In today’s quickly-evolving corporate landscape, companies must often engage in intense competition to secure users and customers. In the age of big data and in-depth analysis of customer behavior, artificial intelligence (AI) and machine learning (ML) solutions are emerging as the de facto way for companies to gain a competitive edge.
Today, it is easier to harvest large amounts of data from the customer. This large volume of data or big data powers the current generation of AI algorithms. The advancement of the AI field has resulted in the creation and adoption of machine learning.
Machine learning was then discovered to be a good fit for the corporate landscape, providing cost-effective solutions to problems that previously required a lot of resources. Driven by market leaders, such as Google, Microsoft, and Amazon, the corporate landscape as a whole has gravitated towards using ML.
In this article, we will go through the list of ten businesses that are utilizing artificial intelligence and machine learning in innovative ways.
Table of Contents
Business Applications of Machine Learning
- Image Classification
- Text Parsing
- Recommendation Engines
- Predictive Modeling
- Customer Service (Chatbots and NLP)
List of Companies Using Machine Learning in Smart Ways
Business Applications of Machine Learning
Machine learning has a variety of applications in the corporate sector, as its capabilities have made it a natural fit for the requirements of an ever-increasing market. Smart automation has enabled businesses to effectively deploy low-cost, high-accuracy AI and ML solutions to replace low-skilled workers.
Apart from this, AI solutions are evolving to execute more complex and narrow tasks, thus taking the place of highly-paid professional roles. In more complicated job roles, AI solutions have taken the position of assisting human labor to do their job faster and more efficiently.
Either way, a company adopting AI for a suitable use-case will find an increase in efficiency with reduced expenses. Solutions offered by cloud service providers, such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure allow companies to effectively engage in a plug-and-play model for their required AI solution.
Due to these factors, ML is seeing acceptance in enterprises. The disruptive potential of the technology has also helped in its adoption. A technology as diverse as ML has prominent business applications.
Let’s take a look at some of them.
1. Image Classification
Image classification is the process by which algorithms are trained to analyze images and find out what they contain. While it may be easy for humans to look at an image and determine what it contains, specialized AI algorithms must be created for computers to analyze images. Today, image classification algorithms have progressed to be on par with human ability.
By utilizing AI algorithms that are built to analyze images, companies can deploy image classification solutions that significantly increase efficiency while reducing the rate of errors. Companies can also deploy such solutions to employ tasks like know-your-customer and identity verification.
Image classification as a machine learning solution has become highly popular in the enterprise sector, mainly due to its capability to disrupt existing systems created for the same purpose. Previously, human labor was required to go through vast amounts of data and label them. Today, giants like Facebook, Twitter, and Google are using image classification to prevent unwanted content from going viral.
2. Text Parsing
AI algorithms can also be trained to understand and process human-generated text. This process is known as text parsing and comes under natural language processing. By teaching the rules of language and grammar to an AI, it is possible to process a large amount of data in lesser time. Text parsing is useful for both, analyzing existing data and harvesting new data, either from user-generated content or competitors’ materials.
As expected, this holds a lot of advantages for companies that deal with a large amount of text data. For such companies, text parsing holds the potential to replace many low-skilled workers. By using AI to process large amounts of text at a fast rate, companies stand to benefit from text parsing solutions.
Using text parsing, computers can interpret large amounts of text as humans would. It enables companies to use a fast search engine for basic tasks and a more complex algorithm for advanced needs like bibliography. This reduces the need for hiring low-skill labor to parse text, improving the bottom line of the company.
3. Recommendation Engines
Algorithms can be trained to recommend things to the user by collecting user data and utilizing deep learning and neural networks. These algorithms, known as recommendation engines, are usually used to collect and store data about the preferences of users. By knowing what the user likes and dislikes, it becomes possible to create a model of what the user would prefer to consume or purchase. This model is used to provide customized recommendations to the user.
Today, recommendation engines have become the industry standard for curating and customizing content for each user. Amazon uses recommendation engines to suggest products that a user may like based on previous purchases, and Netflix uses the technology to recommend movies and shows based on the user’s taste. Recommendation engines improve customer experience significantly.
Recommendation engines also represent a new move forward in customer service and experience, as evidenced by its adoption across different verticals. By accurately analyzing and processing user data, recommendation engines make it possible for companies to vastly improve the customer service they offer. For example, Amazon offers a customized home page to millions of users every day. Even though this change might seem like a small one, placing recommended products on the homepage greatly increases the likelihood of customer conversion.
4. Predictive Modeling
Predictive modeling is a category of machine learning solutions that mines large amounts of data to predict the outcomes of potential scenarios. These predictions can then be utilized to make informed business decisions. Predictive modeling algorithms essentially provide a forecast of the future based on past data, allowing companies to make business moves in preparation for these predictions.
