“The Best Airlines Will Use AI to Build Trust, Not Just Revenue,” Says Shingai George, Aviation Expert

July 16, 2026

Shingai George, Aviation Consultant specializing in data analytics, AI, and sustainability, shares how modern data platforms, AI-driven decision-making, and shared data ecosystems are helping the aviation industry adapt to a new era of geopolitical volatility, regulatory pressure, and sustainability demands. He explains how airlines are moving beyond decades of cost-and-efficiency optimization toward resilience as a competitive advantage, and how AI is reshaping everything from flight planning and predictive maintenance to passenger experience and emissions management. He also explores why the future of airline success lies in shifting from short-term yield to long-term customer value, using AI to build loyalty and trust, not just revenue. Drawing on experience across customer service, flight operations, route development, flight safety, MRO, and sustainability, he highlights why aviation, one of the world’s most interconnected industries, must move from fragmented optimization toward network-wide intelligence. 

This episode will answer:  

• How can airlines build resilience into flight planning, fuel forecasting, and operational decisions when geopolitical shocks can reroute entire networks overnight? 
• What role does AI play in transforming the passenger journey from reactive service to predictive engagement, without crossing the line between personalization and perceived unfairness? 
• How can the industry responsibly share data across airlines, airports, and regulators, and why is federated data sharing key to the future of air traffic management, sustainability, and network resilience? 

Watch the interview: 

Read the podcast as an interview: 

(The interview was shortened and edited using ChatGPT) 

Mark Kohout: Hello and welcome to this Adastra podcast. My name is Mark Kohout, and I lead the North American Data and AI Governance practice for Adastra, a global data, AI, and cloud systems integrator. Joining me today to talk about all things aviation is Shingai George, coming to us from Valencia, Spain. 

Shingai is an aviation consultant specializing in data analytics, artificial intelligence, and sustainability. He began his career in the airline industry, working in customer service and flight operations, where he gained first-hand experience of the operational challenges that shape the sector. Since then, he has worked across route development, flight safety, maintenance, repair and overhaul (MRO), and sustainability, helping organizations leverage data-driven insights to improve efficiency, resilience, and environmental performance. 

Shingai, that is quite a CV. Welcome to the podcast. How are you? 

Shingai George: Thank you, Mark. Good afternoon to you. 

Geopolitics and Aviation Resilience 

Mark Kohout: Let’s start with a helicopter view. Geopolitical instability has reemerged as a major operational pressure in aviation. Jet fuel prices nearly doubled after the Strait of Hormuz disruptions, and airlines have been cutting or rerouting flights. How do events like the U.S.–Iran conflict influence the way aviation leaders think about resilience and long-term planning? 

Shingai George: The current geopolitical environment is reinforcing the idea that resilience can no longer be treated as a secondary operational function. It has become a core strategic priority. Historically, airlines optimized primarily for efficiency and cost, but recent disruptions have shown that highly efficient networks can also be highly vulnerable. When conflicts affect major air corridors or fuel markets, airlines suddenly face longer routes, increased fuel burn, scheduling instability, and higher operating costs. 

As a result, aviation leaders are investing in scenario-based planning, diversified routing strategies, stronger fuel hedging approaches, and more agile operating models. There is also greater emphasis on operational flexibility through fleet allocation, crew planning, and dynamic network management. Ultimately, resilience is becoming a competitive advantage. The airlines that adapt quickly will be better positioned operationally and financially. 

Mark Kohout: Given the blockage of key Middle Eastern corridors and the global rerouting we’re seeing, how important is a modern data and cloud foundation in helping airlines adapt flight planning, fuel forecasting, and real-time operational decision-making? 

Shingai George: It is absolutely essential. When airlines reroute around restricted airspace, the impact cascades across the entire network, affecting fuel requirements, crew duty times, aircraft rotations, maintenance schedules, and passenger connections simultaneously. 

