The Power of Machine Learning for Business: Lessons from Aviation

"AI maturity is not optional - it is correlated with returns. [However,] the aspiration is much higher than the results in many cases. There’s a way to do and operationalize analytics, which is less building everything from scratch the first time and more thinking how to build with reusable components and infrastructure."

- Kia Javanmardian, Senior Partner, QuantumBlack, McKinsey & Company.

Machine learning has been evolving for decades, mainly within research labs. Early machine learning milestones include Alan Turing’s 1950 paper, “Computing Machinery and Intelligence,” which introduced the concept of the Turing Test, Arthur Samuel’s self-learning checkers program in 1952, and the creation of the first neural networks in 1957. Despite these early breakthroughs, machine learning applications largely remained theoretical for many years. It wasn’t until the late 1970s and early 1980s when computing power increased that machine learning for business innovations began to be gradually adopted.

One of the earliest examples was XCON, developed in 1978 for Digital Equipment Corporation (DEC). XCON utilized machine learning techniques to facilitate the automatic configuration of computer systems based on customer orders. It applied a set of rules and learned from past configurations to select the correct parts for each order, making the process faster and more accurate, and ultimately saving the company a significant amount of money.

Today, machine learning, the branch of artificial intelligence that enables computers to learn from data and improve their performance over time without being explicitly programmed, plays a central role in many significant advancements in artificial intelligence. 

As MIT Sloan professor Thomas W. Malone, founding director of the MIT Center for Collective Intelligence, said, "In just the last five or 10 years, machine learning has become a critical way, arguably the most important way, most parts of AI are done. So that's why some people use the terms AI and machine learning almost as synonymous … most of the current advances in AI have involved machine learning."

This close relationship between AI and machine learning underscores why machine learning for business analytics is no longer just experimental – it is now essential. Businesses today are asking: How is machine learning used in business, and what value can it bring to their operations?

‍

The Role of Machine Learning for Business in Improving Operations

All enterprise companies generate vast amounts of data, from customer interactions to operational metrics and financial data. Yet, according to studies, between 60% and 73% of all enterprise data is unused for analytics. Without the right tools, much of this information remains untapped.

By applying machine learning in business operations, companies can automate tasks like identifying key customer segments, predicting future buying behavior, optimizing operations, and delivering highly personalized services or marketing campaigns, dramatically improving results while reducing manual effort.

Organizations that apply machine learning business applications are already seeing measurable outcomes. Many report improving operational efficiency by 30% or more and increasing revenues by 5-10%.

For instance, travel is one of the sectors with the highest potential for AI-driven value. A McKinsey analysis found that artificial intelligence can deliver a 128% performance boost over traditional analytics techniques in the travel sector - the most significant improvement across all industries analyzed. This advantage is primarily driven by machine learning models, especially deep learning. 

A good example of this is enterprise machine learning being used to optimize flight routes, predict maintenance needs, and personalize customer service - efforts that have the potential to unlock up to $10 billion in new value across the aviation industry.

The Growing Impact of Machine Learning in Aviation

‍

While artificial intelligence has been around since the 1950s, the aviation industry has only recently started integrating it into operational workflows. The growing demand for air travel is a big reason behind this. According to the International Air Transport Association (IATA), the number of airline passengers is expected to double over the next 20 years, placing significant pressure on airlines to find more efficient ways to manage their operations.

‍

"Machine learning, leveraging today’s computing power, will enable us to run better operations,"

- British Airways CEO Sean Doyle. 

‍

Airlines now view machine learning as a crucial tool for enhancing performance and maintaining competitiveness. Major carriers, including Southwest, American Airlines, Delta, JetBlue, Swiss International Air Lines, and Lufthansa, are already utilizing machine learning to address real business challenges. For example, Swiss International Air Lines recently used AI to optimize more than half of its flight network, saving nearly 5 million Swiss francs (about $5.4 million), according to a report from Fortune.

Fuel management is another key focus. Since fuel accounts for 20% to 30% of an airline's operating expenses, even a 1% improvement in fuel efficiency can save millions of dollars a year. That’s why machine learning-powered route optimization and predictive maintenance are becoming essential parts of airline strategies.

And here’s the bigger takeaway: It’s not just airlines that can benefit. Any business facing complex operations, tight margins, or unpredictable demand can use machine learning to work smarter, cut costs, and stay ahead.

Next, let’s walk through a few aviation case studies that showcase how machine learning for business delivers real-world results - and explore insights that can be applied across various industries.

‍

How Airlines Are Reducing Costs and Delays Using Machine Learning

Predictive maintenance driven by machine learning is becoming a necessity for industries where reliability is paramount. In the aviation industry, unexpected maintenance issues have long been a significant challenge for airlines, resulting in delays, disrupted schedules, and passenger frustration. In the United States alone, airlines have seen maintenance costs rise by 15% over the past five years, while the share of delayed flights has increased by 14%.

However, experts agree that using AI to predict and prevent equipment issues can significantly lower maintenance costs and reduce delays caused by technical problems.

"Machine learning-powered anomaly detection is a proven technology. When applied in aviation, it improves safety, service quality, and operational efficiency." 

- Alexis Lope-Bello, CEO at ComTrade Group.

Southwest Airlines is a prime example of how this shift is unfolding. Known for keeping operations running smoothly, Southwest realized that fixing planes after something went wrong wasn’t enough anymore. Instead, they began using machine learning tools to monitor aircraft sensor data and maintenance records, identifying early signs of mechanical issues before they developed into major problems.

By applying business machine learning, Southwest was able to plan maintenance more efficiently, avoid unexpected downtime, and keep more planes in the air - all while making flights more reliable for passengers.

Beyond aviation, predictive maintenance using machine learning can also benefit businesses in sectors such as manufacturing, logistics, and energy. Anywhere that reducing downtime, cutting costs, and delivering better service matter, machine learning can offer a more innovative and faster way to stay ahead of problems.

 Handling Peak Travel Challenges with Enterprise Machine Learning

It's not just airlines that can benefit from machine learning. Small airports, facing very different challenges, are finding ways to use machine learning for business efficiency too.

Take Kittilä Airport in Lapland, Finland. During most of the year, it’s a quiet regional airport. However, around Christmas, traffic surges, with up to 58 flights a day, and 70–80% of them arriving within a four-hour window. Expanding infrastructure wasn’t an option, so Finavia, the airport operator, needed a more innovative way to handle peak demand with limited resources.

Rather than starting with available data or technology, Finavia focused on solving a clear business problem: how to manage a massive but temporary spike in flights and passengers. Working with technology experts, they developed an optimization model powered by machine learning.

The solution utilized historical and live flight data to predict arrival times and passenger volumes, generate optimal aircraft parking plans, and inform staffing decisions within the airport. After deploying the model, Kittilä increased the number of flights it could handle during peak season by 12%, reduced average delays by two-thirds, and saved around €500,000 in a single year.

This case demonstrates that machine learning for business has the greatest impact when applied to solve clear, high-value problems. Organizations create real value by focusing on targeted issues, not by forcing AI across every operation. 

How Lufthansa Connected Data and Teams with Machine Learning

‍

While some airlines serve regional hubs or limited networks, for global carriers like Lufthansa, the challenge is managing complex operations across hundreds of airports and millions of passengers. With data spread across various systems, Lufthansa needed a more efficient way to manage operations, enhance customer service, and remain flexible across its global network.

They partnered with IBM to build a cloud-based data science platform enabled by machine learning. By connecting scattered information and making it accessible through natural language tools like IBM Watson Assistant and Watson Explorer, Lufthansa provided its teams with improved methods to predict boarding times, optimize staffing, and manage nearly 100,000 customer queries annually.

Lufthansa was able to move beyond isolated AI pilots and scale machine learning across the organization, improving workflows, reducing passenger delays, and delivering a stronger customer experience.

As Mirco Bharpalania, Senior Director at Lufthansa Group, explained: "For Lufthansa, AI is so critical because it actually opens up the world of the data that we’re sitting on. It actually helps us to unlock all the potential that we somehow or somewhere in our databases already have."

Lufthansa's machine learning solution shows that scaling machine learning for business operations isn't just about using new technology - it's about connecting the right data, people, and processes. When organizations focus on connecting the right data points, machine learning becomes a reliable tool for uncovering insights, improving decisions, and driving measurable results.

‍

Machine Learning for Business: Lessons in Solving Real Challenges

‍

Machine learning forms the core of many AI solutions that deliver a return on investment (ROI) across various industries today. For example, in aviation, machine learning is already addressing real business challenges, including optimizing flight routes and staffing, reducing delays, and enhancing the customer experience. These aren’t theoretical ideas - they’re real-world improvements happening right now.

Many successful implementations of machine learning in business showcase that the real value comes from solving specific, high-impact problems, not from applying AI everywhere. When organizations focus on connecting the right data, people, and processes, machine learning becomes a powerful driver of faster decision-making, stronger operations, and better outcomes.

‍