How AI can unlock connected airport operations

Airports are under increasing pressure to unlock value from AI investments while delivering operational excellence across a complex ecosystem. Yet for most airport leaders, the immediate challenge isn't AI adoption. It's managing the day-to-day realities of running a highly interconnected operation, where passenger flows, baggage handling, airside activities, workforce availability, and disruption management must all work together seamlessly. Delays or bottlenecks in one area can quickly create knock-on effects elsewhere, impacting efficiency, passenger experience, and commercial performance. In this environment, improving performance requires more than optimising individual functions.

As AI becomes embedded across airport operations, from workforce planning and intelligent infrastructure to passenger flows and operational control, many airport leaders are discovering that deploying AI and generating value from AI are two very different things.

The issue is rarely a shortage of use cases. Airports are full of promising opportunities to improve aircraft turnaround, passenger flow, asset management, baggage handling, commercial performance, and workforce planning. The challenge lies in the environment in which those solutions operate.

Airports have spent years solving individual operational problems with individual pieces of technology. The result is often hundreds of systems, each performing a useful function, but rarely working as part of a joined-up whole. The true potential from AI will come when airports create the conditions for AI to improve the performance of the wider airport community.

This requires moving beyond pilots, dashboards, and fragmented tools towards shared business platforms, redesigning the airport operating model around connected data, coordinated decisions, and empowered people. All the while bringing the workforce along for the journey through the transformation, so that new tools are trusted, understood, and embedded in everyday operational decisions.

You can’t improve what you don’t know

One of the biggest barriers to making AI to work across the wider airport community is that airports rarely have access to the complete picture. Airlines often know passenger demand weeks before an airport does. Ground handlers, air navigation service providers, and other stakeholders each hold their own operational data. Valuable information exists across the ecosystem but isn't always shared in a way that supports coordinated decision-making.

This makes it difficult for AI to generate enterprise-level value. A model can only be as effective as the operational context it understands.

Even within airports, commercial, asset management, terminal operations, airside operations, and security teams often work in silos – with different systems, planning assumptions, and performance measures. This makes it difficult to gain efficiency when decisions aren’t coordinated.

Access to richer operational data from across the airport community allows a different approach. Instead of responding after issues crop up, airports can combine data from across stakeholders with AI technologies to help identify where pressure is likely to appear and intervene earlier. Passenger volumes, staffing requirements, asset utilisation, aircraft delays, queue formation, and operational disruptions become easier to anticipate creating opportunities to improve decisions before performance is affected.

Unify operational and passenger data to enable cross-system decision-making

The airports making the greatest progress across their operations are focusing on creating environments where information can be shared securely across stakeholders and translated into a common view of airport performance. The objective is not simply to connect more information, but to improve the quality and speed of operational decisions.

Our work with Heathrow Airport to unlock lasting improvements in punctuality involved collaborating with the wider airport community, including airlines and ground handlers, and included a renewed focus on sharing information to support more effective delivery planning.

The goal with better cross-system decision-making is to establish a shared operational picture with a fuller understanding of how actions in one area affect outcomes elsewhere in the airport. A change in security throughput, for example, has implications for immigration, retail, gate readiness, baggage handling, and passenger experience. A delayed departure may affect stand allocation, connections, cleaning, turnaround resources, and downstream disruption at another airport.

To turn AI into everyday operational value, airport leaders should focus on three priorities:

• Break down data silos between operational, asset, commercial, and passenger systems
• Enable real-time data sharing across airport stakeholders to reposition data as a shared strategic asset and not proprietary information that must always be guarded
• Build data environments that support predictive and scenario-based decision-making rather than historical reporting alone.

A shared business platform acts as the central hub that connects an airport's data, systems, processes and decision-making. It allows airports to bring everything together and work more effectively, without having to replace all their existing technology at once.

Redesign operating models to act on AI-driven insights

Leading airports are bringing their data, systems and operational processes together through integrated platforms that help coordinate day-to-day operations and support better decision-making. The point is not to replace every legacy system overnight. Airports can’t just remove the technology they depend on to run safe and reliable operations. Instead, they should create digital environments that can gradually integrate existing tools, connect new capabilities, and orchestrate processes across functions.

A more efficient security operation may appear to be a success. However, if immigration processing, baggage handling, or passenger flows can't keep pace, the airport has simply relocated the bottleneck. Similarly, optimising one asset, team, or process may create unintended consequences elsewhere if decision-making is not aligned to wider strategic priorities.

Orchestration across the airport community was key in our work with Amsterdam Airport Schiphol to deliver its largest technical and strategic transformation in the airport’s history. This included managing technological dependencies, establishing new cross-functional governance, and creating a structured portfolio management process to prioritise strategic initiatives.

Before airports can effectively connect their systems, data and operations, they need a clear plan for how the business should operate. Airports must define a common vision for future operations, establish a shared data foundation, and create a single source of truth across stakeholders. From here, airports can progress through a clear AI maturity journey: improve how they react to operational events, predict issues before they occur, adapt operations dynamically, and automate selected decisions where it is safe and appropriate to do so.

This phased approach matters. Many AI proof of concepts fail to scale because they’re designed around a narrow use case rather than a broader operational vision. Each pilot should strengthen the foundation for future capabilities, not create another standalone tool.

To move AI from pilots to joined-up operation, airport leaders should:

• Shift from reactive decision-making to predictive, insight-led operations
• Coordinate decisions across teams rather than within functional silos
• Use AI to anticipate bottlenecks in passenger flow, aircraft turnaround, asset performance, and workforce deployment
• Equip frontline employees with the tools, skills, and authority to act on AI-driven recommendations
• Embed AI into everyday workflows rather than treating it as a standalone technology initiative.

Bring your workforce into transformation initiatives

The next generation of intelligent airports will be defined by how effectively AI is adopted and embedded into is daily workforce practices.

Airports rely on experienced operational teams who understand how the airport really works. In many cases, those teams are facing growing pressure from labour shortages, changing employee expectations, and an ageing workforce. AI can help by improving planning, reducing avoidable disruption, and supporting more proactive decision-making, but only if employees trust the tools and understand how their roles will evolve.

Resistance is understandable when AI is perceived as roadblock – increasing monitoring, loss of autonomy, or a step towards replacing people. Successful adoption therefore depends on clear communication, early engagement, and practical input from frontline teams as new tools are designed and deployed.

The aim should be to redesign work around better information and more effective decision-making, reducing avoidable firefighting while giving experienced employees greater capacity to support passengers and manage exceptions. Human judgement remains essential, especially in safety-critical environments where passenger experience depends on empathy, reassurance, and personal interaction.

To foster workforce confidence as AI transformations take flight, airport leaders should:

• Involve operational teams early in the design of AI-enabled processes
• Be clear about which decisions AI will support and which will remain human-led
• Provide training to help employees understand, challenge, and act on AI recommendations
• Use AI to reduce friction in daily work
• Connect workforce change to better passenger, employee, and operational outcomes.

Build trust across the airport community to scale safely and sustainably

As data sharing increases and AI becomes embedded in operational decision-making, governance and accountability cannot be diluted. Greater connectivity creates opportunities for better coordination, but it also introduces dependencies and risks, particularly where AI informs decisions across multiple stakeholders.

Airport leaders need clarity around which decisions remain under human control, where AI is being used to inform operational choices, and how recommendations can be explained and challenged when necessary.

In practice, this means designing governance frameworks that combine automated insight with human oversight. Frontline teams and operational leaders remain responsible for critical decisions, while AI helps identify risks, recommend actions and improve the speed and consistency of decision-making. Trust depends on people understanding how recommendations are generated and how accountability is retained.

This is particularly pertinent when airport, airline, and air navigation systems are frequent targets for cyber threats. Better connected data environments must therefore be designed with security, resilience, and compliance from the outset.

To scale AI data connectivity responsibly, airport leaders should:

• Establish clear frameworks for oversight, explainability, and accountability
• Build safety, compliance, cyber security, and resilience into deployment from the outset
• Align AI deployment with applicable safety, compliance, cyber security, and responsible AI requirements
• Create trust through transparency, employee engagement, and clear communication about workforce impacts
• Preserve human judgement and interaction where essential to passenger safety and experience.

A new phase for airport operations

The benefits of connected operations extend beyond operational efficiency. More predictable operations improve resource utilisation, better visibility supports workforce planning, smoother passenger journeys improve customer experience, and happier, better-supported employees can spend more time helping passengers and less time reacting to avoidable disruption.

Commercial performance also benefits. When journeys are less stressful and more predictable, passengers are more likely to have the time and confidence to use airport facilities.

Airport groups also have an opportunity to scale innovation across their portfolios. New capabilities can be tested at smaller, less complex sites before implementing the transformation at larger hubs. Successful solutions can also become part of a broader innovation proposition.

Scaling AI across operations is a phased journey. Airports must build a shared understanding of data, improve visibility of performance, introduce predictive capabilities, adapt operations dynamically, and gradually increase automation where appropriate. This progression ensures technology, people, and processes evolve together.

AI will not transform airports simply because more use cases are deployed. It will create true operational value when airports use it to redesign how decisions are made, how stakeholders collaborate, and how people work.