Legacy systems were not designed for this level of real-time complexity. Modern cloud-based platforms allow airlines to ingest and process live operational data at scale, enabling faster, more coordinated decisions. For example, airlines can dynamically recalculate fuel uplift based on changing winds and rerouting scenarios while updating crew legality constraints and passenger accommodations. The real value is not just computing power. It is integrated situational awareness across the entire ecosystem. 

Mark Kohout: U.S. airlines are reporting increases in GNSS jamming, spoofing, and airspace detours linked to this and other conflicts. How should organizations evolve their governance, AI-assisted flight operations, and safety frameworks to responsibly navigate these risks? 

Shingai George: GNSS interference highlights the growing intersection between geopolitics, cybersecurity, and operational safety. Organizations need to evolve across three dimensions: governance, technology, and operational readiness. 

From a governance perspective, airlines and regulators need clearer frameworks for operating in degraded navigation environments, including stronger cross-border coordination and risk-sharing mechanisms. From a technology perspective, AI can detect anomalies by identifying inconsistencies between GNSS/GPS inputs, inertial reference systems, surveillance data, and aircraft trajectory behavior. However, AI should support, not replace, human judgment. Operationally, airlines need enhanced pilot training, alternative navigation procedures, and stronger collaboration between air navigation service providers and manufacturers. 

The key principle is maintaining trust in operational data while ensuring robust fallback mechanisms when that trust is compromised. 

Fuel Optimization and Sustainability 

Mark Kohout: Let’s turn to operations. As airlines work toward more sustainable operations, how can AI-driven fuel planning engines help determine the most efficient climb, cruise, and descent profiles? What data inputs make these “eco-trajectories” operationally feasible? 

Shingai George: AI-driven fuel optimization engines allow airlines to move beyond static flight planning toward continuously adaptive trajectory optimization. These systems combine meteorological data, aircraft performance models, historical route efficiency, airspace congestion information, and real-time operational constraints to identify the most fuel-efficient profiles. For example, AI can optimize step climbs, speed adjustments, and descent planning based on changing atmospheric conditions and traffic flow restrictions. 

What makes eco-trajectories operationally realistic is the integration of operational constraints such as air traffic control restrictions, airport sequencing, weather deviations, and schedule integrity. The result is lower fuel burn, reduced emissions, and improved efficiency without compromising safety or punctuality. 

Mark Kohout: As the industry looks beyond sustainable aviation fuel (SAF) toward synthetic fuels produced from green hydrogen and CO₂, how can AI help optimize production, certification, and deployment of these new fuels? 

Shingai George: Advanced analytics is becoming essential as aviation transitions from estimated sustainability reporting toward verifiable, near real-time emissions management. Traditionally, emissions reporting relied on post-flight estimates and aggregated datasets. AI enables far more granular tracking by integrating flight data, fuel burn calculations, SAF blending information, and trajectory optimization metrics in real time. 

This is increasingly important as airlines navigate growing regulatory requirements. With the second phase of CORSIA beginning in 2027, airlines will face greater expectations around emissions monitoring, reporting, and verification. Carriers operating in Europe must also comply with the EU ETS scheme and the ReFuelEU Aviation regulation, which mandates increasing SAF use. AI can automate data collection, strengthen auditability, verify SAF-related claims, and provide a transparent chain of evidence across all three frameworks. 

Looking ahead, emissions intelligence will be embedded directly into operational decision-making rather than treated as a separate reporting exercise. Airlines that combine operational, sustainability, and compliance data into a single decision-making framework will be best positioned to manage costs, meet regulations, and achieve their decarbonization objectives. 

Air Traffic Management 

Mark Kohout: Let’s talk about airspace and air traffic management. What challenges remain when integrating predictive AI models into legacy air traffic control systems? 

Shingai George: One of the biggest challenges is that many ATC systems were designed around highly procedural workflows, whereas predictive AI models are probabilistic by nature. This creates challenges around trust and operational integration. Controllers need to understand why a recommendation is being made before relying on it in safety-critical environments. 

