Merrill Lynch uses machine learning to automate investors’ portfolios. Facebook employs it to curate newsfeed content. Professional soccer teams utilize machine learning to design personalized training programs to keep players injury-free. And, of course, Google’s self-driving cars rely on it to navigate the roads.

Thanks to machine learning’s ability to analyze data with greater speed and more accuracy than any human — in a more cost-effective way — the technology is now being embraced by countless industries.

Recognizing machine learning’s immense value, businesses have big plans for it in 2017. According to a survey conducted by MIT Technology Review Custom in partnership with Google Cloud, 50% of organizations plan to use machine learning to better understand customers and 48% anticipate using it to gain a competitive advantage.

Businesses with customer loyalty programs are ripe to adopt this technology and use it to better their offerings.

Technology has long helped companies get better and faster at capturing customer data, but the analytical capabilities have been limited. Historically, the data that is collected measures information at face value, such as the number of engaged members in a loyalty program or when people stop participating. Now, machine learning can help these businesses take their loyalty offerings to the next level.

How so?

Machine learning won’t just look at data and point out obvious facts. Instead, it will uncover correlations between groups of data sets. In other words, machine learning will comprehend exactly what the data is saying.

It’s precisely this insightful aspect of machine learning — its ability to see patterns and understand consumer behavior — that equips marketers with the information they need to better segment and target their customers.

For example, the technology can be applied to the creation of a recommendation engine, which is based on a customer’s prior behavior instead of an artificial set of perimeters or rules. It can predict the preferences of shoppers, travelers and diners and develop actionable insights such as which customers get which types of offers. Predictive analytics can also detect potentially improper or even fraudulent behavior.

Overall, marketers can use the technology to uncover the touch points a customer will encounter along the path to purchase and predict which ones will lead to conversion. This is particularly beneficial as a drastic increase in the number of product choices and digital channels make it more difficult to use traditional methods to locate the gaps in the customer funnel.

Using machine learning to optimize business may sound like a luxury, but it’s a must in today’s world of micro-personalization. Consumers’ expectations are higher than ever. Brands have been focused on determining “the next best loyalty offer,” but they need to elevate their line of thinking. Brands should instead look to “the next best action” that will truly improve the customer experience — and in turn, improve engagement.

Loyalty marketers might be thinking to themselves, “This machine learning sounds like science fiction that maybe, one day, we’ll use.” Turns out, the future is now. It isn’t just Fortune 500 companies and the so-called FANG stocks (Facebook, Amazon, Netflix, and Google) using machine learning. Numerous open-source machine-learning algorithms are already available. It’s just a matter of marketers combining platform architecture with staff expertise in order to produce insights they need to better their offerings.

It’s imperative to note, however, that machine learning cannot replace human marketers. The industry cannot be fully automated. In fact, machine learning is a tool that only works when it’s used in combination with human expertise and analysis.

Turns out, two brains — one human, one machine — really are better than one.