Across the globe, modern air forces are quickly adopting Artificial Intelligence (AI) to boost their advantage in everything from sensing threats and making faster decisions to enabling autonomous aircraft, improving logistics, and fostering better collaboration between people and machines For the Indian Air Force (IAF), the challenge—and the huge opportunity—is to create a structured plan that is both ambitious and practical. The proposed path forward involves building a range of capabilities, including safe autonomous systems for intelligence, surveillance, and reconnaissance (ISR) and logistics; AI-powered tools for mission planning and command-and-control; systems for predicting maintenance issues; collaborative drone swarms; and robust, secure computing for operational environments where communication might be difficult. Just as important as the technology are the essential frameworks for how AI will be governed, tested, and how the workforce will be developed to ensure the technology delivers real results while remaining accountable and reliable.
Why AI matters for air power now?
Global conflicts and defence research over the last five years have made one thing crystal clear: having dominant information, being able to make rapid decisions, and using affordable, expendable unmanned systems are huge advantages. AI helps speed up the decision-making cycle—the process of sensing, deciding, and acting—and significantly reduces the burden on human operators during data-intensive tasks like continuous, wide-area surveillance. It also allows for autonomous systems to handle routine or dangerous jobs, like resupply, forward logistics, and ISR, and improves fleet readiness by predicting when maintenance is needed. Large military organisations and alliances, such as North Atlantic Treaty Organization (NATO), have already created formal strategies to use data and AI on a massive scale, focusing on core activities like experimentation, ethics, and interoperability. At the same time, rival nations are deploying thousands of low-cost drones, experimenting with swarm tactics, and fielding AI-enabled targeting and autonomy systems, constantly raising the bar for modern military operations. Programmes like the US Replicator, along with the growing use of swarms in active conflict zones, prove both the military effectiveness of these technologies and the speed at which they are being adopted. Crucially, AI technology in areas like logistics (predictive maintenance), local processing on aircraft (edge inference), and high-fidelity training simulations is now mature enough that functional demonstrators are affordable and ready to be scaled up. Numerous studies and industry reports have already shown tangible benefits in improved readiness and reduced costs through the use of machine learning for maintenance.
Key Capability Areas the IAF Should Prioritise
By studying international practices, six interconnected AI capability areas stand out as especially important for the IAF. Together, these clusters form the foundation of a modern military portfolio, enhancing operational effectiveness while maintaining resilience and accountability.
AI-Assisted Intelligence, Surveillance and Reconnaissance (AI-ISR)
AI has the potential to dramatically improve how air forces process and act upon the enormous amounts of data gathered from surveillance. By bringing together inputs from various sensors, such as optical cameras, infrared systems, synthetic aperture radar (SAR), and signals intelligence, AI can automatically detect targets, pinpoint unusual activity in wide-area video feeds, and track changes across different missions. These capabilities do more than just reduce false alarms; they also enable continuous surveillance and allow targets to be handed off to manned platforms much faster, ultimately strengthening situational awareness. To achieve this, the IAF must focus on creating automated systems to merge data from multiple sensors, deploying processing engines on board aircraft and at ground stations, and developing tools to generate the labelled data sets needed to continuously refine the AI models. The value of AI-assisted ISR is already being demonstrated in conflict zones worldwide, where it has increased the speed of operations, lessened operator fatigue, and improved the automation of the entire process from tracking a target to engaging it.
Predictive Maintenance and Logistics Optimisation
Maintenance is absolutely essential for keeping aircraft ready, and AI can play a critical role in predicting equipment failures before they happen. By applying machine learning to technical data like vibration patterns, electrical current draw, and environmental conditions, AI can estimate the Remaining Useful Life (RUL) of components and recommend when to intervene. This forward-looking approach increases the availability of aircraft while reducing both unexpected downtime and costs. To make this possible, the IAF needs to establish a secure data repository for telemetry from its fleet, develop the modelling systems for predictive analysis, and seamlessly integrate these new systems with its existing maintenance infrastructure. International defence studies and programmes have already shown measurable improvements in fleet readiness and reductions in lifecycle costs thanks to predictive maintenance, making it one of the most immediate and practical uses of AI in air power.
