The Air India Tragedy That Could Have Been Prevented by AI
Since its introduction 14 years ago, the Boeing 787 Dreamliner has faced several safety issues. Flight data from the Aviation Safety Network shows that six such occurrences were linked to technical difficulties in 2025 alone. One of the most devastating mishaps was the recent crash of Air India Flight 171, which claimed at least 270 lives, including 230 passengers and 12 crew members, after the aircraft crashed into a hostel block at BJ Medical College in Ahmedabad. However, solutions cannot be found only after accidents; they must be identified and tested before failures occur. This tragedy, which left only one survivor, has sparked widespread fear and raised urgent questions about aircraft safety. It underscores a critical truth: solutions must be identified and tested before failures occur, not after lives are lost. While human error has often been cited as a leading cause for aviation accidents, it raises an important question: Could AI play a significant role in preventing such tragedies through various strategies? Revolutionising Aviation Operations As commercial aviation experiences growing demand and stricter safety regulations, Boeing has incorporated robotics, AI-based analytics, digital twin technology, and machine vision systems to improve manufacturing efficiency and product quality. A research paper titled ‘Artificial Intelligence in Aviation Safety: Systematic Review and Biometric Analysis’ reveals that AI enhances safety protocols through predictive analytics, real-time monitoring, and proactive risk management. Machine learning (ML) algorithms analyse large datasets to identify patterns, enabling the prediction of safety issues before they arise. “Machine learning – anomaly detection is a mature and well-understood technology with a proven positive impact. When applied to the aviation industry, it can significantly improve safety, quality of service, and reliability, reduce maintenance costs, and increase the efficiency of aircraft operators,” Alexis Lope-Bello, CEO at ComTrade Group, reportedly highlighted the importance of these technologies. Moreover, AI also supports the development of decision-making systems for pilots and air traffic controllers, improving the timeliness and quality of their decisions. By implementing AI-driven predictive maintenance, airlines can identify and resolve mechanical problems early by assessing aircraft performance and maintenance records, thus reducing the chances of in-flight failures. According to the research paper, data science and big data analytics are increasingly critical in optimising aviation safety and operations. “The data-driven methodology for detecting safety risks through aviation data analysis illustrates the growing use of big data analytics in aviation. Applying Natural Language Processing (NLP) techniques to detect human factors in aircraft accidents represents a crucial step toward minimising human errors,” the paper read. Proactive Measures Through Data Science and AI AI algorithms can analyse extensive data from aircraft sensors to predict mechanical failures before they occur. This early detection allows airlines to perform proactive maintenance, reducing the risk of in-flight emergencies. By examining flight data, AI identifies patterns that may indicate safety risks or operational inefficiencies. The paper shows that continuous monitoring of flight metrics helps airlines spot deviations and take corrective actions to prevent accidents. “AI is already playing a crucial role in reducing aviation accidents and loss of life by improving situational awareness, predictive maintenance and decision-making processes. In air traffic control (ATC) and collision avoidance, AI is helping out tremendously,” Freshta Farzam, CEO and founder of LYTE Aviation, told Al Jazeera. “When it comes to aviation, there’s a lot of advancement in AI, even though a lot of that has not trickled through to the commercial aviation because of the processes and certification,” Amad Malik, chief AI officer at Airport AI Exchange, told Al Jazeera. Various companies are incorporating AI models such as the ones mentioned earlier. In collaboration with Microsoft, Lufthansa Technik has employed AI algorithms to examine aircraft sensor data and forecast possible mechanical problems before they arise. This strategy enables them to arrange maintenance activities more effectively, minimise aircraft downtime, and avoid expensive interruptions to airline operations. “The way the regulations are set up is that years and years of data are required before you can use anything in the commercial landscape. What we do have right now is something that started in the 1960s. But there’s also a concept of having AI as a local intelligence within the aircraft that can detect and mitigate even if the pilot or ATC are making a mistake,” Malik added. Forbes recommends that Boeing adopt Predictive Maintenance Systems that utilise AI to analyse aircraft sensor data and identify safety risks early. For example, Lufthansa Technik’s Condition Analytics uses machine learning to reduce unexpected maintenance and aircraft downtime. How AI is Enhancing Seamless Airline Experience Air India has also actively leveraged AI into its operations, but it is restricted to customer experience and streamlining various processes, such as quicker ticket bookings, personalised customer service, and baggage tracking. Satya Ramaswamy, chief digital and technology officer of Air India, wrote on LinkedIn, “With Copilot, our leaders gain quick, clear insights without information overload, enabling timely, effective decisions that drive the airline forward. Being in a business where there are human copilots, we understand the spirit in which Microsoft Copilot, as a technology, is to be used in assisting and amplifying human performance! Take the example of our On-time Performance (OTP) Copilot. Notably, the air-taxi industry is expected to expand rapidly by the close of the decade, though experts caution that its viability relies on a thoughtfully designed network of vertiports that must be incorporated into current infrastructure and backed by sophisticated air traffic management systems. “We will see more vertiports being developed or planned in the short term, particularly in congested areas where helicopter and traditional aerial transportation access is limited,” Nicolas Zart, founder of Electric Air Mobility, reportedly said. “Initially, these vertiports may be located on the outskirts of major cities as they test the concept of AAM before moving into urban centres.”
While many have blamed human error for the fatal accident, deploying AI analytics systems could assist airlines in managing risk.
While many have blamed human error for the fatal accident, deploying AI analytics systems could assist airlines in managing risk.
["Air India", "artifical intelligence"]
["data science", "machine learning", "artificial intelligence", "AI", "ML", "Air India", "RAG", "NLP", "Aim", "analytics", "copilots", "artifical intelligence"]
["AI", "artificial intelligence", "machine learning", "ML", "NLP", "data science", "analytics", "Aim", "RAG", "copilots"]
Smruthi brings over two years of experience in reporting on the global energy industry. They hold a Master's Degree from the University of Leeds in International Journalism and a Bachelor's Degree from Christ University in Media Studies, Economics and Political Science.