Generative AI

Taking Flight: The Airline’s Roadmap to Generative AI Integration

Unveiling the Process, Benefits, Investment Considerations, and Security Guardrails for the Airline Industry’s Shift to Generative AI

Futuristic 3D image of an airline landing in a GPU architecture. This picture was created using Midjourney.

My twin passions have always been travel and technology. Over the years, I’ve had the privilege of visiting various countries across the globe, hopping from one flight to another through different airline companies. Like many travelers, I’ve encountered a wide spectrum of experiences, ranging from the mediocre to the truly exceptional. Recently, the growing buzz around artificial intelligence (AI) and its transformative impact on real-world scenarios have significantly captivated the tech and business landscapes. It made me wonder: how could airlines leverage this disruptive technology to offer a more personalized, superior customer experience?

In this piece, I will endeavor to lift the veil on this fascinating subject. We will explore the process, benefits, investment considerations, and essential security guardrails required for the airline industry’s shift toward generative AI. So, buckle up and join me on this exciting journey into the future of aviation.

Introduction to Generative AI

So, what exactly is this game-changing technology that’s taking the world by storm? Let’s break it down:

• Core Concept: A branch of artificial intelligence that’s all about creating new content.
• Content Generation: From existing data, generative AI innovatively crafts new and original material.
• Recent Advances: Significant progression in the field with the introduction of models like ChatGPT, GPT-4, Auto-GPT, Midjourney, and Stable Diffusion.
• Wide Applications: It’s not just about text — generative AI can create images, audio, and video content too.

Let’s explore the growth and impact in the generative AI domain together …

Generative AI: Key Figures and Impact

The power of generative AI has not gone unnoticed. Here are some key figures:

• Venture capitalists have recognized the potential, investing over $2.1 billion in AI startups since 2020.
• The global market for AI-augmented content is projected to reach $2.3 billion in 2022.
• Generative AI data is set to make up 10% of all data produced by 2025.

Generative AI can work hand in hand with human expertise to revolutionize the aviation industry:

• It can create a symbiotic relationship with human expertise, augmenting and automating human labor.
• It undertakes tasks in ways that approximate human behavior, pushing technology further into human realms.
• By leveraging its inputs and experiences, generative AI generates entirely new and unique content.
• It puts a premium on human expertise. This collaboration between generative AI and human expertise can lead to significant advancements and efficiencies in the aviation industry and across various sectors.

Having set the stage, let’s now delve into how this technology intersects with the aviation industry …

Exciting Use Cases for Generative AI in Aviation

Pilot and his crew in the cockpit using AI-integrated dashboards. This picture was created using Midjourney.

In the aviation industry, generative AI can transform everything from marketing to operations. Here are some applications:

• Predictive Maintenance: Generative AI can analyze aircraft sensor data and maintenance records, predicting potential failures. This lets airlines optimize maintenance schedules, reducing downtime and saving millions of dollars.
• Pilot Training: AI-driven simulators create realistic, tailored training scenarios. They adapt to each pilot’s performance, providing personalized feedback and improving safety.
• Flight Planning: Generative AI can optimize flight routes and fuel efficiency by analyzing historical data, weather patterns, and more.
• Passenger Experience (My Favorite!): AI-driven destination matching allows airlines to offer personalized travel recommendations. This transforms how customers plan their trips.
• Pricing Strategy: Generative AI can develop effective pricing strategies by analyzing market trends, customer preferences, and competitor pricing.
• Customer Service: AI-powered chatbots and virtual assistants can provide quick, accurate responses to customer inquiries, improving customer satisfaction and loyalty.

Beyond these use cases, let’s envision a new paradigm in travel planning …

Redefining Travel Planning and Passenger Experience with AI-Driven Destination Matching

The future of travel planning is all about personalization.

“Imagine an airline that doesn’t just ask ‘Where and When?’ but instead focuses on ‘Who are you?’ and ‘What do you want?’”