There are also technical challenges: interoperability, fragmented infrastructure, and varying levels of digital maturity across air navigation service providers. Integration therefore needs to be gradual, with AI initially acting as decision support rather than a fully autonomous operational layer. 

Mark Kohout: Wearing the hat of a long-suffering business traveler, what opportunities exist for AI to synchronize departures, arrivals, surface movements, and airspace flow in a more integrated, system-wide manner? 

Shingai George: AI has the potential to shift the industry from reactive flow management to predictive network optimization. Today, decisions around departures, arrivals, airport capacity, and airspace are often made in separate operational domains. AI can create a shared operational picture by combining flight trajectories, weather forecasts, airspace constraints, airport capacity, and airline schedules to predict congestion before it occurs. 

Instead of managing delays after bottlenecks develop, AI could recommend adjusted departure times, alternative routings, or arrival sequencing hours in advance. This reduces airborne holding, minimizes unnecessary vectoring, improves traffic flow, lowers fuel consumption, and maximizes airspace capacity. 

Air traffic management is also integral to aviation’s sustainability journey. While much attention rightly focuses on SAF and future propulsion, more efficient airspace management can deliver immediate emissions reductions by enabling more optimal trajectories and less time taxiing or holding. 

Initiatives such as the SESAR program in Europe are laying the foundations through concepts like trajectory-based operations and System-Wide Information Management (SWIM). AI can accelerate these efforts by turning vast operational data into actionable insights, serving as a key enabler of a truly connected, optimized air traffic management system. 

Predictive Maintenance 

Mark Kohout: Nice catch on your earpiece there. You didn’t miss a beat! Let’s move to predictive maintenance. How is AI transforming day-to-day aircraft operations, and which data streams are proving most valuable? 

Shingai George: AI-driven predictive maintenance is fundamentally shifting aviation from reactive to anticipatory operations. Instead of fixing issues after failure or following rigid schedules, airlines can now predict faults before they occur. 

The most valuable data streams include engine health monitoring, sensor outputs across flight phases, and historical maintenance logs. Combined, these datasets allow machine learning models to detect subtle performance deviations early. Operationally, this reduces unscheduled maintenance and improves fleet availability. For technicians, the role is evolving toward interpreting predictive insights and making risk-based decisions. Maintenance becomes more strategic and less reactive. 

One often overlooked benefit is reducing Aircraft-on-Ground (AOG) events, which are extraordinarily expensive. AOG costs extend far beyond repair itself and include flight delays, passenger compensation, crew disruptions, and aircraft repositioning. By identifying potential failures early, AI helps airlines avoid these disruptions and protect both revenue and customer satisfaction. In many cases, the greatest value lies not in reducing maintenance costs but in preventing the commercial consequences of unexpected groundings. 

Personalized Passenger Journeys 

Mark Kohout: Turning to airports and passengers, personalization has been a long-promised goal for airlines. What role does AI play in personalizing the journey from booking to arrival? 

Shingai George: AI allows airlines to deliver a more context-aware experience by analyzing booking behavior, loyalty data, and real-time journey context. Airlines can offer tailored services such as upgrades, ancillary products, or proactive disruption support. For example, passengers can be rebooked automatically before a missed connection occurs or receive relevant offers during delays. This transforms the experience from reactive service to predictive, personalized engagement. 

Airport and Turnaround Operations 

Mark Kohout: Can you share some concrete examples of AI-powered tools enhancing airport operations or aircraft turnaround times? 

Shingai George: AI is increasingly used to improve predictability and coordination across airport operations. One example is predictive gate allocation, where AI analyzes inbound flight progress, weather conditions, airport congestion, and historical delays to optimize gate assignments and reduce conflicts. 

