Looking for inspiration on how to use machine learning in your business? Join the club. The technology is hot right now, and with good reason. It can help you do everything from predicting customer behavior to improving your supply chain.
In fact, a recent PwC survey of over 1,000 businesses across nine sectors, such as banking, consumer markets, and insurance, found that 86 percent of them planned to implement AI as a "mainstream technology." Given the ability of machine learning for business to boost efficiency and productivity, it's expected that the technology will add $15 trillion to the world economy by 2030, according to another report by PwC.
And it's not just big businesses that are benefiting from machine learning. AI startup funding is hitting record highs, and AI adoption has skyrocketed across sectors during the pandemic.
The post-pandemic world also demands faster, more flexible machine learning solutions. No-code machine learning is uniquely positioned to meet this need, as it allows businesses to quickly deploy models and get results without the need for expensive data science teams.
Machine learning is a type of artificial intelligence that enables computers to learn from data without being explicitly programmed. Over the past few years, the field of machine learning has exploded in many different industries. In this article, we'll take a look at 20 companies that are using machine learning in innovative ways to improve their business.
Before we dive into the list, let's take a quick look at what machine learning is and how it's used.
The way machine learning "learns" from data is by using algorithms that iteratively improve as they are "trained" on historical examples. There are many different types of machine learning algorithms, but some of the most common are linear regression, random forest, and support vector machines.
While traditional algorithms are deterministic, meaning that they always give the same result for a given set of inputs, machine learning algorithms are probabilistic, meaning that they provide a range of possible outcomes. This is what allows them to handle highly complex tasks from self-driving to natural language processing.
Let's look at some of the top machine learning for business applications.
Netflix is a great example of a company using machine learning to improve the customer experience. The streaming giant has long been using machine learning algorithms to personalize its recommendations for viewers.
By using data like a customer's viewing history, the viewing history of customers with similar entertainment interests, information about individual shows and movies, and even data about when a customer is likely to be most active on the platform, Netflix is able to recommend relevant content to individual viewers. This recommendation system is estimated to drive “80 percent of hours of content streamed on the platform.”
Of course, Netflix isn’t the only giant using AI for recommendations, as this is a common technique used by e-commerce firms like Amazon to boost sales metrics.
Beyond the personalization of recommendations, Netflix uses AI to auto-generate and even personalize thumbnails and optimize streaming quality by predicting bandwidth usage, providing insights into when to cache regional servers for faster load times. This helps keep viewers engaged with the service and reduces the chances that they'll cancel their subscriptions.
Netflix even uses AI to determine how to market its shows, via a “similarity map” that helps determine audience sizes.
YouTube is another company that has been using machine learning for business for a long time. The site uses a technique called deep learning to recommend videos to viewers, which is based on modeling massive amounts of historical data.
This approach relies on the fact that people tend to like and watch videos that similar people have watched. By analyzing the viewing history of individual users and the viewing history of users with similar interests, YouTube can recommend videos that individual viewers are likely to enjoy.
YouTube has been building this system since 2008. Prior to then, recommendations were based simply on what had the most views. Now, your recommendations are based on "over 80 billion pieces of information" about you, according to YouTube. Given this massive amount of data, YouTube uses large neural networks for a variety of use cases.
If you're a content creator or publisher, then you know that monetizing your content can be tricky. However, machine learning can help.
Companies are using machine learning algorithms to help broadcasters and content publishers better monetize their content. Machine learning algorithms analyze data about the content, such as genre, length, and viewership demographics, to help identify which types of advertising are most likely to be successful. This helps publishers and broadcasters to better target their ads and increase their overall ad revenue.
Further, the Broadcast Audience Research Councils (BARC) of India process 7.5 Petabytes of data annually to build models that help broadcasters improve their programming and targeting.
In 2015, The North Face launched the Expert Personal Shopper through a collaboration with IBM Watson. The North Face used Watson's machine learning for business capabilities to create a virtual assistant that helps customers find the perfect product for their needs.
The assistant is based on a chatbot that asks the customer a series of questions about their preferences, such as climate, activity, and budget. It then uses this data to recommend jackets that are a good fit for the customer. This service has been a huge success for The North Face, with over 60 percent of customers using it clicking-through to make a purchase.