Companies with large troves of data can implement machine learning models to find patterns in the data. These patterns can be used to identify a pain point in the company’s procedures or a potentially profitable undertaking. This is also a common application of predictive modeling algorithms, as they not only provide the opportunity to make informed decisions but also improve existing processes by finding patterns in company data.
For example, predictive modeling algorithms can be utilized to gauge customer demand for a specific product in a retail environment. This prediction can then be used to find the optimal amount of stock to ship, thus reducing overhead costs. This is a sizeable competitive advantage that can save the company a considerable amount of resources while maximizing sales and avoiding shortages.
5. Customer Service
Algorithms can also be trained to act as customer service executives by deploying natural language processing trained on common customer complaints. AI has taken over the customer service vertical with a high number of accessible chatbots and natural language processing solutions on the market.
Chatbots have effectively taken up the role of customer-facing support executives, as they can respond to the customer almost instantaneously. After being taught a set of common customer complaints and their solutions, the chatbot can provide easy and accessible solutions to the customer in a timely manner. If the customer’s complaint is not resolved, it is transferred to a human customer service executive.
Apart from this, NLP is also heavily used to parse large amounts of training and support material to create an accessible knowledge base that can be used by human labor to resolve issues quickly. B2C companies previously spent a lot of resources on this segment. However, once AI came into the picture, these expenses were cut down by a significant margin.
List of Companies Using Machine Learning in Smart Ways
The above-mentioned applications of machine learning and artificial intelligence are only the tip of the iceberg. Companies have come up with several innovative ways to use AI and ML for addressing different business problems. The cost savings offered by using such solutions, along with the competitive advantage offered by them, are huge incentives for companies to create innovative solutions.
Let’s take a look at a list of companies that are using machine learning solutions in innovative ways.
1. Go-Jek
Go-Jek is an Indonesian company that tackles problems with an AI-first approach. Go-Jek is best described as an app ecosystem that contains elements from hyper-local, payments, social, and services verticals. The company aims to provide a vast range of solutions to everyday problems.
The app contains 18 different services under one interface, which means that the company has to processOpens a new window over 5TB of data every day. This has led them to use AI innovatively to optimize their delivery routes and cut down on human error rates. The company mainly uses machine learning solutions to assign drivers to tasks and find the best route for them to take an order to save time and resources.
They have also developed an in-house solution called “Jaegerâ€, which was built to assign tasks to many drivers simultaneously. The solution has already helped the company to complete over 1 million trips since it was deployed.
2. Pinterest
Pinterest is an online platform where users can discover new ideas for a variety of projects. This platform uses AI, more specifically deep learning, to recommend new ideas to the user. The website utilizes deep learning to discover the likes and dislikes of the users and recommend ideas based on this data.
Deploying deep learning has enabled Pinterest to vastly optimize the discovery of new ideas, which is an integral part of the site’s user experience. Moreover, the solution also allows them to personalize each user’s feed with ideas that are tailored to their preferences.
Deep learning solutions have also been deployed to Pinterest’s search feature, thus enabling the platform to provide better search results by deciphering the user’s intent. This has enabled the company to grow from a simple internet platform to a data and AI company at its coreOpens a new window .
3. PayPal
PayPal has become synonymous with seamless online payments, especially in the age of the gig economy. Due to its vast, global scale, the company had its hands full when it came to preventing fraud on its platform. This changed when they discovered the effectiveness of easily accessible cloud AI services.
They utilized these technologies to offer a service known as PayPal OneTouch, where users could pay for transactions with a single tap. While this may ring alarm bells for many users, AI and ML solutions have helped PayPal make safe transactions for millions of users.
Apart from OneTouch, the company heavily utilizes AI and ML to detect fraudulent transactions and evaluate the risk of offering business to a certain user. By ingesting millions of data points collected over the time of PayPal’s operations, their solutions can predict whether a given transaction is fraudulent or not, thus offering higher security for users all over the world.
4. T-Mobile
T-Mobile is one of the United States’ biggest mobile network providers. It offers calling and data services to over 83 million customers. With a customer base this large, it quickly becomes financially impractical to provide customer service. Instead of going with the norm of creating a chatbot, T-Mobile tried to help human agents handle customers better.
The company uses NLP to understand the problems of customers through text. Owing to the large amount of incoming data from customers, T-Mobile was able to train its algorithms to understand text with human-like accuracy.
The most commonly faced issues are collected in a database, and the algorithm parses the issues to find the relevant solution. This information is then passed on to the customer service executives, thus allowing them to resolve issues at a much faster rate.
