Companies that have adopted artificial intelligence (AI) and machine learning (ML) in their operations have seen great success. Apart from freeing up human labor from mundane tasks and automating repetitive ones, AI has fundamentally changed the way some companies operate. Let’s look at some companies that were highly successful in adopting AI.
Table of Contents
Why Is AI Adoption on the Rise?
Top 5 Businesses That Benefited From AI
Adoption of AI in 2019
In the fast-moving enterprise world, adopting AI seems to have become a move that can make or break a company. Companies that deploy the technology in a way that is fitting for their vertical, face great success and gain a competitive advantage, while those that do not, fall behind in relevance.
As per a surveyOpens a new window conducted by Narrative Science and the National Business Research Institute in February this year, 61% of companies had already adopted AI in 2018 as compared to just 38% in 2017. This is only a part of the bigger trend wherein the whole sector has recognized the disruptive potential of AI. Moreover, the benefits of implementing AI solutions have become clear to companies.
Artificial intelligence, machine learning, and deep learning stand to fundamentally change the way some companies operate. The adoption of such technologies also allows companies to expand in a way that wasn’t possible before.
Among the organizations that adopted AI, few stand apart as implementing highly successful solutions. In this article, we will delve deeper into the possible artificial intelligence use-cases and look at some case studies of companies who have seen success with the technology.
Why Is AI Adoption on the Rise?
Before looking at the potential disruptive advancements offered by AI, we must first delve into why AI is being adopted today. Due to its position as an industry-standard in the modern corporate landscape, companies that adopt the technology enjoy a sizeable competitive and financial advantage over those who don’t.
AI technology holds the capability to fundamentally change how a company functions. It is important to note that artificial intelligence solutions must be created, keeping the company’s needs in mind. This technology cannot be deployed as a catch-all solution, despite the common misconception. Today, it has become easier and more accessible to deploy AI solutions in the enterprise, with a gamut of benefits to be seen.
With the rise of accessible cloud solutions, companies across verticals can easily utilize AI, ML, and deep learning as they wish. These services offer plug-and-play solutions like image recognition, voice recognition, chatbots, predictive models, and more.
Aimed at solving common enterprise problems, these services reduce the barrier of entry into AI. These services can even be added to existing AI solutions already deployed by the company. Deploying AI in the cloud also offers cost-savings over on-premise solutions, thus reducing overall expenses and saving resources.
While a full-blown AI solution would require a company-wide restructuring of how data flows, the results seem to be worth it. Tailored AI solutions offer many benefits. Some of these include:
â— Intelligent Automation: AI solutions can automate repetitive tasks with intelligence, replacing low-cost labor. This saves costs for companies while increasing efficiency among the workforce.
â— Business Intelligence: AI solutions that are focused on deciphering the way businesses work can also improve the quality of operations for companies. Business intelligence technology can help companies make faster and more informed business decisions by analyzing large amounts of data.
â— Data Mining: Companies have found AI to be integral in data mining processes, allowing them to pick up on information that could be used to increase their customer base. This data can also allow AI to delve deeper into company processes and offer visualizations for important company information.
â— Customer Experience: AI has the potential to change the way a company approaches and interacts with customers. Common solutions include implementing chatbots to quickly address customer FAQs, recommendation engines for customized user experience, and 24/7 customer support using AI agents.
Top 5 Businesses That Benefited From AI
AI has many applications in the enterprise sector, offering sizeable benefits for companies that adopt the technology. There is a small number of companies that adopt AI for truly edge-case scenarios. These are the ones that stand to vastly increase the rate of their success by utilizing AI. Such companies utilize AI for truly unexpected solutions to problems. Let’s look at some of these companies.
1. Amazon
Before Amazon adopted AI, it was already one of the world’s leading e-commerce marketplaces. In 2014, the company started making a move towards deep learning for recommendation engines – a staple of the website and its services today. This was previously unheard of in the enterprise space, and many companies did not know that deep learning could be used for this purpose.
This marked the move of the company into AI, with the technology being an integral part of Amazon’s DNA today. The first place that AI is visible is the homepage of the Amazon website, which is uniquely tailored to each one of its 300 million users. With the help of the users’ browsing habits across the Internet, Amazon can accurately predict what they are most likely to buy. The site is built around this and places several recommended products in a prominent visual position that helps users notice it.
