Artificial intelligence is no longer a distant ambition in aviation - it’s actively reshaping day-to-day operations across the industry. From reducing delays and streamlining maintenance to optimizing crew scheduling and gate planning, AI is driving measurable improvements in efficiency, reliability, and cost control.

This summary highlights key findings, from our in-depth report, on how leading airlines and airports are deploying AI to solve persistent operational challenges, and what that means for the future of aviation performance.

1) Flight Delay Mitigation

AI enables more proactive delay mitigation by analyzing real-time data from multiple sources (weather, staffing, aircraft readiness). Predictive models help operations teams anticipate disruptions and adjust routing or scheduling before problems cascade.

Example:

• United Airlines and JetBlue use Tomorrow.io’s AI-powered forecasts to predict severe weather conditions in real time, allowing them to adjust flight routes, delay departures preemptively, or reroute aircraft before disruptions escalate.

2)  Turnaround Improvements

AI and computer vision are transforming turnaround efficiency by providing real-time visibility of ramp activities. These systems detect delays in tasks like fueling or baggage handling and trigger early interventions.

Example:

• Rome Fiumicino Airport (FCO) has adopted Assaia’s “ApronAI” solution, which generates real-time timestamps for key turnaround activities including passenger deboarding, refueling, and catering, to estimate departure readiness and pushback times with high accuracy. This foresight allows ground handlers and airline operators to act earlier and more precisely, preventing last-minute scrambles and ensuring tighter on-time performance. In airports where ApronAI was deployed in 2023 and 2024, overall ground delays dropped by 6%, and turnaround times improved by 4%, despite increased traffic.

3) Resource Allocation Optimization

AI tools now automate crew pairing, reassignments, and post-flight admin tasks - improving compliance and speed. These systems integrate real-time availability, rest limits, and operational constraints to optimize task allocation.

Examples:

• Air India has integrated AI-powered data analysis into its operational workflow using Microsoft Copilot, enabling operations teams to quickly analyze flight performance, identify inefficiencies, and make real-time adjustments – all through natural language queries. Instead of relying on siloed reports and manual data interpretation, teams now have instant access to actionable insights on aircraft availability, crew scheduling, and airport constraints.
• Japan Airlines' JAL-AI mobile app cut post-flight report time by 67%, easing admin load on cabin crew and freeing up staff capacity for more meaningful work.

4) Quicker Aircraft Diagnostics and Maintenance

AI is revolutionizing maintenance by enabling rapid troubleshooting and smarter data retrieval. Generative AI tools process scattered technical data to diagnose faults quickly and surface asset records in seconds.

Examples:

• Textron’s TAMI assistant - a Generative AI assistant powered by Azure OpenAI Service and Microsoft Cloud for Manufacturing - cut troubleshooting time from 20 to two minutes.
• GE Aerospace’s AI-powered platform – built on Microsoft Azure and Azure OpenAI Service - simplifies historical maintenance records access by extracting key asset insights. Traditionally access to these records required days or even weeks of manual document reviews, slowing critical decision-making and compliance checks.

5) More Efficient Gate Assignment

AI-driven gate planning systems assess flight arrival patterns, aircraft specs, and real-time congestion to assign gates more efficiently - reducing taxi times, fuel use, and passenger delays.

Example:

• American Airlines’ AI-powered gating technology (built on Microsoft Azure) helps reduce downstream delays by dynamically assigning the most optimal gate for arriving aircraft. At DFW Airport this has reduced taxi times by over a minute per flight, eliminating up to 10 hours of taxi time daily and saving 870,000 gallons of jet fuel annually.

Read the full report: