How AI Boosts Profits: Real-World Examples

However, for AI to be truly successful, it not only needs to pay off its costs but also generate additional revenue for the organization or at least reduce expenses, thereby contributing to profit growth.

Since AI helps improve various factors—eliminating inefficiencies, reducing errors, and providing better business insights—AI often results in long-term profit increases for companies. Let's look at a few examples of how this happens.

Less inefficiency reduces costs

In August 2016, a 5-hour power outage at Delta Airlines' operations center caused 2,000 flight cancellations, with losses estimated at $150 million. Unplanned outages like this are extremely expensive. They reduce revenue, disrupt service chains, increase maintenance costs, and raise customer dissatisfaction.

When such failures occur, energy transmission companies have to make emergency repairs. This includes installing new equipment, sending experts into the field, handling logistics, and other costly activities. All of this increases costs and requires a lot of engagement. San Diego Gas & Electric Company (SDG&E) aims to avoid such expensive power supply interruptions.

With the help of AI experts, SDG&E is developing an AI system to prevent transmission system failures by predicting them weeks before they happen. Their system reaches an 80% success rate in predicting equipment failures two weeks in advance, and 90% three days in advance.

By predicting where failures are most likely to occur, SDG&E will be able to plan repairs in advance before an outage occurs, thus preventing large-scale power outages. This not only prevents interruptions in power supply but also reduces costs associated with unplanned interventions. Additionally, it ensures that critical services such as hospitals, firefighters, and rescuers are always operational.

Increased personalization raises revenue

We've already experienced how AI can hyper-personalize the customer experience. Take Amazon as an example. How many times has it happened that, instead of the product we are looking at, we end up purchasing the one recommended by their automatic system, which offers products related to the one we’re viewing? Amazon's AI system manages to keep customers in the store longer and provides more options related to our interests until we find what we want.

According to McKinsey, 35% of Amazon's revenue is generated through its recommendation system. Similarly, Netflix estimates that its recommendation engine saves more than a billion dollars a year by retaining customers who would otherwise drop off. Netflix estimates that only 20% of movies are found through search, while 80% come from its recommendation system. That's why it's crucial for Netflix to continue improving its personalized customer experience.

Personalization has the power to significantly boost revenue, and AI is the tool that enables exceptional results in personalization.

Innovation driven by data analysis

Ocean Spray, an agricultural cooperative of cranberry and grapefruit growers, faced declining demand and turned to AI to enhance sales. They first wanted to understand how consumers like to use cranberries. In such situations, focus groups of 10 to 15 people are usually used. However, relying on the opinions of such small groups to predict the behavior of millions of potential future customers is risky. You can never be sure whether the views of a small focus group truly represent real market demand.

Therefore, Ocean Spray decided to avoid traditional research methods. Instead, they conducted AI-assisted analysis of hundreds of thousands of online conversations, including user reviews and tweets related to cranberry juice, to uncover the opinions of a much larger number of potential consumers.

Through such analysis, they understood how consumers use cranberry juice daily, which gave them an idea of how to innovate and fill market gaps. Shortly after, Ocean Spray launched a new line of drinks, Ocean Spray Mocktails and Ocean Spray Pact. Revenue from this move was estimated at $100 million. By launching these cranberry-infused water beverages, they significantly expanded their existing market.

This example shows how Ocean Spray, by using AI analysis, gained insight into consumer habits, enabling them to step out of their saturated market and open new revenue streams.

A similar AI-assisted decision-making system can be applied in many types of businesses to understand the behavior of potential customers. This can lead to the creation of new products or provide ideas for improving existing ones. In either case, the result is increased revenue or at least savings by improving existing processes.