When John McCarthy coined the term ‘artificial intelligence’ (AI) in 1956, few could have imagined how transformative it would become. Over the past decade, AI’s progress has accelerated dramatically, fueled by the explosion of data, advanced algorithms, and powerful computing. From healthcare to banking, AI is redefining industries. Aviation, too, stands to benefit enormously, yet the adoption of AI for turnaround management and operational optimisation remains surprisingly limited.
Introduction: The paradox of AI in aviation
Airlines and airports operate in one of the most complex environments in the world, where minutes matter and efficiency directly impact profitability and passenger satisfaction. AI can revolutionise this space by:
- Enabling and enhancing real-time visibility across operations
- Predicting and preventing delays
- Optimising stand management
- Improving asset and resource utilisation
- Supporting predictive maintenance
- Reducing downtime and operational costs
- Improving collaboration among multiple stakeholders
These capabilities directly address aviation’s biggest challenges: rising fuel costs, growing passenger expectations, regulatory pressures, infrastructure constraints, and the need for faster, safer, and more cost-efficient operations.
The numbers speak for themselves. According to Straits Research (2024), the global AI in aviation market is projected to reach US$32.5 billion by 2033, growing at a staggering CAGR of 46.97%. Yet, despite the clear opportunity, many airlines and airports remain hesitant.
So, what’s holding them back?
Barriers to AI adoption in aviation
1. High implementation costs
AI requires significant upfront investment in infrastructure, software, and skilled personnel. While studies suggest that AI can lower maintenance costs and enhance fuel efficiency, the initial outlay remains daunting—particularly for regional airports and smaller carriers. For example, one European low-cost airline reported saving about 7 kg of fuel per flight through AI-powered fuel planning, with additional reductions achieved via optimised climb speeds and taxi operations.
Possible solutions:
- Start with pilot projects underpinned with base models and measurable KPIs
- Flexibility in options between cloud, hybrid and on-premises systems.
- Explore flexible models like “AI-as-a-Service” or pay-per-use.
- See AI adoption as an enabler for real return on investment and cost savings, not as pure cost.
2. Integration with legacy systems
Aviation still relies on decades-old legacy platforms that were never designed for AI. Integrating modern tools often requires costly upgrades, customisation, and phased rollouts. In addition, the aviation industry revolves around siloed systems that cannot communicate with each other.
Possible solutions:
- Deploy APIs to connect AI with existing systems and break down silos.
- Consider AI as tool to leverage and enhance existing systems, and not just “another additional system”
- Use “layered” AI applications that gradually integrate.
- Leverage digital twins to replicate operations in real time, allowing AI to work alongside legacy systems without full replacement.
3. Accountability and transparency
The “black box” nature of AI creates trust issues. In an industry where safety is non-negotiable, regulators and operators demand explainable, auditable AI.
Possible solutions:
- Adopt explainable AI models that provide traceable reasoning.
- Establish clear accountability protocols and audit trails.
- Maintain human oversight for safety-critical decisions.
- Treat AI as a highly efficient colleague that drastically aids decision making (but does not necessarily make the decision for you)
4. Data infrastructure and security
AI thrives on data—passenger records, aircraft telemetry, weather updates, and more. But managing, securing, and harmonising such vast datasets is a formidable task. Regulations like GDPR add further complexity.
Possible solutions:
- Use unified dashboards to consolidate fragmented data sources.
- Implement strong encryption, intrusion detection, and compliance frameworks (GDPR, ISO 27001).
- Build secure data-sharing ecosystems between airports, airlines, and ground handlers.
5. Workforce readiness and trust
AI adoption is as cultural as it is technological. The shortage of aviation-focused AI talent slows progress, while frontline staff and managers may hesitate to trust AI recommendations.
Possible solutions:
- Run change management programs with workshops, training, and continuous on-site support.
- Clearly communicate the benefits of AI to operational teams and actively demonstrate the value
- Position AI as an enabler, not a replacement, of human expertise.
- Use consultancy companies to create reference and base models to review performance before and during AI enhanced operations
- Actively seek user feedback and address concerns
6. Risks of bias and errors
AI models can inherit bias from incomplete or poor-quality data. In aviation, even small errors can have outsized consequences, creating resistance to adoption.
Possible solutions:
- Keep humans in the loop for mission-critical decisions.
- Continuously audit and retrain AI models.
- Ensure robust data cleaning and validation processes.
7. Regulatory complexity
Aviation is one of the most heavily regulated industries. Introducing AI into predictive maintenance, flight planning, or turnaround management requires rigorous validation and approval from authorities like the FAA and EASA. This process is long and resource intensive.
Possible solutions:
- Map regulatory requirements early in AI development.
- Build compliance into AI models from the start.
- Collaborate with regulators to define safe, transparent adoption pathways.
Why the time to act is now
Despite these barriers, delaying AI adoption comes at a cost. Early adopters such as Flydubai, Ethiopian Airlines, and Fraport are already reaping benefits including:
- Improved on-time performance through predictive scheduling
- Reduced unplanned downtime via intelligent maintenance forecasting
- Enhanced safety monitoring and regulatory compliance
- Streamlined resource allocation and turnaround optimisation
- Superior passenger experiences through smoother operations
- Increased non-aeronautical revenue through AI-driven retail optimisation
- Better drive sustainability initiatives
Conclusion: The strategic imperative
The aviation industry stands at a crossroads. AI is no longer a futuristic concept—it’s a proven operational enhancer available today. Organisations that address the cost, integration, and trust barriers systematically will unlock significant efficiencies, cost savings, and safety improvements.
The competitive landscape is shifting rapidly. Airlines, airports, and ground handlers that embrace AI now will establish new benchmarks for safety, efficiency, and passenger satisfaction. Those that delay risk falling behind in an increasingly AI-driven industry.
AI adoption is not merely a technology investment—it is a strategic imperative for the future of aviation.
The real question is not “Should aviation adopt AI?” but rather:
“How quickly can it adapt to remain competitive in an AI-driven future?”
We at ZestIoT are leveraging AI to drastically enhance Aircraft Turnaround and Resource Management as well as improving the passenger journey. Come and speak to us to learn more.
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