By Anurita Das, Founder and CEO, Genovation Technological Solutions
In Aerospace and Defence, the margin for error is almost non-existent. Every mission, every system, and every decision must operate with precision, security, and accountability. These are environments built on trust, where even a small oversight can have significant consequences. For decades, automation has played a key role in these sectors, but traditional automation can only follow instructions. It cannot adapt, reason, or understand intent. That is now changing with agentic AI, intelligent systems that can perceive context, make decisions based on goals, and act autonomously while remaining fully explainable and traceable.
An Industry on the Edge of Intelligent Transformation
According to Statista, the AI Robotics market is projected to reach US $22.63 billion in 2025, with an expected annual growth rate of 26.82 percent between 2025 and 2031. By the end of that period, the market is estimated to reach US $94.14 billion, with the United States accounting for the largest share at about US $9.49 billion in 2025. This impressive growth reflects how quickly intelligent automation and robotics are becoming fundamental to aerospace and defence operations.
These figures highlight a clear shift: repetitive, rule-based tasks are steadily giving way to intelligent systems capable of reasoning, optimizing, and supporting human operators in complex, dynamic environments.
Autonomy with Accountability
Agentic AI represents a move from automated execution to intelligent decision support. Unlike conventional AI models that generate outputs without context, agentic systems can reason about objectives, act within operational boundaries, and provide clear explanations for every action.
Where Agentic AI Is Already Making a Difference
Mission and Flight Operations
Modern flight control involves countless variables that can change in seconds, weather conditions, sensor data, radar inputs, and communications. Agentic AI can analyse all of this information simultaneously, recommend optimal manoeuvres, and alert pilots or commanders to anomalies in real time. It functions as a dependable digital co-pilot that enhances human decision-making under pressure.
Predictive Maintenance and Asset Management
Aerospace and defence equipment are highly complex, with thousands of components that degrade differently over time. Agentic AI continuously monitors operational data to predict when parts might fail, schedule maintenance proactively, and coordinate spare-part logistics. This minimizes downtime, lowers costs, and improves fleet readiness.
Threat Detection and Strategic Planning
Defence operations depend on the fusion of radar, satellite, and intelligence data. Agentic AI can integrate and interpret these inputs, detect potential threats, and simulate adversarial scenarios to support more informed strategic decisions. This enables commanders to act with both speed and confidence.
Synthetic Data and Training Pipelines
Because most defence data is classified, organizations often struggle to build or train models safely. Agentic AI can generate synthetic datasets that simulate real-world operational conditions, such as flight paths, maintenance events, or sensor anomalies, allowing the development of secure computer vision systems.
Security, Compliance, and Trust
In aerospace and defence, data sovereignty and confidentiality are paramount. Agentic AI systems are therefore deployed in air-gapped or on-premise environments, protected by strong encryption and multi-layered access control. Every action, inference, and human intervention is recorded, creating comprehensive audit trails that ensure transparency and regulatory compliance.
Challenges and the Road Ahead
Despite its potential, agentic AI faces notable challenges. Ethical responsibility must be clearly defined when AI contributes to mission-critical decisions. According to Gartner, more than 40 percent of early agentic AI projects may be discontinued by 2027 due to cost, complexity, or unclear ROI. These figures underline an important lesson: explainability and oversight are not optional; they are prerequisites for success.
One promising answer to these challenges lies in the use of powerful small language models (SLMs), compact yet highly capable AI models that can be trained or fine-tuned on sensitive, domain-specific data at a fraction of the cost of large-scale systems. For aerospace and defence, these SLMs offer the flexibility to operate securely within air gapped networks, adapt quickly to mission data, and comply with strict regulatory standards. They consume less compute power, can be retrained frequently as environments evolve, and allow organizations to retain data sovereignty.
The future of aerospace and defence will not be a contest between humans and machines, but a collaboration between humans and intelligent agents. Agentic AI is not replacing human judgment; it is expanding it. Together, they can achieve faster insights, improved efficiency, and greater operational resilience.