Machine learning has become a buzzword that mixes tremendous potential and many unrealistic promises. This is somewhat unfortunate, as ML can truly be a game changer for many companies across many industries.
A subset of Artificial Intelligence, ML can be used to tackle real obstacles and problems in industries ranging from healthcare to finance to education and more. Still, considering how complex this technology really is and the fact that it is still gaining sophistication, it is commonly misunderstood.
Without understanding its true capabilities, when to use it and when not to use it, business leaders run the risk of mishandling machine learning in their organizations. So what's the right way to incorporate machine learning into your business model? What are the limitations?
What is machine learning?
AI involves harnessing human intelligence to perform a series of tasks traditionally performed by humans. As a subset of AI, machine learning uses algorithms to analyze and comb data to identify trends and predict outcomes without human intervention. As you gain experience and familiarity with the data you process, you become more sophisticated and begin to “learn” new, more efficient ways of evaluating information to provide more accurate results.
There are numerous examples of machine learning that are relevant and applicable to our daily lives, from voice recognition to facial recognition to preference detection.
Ways to Incorporate Machine Learning into Your Business
1. Security
Looking for ways to improve your organization's cybersecurity? Given the real threats and risks posed in today's world, this is completely understandable – and advisable. Fortunately, machine learning can play a key role in protecting your business.
For example, machine learning applications include facial detection. As it learns to recognize employees' faces, it can quickly and easily grant access to various systems to authorized employees, preventing others from hacking. You've probably already had experience with this feature if you have an iPhone – it's a way to unlock your device without entering your passcode.
Machine learning can also help ensure customer safety. Credit card companies, for example, have leveraged technology to detect and track potentially fraudulent activity. This is because it familiarizes itself with customers' purchasing and spending patterns to detect inconsistencies, before alerting the user to suspicious activity.
2. Customer Pattern Detection
Machine learning can also be used to detect more general consumer patterns – their likes and dislikes, their purchasing habits, and more. This is important data that companies can leverage to make decisions about how to retain customers and acquire new ones.
Based on the insights gained from the data collected, a company can better target marketing campaigns, make offers and consider how to build its audience, all based on data such as purchasing and browsing history, as well as other factors about their habits. They can also discover trends among different demographic groups and in different areas.
3. Personalized Customer Experiences
In another category of exemplary customer service and marketing, companies are able to create more personalized and personalized experiences.
Take Netflix, for example. How is the streamlining giant able to offer exclusive film and television recommendations? The answer is machine learning. As the platform learns about your entertainment preferences based on your viewing patterns and ratings, it will make new recommendations that it thinks you might like based on past activity.
4. Easy Repairs
Predictive maintenance is useful for a variety of industries, especially manufacturing. Thanks to machine learning, companies can find out when they are likely to need maintenance or repairs well in advance, rather than risking equipment failing unexpectedly.
This is because machine learning will find patterns and gain insights into what will happen in the future based on what it has learned about past behaviors. This is cheaper, more efficient and generally better for the business as a whole.
Tips for getting started
So how will you reap the rewards of machine learning in your organization? Here are some important steps.
1. Learn about AI
Start by familiarizing yourself with AI and its potential. It's important to take this step so you can fully understand how the technology can be applied to your specific needs.
In addition to reading research and articles, consider taking an online course to gain an overview of AI and machine learning. There are many sites to check out, like edX, Udacity, and others. Some are even free.
2. Consider how machine learning can be applied to your business
Once you have gained some knowledge about the potential of AI, think about how it can be applied to your business. Talk to your company's managers about your weaknesses and the areas they have identified that could be improved. Also consider your goals for improving the service or product your company offers. Is there a faster or more efficient way to make this happen? Could you add new features or improvements?
3. Solve problems, don't create them
This is important: don't try to make machine learning fit a specific business model if it doesn't. Not everything needs or will benefit from machine learning.
Instead, think about the problems or inefficiencies that exist. Could machine learning improve them? The answer could be yes, but it could also be no. Accept this.
4. Hire experts
You may have a full-time local team with extensive AI knowledge, so you can use their expertise. But if you don't, you will certainly benefit from the involvement of experts. Even if you have talent in place, consider turning to an outsourcing partner to help tailor specific machine learning solutions to your needs. It is highly likely that a niche specialist will find applications that you have not considered.
What’s next for your business? Whatever your goals and objectives, machine learning can help you achieve them. But remember to use this and other technologies and innovations responsibly. Although the implications are vast, remember to use them sparingly so that the impact is even greater.
