical domains like robotics and humanoid systems, the demand for domain-specific expertise will intensify. Lived experience in complex environments like factories, hospitals, and emergency response zones will establish the essential guardrails for these autonomous systems. Consequently, organizational success depends on leaders who prioritize strategic problem-solving over mere technical tool adoption to navigate this rapid technological shift, ensuring execution does not outpace thoughtful planning.

Comment

The integration of artificial intelligence into military decision-making frameworks exposes a fundamental tension between rapid algorithmic execution and structured doctrinal planning. The Marine Corps Planning Process (MCPP) relies on iterative problem framing and assumption testing to mitigate the friction of the physical battlefield. When applied to machine learning systems, this structured methodology serves as a vital cognitive framework to prevent algorithmic drift. Without these human-defined constraints, automated systems lack the contextual awareness required to operate within complex, non-linear environments.

Specifically, the MCPP's emphasis on Commander's Intent and Center of Gravity analysis translates directly into the hyper-parameters and reward functions that govern neural networks. By formalising these qualitative military concepts into explicit operational boundaries, planners can systematically bound the solution space of generative models. This translation mechanism ensures that tactical AI outputs generated during simulated exercises like Bold Alligator remain aligned with broader strategic objectives.

Strategic Question for Discussion

How does the digitisation of the Marine Corps Planning Process (MCPP) alter the balance between rapid algorithmic execution and the deliberate human cognitive friction required to stress-test assumptions during exercises like Bold Alligator?

The transition toward digitised planning frameworks suggests that while algorithms can rapidly generate multiple courses of action, they frequently overlook the qualitative friction points inherent in amphibious operations. Observations from exercises like Bold Alligator indicate that human-in-the-loop validation remains essential to prevent automated systems from optimising for mathematically logical but tactically unfeasible manoeuvres. Consequently, the integration of the Marine Corps Planning Process into AI architectures is likely to function as a hybrid cognitive filter rather than a fully autonomous replacement.

Share your assessment in the comments below.

To read in details click here