The future of travel planning is all about personalization. AI-driven destination matching can use comprehensive customer profiles, immersive multimodal interface, and identity verification (via Iris Biometrics or Soulbound NFT technology) to offer:

• Tailored experiences
• Personalized travel suggestions
• Recommendations for activities, accommodations, and local experiences packages

Now that we’ve envisioned a future of personalized travel, let’s identify the most promising opportunities to make this vision a reality — the ‘golden’ use cases …

Discovering the “Golden” Use Cases for Airline Companies

To unlock the full potential of generative AI, airline companies must identify the most valuable opportunities — the “golden” use cases. Here are the key factors to consider:

1. Uniqueness: When it comes to uniqueness, what are our areas of exclusive strengths, resources, or data? How can we combine these with generative AI to create a competitive advantage?
2. Customization: We need to tailor AI models to align with our specific goals, requirements, and workflows. What would that look like?
3. Integration: What steps can we take to ensure AI solutions complement our existing infrastructure and are easily accessible to our employees?
4. Tolerance for Variability: Given that AI is still limited by its propensity for error, we should primarily implement it for use cases with a high tolerance for variability. What might these use cases be?

After identifying the ‘golden’ use cases, it’s time to explore various generative AI models suitable for airline companies…

Picking the Perfect Generative AI Model for Airline Companies

Selecting the ideal generative AI model involves evaluating several options:

1. Training a Proprietary Model: When a unique model is a must-have, we should ask ourselves: do we have the budget and the talent to design and manage it?
2. Partnering with a Provider: If we find ourselves short of resources for a proprietary model, could a collaboration with a provider that supports training with proprietary data be the solution?
3. Adjusting an Existing Model: Could fine-tuning a ready-made model be the sweet spot between performance and cost? We should consider open-source models for budget-friendly solutions, or accessing a model from a provider for superior performance?

Now that we’ve explored selecting the right generative AI model, let’s examine the investment budget required for this technology…

Investment Considerations for Generative AI

Successful implementation of generative AI involves careful financial planning and timing assessment. Beyond talent training costs, this budget also covers expenses for model training, in-house GPUs, and cloud-based GPU services, among other considerations:

• Training a New or Existing Model: Opting for a custom LLM offers maximum flexibility but comes at a steep price. As per AI21 Labs, training a 1.5-billion-parameter model with two configurations and 10 runs per configuration could cost an estimated $1.6 million.
• In-house Hardware Costs: The price for an in-house NVIDIA A100 GPU stands around $10,000.
• Cloud-based GPU Services: Utilizing AWS’s A100 GPU services is billed at $4.10 per hour.

With a clear understanding of the required investment, we now turn to the importance of preparing the workforce…

Equipping the Airline Workforce for Generative AI Adoption

A successful generative AI implementation requires a prepared workforce:

1. Navigating Organizational and Operating Models: How might generative AI impact existing roles? Could the appointment of a Generative AI leader be a worthwhile consideration?
2. Cultivating Talent and Skill Development: Let’s think about cultivating talent and skill development. Where might there be talent or skill gaps, and what competencies should we expect from managers guiding an AI-augmented workforce?
3. Fostering a Dynamic Culture of Change: How about fostering a dynamic culture of change? How could we design training programs that emphasize the ethical use of generative AI, encourage collaboration between human and AI tools, and foster a cultural shift that welcomes AI?

Finally, as we fully embrace generative AI, it’s crucial to navigate the inherent risks and implement appropriate safeguards…

Putting Quality and Security Guardrails in Place

With the adoption of generative AI comes the need to address potential risks. Here’s how to tackle these challenges:

Navigating the Risks: Generative AI brings risks such as lack of truth function, copyright infringement, data leaks, biased outputs, phishing, fraud, and capability overhang. With clear policies and preparation, these can be managed.

Safeguarding the Future: A Four-Step Action Plan:

1. Crafting Clear Policies: Let’s look at crafting clear policies. What might be the best ways to guide employees on safe generative AI use?
2. Owning and Protecting Data: How about owning and protecting data? What review processes could we establish to prevent the publication of incorrect or harmful content?
3. Empowering Employees with Training: How can we empower our employees with training? What knowledge do they need to use generative AI responsibly?
4. Cultivating Healthy Skepticism: How can we encourage them to question and analyze AI-generated content?

Generative AI holds great promise for the aviation industry, offering benefits like improved safety, increased efficiency, and cost savings. However, it also presents challenges in data quality, safety and regulation, system integration, and ethics. The industry’s success in adopting generative AI will depend on how effectively it addresses these challenges.

I welcome your feedback. If this article sparked some thoughts or ideas, I’d love to hear them in the comments section.

Written By Marouane Ziani. I am a Tech enthusiast with startup experience in market research and consulting across Africa, Europe, and Asia. I possess a deep curiosity and passion for tech and innovation trends. I enjoy collaborating with others and testing ideas in the real world.