However, revamping the site is simply one side of Amazon’s success with AI. Using natural language processing and speech recognition algorithms, the company developed and released the Amazon Echo. The Echo is a smart home device that shares information with the help of natural conversation. After a host of AI-focused acquisitions, the company released Alexa to increase user engagement across all their platforms. The service allows users to easily access Amazon Music, Amazon Prime Video, and even place orders for products available on the site.
The third, and arguably the most important move Amazon made towards AI was to offer machine learning as a cloud service. Through its enterprise cloud arm known as Amazon Web Services (AWS); Amazon began offering accessible AI solutions for common enterprise problems. By utilizing its sizeable back-end infrastructure of cloud computing and combining it with its developed AI solutions, Amazon was able to capitalize on the initial part of the AI wave.
This netted it a sizeable profit and cemented AWS as the leader in cloud services, a position it still holds today. According to a 2018 Goldman Sachs reportOpens a new window , Amazon holds a leadership role in the cloud services market. The company has 47% of the market share for enterprise cloud service customers. The next closest competitor, Microsoft Azure, stands at a 15% market share.
2. Anheuser-Busch InBev
Anheuser-Busch InBev, or AB InBev is the world’s largest brewer of beer. The company brews over 500 well-known brands of beer, such as Budweiser, Corona, Stella Artois, Beck’s, Hoegaarden, and more. The company has a global reach, operating in more than 50 countries, and its brands are well known among the general populace. With an operation of this scale, it didn’t take long for the company to see the potential benefits of adopting AI.
By using services across Microsoft Azure and Google Cloud Platform, AB InBev brought analytics, AI, and predictive modeling to their processes. They began by using a platform known as Smart Barley to encourage sustainable and healthier farming of barley, the integral ingredient in beer. This platform aims to reduce water and fertilizer usage while maximizing yields by using data from past yields. Smart Barley features an artificial intelligence algorithm that analyzes this data and aims to allow farmers to harvest their crops sustainably.
In addition to this, they began to utilize AI for their back-end operations, starting with the supply chain. As a global brand that moves a lot of its products around the world, AI had the potential to vastly change the way AB InBev conducted its operations. By optimizing their data pipeline and deploying AI solutions from Azure, they were able to cut down on shipping times and fuel costs.
AB InBev also utilized the Google Cloud Platform to enhance their beer filtration process. The last stage of filtering a beer is known as the K Filter, which removes any remnants of the brewing process to create the final product. By harvesting data from sensors in the filtration equipment, the company used AI and ML to find the sweet spot for filtering the finished product. This not only reduced the cost of filtering it but also put AB InBev at an advantage over its competitors.
Over the past three years, the company has taken a much more proactive attitude towards adopting and using AI in its operations. It deployed an AI solution for maintaining the brewing machinery and is also developing a chatbot for customer experience. The company also improved back-end pricing operations using ML.
These AI innovations paid off, as the company saw revenue growthOpens a new window of 4.8% in 2018.
3. Starbucks
Starbucks started as a simple coffee shop in Seattle almost 40 years ago and has since been at the forefront of culture due to its target audience of young millennials. Today, it has over 30,000 locations worldwide and is the third-largest fast-food restaurant chain in the world. The fast-food chain has been looking at more ways to harness the vast amount of data offered by its customers.
In 2018, it was reported that the company conducts over 90 million transactions across its shops. This holds a vast trove of information regarding the customers’ buying habits and the products that are most likely to sell. Starbucks began harvesting this data and deriving insights with the help of AI. These insights enabled the company to make better decisions when it came to sales and the overall direction of the business. In addition to this, they also improved upon their direct marketing capabilities, enabling targeted advertising and other such strategies.
Another part of the Starbucks AI formula was utilizing its rewards program to harvest more data. With 13 million users in its loyalty program, it offered even more data on customers’ favorite drinks, especially overlaid over other data of weather and special promotions. By utilizing these data points, the company can serve a customer’s preferred drink correctly, even when they visit a Starbucks they haven’t visited before.