Another key application is baggage and ground handling. AI-powered systems can monitor baggage flows in real time, identify bottlenecks before they occur, and optimize routing throughout the airport. AI-driven turnaround platforms can also coordinate activities such as refueling, catering, cleaning, boarding, and baggage loading to ensure aircraft depart on schedule. 

The biggest benefit is improved turnaround predictability. By helping airports and ground handlers anticipate disruptions and allocate resources effectively, AI improves aircraft utilization, reduces delays, and strengthens overall network reliability. 

Revenue Management and Dynamic Pricing

Mark Kohout: Let’s turn to a more strategic dimension: revenue management and dynamic pricing. How can airlines leverage data and AI to improve pricing without alienating customers or eroding loyalty? 

Shingai George: AI is transforming revenue management by allowing airlines to move beyond traditional fare buckets toward more dynamic, demand-responsive pricing. By analyzing booking patterns, market demand, competitor activity, seasonality, and customer behavior, airlines can make more informed pricing decisions in real time. 

However, customer trust must remain central. There is a fine line between personalization and perceived unfairness. While AI can help airlines better understand customer preferences and willingness to pay, pricing strategies must remain transparent, explainable, and consistent. Customers are generally comfortable paying different prices based on flexibility, timing, or product attributes, but they are less comfortable if they feel prices are arbitrary. 

Beyond ticket pricing, AI creates opportunities to optimize ancillary revenues by offering more relevant products and services. For example, airlines can personalize seat selection, baggage options, lounge access, or upgrades based on traveler preferences and journey context. This allows airlines to increase revenue while delivering greater value to customers, rather than simply charging higher fares. 

Ultimately, the focus should shift from maximizing short-term yield to maximizing long-term customer value. The most successful airlines will use AI not only to optimize revenue, but also to strengthen loyalty, improve customer satisfaction, and build trust through a more personalized and relevant travel experience. 

Shared Data Ecosystems and Industry Collaboration

Mark Kohout: That makes me think about the foundations for all of this: the data ecosystem itself. Aviation relies on collaboration between airlines, airports, and regulators. What opportunities and challenges arise when creating shared data ecosystems, and can the industry responsibly unlock more value from cross-organizational data given its strict regulatory context? 

Shingai George: Aviation is one of the most interconnected industries in the world, yet much of its operational data still exists in silos. Shared data ecosystems create a major opportunity to improve coordination, efficiency, and system-wide decision-making across airlines, airports, air navigation service providers, and aircraft manufacturers. 

When stakeholders share operational data more effectively, the industry can improve demand forecasting, reduce congestion, optimize turnaround coordination, and enhance disruption management. Shared situational awareness becomes especially valuable during irregular operations, such as severe weather events or geopolitical disruptions. 

However, the challenge is not just technological. It is also organizational and regulatory. Different stakeholders operate with different systems, governance models, and commercial sensitivities around data ownership and access. The industry therefore needs trusted governance frameworks that define how data is shared, protected, standardized, and used responsibly. 

I believe the future lies in federated data ecosystems, where organizations can securely share operational insights without exposing all the underlying proprietary data. Ultimately, the goal is to move from fragmented optimization toward network-wide optimization across the aviation ecosystem. 

Mark Kohout: Sounds like industry-wide standards and federated ecosystems will let everybody play their role while working to the same standard. I’m afraid we’re out of time, Shingai, but I’d like to thank you for battling our technical synchronization issues and working across time zones to share your expertise and insights about how data and AI are reshaping the future of the aviation industry. I’m taking away a great deal about the interdependencies and the focus shift that AI-driven optimization enables. Thank you very much for joining me today. It was a pleasure to have you. 

Shingai George: It’s a pleasure, Mark. Thank you for your time. 

Mark Kohout: And to our audience, if you’ve enjoyed today’s discussion, please be sure to like and subscribe to this podcast series for more insights on data and AI business transformation. Until next time, thanks for listening. So long for now from Adastra.