This is a powerful example of natural language processing (NLP) in business, which is also used extensively for customer support and social media bots.
The MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) created a program called ICU Intervene that uses machine learning to predict possible treatments.
It learns from massive amounts of intensive-care-unit (ICU) data, including symptom data, to make real-time predictions. It also explains the reasons behind the decisions it makes, which helps to improve patient care. This could ultimately make a huge difference in the quality of care that patients receive.
There are many other AI and machine learning applications in healthcare, from image recognition and computer vision to predict cancer malignancy to healthcare fraud detection. These applications of machine learning technology both fuel unsupervised learning algorithms with big data for automation in decision-making.
Uganda has a long history of cassava production, but a lack of resources and knowledge has resulted in many farmers losing crops to disease.
To address this problem, Makerere University has launched the Mcrops project, which uses machine learning to detect diseases in cassava crops.
This algorithm can identify early signs of disease in crops, which allows farmers to treat the plants before they are completely destroyed. The project has already been implemented in Uganda and is helping to improve the quality of cassava crops in the area.
In April 2017, Wells Fargo launched a chatbot for Facebook Messenger that helps customers with their banking needs.
Wells Fargo’s bot for Messenger focuses on incorporating financial services into third-party environments to meet customers where they are and into the moments they want to use them. Their goal is to deliver information ‘in the moment’ to help customers make better informed financial decisions using AI technology instead of requiring customers to navigate through several pages on our website, and turn it into a simple conversation in a chat environment.
The bot is based on machine learning algorithms that learn from the customer's interactions. This enables the bot to provide a more personalized experience for each customer. Since its launch, the bot has been a huge success, providing business value by boosting operational efficiency and strengthening the customer relationship.
Many business processes can be automated by this implementation of AI, from password resets to account upgrades. Additionally, AI can automatically conduct customer segmentation to better address customer needs.
Recruitment is one of the areas where machine learning for business can have the biggest impact.
Peoplise is a company that is using machine learning to help businesses find the best candidates for their open positions. The company's algorithms analyze data about potential candidates, such as their skills, experience, and education, to calculate a "fit grade" for them.
This helps businesses to quickly and easily identify the best candidates for their open positions. Peoplise has already been successful in helping businesses hire over 2,000 employees, get 45% more candidates, and end up with 3 times faster processes.
There are many other business applications of AI in recruitment, from forecasting staffing needs to employee attrition prediction.
Decathlon is a company that sells sports equipment and apparel. The company uses machine learning to personalize the shopping experience for each customer.
Its algorithms analyze data about the customer, such as their sports preference, past browsing history, and past purchases, to recommend products that are a good fit for them. This helps to ensure that each customer finds something that they like and that they are likely to purchase.
The recommendation engine also suggests nearby sporting events, and ultimately creates a personalized experience for every user.
PayPal is a company that is well-known for using machine learning to prevent fraud.
The company's algorithms analyze data about transactions, such as the location of the purchaser, the IP address of the seller, and the type of product being sold, to identify potentially fraudulent transactions.
This helps to ensure that PayPal's customers are protected from fraudulent activities. In short, AI takes cybersecurity to the next level, which is vital for any modern business model. After all, data breaches and hacking are on the rise, and with the majority of businesses having experienced a cyberattack, ML algorithms are vital to solving security-related business problems.
Traditional data analysis methods for fraud detection fall short, because they can’t accurately model the complexity of billions of data points in various formats.
Amazon is another company that's using machine learning in a number of different ways.
For instance, they're using it to improve their search algorithms, to make product recommendations, and to detect fraud. They're also using machine learning to improve the accuracy of their inventory management system and to automate customer service tasks.
The firm also uses AI to support their climate pledge, which is to reach the goals of the Paris Agreement 10 years early and achieve net zero carbon by 2040. Through ML-powered sustainable packaging mix, they've reduced the use of boxes from 69% to 42%, eliminating close to a million tons in packaging. Packaging often ends up in the world's waterways and oceans, making this an important win for sustainability efforts.
Facebook is using machine learning for a variety of tasks, including facial recognition, content moderation, and ad targeting. In addition, they're using it to build chatbots and to improve the performance of their search engine.
Like a search engine, the feed you view is driven by AI to provide recommended content and keep you engaged, while minimizing spam and hateful content.