The Starbucks mobile application is also an integral part of their move towards AI and machine learning. With 17 million app users, the company can not only harvest data but also reach users in a more targeted manner. The app also features a virtual barista that helps users place their orders, thus allowing for a truly seamless experience powered by AI.
4. Twitter
Twitter has long been a popular social media service and has emerged into pop culture as an alternative to Facebook. However, as it grew in the latter half of the 2010s, the platform began seeing an increase in hate speech, terrorism, and illegal content. As the responsibility for such content fell on the company, it started utilizing artificial intelligence and machine learning tools to redefine the way it handled such content.
Twitter acquired a company known as Magic Pony Technology in 2016 to handle these problems, moving the talent to their in-house engineering team. The platform not only utilizes recommendation engines heavily but also uses them as a tool to filter unwanted content, thereby preventing it from going viral for the wrong reasons. The Twitter timeline also underwent a revamp – it went from showing the latest tweets first to a complex system of recommendations tailored to each user’s tweeting habits.
The social media platform also utilizes AI to crop images in a specific way, with the end goal of driving engagement. This AI even detects whether an image has adult content and then proceeds to display a message while hiding the image. The company has also deployed video recognition software to accurately categorize what a video is, thus improving its searchability.
The platform’s timeline is powered by a complex deep learning algorithm powered by neural networks. It was trained on what content would be relevant for a user by looking through millions of tweets and their accompanying reactions. In addition to tailoring its algorithms to remove the incidents of hate speech, the company also utilized machine learning to take down over 300,000 terrorist accounts in the first six months of 2017.
These new changes seem to have helped the company keep users engaged longer, as the website reportedOpens a new window 126 million monthly daily active users at the end of 2018. This represents a 9% growth year-over-year, with the company seeing its first profitable year in 2018.
5. Netflix
While Netflix started as a platform where users could simply pick whatever they wanted to watch, it has evolved into a platform that routinely recommends new shows and movies to users. These recommendations are on-point for most users, thus driving engagement on the platform by showing content that users are more likely to consume. However, the recommendations go beyond TV shows or movies, as the platform moves towards AI at each step.
The recommendation engine is at the heart of the Netflix experience. The engine first looks at a user’s likes and dislikes, provided by an accessible feedback system on the site and engagement of a user for a specific movie or show. Based on this data, it finds other media with similar attributes and recommends it to the user. In addition to this, the recommendation engine also looks at the browsing history of users with similar tastes and recommends shows or movies that they have previously watched.
Apart from recommending shows and movies, Netflix has a priority to drive engagement on its platform. Once an individual clicks on a movie or show, they are more likely to watch it. Keeping this in mind, the platform also generates and personalizes the thumbnail for recommended movies or shows. These thumbnails are recommended to users based on their past actions. For example, if a user clicks on a thumbnail with Brad Pitt, the algorithm will generate and recommend similar thumbnails to the user. Other attributes, like the genre of the movie, also play a role in this recommendation.
The platform also utilizes AI and data science to determine the best streaming quality to be provided to the user. This is integral to the user experience, as an inaccessible stream does not allow the user to watch what s/he would want to – using the data of what is watched most in a certain region, Netflix caches movies and shows accordingly. This decreases the time required to stream a show or series, thereby improving the overall customer experience.
This push towards personalizing the customer experience using AI seems to have netted Netflix a sizeable increase in revenue. Apart from other initiatives, such as producing more original content, Netflix has also doubled down on its AI capabilities. The net incomeOpens a new window of the company rose from $550 million to $1.2 billion from 2017 to 2018, marking a steady growth for the service.
Closing Thoughts
After looking at the trend of AI adoption in the enterprise, it becomes easy to think that every company can stand to benefit from deploying the technology. Implementing AI on a wide scale requires a company to redefine how it manages its data. However, a specific use-case or scenario is required for AI to bring success to the company.
Before implementing AI, it is crucial to first understand the needs of a company to identify the pain points where AI can help. Some companies simply cannot benefit from deploying AI in a cost-effective way. Implementing an AI solution requires fundamental restructuring of how data flows in the company.
With the rise of accessible cloud AI solutions, taking on the mantle of being an AI company seems like a natural choice. However, just as with any other technology, it is important to see how AI will impact the bottom line when deployed.
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