Putting AI into military operations is going to completely change how we prep for and fight the next war. To get a handle on it, you can’t just look at the tech. You’ve got to dig deep into military history and see what happened before when new stuff came along. So what can the chaos caused by gunpowder, aviation, or even cyber warfare teach us about getting AI right in 2026?
Key Takeaways
- Go back and read the old military doctrine, looking for direct parallels to AI. You need to see exactly how things like tanks or planes forced a total rethink of command and control.
- Make human-AI teaming protocols a top priority right now, using lessons from past joint ops where clear communication and trust between different units were the only things that led to a win.
- Build tough, layered cybersecurity for every military AI system because, as history shows, your enemy’s first move is always to find and exploit the new guy’s tech vulnerabilities.
- Get clear ethical frameworks for autonomous combat AI on paper now, basing them on historical precedents for rules of engagement and who’s held accountable when things go wrong on the battlefield.
1. Analyze Doctrinal Shifts from Past Technological Revolutions
Every time a disruptive technology shows up in war, the old rulebooks get thrown out. Think about the machine gun in the late 19th century. At first, military leaders treated it like a faster-firing rifle and completely missed how it would make massed infantry charges suicidal. It took the staggering losses in World War I at places like the Somme for armies to finally get it and adapt with trench warfare and dispersed formations. AI isn’t just going to speed up what we already do. It’s going to change the entire calculus of decision-making, intel, and kinetic strikes.
Pro Tip: Concentrate on specific moments where one piece of tech turned the battlefield upside down. The debates over tank warfare during the interwar period are full of priceless insights. Guys like Basil Liddell Hart and J.F.C. Fuller pushed for mechanized warfare when most people resisted, but their ideas eventually reshaped all of land combat, and their arguments about speed, maneuver, and combined arms are directly applicable to thinking about how AI-driven systems could create rapid, distributed effects across a battlespace.
Common Mistake: Thinking you can just plug AI into current systems without a fundamental shakeup of your org chart or command structure. That’s the exact same mistake early 20th-century navies made when they bolted aircraft onto battleships but failed to really integrate naval aviation into the fleet’s core doctrine, resulting in a new capability that was almost always used badly.
2. Study the Evolution of Command and Control with New Technologies
Command and control (C2) has always been the nervous system of an army, and it’s had to evolve with every new piece of tech. From semaphore flags to encrypted radio, every leap forward created new opportunities and new weak points. AI is set to accelerate the OODA loop (Observe, Orient, Decide, Act) to a speed we’ve never seen before, but that speed comes with its own set of problems. During the Cold War, the invention of sophisticated radar and air defense networks forced decision-making cycles that were already pushing the limits of what a human staff could handle. AI can now crunch those datasets and recommend actions far faster than any person.
You can see this playing out in real time at the U.S. Army’s Project Convergence exercises, which are all about integrating AI for multi-domain ops and linking any sensor to any shooter, no matter the domain. It’s the 21st-century version of the complex coordination needed for something like the amphibious landings in World War II, where you had to time naval gunfire, air support, and ground troop movements perfectly. The difference is the sheer volume and velocity of the information, which means you need AI that can filter, prioritize, and show a human commander what matters right now.
Pro Tip: Go look at historical C2 failures that happened during a tech transition. When aerial reconnaissance first started, for example, the process of getting photos from the plane to the commander who needed them was so slow that the intelligence was often useless by the time it arrived. AI can solve that lag, but only if the interface between the human and the machine is clean and inspires confidence. It’s not unlike how the U.S. Air Force developed the Airborne Warning and Control System (AWACS) to create a real-time command post in the sky, a direct ancestor of today’s AI-driven battlespace awareness platforms.
3. Prioritize Human-AI Teaming and Trust Development
It doesn’t matter how smart the AI gets, a human has to be in the loop, especially when it comes to pulling the trigger. History is a long story of tech failing and battle plans going sideways, situations that demanded human improvisation and judgment to fix. The rigid battle plans of the Napoleonic Wars often fell apart the second an enemy force did something unexpected or the terrain wasn’t what the map said it was. A commander’s gut instinct and ability to adapt on the fly was what won the day, and that’s not changing.
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Building trust between a human operator and an AI isn’t something you can just wish for. It takes tons of training, a very clear-eyed view of what the AI can’t do, and processes that let you see *why* the AI made a certain recommendation. The U.S. Department of Defense’s Ethical Principles for Artificial Intelligence, adopted in 2020, aren’t just some philosophical document. They’re an operational necessity. If a soldier on the ground doesn’t trust the targeting box an AI system just drew, they’ll either ignore it or hesitate, and that hesitation completely erases the speed advantage the AI was supposed to provide.
Pro Tip: Look at how we built trust between different services or between soldiers and new gear in the past. Getting close air support right in the Korean War took a massive effort to build confidence and a shared language between the infantry on the ground and the pilots in the air. The solution was constant joint exercises. For AI, that means countless hours in sims and in real-world tests where humans and AI work together, learning each other’s quirks and strengths.