Twitter is using machine learning to detect malicious content on their platform. In particular, they're using it to identify spam accounts, to remove abusive tweets, and to flag potentially dangerous content.
IBM is using machine learning in a number of different industries, but one of the most interesting applications is in healthcare. They're using it to develop new treatments for diseases, to improve patient care, and to make medical data more accessible.
Google is using machine learning for a variety of tasks, including image recognition, email spam filtering, and ad targeting. They're also using it to improve the performance of their search engine and to develop new features for their products.
Google uses AI to such an extent they're virtually an AI company, with AI at the heart of Google products, including Google Search, Google Ads, Google Maps, Youtube, Google Photos, Google Drive, and more.
Walmart is using machine learning to improve their supply chain management. In particular, they're using it to track inventory levels, to predict consumer demand, and to route products more efficiently.
A recent VentureBeat article highlights that AI is "embedded everywhere" at Walmart.
RenTec is infamous for not just its secrecy, but its consistently extraordinary returns. They've generated tens of billions of dollars practically out of thin air.
As a Harvard Business School case study explores, RenTec also uses machine learning. They have a system called Medallion that makes high-frequency trades. A team of 90 PhDs work on and constantly refine this system.
Long-gone are the days of searching radio stations for new songs or artists similar to the ones you like. Spotify uses machine learning to personalize your listening experience in a way that feels eerily accurate sometimes.
They do this by analyzing the music you listen to, the music you save, and even the music you skip. They also consider factors such as time of day and your location. All these data points contribute to the algorithm that creates your personal Spotify experience.
Modern dating wouldn't be the same without Tinder. This app has changed the game by using machine learning to create a smooth user experience and perfect matches.
Tinder's algorithm looks at a variety of factors, such as your age, location, and interests, to find matches for you. It also takes into account your interactions with other users, such as whether you swipe right or left on their profile. All this data contributes to the algorithm that makes Tinder work.
Airbnb is another company that relies heavily on machine learning. They use it to personalize your search results, to match you with potential hosts, and to help you find the perfect listing.
Traditionally, implementing AI in a business has required coding skills and extensive knowledge of AI algorithms, cloud computing, and data science. This meant that businesses had to hire experts in AI in order to use machine learning, which created a high barrier to entry.
For one, that talent is in short supply, with the demand for AI experts far outpacing the number of available professionals. And even if your business can find the talent, it can be prohibitively expensive to bring on board. After all, experienced data scientist salaries have been growing at a rate of about 12-13% annually over the past few years.
But the days of needing an army of data scientists are over. The rise of no-code AI tools is making it possible for businesses of all sizes to implement AI without extensive coding skills or AI expertise. These tools allow users to drag and drop pre-built AI models into their workflows, making it possible to create sophisticated AI applications with little more than a basic understanding of how AI works.
Akkio is one such tool. With Akkio, businesses can quickly build and deploy custom AI models without any coding or machine learning expertise. And because Akkio is cloud-based, businesses of all sizes can use it, regardless of their location or IT infrastructure.
As Ajay Agarwal, Partner at Bain Capital Ventures, recently said, “The best companies are leveraging AI across their enterprise. With Akkio, business users can leverage the power of AI without the burdens of long and expensive traditional AI solutions.” With no-code AI tools, that’s truer than ever before. Whether you want to detect fraud, predict churn, score sales leads, or reduce employee attrition, Akkio can help.
To get started, simply sign up for a free trial. You can try out a wide-range of existing demos, or AI Flows with datasets already pre-loaded, or you can directly connect your data, whether it's a CSV file, an Excel sheet, or in a business tool like Salesforce. After connecting your dataset, it's as easy as selecting the column you want to predict and deploying your model - all in a few clicks!
Machine learning in business has traditionally been the domain of experts, requiring coding skills and extensive knowledge of AI algorithms.
That is changing with the rise of no-code AI tools, which allow business users to drag and drop pre-built AI models into their workflows. Leading companies like Netflix, YouTube, and Wells Fargo use machine learning in business to power their customer experiences. Now, any company can do the same.
Akkio is built for non-coders to use so you can be up and running in minutes. To get started with no-code AI, sign up for a free trial of Akkio.