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Why Executives Should Rethink How They Prepare Visual Data for AI Success
The aviation and travel industry is no stranger to digital transformation. AI is already being explored or implemented across key operational areas—from aircraft maintenance and passenger flow optimization to smarter security and faster aircraft turnaround. Yet despite all the interest and investment, many AI initiatives never surpass the prototype stage.
Modern aviation operations generate vast volumes of image and video data—from flight decks and terminals to hangars and supply zones. But much of this rich visual data remains unused by AI systems.
What’s holding them back?
It’s not model accuracy. It’s not a lack of compute.
The biggest hurdle is that the AI can only learn from what it’s shown and how it’s labeled.
The Untapped Power of Visual Data
Every day, airports and airlines capture vast amounts of imagery and video: ramp operations, terminal surveillance, maintenance inspections, and more. This data has immense potential to fuel AI systems that can identify risks, automate tasks, and predict delays. But here’s the reality:
Most of this data remains untapped because it lacks structure and annotated context.
AI doesn’t just “watch” video—it learns from labeled examples of what to look for:
- What is a regular ramp operation?
- What does a delayed procedure look like?
- When is a part missing or misplaced?
- How do passengers move through a congested terminal?
Without this labeled context (applied consistently and at scale), AI models can't recognize patterns, detect anomalies, or offer practical recommendations.
Manual Annotation is the Hidden Cost of AI
Many organizations underestimate how resource-intensive annotation can be:
- Manual labeling slows projects down
- Inconsistent tagging introduces bias
- Lack of domain understanding leads to irrelevant or inaccurate datasets
The result? AI models that don’t reflect real-world operations and never reach production.
This is where new-generation, domain-aware annotation solutions come into play.
Annotation as a Strategic Enabler
To fully capitalize on AI’s potential in aviation and travel, data annotation needs to be:
Scalable
From thousands to millions of labels across formatsContext-aware
Reflecting industry-specific workflows and compliance needsConsistent and unbiased
Supported by rigorous quality controlSecure
Especially when dealing with sensitive or regulated environmentsReal-World Impact: Ground Handling, MRO, and More
When annotation is done right, the results are measurable. AI can:
- Detect tailgating or unauthorized access in real-time
- Monitor turnaround workflows for compliance and efficiency
- Track passenger movement to inform terminal design
- Audit maintenance steps without manual logs
- Improve inventory control through video-based part tracking
One recent example is a major Indian airline that used structured video annotation to solve turnaround inefficiencies—a challenge costing the global industry over $8B annually. The result? A 10% improvement in on-time performance and a 99% reduction in manual data collection.
iTaG.AI, IGTx’s AI-powered annotation platform, bridged this gap. By enabling scalable, accurate labeling of video and image data—including object states, movement patterns, and sequences of activity—it empowers smarter AI systems across safety, efficiency, and maintenance operations.
To explore specific, high-impact AI use cases powered by structured visual data, see the iTaG.AI Use Cases Brief —a practical look at how annotation enables real-world results across security, operations, and maintenance.
Arkaprava Chongdar
Senior Business Analyst