AI and cloud innovation create the airports of the future

AI can redefine the way air travel and airports work. Agentic AI—capable of autonomous decision-making and action—has increased that potential. The payoff could be significant, but so is the implementation challenge.

Airports are among the most complex environments in the world. These mini cities operate around the clock and coordinate thousands of passengers, staff, and systems.

For airport operators, the challenge is how to harness new technologies like AI and now agentic AI to improve passenger experiences and grow revenues. The AI in aviation market is forecast to exceed USD $4 billion by 2032.

This is a highly regulated and complex sector, creating an implementation challenge. The AI Adoption Alliance is a collaborative initiative designed to accelerate responsible AI deployment. The alliance, between Amazon Web Services (AWS), the Cities Today Institute, Zensors AI, and NVIDIA, helps airport operators navigate data governance, legacy integration, and use case evaluation to support AI adoption.

AI addresses airports’ big data challenge

It’s important to work backwards from the problem you want to solve. That usually means starting with the passenger experience or operational improvement. Identify the business value that could be unlocked, then experiment and test with new or proven technologies to find the best solution for your airport. When you’ve established a positive impact, implement that technology at scale.

Airports’ interdependent systems add up to a big data challenge that AI can address, as Nick Woods, managing director of CAVU, founded by Manchester Airports Group (MAG), explains in our AWS paper on the future of intelligent air travel. He says, “AI agents give us the ability to break down those processes into the smallest composable elements and then get them to work together in a streamlined way.”

He describes three core journeys: the passenger, the aircraft, and the luggage journey. Within each, AI can optimize check-in, security, boarding, runway, and control tower operations. By focusing on these journeys, airports can target high-impact areas for AI-driven improvement.

AI transforms passenger experience

The top three AI use cases for airports worldwide are cybersecurity, customer service, and passenger processing. Agentic AI is advancing these applications further, enhancing efficiency, delivering proactive support, and creating personalized experiences that radically improve air travel.

At Aena, the world’s largest airport operator, the “Oli” assistant helps passengers with connections, document verification, and entertainment. Accessible through familiar platforms such as WhatsApp, Facebook, or WeChat, Oli’s real-time support is not only easy to use, it extends seamlessly to airlines and partner airports. Vinci Airports, which manages more than 70 airports in 14 countries, has tested a conversational AI assistant at Lyon Airport capable of understanding natural speech and responding contextually to complex passenger queries. Meanwhile, Air Canada uses Amazon Rekognition for biometric identity verification with 99.9% accuracy, speeding up the boarding process. At Rome’s Fiumicino Airport, Aeroporti di Roma created a smart helper called Virtual Assistant that answers travelers’ questions about flights, baggage, and airport services through WhatsApp and their website—like having a friendly airport guide in your pocket. This AI assistant, powered by Amazon Bedrock and developed by the AWS Generative AI Innovation Center and Storm Reply, talks to passengers in their own language and helps them find their way around one of Europe’s busiest airports.

Operational and revenue benefits

AI transforms how airports operate. One of their biggest challenges is that airport capacity is in constant flux. Accurately forecasting the day-to-day realities of air travel with predictive analytics can have an immediate impact on the bottom line.

At Riyadh Airport, an AI-enabled baggage-handling system developed with AWS cut unexpected failures by up to 50% and reduced repair times by 60%. Brussels Airport uses AI-powered demand forecasting to optimize check-in, border control, and baggage handling. At Ljublijana Airport in Slovenia, the operator Fraport uses ApronAI from Assia to reduce aircraft turnaround delays, assigning staff based on actual events rather than pre-planned schedules.

At San Sebastián Airport, a combination of AI, drones, and a private 5G network detect foreign object debris (FOD) on the runway, a safety-critical issue that is time-consuming to rectify. At Gatwick, Veovo is building an Integrated Airport Control (IAC) System that can predict challenges in real time. The system provides live situational awareness using aggregated flight, passenger, and transport data. It sends recommendations to security, immigration, airlines, ground staff, air traffic control, and management to help them make coordinated, better-informed decisions.

The agentic AI journey to autonomous airports

AI in aviation is evolving from AI-supported to AI-managed experiences and operations. This next phase, powered by agentic AI, will drive autonomous coordination across complex airport systems. Agentic AI is potentially a breakthrough technology to support airport operations. Analytical AI is good at forecasting, but it’s not so powerful in decision support in a highly complex environment where you need to ask many different stakeholders what their status is and determine what the impact could be. This is where AI agents come into play.

For example, in the case of a delayed flight, the AI agent detects an issue, reallocates the stand, notifies passengers, dispatches ground handlers, and can even direct autonomous wheelchairs to meet the arrival. These capabilities are already being tested within an industry embracing a culture of experimentation and collaboration. As systems will eventually start coordinating themselves, this will change the role of airport staff. Instead of reacting to situations, people will focus on forward planning and refining the underlying rules and policies that drive AI automation, using their real-world experience to continuously improve the system.

Manchester Airports Group developed an AI-powered digital colleague to automate workforce absence management across thousands of employees in around-the-clock airport operations, using Amazon Bedrock foundation models (FMs) and Model Context Protocol (MCP) to process text and speech interactions with over 90% accuracy while maintaining compliance with employment regulations through built-in guardrails. The serverless solution automatically validates absence requests against company policies and updates rostering systems in real time, reducing operational costs and freeing HR staff to focus on complex employee relations issues rather than routine administrative tasks. This implementation demonstrates how agentic AI architectures can transform labor-intensive processes in regulated, safety-critical environments while maintaining the consistency and reliability required for airport operations.

The aviation industry is on a path towards creating resilient, efficient, and environmentally conscious airports through measured integration of AI technologies. It’s an evolution that will reimagine air travel—unlocking the efficiencies that operators need and the travel experience that passengers expect.

To learn more, read How AI and autonomous systems shape airports of the future. To learn about the AI-powered digital colleague, watch this AWS re:Invent 2025 session.