The air travel industry today faces numerous challenges for travelers, from customer service frustrations to the complexities of navigating airport terminals. Long wait times, cumbersome security protocols, and inefficient logistics can test even the most seasoned travelers’ patience. In 2023, Contact Week reported a staggering 57% decline in customer satisfaction over the past year, while Forrester Research revealed that US airlines miss out on about $1.4 billion annually due to poor customer experiences.

To tackle these issues, the industry is turning to infusing high-end AI-based solutions to enhance both customer-facing and operational aspects of the air travel experience. For example, Dubai Airports has implemented an AI-driven demand planning and forecasting system, revolutionizing its behind-the-scenes supply management operations. This advanced approach has improved service levels, streamlined inventory management, and significantly boosted operational efficiency, all contributing to a superior experience for airport guests.

Similarly, Lufthansa leverages AI/ML algorithms to predict winds blowing from the northeast to southwest across Switzerland. The winds, causing delays, reduce the capacity of Zurich airport by up to 30%. By using Google Cloud’s forecasting models, Lufthansa can anticipate and mitigate delays, providing a smoother travel experience for passengers.

As the air travel industry increasingly prioritizes customer experience, collaboration with tech partners to deploy cutting-edge AI solutions becomes essential. This leads to an important new question…

 

Is your company ready to embrace AI?

Navigating the Generative AI (GAI) market is complex due to loud hype and big promises. It is easy to chase solutions that do not align with your business needs or end up impractical, wasting time and resources. That’s why you need a clear vision, careful planning, and technical expertise to assess your company’s AI maturity, identify the most suitable use cases that align with your goals, and ensure a successful AI implementation.

Taking a strategic approach can help you move efficiently from concept to production, generating tangible results without the risk of embarking on new projects that lack a clear plan.

 

How to implement an AI solution successfully?

Implementing AI leads to a return on investment only when the right solutions are chosen and aligned with appropriate use cases. Success is grounded in real-world experience, which tempers expectations and guides the application of AI to areas where it truly adds value.

At DataArt, our process begins with a Proof of Concept (PoC) to identify multiple use cases. A PoC allows organizations to quickly test-drive new solutions and technologies without long wait times. With DataArt’s precision MIA-DAMA™ framework, companies can develop a GAI prototype in just 6 weeks, allowing them to assess feasibility with minimal upfront investment.

MIA-DAMA™ includes:

  • Proof-of-Concept (PoC): Identify your business objectives and AI requirements, explore data, develop a foundational model, and create a prototype UI or API.
  • Implementation: Transition the PoC into a production environment, scale model deployment, integrate with external systems, establish data pipelines, conduct retraining and testing, and set up monitoring systems.
  • Maintenance: Continuously refine the model, monitor its performance, and make improvements to meet evolving needs.

 

DataArt applied this approach when working with a UK airport to address the challenge of managing repetitive and time-consuming queries. Duty managers used to handle these queries manually after their shifts, adding to their workload. To alleviate this, we utilized AWS’s Bedrock foundation models and DataArt’s TRAG Accelerator to create a solution that automatically responds to most queries.

Using a Retrieval Augmented Generation (RAG) approach, the solution retrieves the most relevant information from the airport’s FAQ section, stored in a vector database. This enables quick, accurate replies generated through semantic search on text embeddings.

By automating these repetitive tasks, the solution helps reduce the time required to handle each request by more than 95%. The system achieved 100% accuracy in generating draft replies for frequently asked questions, effectively filtering out irrelevant requests that required human intervention. Over a year, this efficiency improvement could potentially reduce the workload for several full-time employees, leading to an annualized ROI of over 4%. These time savings would not only help reduce operational costs but also enable the team to shift their focus to more critical tasks, ultimately enhancing productivity, improving customer experience, and driving overall business performance.

Additionally, the production-ready architecture and MVP accuracy in the PoC phase enabled the team to iterate four times faster, allowing them to develop production solutions in just 2-4 weeks.

 

Bottom line

The air travel industry is at a pivotal moment, where the integration of AI is no longer an option but a necessity for enhancing both operational efficiency and customer experience. As demonstrated by leading airports and airlines, AI solutions have the power to revolutionize how the industry handles everything from supply management to real-time flight planning and customer service.

However, a successful AI implementation requires more than just adopting the latest technology; it demands a strategic approach that aligns with your specific business needs. Contact DataArt at aviation@dataart.com to explore how a strategic AI adoption plan can revolutionize your aviation operations.

Don’t miss the opportunity to join our upcoming session at the World Aviation Festival on October 9 in Amsterdam, where Tim McMullen and Dmytro Baikov will discuss the topic “From Idea to Production: Real-world Applications of AI in Aviation.”

 

Article by DataArt