Common Mistake: You can’t overestimate what AI can do today or underestimate the psychological wall a person has to climb to trust a machine in a life-or-death situation. “Automation bias”, the tendency for people to blindly trust a machine even when their own eyes tell them something is wrong, is a serious risk that you have to train against and design your systems to counter.
4. Learn from Historical Intelligence and Counter-Intelligence Battles
The fight for information is as old as fighting itself. From Sun Tzu telling you to know your enemy to the code-breakers at Bletchley Park in World War II, good intel has always been the ultimate advantage. AI is about to turn intelligence gathering and analysis on its head, but it’s also opening up new ways for the enemy to mess with us. The long history of deception and camouflage (from the Trojan horse to electronic warfare) proves that your opponent will always try to turn your tech against you and create confusion.
An AI can sift through incredible amounts of open-source intelligence (OSINT) and signals intelligence (SIGINT), giving you a picture of the enemy’s plans that was unimaginable a decade ago. But that also means the enemy will use their own AI to pump out disinformation, launch complex cyber attacks, and build AI-powered countermeasures. The constant back-and-forth in the cyber domain, which you can see in the annual reports from an agency like CISA, is a preview of this future. It’s a cat-and-mouse game, and AI is about to hit the accelerator.
Pro Tip: Study classic examples of intelligence deception and how they were broken. Operation Mincemeat in WWII, where the British fooled the Axis about the invasion of Sicily, worked because they understood their enemy’s assumptions and biases. An AI model can be taught to spot deception, but a clever enemy can also feed it poisoned data to trick it. That’s why you always need strong data validation and a human analyst in the loop asking “Does this actually make sense?”
5. Examine the Ethical and Legal Precedents of New Weapon Systems
Every new weapon that comes along, from the crossbow to the atomic bomb, kicks off a huge debate about ethics and legality. AI-powered autonomous weapon systems (AWS) are having their moment now. The arguments happening today about whether AWS can comply with international humanitarian law (IHL) sound a lot like past arguments over chemical weapons or cluster bombs. The core principles of distinction (only hitting combatants) and proportionality (not causing excessive civilian damage) are at the heart of it all.
The DoD’s own Directive 3000.09, “Autonomy in Weapon Systems,” lays out the strict requirements for keeping a human in the loop on lethal force decisions. That directive shows a very careful and deliberate approach because everyone recognizes the deep ethical questions involved with fully autonomous systems. We’ve been here before. The creation of rules like the Geneva Conventions after World War II was our attempt to put some guardrails on the conduct of war. The speed and scale of AI just makes getting these ethical calls right even more urgent.
Pro Tip: Go back and read up on the history of the international laws of armed conflict. The Lieber Code, written during the American Civil War, was one of the first real attempts to codify the rules of war. If you understand where those rules came from, it’s easier to see how they apply to a new technology like AI. The modern debate about who is accountable for an action taken by an autonomous system, for instance, is a direct continuation of old discussions about a commander’s responsibility for their soldiers’ actions.
Common Mistake: Thinking that tech will solve your ethical problems. It won’t. You have to develop the ethical frameworks and legal rules at the same time you’re developing the technology. If you don’t, you risk losing legitimacy, facing international condemnation, and handing your adversary a strategic win.
Bringing AI into our military strategy requires us to keep one foot firmly planted in military history. By looking hard at past doctrinal changes, C2 evolutions, the messy business of building trust in new tech, intelligence fights, and ethical debates, we can better prepare for the weirdness of the next conflict. The whole trick is applying those hard-won lessons to the specific problems AI creates, letting us innovate responsibly while keeping our edge. You can learn more about the business side of this with Veteran AI Tech and DoD Contracts in 2026.
How does AI impact the OODA loop in future conflicts?
AI gives a massive speed boost to the Observe and Orient parts of the OODA loop. It can process data from hundreds of sensors at a speed no human team could ever match, allowing a commander to understand the battlespace, make a decision, and act much faster. That speed can be the difference between winning and losing.
What historical military technology offers the most relevant lessons for AI integration?
The introduction of aviation and the tank in the early 20th century are the best parallels. Both completely changed the speed, range, and coordination required on the battlefield, which forced a total rewrite of doctrine and command structures. We’re facing the same level of disruption with AI.
Why is trust between humans and AI critical in military applications?
If an operator doesn’t trust the AI, they’ll second-guess it or turn it off, which completely defeats the purpose of having it. That hesitation wastes the speed and analytical advantage the AI provides. Just like in past human-machine teams, trust comes from understanding the gear’s reliability and its limits, and you only get that through training.
How can historical intelligence failures inform AI-driven intelligence operations?
Looking at old intelligence failures, especially the ones caused by deception, shows why you can’t blindly trust AI-generated intel. You need strong systems to validate the data, you need to pull from different sources, and you always need a human analyst asking if the conclusion makes sense. Adversaries will absolutely use their own AI to try and fool ours.
What are the primary ethical considerations for AI in future warfare?
The big ones are keeping a human in ultimate control of lethal force, figuring out who is held accountable when an autonomous system makes a mistake, and making sure the system can follow the laws of war like distinction and proportionality. These are the same kinds of hard questions that have been asked every time a powerful new weapon has been invented.