Autonomous Navigation and Adaptive Flight Control In hostile or electronically degraded environments, navigation becomes challenging, especially when GPS signals are denied or jammed. AI provides solutions through Simultaneous Localisation and Mapping (SLAM), adaptive control algorithms that can overcome disturbances, and machine learning techniques for dynamic path planning. Together, these tools allow both unmanned systems and those working collaboratively with manned aircraft to operate reliably and resiliently in difficult conditions. To implement this, the IAF should invest in test facilities, both indoor and outdoor, for SLAM and adaptive controllers, along with hardware-in-the-loop simulation systems, powerful computing resources for model training, and safe procedures for transferring models from simulation to real-world use. This autonomy decreases the pilot’s workload while enabling continuous ISR and logistics missions in environments where GPS and communications support might be unavailable.
Maintenance is absolutely essential for keeping aircraft ready, and AI can play a critical role in predicting equipment failures before they happen. By using machine learning on telemetry data, AI can estimate the Remaining Useful Life (RUL) of components and recommend timely interventions, which increases the availability of aircraft while reducing unexpected downtime and costs
Swarm and Collaborative Unmanned Systems
Swarm intelligence is rapidly becoming a force multiplier in modern warfare, offering new ways to conduct distributed and resilient missions. Multiple drones working together can achieve saturation effects, cover large areas, execute deception tactics, or cooperatively deliver payloads—capabilities that no single platform can match. Lightweight communication protocols and AI autonomy at the unit level allow these swarms to operate efficiently at scale and at a lower cost per unit. To enable this, the IAF must focus on creating scalable testbeds for multiple drones, establishing mesh communication networks, developing distributed algorithms for coordination and task assignment, and setting up strict safety protocols for experimentation. Current conflicts have already demonstrated the disruptive potential of swarms, and many nations are heavily investing in large-scale production programmes. For India, swarm technology could provide asymmetric advantages in ISR, anti-access operations, and tactics to overwhelm advanced defences.
Edge AI and Hardened Inference
Relying solely on ground stations or cloud analysis creates vulnerabilities in contested environments. Edge AI solves this by deploying optimised, compact models directly onto drones using small, powerful processors. This approach allows for real-time perception, closed-loop autonomy, and fast decision-making without needing constant connectivity. Implementation efforts should focus on creating systems to optimise models, adopting efficient runtime software, and designing methods that balance the computational demands with the drone’s flight endurance and thermal performance. International field trials have confirmed the operational value of edge AI, proving its essential role in reducing bandwidth needs and sustaining autonomous operations where communications are denied.
Gen AI based knowledge management
A dedicated, on-premises Generative AI (Gen AI) solution, custom-built for the IAF, prioritises security and operational sensitivity by operating completely within the IAF’s secure digital ecosystem, entirely independent of external cloud infrastructure. This ensures complete control over data while allowing commanders, staff officers, and operational units to rapidly create mission-ready documents, intelligence briefs, procurement drafts, and decision-support material with precision and accuracy. By using structured inputs, such as mission specifics, procedural guidelines, and financial authorities, the on-prem Gen AI produces context-specific, compliant, and correctly formatted outputs. This not only guarantees confidentiality and control but also significantly accelerates planning cycles, improves efficiency, and reduces manual work, making it a powerful force multiplier in the IAF’s digital evolution. As an example of this capability, Zenerative Minds, a firm incubated at IIIT Hyderabad, has already developed a platform called ZM-Tathya under the IDEX initiative, specifically for organisations that handle highly sensitive data.
Responsible AI, Governance, and Legal Compliance
As AI capabilities advance, so do the ethical, legal, and strategic worries surrounding their use in warfare. International discussions, particularly those led by the United Nations regarding lethal autonomous weapons systems (LAWS), emphasise the need for accountability and oversight. For the IAF, it is crucial to embed governance mechanisms from the very beginning. This involves setting up an AI governance cell, creating adversarial red-team testing capabilities, enforcing legal review processes, and incorporating features for explainability and logging into AI deployments. Most critically, human-in-the-loop (HITL) safeguards must remain the central principle for any system with lethal implications. By adopting strong governance and compliance frameworks, the IAF can ensure its operations are legitimate, adhere to international law, and preserve human accountability, thereby building trust in its AI-enabled systems.