5. Expedia
Expedia is a travel and hotel company which provides a platform where users can compare the prices of flights and hotels across different offerings. They also tie-up with hotels to offer better deals and holiday packages to customers.
Recently, they developed a deep learning solution to improve the user experience of booking hotels. This solution was deployed in three stages, starting with adding new properties to the Expedia lineup, sorting them in real-time according to user preference, and finishing up with a recommendation application programming interface to bring new customers to relevant properties.
Each of these steps utilizes deep learning to recommend a customized travel plan. For example, the platform will suggest bigger properties to a user who usually travels with family, and more compact options to those who travel alone.
6. Lenovo
Lenovo, a worldwide manufacturer of computers, has taken to artificial intelligence to help it predict the supply and demand in certain markets. For instance, Lenovo Brazil engaged in the rollout of an advanced ML solution to find a profitable location in the South American market.
This arm of Lenovo faced a lot of problems in accurately forecasting the potential sell-out volume for laptops. Knowing the volume allows for better control over the supply chain and the logistics of stocking retail shops, which provides a competitive advantage.
By using a modeling cloud service, Lenovo was able to accurately predict the volume of products they should manufacture. Using this data, the company then developed strategies for each product launch. ReportedlyOpens a new window , this solution was able to predict the sell-out volume for weeks in the future, thus allowing the company to have foresight on potential market movements.
7. Adobe
Adobe is known all over the world for its powerful Creative Cloud software suite, which contains software, such as Photoshop, Illustrator, and Premiere Pro. This software suite is used by millions of creative professionals all over the world and share a large amount of data with Adobe which it uses to model complex AI algorithms.
Keeping this in mind, the company created the Adobe Sensei solution for Creative Cloud products. Professionals usually find their time consumed by tasks that take a lot of resources to do manually. Adobe applied intelligent automation to these tasks, with an ever-increasing set of AI-enabled tools to help optimize the user’s workflow.
Apart from its applications in the Creative Cloud suite, Adobe Sensei is also used in other Adobe offerings. The solution offers predictive analysis and personalization features in Adobe Experience Cloud and creates digital copies of paper documents and performs form field recognition in Adobe Document Cloud.
8. HP
Hewlett Packard or HP manufactures computer hardware, printers, and other peripherals. The company is prominent in the enterprise sector, handling over 600 million technical support contracts in a year. HP capitalized on the easy availability of cloud services regarding AI for customer service.
Using the Microsoft Azure platform, HP created a virtual agent for handling customer support inquiries. Apart from interacting with the customers, this solution was also installed in every computer that HP sold, in the form of the ‘HP Support Assistant’. The solution engages in a conversation with the customer and tries to solve their problem by referring to its database of support manuals.
9. Twitter
Twitter is one of the world’s most popular social media platforms, with over 126 million active users every day. Any company operating at this scale is required to keep a close tab on the content appearing on their platform to prevent anti-social elements from misusing it.
In 2017, the platform announced that it would be deploying AI to fight inappropriate content regarding racism, terrorism, and hate speech. While this was effective against anti-social elements, the platform saw the potential of neural networks to change its platform. Twitter also redesigned its timeline and how tweets appear to individuals. Powered by neural networks, the platform suggests relevant content to users, thus creating a personalized experience for each user.
10. Facebook
Facebook, the world’s biggest social media platform, utilizes artificial intelligence, machine learning, and deep learning to such a point that they have developed platforms and frameworks for creating AI. In addition to this, the company has also expressed its plans to develop an in-house, specialized chip for AI tasks.
The platform uses AI for everything, from serving the latest viral videos and posts to users based on their preference, to flagging inappropriate or anti-social content, to recognizing what is in the pictures that users upload. The site itself is moving towards collecting different kinds of user data to make its algorithms more powerful, accurate, and fast.
Facebook also utilizes AI to find the best match between an advertiser and their target audience. By harvesting data about the user’s preferences from comments, shared posts, images, and other text data, Facebook is able to find exactly what kind of product a specific user would like. This increases the conversion rate of the advertisement, as it will be highly relevant to its target audience.
Closing Thoughts for Techies
The world around us is utilizing artificial intelligence, machine learning, and deep learning solutions to solve real problems. From everyday problems like ensuring security in payment transactions, to vastly applicable customer service experience chatbots, companies are seeing the disruptive potential of AI in the enterprise space.
While there is a group of companies that have evolved to adopt AI and make use of it, newer companies today are emerging with a ‘data-first’ approach. This means that AI and ML are part of the company’s DNA from the very beginning, giving them a competitive advantage in any market.
Do you know of any other innovative use cases of AI and ML? Let us know on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We’d love to hear from you.
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