While the industry debates the latest generative AI model, leading airlines are focusing on a quieter but far more consequential challenge: Data Logistics. In aviation, operational disruptions can cost thousands of dollars per minute, and in many cases, the root cause is not a lack of intelligence but a failure in how data is managed. In this scenario, the difference between constant value addition and system breakdown will come from an architecture capable of reliably moving, governing, and activating large volumes of distributed data, transforming fragmented information into real-time operational decisions.
Before AI, There Is Data
A recurring question within data engineering teams is: What will be the most consequential technology by 2026?
In aviation, the answer is increasingly clear: Hybrid Data Engineering. There is no truly “always-on” cloud in this industry. Aircraft, maintenance crews, and ground operations function at the edge, often under intermittent satellite or 3G connectivity. Before AI can deliver value, it is essential to understand the anatomy of the data itself.
Across the sector, many real-time decisions are delayed, and opportunities lost, not because AI models lack sophistication, but because the data supply chain breaks down. In 2025 alone, these inefficiencies cost the airline industry at least $11 billion, even as passenger demand continued to grow.
At Globant, we approach this challenge through a strategy that focuses on three critical, yet often invisible, layers:
1. Intelligent Ingestion: The Edge Reality
Not all data can, or should, be pushed to the cloud. For airlines, this is not just a technical limitation but a structural condition. As edge computing adoption accelerates, with global spending surpassing $265 billion in 2025, enterprises are shifting toward distributed systems capable of real-time decision-making, bringing computation closer to where data is generated.
However, bandwidth and latency remain critical bottlenecks: transmitting raw telemetry or continuous video streams from aircraft or airport sensors to centralized servers is impractical at scale. Edge ingestion addresses this by applying compression, filtering, and deduplication directly at the source. Instead of transmitting every byte captured, systems prioritize and send only what is operationally relevant.
Whether dealing with engine telemetry or computer vision streams in airports, data is refined before it moves. The result is greater resilience in constrained environments and a significantly faster time-to-decision.
2. The Foundation of Trust: Compliance First
Aviation operates under strict regulatory constraints. Proprietary aircraft manuals, passenger PII, and financial data cannot be introduced to uncontrolled AI systems or public inference layers. The risk of weak governance is well documented: 63% of organizations experiencing AI-related breaches lacked formal governance frameworks, while 97% of incidents were linked to missing access controls.
This makes Data Governance non-negotiable. Sensitive data must be isolated through encryption, access controls, and auditable architectures, while unstructured “dark data” – legal documents, technical manuals, and contracts – is transformed into governed, traceable assets.
In this context, governance is not just about compliance: it is a prerequisite for reliable AI. Without it, systems remain vulnerable to hallucinations, bias propagation, and regulatory exposure. With it, organizations can safely operationalize data at scale while maintaining trust and auditability.
3. AI Only Works When the System Does
Only when ingestion and governance are in place does AI begin to deliver value. Here, AI is not deployed for novelty, but applied where operational friction exists and timely decisions matter most.
For airlines, this often means embedding intelligence directly into critical workflows. For instance, in maintenance operations, AI-driven systems are already reducing unplanned downtime by up to 25–30% and enabling earlier detection of potential failures, shifting operations from reactive to predictive.
Similar patterns extend across operational and decision-making domains. Machine learning models help identify risk signals earlier and support more consistent, data-informed decisions at scale.Â
The value of these systems does not come from the models themselves; it comes from their ability to operate on transparent, well-governed data. When integrated effectively, AI is transformed from isolated experimentation to a core component of operational execution, where intelligence is the result of a system that works.
The Future Is Orchestration
Success will not be measured by how many models an airline deploys, but by how quickly those models translate into operational value. Moving from months to days in data product deployment requires orchestration layers that can operate across distributed environments, enabling flexibility without compromising legacy systems.
In this context, orchestration is not just a technical capability; it is what connects data, governance, and intelligence into a functioning system. A strong on-time performance or efficient aircraft utilization is rarely the result of a single decision. It is the outcome of many interconnected, data-driven decisions operating in sequence, each one dependent on the reliability of the system behind it. Each one backed by expert human supervision.Â
The next time you see an aircraft depart on schedule, consider what makes that possible: an invisible, secure, and continuously operating data system supporting those decisions in real time.
At Globant’s Airlines Studio, we partner with leading airlines to design and scale the systems behind these decisions, from edge data architectures to AI governance and real-time operational platforms. If these challenges resonate with you, let’s continue the conversation.