In hostile or electronically degraded environments, navigation becomes challenging, especially when GPS signals are denied or jammed. AI provides solutions through Simultaneous Localisation and Mapping (SLAM) and adaptive control algorithms, allowing both unmanned and manned–unmanned systems to operate reliably and resiliently in difficult conditions
Building an AI-Driven Future IAF (A Three-Pillar Programme)
After Operation Sindoor, the IAF has once again demonstrated its operational competence and ability to adapt in a battlespace increasingly defined by new technologies, contested domains, and the faster pace of modern warfare. To stay ahead of adversaries and fully leverage the potential of AI, autonomy, and advanced data analytics, the IAF needs a structured and scalable approach to developing these capabilities. This approach must strike a balance between short-term experimentation and long-term integration, ensuring security, efficiency, and interoperability while building a truly future-ready digital force. Today’s air power is defined not just by the aircraft themselves, but by the effective use of data and software, with AI integration becoming a decisive factor in operational advantage. While the IAF already collects vast amounts of data, from flight telemetry to ISR feeds, much of its potential may still be untapped due to fragmented infrastructure and limited paths for applying AI. Meanwhile, global peers and potential rivals are rapidly making predictive maintenance, AI-driven ISR, autonomous navigation, and swarming capabilities operational. Without a coordinated plan, India risks pursuing isolated projects that fail to become force-level assets. To address this, a three-pillar programme structure is proposed. This framework offers a systematic path to transform experimental technology clusters into operational capabilities, safeguarding national sovereignty, enhancing mission readiness, and positioning the IAF as a leader in AI-enabled air power.
Pillar 1: Foundations (Year 0–1)
This first pillar will focus on establishing the essential groundwork in terms of data, infrastructure, and talent. The phase will begin by creating a centralised, secure data lake to combine flight telemetry, sensor feeds, and maintenance logs, starting with a small selection of aircraft: one transport, one ISR, and one rotary-wing series. At the same time, two R&D testbeds will be built: an Autonomy Lab featuring an enclosed space with motion capture for testing SLAM in GPS-denied environments, and a Swarm & Edge Lab designed as a multi-vehicle testbed with mesh communications and advanced edge computing modules. To handle the necessary computing, a GPU-equipped modelling cluster will be deployed in a hybrid configuration, using on-premises resources supplemented by a controlled cloud-burst capability, all integrated with MLOps pipelines for effective data and model versioning. In parallel, training partnerships will be formed with premier institutions like IITs and IIITs, DRDO labs, and industry stakeholders including startups and defence prime contractors to build a strong talent pipeline. This foundation stage is critical, as access to data and experimental infrastructure are necessary steps for adopting AI. With a relatively modest investment, this phase promises huge returns by enabling a wide array of future projects.
Pillar 2: Capability Prototypes (Year 1–3)
This second pillar will focus on developing and validating operational pilot projects with clear, measurable evaluation metrics. Priority initiatives include a predictive maintenance programme, where 5–10 aircraft within a squadron will be equipped with enhanced data collection systems to deploy Remaining Useful Life (RUL) models, aiming to reduce unscheduled maintenance compared to current standards. An AI-enabled ISR pipeline will be developed to automate cueing for wide-area motion imagery (WAMI), integrated with on-board edge detection for persistent surveillance of high-value areas. Efforts will also concentrate on autonomous navigation in GPS-denied environments, starting with SLAM and adaptive control trials in enclosed testbeds before moving on to outdoor tactical resupply and ISR missions. Additionally, a swarm demonstrator will test leader-follower formations and distributed search operations, with metrics focusing on mission efficiency and resilience to the loss of individual agents. These pilot projects will be evaluated using objective criteria such as detection accuracy, false alarm rates, RUL prediction error margins, and mission success rates under contested conditions like jamming. All experimentation will follow a strict progression: simulation first, then hardware-in-the-loop (HIL) testing, and finally flight, with rigorous safety rules enforced at every stage.
