Beyond the hype: aviation's urgent need for strategic AI implementation.
aviation,
travel,
tourism,
Agentic AI,
AI,
Generative AI,
CX,
technology,
Published on Mon Nov 10 2025
Updated on Mon Nov 10 2025
2 minute read
Humans have continuously evolved by embracing monumental shifts, from harnessing fire and the steam power that drove the first industrial revolution right through to the electricity and computers that define our modern world. Today, we stand at the brink of the agentic AI era, and just like those early inventors, we are all actively exploring its potential uses - both in aviation and other industries. Science fiction is no longer a myth but a reality.
The key conversations at the World Aviation Festival (WAF) made one thing clear: AI’s potential is real, but its implementation is currently suffering from a severe case of overconfidence.
The AI confidence gap: are we building castles on fragmented data?
The data speaks volumes. As stated by Rafa Mercado, Vice President and Consumer & Travel Consult Partner for Kyndryl, a staggering 86% of leaders believe their AI is ‘best-in-class’, but only 42% report seeing a positive ROI. This gap is the industry's most urgent technical debt.
Everyone I spoke to and listened to at the World Aviation Festival event feels nervous but hopeful about the technology. We must embrace the unknown to grow, even though fear is a normal part of change. Our collective anxieties make us question many aspects, such as:
- How will we change our perspective on data analysis to discover new patterns?
- Will AI truly help us negotiate better contracts and predict maintenance needs?
- Can engaging with next-gen chatbots truly transform customer support, or just complicate it?
- How will we ensure we aren't just selecting a "flashy use case" that ultimately delivers zero business value?
The airline industry has to adapt to the cold, hard fact that the path to ROI requires more than just buying a new system. As Mecardo explains, when proofs of concept (POCs) and pilots are scaled up into production, organisations often confront the challenge of having a lot of technical debt, legacy systems, and fragmented silos of data. Because of these issues, they may not have the necessary computer resources, security posture, or resiliency to take things into production. The airline industry must meet these demands aggressively, tackling technical debt and fragmented data silos - the shaky sandcastle foundation many aviation companies are trying to build upon.
The human factor: why technology shouldn't just replace a job title.
AI is a tool for people. Success hinges on the cultural aspect of companies and industries: enabling and empowering the frontline employees and users who use the system.
Technology should enhance productivity and decision-making, not immediately replace headcount. While AI is excellent for autonomous, "business as usual" tasks, complex disruption demands human intervention and empathetic communication.
Entering the next era: agentic AI.
Just a few short years after mastering generative AI, we are already moving towards agentic AI. Think about how a Large Language Model (LLM) combines multiple building blocks of language, similar to how various words create sentences that can be rearranged to change the context. Agentic AI is that, but for control towers. This technology promises to create "autonomous control towers" capable of generating personalised recovery plans for passengers during disruption in milliseconds.
The industry needs to continuously update its strategies and technology to handle the millions of data points needed for hyper-personalisation in dynamic pricing and offer management. They can achieve this only by embracing the speed of change and challenging themselves to get the implementation right from all angles.
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Created at Thu Sep 17 2026
6 min read
Leading Voices: most brands fail at agentic AI. Our solutions experts share how yours can win.
Something happens when a technology concept outpaces the terminology we have to describe it. Across the customer experience landscape, vendors start applying the same label to wildly different things. For instance, industry leaders start buying into the AI label instead of its real operational capability. And somewhere between the pitch deck and the production environment, the real question gets lost: does any of this actually address a real operational need? Agentic AI has reached that stage.
