Airport AI: What it is, why it matters, and how it is shaping the intelligent airport
Artificial Intelligence (AI) is no longer just a buzzword—especially not in the aviation industry. From predicting passenger flows to optimizing security staffing, AI is already transforming how airports operate. But what does “Airport AI” really mean, and how does it go beyond the likes of ChatGPT or generative AI?
In this post, we unpack what AI in airports truly entails, why it’s critical to future-proof operations, and how Copenhagen Optimization’s Better Airport software is enabling airports worldwide to become truly intelligent.
What is Airport AI?
“Airport AI” is an umbrella term that encompasses the use of artificial intelligence technologies in airport environments to improve efficiency, planning, decision-making, and ultimately the passenger experience.
AI in airports spans several types of technologies:
- Machine learning for demand forecasting and predictive modeling
- Optimization algorithms for resource allocation
- Decision support systems for operational planning
- Natural language processing in passenger services
- Computer vision for security and baggage tracking
Unlike the often-hyped generative AI (like ChatGPT), the kind of AI that truly impacts airport operations today is grounded in robust, practical applications that solve specific, high-value problems.
Your might also want to read: 4 reasons your airport needs to embrace new technologies in 2025
Why is AI important for airports?
To put it simply: you can no longer build or run a modern airport without AI. The complexity of airport operations—interfacing thousands of flights, passengers, and resources daily—demands smarter tools.
Anders Dohn, Co-CEO of Copenhagen Optimization, puts it well:
“AI is many things. It’s not just machine learning or ChatGPT. It’s the umbrella under which airports can evolve from manual processes to intelligent systems.”
The intelligent airport isn’t just a concept—it’s a necessity. As air traffic rebounds and traveler expectations rise, AI provides a competitive edge in both cost-efficiency and service quality.
Real-world examples of Airport AI: Driving tangible results
Copenhagen Optimization’s Better Airport suite is a prime example of Airport AI in action. Here’s how it works across airport operations covering everything from check-in to baggage reclaim:
Machine learning for forecasting:
- Passenger forecasting: Anticipating passenger numbers and passenger turn up with precision
- Baggage volume predictions: Improving baggage handling logistics
- Linking flights and passengers: Smarter connections, smoother flows
- Arrival/departure time estimates: Better planning, fewer delays
At Manchester and Stansted airports, for instance, machine learning has improved the forecasted time stamps EXIT (estimated taxi in time) and EXOT (estimated taxi out time) by up to 50% compared to traditional estimates.
Planning and decision-making:
Check-in counter allocation:
Maximize passenger throughput and minimize wait times by dynamically aligning available counter resources with real-time demand and flight schedules.
Security demand optimization:
Ensure seamless passenger flow, reduce bottlenecks, and minimize the number of lanes by precisely forecasting peak periods and staffing demand accordingly — boosting efficiency without overstaffing.
Gate and baggage allocation:
Enhance on-time performance and turnaround efficiency by continuously adapting gate and baggage plans to real-time operational conditions.
Shift planning:
Reduce idle time and last-minute reassignments by aligning staff deployment with highly accurate, AI-driven forecasts of actual arrival patterns. It is about matching capacity to actual demand.
These aren’t just theoretical models. They are active systems used daily by planners who are already seeing measurable improvements in performance and efficiency.
Missing the flight: Why airports can't afford to stay in descriptive mode
Airports generate an enormous amount of data—from flight schedules and passenger flows to baggage handling and security throughput. But data alone isn’t value. It’s what you do with it that defines success.
When airports operate with outdated tools or static planning models, they often get stuck at the lower end of what Gartner defines as the Analytics Maturity Model. Here’s what that looks like—and why moving up the ladder is critical:
1. Descriptive Analytics: What happened?
This is where many airports still reside. Reports tell you yesterday’s wait times or how many passengers missed their connections. It’s reactive and retrospective—valuable, but far from proactive.
2. Diagnostic Analytics: Why did it happen?
Some airports advance to spotting patterns—pinpointing root causes of staffing issues or delays. While it helps explain problems, it doesn’t help prevent them.
3. Predictive Analytics: What will happen?
Here’s where Airport AI begins to make a serious difference. With machine learning models trained on historical and real-time data, airports can forecast passenger surges, baggage demand, or gate usage with remarkable accuracy.
Better Airport, for example, improves time predictions far beyond traditional methods. Instead of reacting to delays, planners can now anticipate and prevent them
4. Prescriptive Analytics: How can we change what will happen?
This is the Intelligent Airport. AI doesn’t just show what’s coming—it helps determine the best action. Whether it’s reallocating security staff in real time or optimizing gate assignment during disruptions, the system supports smarter decisions at scale.
The value of climbing the maturity ladder
This journey is not just technological, but transformational. At the base, we have Business Intelligence: basic dashboards and KPIs. But true advantage lies higher up—where artificial intelligence, machine learning, and optimization intersect to drive real-time, data-backed decisions.
Staying in the descriptive stage is like reading yesterday’s weather report and expecting it to help with tomorrow’s flight operations.
Airports that don’t invest in AI miss out on:
- Proactive planning that adapts to real-time events
- Forecasting passenger volumes with machine learning precision
- Reducing delays through optimized resource allocation
- Making better decisions—not just faster, but smarter
- A scalable path to becoming an Intelligent Airport
Your might also want to read: How aviation data analytics improves your airport operations
The Better Airport advantage
Copenhagen Optimization is proud to be an enabler of the Intelligent Airport. Our software, Better Airport, incorporates AI in ways that are practical, measurable, and ready for deployment.
Why Better Airport is Airport AI:
- It combines machine learning, optimization, and heuristics in one coherent platform
- It delivers predictive insights and real-time decision support
- It moves you away from guesswork and towards data-driven, optimal planning
Manchester Airports Group (MAG) is already on this journey. As their CIO, Nick Woods, put it: “Our mission is to become the world’s most intelligent airports.”
Copenhagen Optimization plays a key role in helping them realize that mission. Through Better Airport, we provide the forecasting accuracy, planning intelligence, and decision-support tools needed to move from reactive operations to data-driven excellence. It’s not just about managing complexity—it’s about mastering it.
“The potential of AI is everywhere—certainly also in airports. And the technology is already mature enough to create tremendous value,” says Anders Dohn, Co-CEO & founder, Copenhagen Optimization.