Swarm intelligence is rapidly becoming a force multiplier in modern warfare, offering new ways to conduct distributed and resilient missions. Multiple drones working together can achieve saturation effects, cover large areas, execute deception tactics, or cooperatively deliver payloads—capabilities that no single platform can match
Pillar 3: Operationalisation and Doctrine (Year 3–7)
The final pillar will focus on scaling, sustaining, and fully integrating proven capabilities into mainstream IAF operations. This phase will begin with comprehensive certification and acceptance testing to validate standards for safety, interoperability, and the human-machine interface. Concurrently, IAF doctrine will be updated to incorporate human-in-the-loop (HITL) decision frameworks for autonomous systems and new concepts for employing swarms. To ensure long-term flexibility, procurement processes will be established around modular and upgradable architectures that can evolve as technology advances. Finally, the IAF will pursue international cooperation with NATO partners and friendly nations to align with global standards, improve interoperability, and ensure compliance with emerging legal and regulatory frameworks.
Implementing Safely: Governance, Testing, and Ethics
Integrating AI into defence comes with distinct risks, including the potential for systems to fail when faced with unexpected inputs, vulnerability to adversarial attacks like data poisoning, cascading failures in complex systems, and the difficult ethical and legal questions surrounding lethal autonomy. To handle these challenges, the IAF must establish a comprehensive governance framework before any operational deployment. This should include an AI Governance Board, made up of policy, technical, and legal experts, to review new proposals and enforce human-in-the-loop (HITL) decision thresholds. In parallel, a Red Team and adversarial testing capability must be created within test environments to simulate hostile conditions, such as electronic warfare, sensor spoofing, and dataset poisoning. All operational AI models must be designed to ensure auditability and explainability, with version control, detailed logging, and the ability to provide human-readable reasons for mission-critical decisions. The IAF should also create safety cases and certification standards for autonomous modes, taking cues from civil aviation benchmarks like RTCA but adapting them for tactical contexts. Equally crucial, every pathway to weaponisation must undergo rigorous legal review to ensure compliance with International Humanitarian Law (IHL) and global norms, with lethal autonomous engagements remaining strictly under human control. Alongside these safeguards, machine learning systems must be designed for continuous improvement and learning, with dedicated Gen AI models and tools generating crucial, context-specific analysis that directly supports the OODA loop (Observe, Orient, Decide, Act) and strengthens decision superiority in operations.
AI capability is not just about technology; it relies equally on people, skills, and collaboration. To build this foundation, the IAF must invest in cross-disciplinary teams that bring together operators, data scientists, controls engineers, and legal experts to tackle both technical and operational challenges. Dedicated career tracks and training programmes should be created to certify AI operators, lab engineers, and field maintainers, supplemented by continuous education through specialised courses and academic collaborations with institutions like IITs for focused MTech programmes. Strong industry partnerships are also essential, including incubators with startups and joint development initiatives with defence public sector undertakings (DPSUs) and private firms to accelerate technology transfer. A robust knowledge management system must be established, containing repositories of labelled datasets, experiment logs, and validated models, accessible to authorised teams to ensure continuity, transparency, and the reuse of lessons learned.
A dedicated, on-premises Generative AI (Gen AI) solution, custom-built for the IAF, prioritises security and operational sensitivity by operating completely within the IAF’s secure digital ecosystem. This not only guarantees confidentiality and control but also significantly accelerates planning cycles and reduces manual effort for commanders and operational units
Key Takeaways
For the Indian Air Force, AI is not a replacement for human pilots or commanders, but rather a force multiplier that enhances the IAF’s reach, decision speed, and overall operational effectiveness. A phased, three-pillar roadmap is the most practical way forward: first, building the necessary data and infrastructure; second, developing high-value prototypes like predictive maintenance, AI-enabled ISR, GPS-denied autonomy, and swarming; and finally, embedding these capabilities through updated doctrine and governance. By balancing high aspirations with responsibility, aligning with global best practices while customizing solutions for India’s unique operational needs, and prioritizing modular systems, human oversight, and indigenous partnerships, the IAF can move beyond mere experimentation to achieving a sustained operational advantage. This approach positions the IAF not just to keep up with international air power trends, but to emerge as a leader in AI-enabled warfare, securing sovereignty, strengthening mission readiness, and shaping the future of combat air power.
–The writer is an SME and independent consultant in military technology. The views expressed are of the writer and do not necessarily reflect the views of Raksha Anirveda
