• Airlines are moving beyond AI experimentation towards targeted applications such as disruption management, resource allocation, safety and compliance, crew certification and tracking, with the aim of improving efficiency and decision-making without replacing operational expertise.
• Successful AI adoption depends on strong foundations, including reliable data, connected systems and a clear understanding of the operational problem. Without these, AI risks adding complexity rather than delivering meaningful value.
• The next phase of adoption will be driven by measurable outcomes rather than technology for its own sake, with AI supporting faster decisions, greater reliability and improved customer experience while retaining human judgement where it matters most.

While adoption of artificial intelligence (AI) remains cautious due to the aerospace sector’s zero-tolerance approach to operational risk, airlines and aviation businesses are increasingly looking beyond experimentation towards targeted applications that can improve efficiency, resilience and customer outcomes.

From disruption management and resource allocation to improving decision-making across complex networks, AI is increasingly being viewed as a capability that can strengthen existing processes rather than replace operational expertise.

“AI is still at an evolutionary stage when it comes to acceptance, because this industry is a zero tolerance industry. You cannot get things wrong,” Balaji Baysham, Chief Strategy Officer, Airlines, Travel & Transportation at TCS, stated.

“Most players are experimenting, looking at where they can test, learn and then start to apply AI in a much more significant manner.”

The focus is now shifting towards practical aviation use cases where AI can deliver measurable improvements. “Where we see the next frontier of AI is in areas like disruption management, safety and compliance, crew certifications and crew tracking,” Baysham explained. “We do not have all of this today, but in the next couple of years, you will start to see results on these use cases.”

Building the foundations for AI-led operations

The implementation challenge for aviation organisations is not simply adopting artificial intelligence, but ensuring the operational foundations are strong enough to support it. Effective AI deployment depends on access to reliable data, connected systems and a clear understanding of where technology can create value.

Aircraft availability, operational constraints, shipment priorities and network conditions all need to be considered together if AI is to support meaningful decisions. Without this level of integration, technology risks becoming another layer of complexity rather than a driver of improvement.

“There is still an element of FOMO at the moment because AI is so pervasive in terms of the media,” Baysham said. “But we always tell customers that AI cannot work by itself; you need to get all your ducks lined up in terms of the data.”

While AI capabilities are developing rapidly, organisations must avoid investing in technology without first defining the operational problem. For cargo businesses, this means identifying where improved intelligence can support network performance, customer experience or operational efficiency.

“Without the right foundations, you are really not building on solid ground,” Baysham noted. “The context of the airline, the airport, the passenger, the network and the crew all have to be joined up, and that is when AI can add that little bit extra to take you over the top in terms of customer experience.”

Outcomes drive the next phase of AI adoption

As artificial intelligence continues to evolve, the aviation sector faces a strategic choice: follow technology developments as they emerge or focus on specific outcomes where innovation can deliver measurable impact. For cargo operators, the second approach is becoming increasingly important as businesses balance investment decisions with operational priorities.

Technology-led adoption can create unnecessary complexity if organisations attempt to solve every challenge simultaneously. A more effective model is to break larger problems into smaller components, apply AI where it provides clear benefits and expand implementation as value is proven.

“We always focus on the outcomes because at the end of the day, an airline is transporting people. That is not going to change,” Baysham stated. “The traveller is going to be different, so we always look at what you are trying to solve.”

The long-term value of AI will come from improving reliability, supporting faster decisions and helping organisations adapt to changing market conditions. It will not remove the need for human judgement, but instead help teams focus on the areas where expertise has the greatest impact.

“Technology should fit the problem you are trying to address,” Baysham concluded. “You can never solve every problem that exists today with today’s technology, even as fast as AI is developing, but when new technology comes up, you can relook at your problem statements and decide whether it can help.”

The post AI strategy shifts from experimentation to operational advantage appeared first on Air Cargo Week.