Artificial Intelligence (AI) is steadily entering the domain of military operations, promising to transform the speed, scale, and precision with which decisions are made and actions are executed. From target prioritisation to logistics optimisation, AI introduces capabilities that were previously unimaginable in terms of data processing and real-time responsiveness.
Yet, despite its transformative potential, AI remains—fundamentally—a technology. And history repeatedly reminds us that no technology, however powerful, operates in isolation or guarantees dominance. Each innovation triggers a cycle of adaptation, countermeasures, and new vulnerabilities. The modern battlefield, as seen in evolving conflicts, continues to validate this pattern.
The resurgence of trench-like conditions, even when tanks are on the battlefield, under the threat of first-person-view (FPV) drones illustrates a familiar reality: technological advantage is often temporary, and warfare remains a dynamic contest between competing adaptations.
Analysis
AI’s most immediate and impactful contribution lies not necessarily at the tactical edge of the battlefield, but in the layers behind it—particularly in command, control, and logistics.
Military headquarters have always been centres of intense analytical activity. Operational and logistics staff continuously process vast amounts of data—troop movements, supply levels, terrain conditions, threat assessments, and more. This data must be interpreted, reinterpreted, and acted upon as situations evolve. AI dramatically enhances this capability by enabling faster data synthesis, predictive analysis, and decision support.
For instance, AI can:
- Prioritise targets based on dynamic battlefield conditions
- Optimise logistics distribution, ensuring scarce resources are allocated efficiently
- Synchronise operations and supply chains, bridging the gap between combat requirements and logistical capabilities
- Support scenario modelling, helping commanders anticipate outcomes of different operational choices
In these domains, AI acts as a force multiplier—not by replacing human decision-makers, but by augmenting their ability to process complexity.
However, the closer one moves to the battlefield itself, the more constraints begin to emerge—particularly around communication.
The battlefield is a highly contested and resource-constrained environment. Communication bandwidth is limited and must be shared across multiple critical functions:
- Command and control communications
- Artillery coordination
- Unit-level coordination and situational awareness
- Intelligence dissemination
AI systems that rely heavily on continuous data exchange may struggle in such environments. If communication networks are degraded, jammed, or destroyed, the effectiveness of AI systems that depend on connectivity is significantly reduced.
This leads to a critical design and deployment consideration: AI in warfare must be capable of operating in degraded or disconnected environments.
Distributed, edge-based AI systems—capable of functioning independently with minimal communication—may offer a partial solution. However, this decentralisation comes at the cost of reduced data sharing and coordination, potentially limiting the overall effectiveness of AI-enabled operations.
Thus, the very environment in which AI is expected to deliver advantage also imposes fundamental constraints on its use.
The Human Dependency Challenge
Another critical dimension is the risk of over-dependence on AI.
Military staff processes have evolved over decades, built on structured methodologies for planning, analysis, and execution. These processes are designed to function under uncertainty, often with incomplete information.
If AI begins to take over a significant portion of analytical tasks, there is a risk that human expertise in these traditional methods may erode over time.
This creates a potential vulnerability: in a contested environment where AI systems may be disrupted—whether through cyber attacks, electronic warfare, or physical destruction—personnel must still be capable of reverting to manual processes.
The challenge, therefore, is not merely technological but organisational and doctrinal:
- How to integrate AI without eroding core competencies
- How to train personnel to operate both with and without AI support
- How to ensure trust in AI outputs without blind reliance
This dual-capability requirement adds complexity to training, doctrine, and operational planning.
Connection to Earlier Thinking
In my earlier writings on this platform, a recurring theme has been the importance of fundamentals in complex systems, particularly within maintenance, repair, and overhaul (MRO) ecosystems and defence capability.
Whether discussing propulsion systems, maintenance strategies, or industrial readiness, the emphasis has consistently been on:
- Reliability over novelty
- Process discipline over technological hype
- Human expertise as the backbone of system effectiveness
The current evolution of AI in warfare reinforces these principles.
Just as advanced machinery cannot compensate for poor maintenance practices, AI cannot compensate for weak operational fundamentals. In fact, the more advanced the technology, the greater the need for strong underlying systems and competencies.
The analogy extends further: in MRO environments, systems must be designed to function even when ideal conditions are absent. Similarly, in warfare, technologies must be robust against disruption, degradation, and uncertainty.
The battlefield, much like an industrial ecosystem under stress, exposes weaknesses rapidly and unforgivingly.
Strategic Implications
The integration of AI into military operations carries several strategic implications.
1. Indigenous Capability Development
Reliance on external AI technologies poses risks to security, adaptability, and sustainability. Developing indigenous AI capabilities tailored to India’s operational environments—characterised by diverse terrains and contested communication spaces—is essential.
2. Focus on Edge Computing and Resilience
Given communication constraints, there is a need to prioritise:
- Edge-based AI systems
- Robust, low-bandwidth communication technologies
- Systems designed for graceful degradation rather than complete failure
This has direct implications for the domestic defence and technology industry.
3. Training and Doctrine Evolution
Military training institutions must adapt to ensure that personnel:
- Understand AI capabilities and limitations
- Retain proficiency in traditional methods
- Can transition seamlessly between AI-assisted and manual operations
This dual competency will be a defining feature of effective forces in the future.
4. MRO and Lifecycle Support
AI-enabled systems introduce new dimensions to maintenance and support:
- Software reliability becomes as critical as hardware reliability
- Continuous updates and validation are required
- Cybersecurity becomes integral to system availability
This expands the scope of the MRO ecosystem, requiring new skills, tools, and organisational structures.
5. Industrial Ecosystem Alignment
The broader industrial base must align with these evolving requirements:
- Integration of AI into defence manufacturing and support systems
- Development of testing and validation frameworks for AI in operational environments
- Collaboration between defence, technology, and academic sectors
Conclusion
Artificial Intelligence undoubtedly represents a powerful addition to the military toolkit. It enhances speed, improves decision-making, and enables more efficient use of resources. However, it does not alter the fundamental nature of warfare as a contested, uncertain, and resource-constrained environment.
History shows that every technological advantage invites countermeasures and adaptations. AI will be no exception.
The true measure of effectiveness will not lie in how extensively AI is deployed, but in how intelligently it is integrated—balancing innovation with resilience, and capability with preparedness for failure.
Ultimately, the decisive factor will remain unchanged: the ability of trained personnel to operate effectively under all conditions.
In that sense, AI is not a replacement for fundamentals—it is a tool that must be built upon them.
