Artificial intelligence (AI) is increasingly getting attention from enterprise decision makers. Given that, it’s no surprise that AI use cases are growing. According research conducted by Gartner, smart machines will achieve mainstream adoption by 2021, with 30 percent of large companies using AI.
These technologies, which can take the form of cognitive computing, machine learning and deep learning, are now tapping advanced capabilities such as image recognition, speech recognition, the use of smart agents, and predictive analytics to reinvent the way organizations do business. Combined with other digital technologies, including the Internet of Things (IoT), a new era of AI promises to transform business.
- Building the Right Environment to Support AI
- Forrester Wave: Machine Learning Data Catalogs
- AI for Executives: Integrating AI into you Analytics Strategy
- Harvard Business Review: The Risks and Rewards of AI
- Making Sense of AI
- The Artificial Intelligence of Things
Here’s a look at 10 leading AI use cases and how organizations can use them to gain a competitive advantage:
Marketing: AI for Real Time Data
The use of real-time data, Web data, historical purchase data, app use data, unstructured data and geolocation information have introduced the ability to deliver information, product recommendations, coupons and incentives at the right time and place. AI allows companies to engage in personalized marketing and slide the dial closer to one-to-one relationships.
In addition, businesses gain competitive advantage by using machine learning and deep learning for sentiment analysis by analyzing e-mail and social media streams. More advanced systems can detect a person’s mood from photos and videos. This helps systems respond contextually and create more targeted marketing and interactions.
Retail Sales: AI for Voice and Image Search
Artificial intelligence in retail is transforming the way people shop and buy items ranging from clothes to cars. Voice search and image search are now widespread. Amazon and many other retailers now incorporate these tools in their apps. Next generation AI is also taking shape. For example, augmented reality (AR) lets shoppers view a sofa or paint color superimposed in their house or office. Virtual reality (VR) allows consumers to sit inside a vehicle and even test drive it without leaving home. Audi, BMW and others have developed VR systems for shoppers.
But the AI use cases don’t stop there. AI in retail extends to bots and virtual assistants that recommend products and provide information; algorithms that helps sales teams focus on high value customers and high probability transactions; and predictive analytics that factor in weather, the price of raw goods and components, or inventory levels to adjust pricing and promotions dynamically. Clothing retailer North Face, for instance, asks customers a series of questions related to a purchase at its website. Not only does this lead customers to the right product, it taps machine learning to gain insights that potentially lead to higher cart values and additional sales.
Customer Support: AI for Natural Language
AI in retail is emerging as a powerful force, but customer support is also harnessing the technology for competitive advantage. Bots and digital assistants are transforming the way support functions take place. These technologies increasingly rely on natural language processing to identify problems and engage in automated conversations. AI algorithms determine how to direct the conversation or route the call to the right human agent, who has the required information on hand. This helps shorten calls and it produces higher customer satisfaction rates. A Forrester study found that 73 percent of customers said that valuing their time is the most important thing a company can do to provide them with good online customer service.
Manufacturing: AI Powers Smart Robots
Robotics has already changed the face of manufacturing. However, robots are becoming far more intelligent and autonomous, thanks to AI. What is machine learning used for in factories? Many companies are building so-called “smart manufacturing” facilities that use AI to optimize labor, speed production and improve product quality. Companies are also turning to predictive analytics to understand when a piece of equipment is likely to require maintenance, repair or replacement.
For example, Siemens is now equipping gas turbine systems with more than 500 sensors that continuously monitor devices and machines. All this data is helping create the manufacturing facility of the future, sometimes referred to as Industry 4.0. Smart manufacturing–which merges the industrial IoT and AI–is projected to grow from $200 billion in 2018 to $320 billion by 2020, according to a study conducted by market research firm TrendForce.
Supply Chains: AI for Management
AI use cases in operations and supply chain management are growing. Organizations are turning to algorithms to improve fleet management, warehouse administration, logistics processes, freight brokering and numerous other tasks. This includes emerging areas such as drone deliveries and automated vehicles. The IoT, which places sensors on raw materials, components and products, is also reshaping business by collecting massive amounts of data, which can be fed into analytics engines that AI to make decisions.
Meanwhile, businesses are finding other AI uses cases. They are turning to image recognition, voice recognition, gesture interfaces and other tools along with AI to simplify input and automation. McKinsey & Company found that AI improves forecasting accuracy by 10 percent to 20 percent. This typically translates into a 5 percent reduction in costs.
Information Technology Management: AI Helps Routing
AI is redefining the way companies consume and manage a wide variety of IT resources. AI can detect traffic patterns as well as how computers, servers and other systems are being used at any given moment and adjust bandwidth dynamically, often through the use of cloud technology. AI use cases also include: preventative maintenance; identifying systems that are not patched or that are out of compliance; data and document classification; optimizing network performance, managing storage, including how data is archived.
Cybersecurity: AI Protects Assets
Protecting enterprise assets is an increasingly challenging proposition. Attackers are becoming more sophisticated and techniques are becoming more stealth. AI, including machine learning and deep learning, is helping reshape cybersecurity in a number of ways. AI algorithms move protection beyond increasingly ineffective whitelists, blacklists and firewalls by detecting unusual activity and patterns, including the movement of data packets.
Among the machine learning use cases: analyzing vast amounts of data about attacks and responses to uncover more effective methods for responding to different scenarios. Another emerging area is User and Entity Behavioral Analytics (UEBA), which relies on deep learning methods. ABI Research forecasts that machine learning in cybersecurity will boost big data, intelligence, and analytics spending to $96 billion by 2021.
Financial Service: AI Enables Intelligent Processing
Banks and other institutions already use AI to detect suspicious activity, including fraud. AI use cases also extend to intelligent process automation (IPA) and robotic process automation (RPA). This includes everything from apps that scan checks and make deposits to system that automate the movement of funds, based on interest rates. Robo-advisors, which use recommendation engines to replace traditional stockbrokers are also becoming commonplace.
Stock trading quants also handle trades using algorithms that incorporate an array of factors, information and variables. But deep learning, machine learning and other AI tools are also helping institutions understand customers and conditions at a much deeper level. This includes predictive analytics tools that can gauge when a person is in the market for vehicle loan or considering changing banks.
Life Sciences and Medicine: AI Leverages Algorithms
Numerous AI uses cases exist in life sciences and medicine. Researchers and healthcare providers are increasingly using machine learning, deep learning and other types of AI to pore over data and, using analytics, spot patterns that help healthcare providers treat at risk groups more effectively. Pharmaceutical companies and biomedical device makers are tapping AI to develop algorithms that produce more effective drugs, smart prosthetics, robotic surgical systems and virtual reality applications that help treat conditions such as depression and post-traumatic stress disorder (PTSD).
Smart Cities: AI Optimizes Myriad Functions
AI algorithms are becoming an integral part of smart city initiatives that aim to automate and improve a wide range of activities and operations. Among other things, they help determine how to operate transportation systems, optimize power plants and utilities, oversee law enforcement functions, and control an array of other functions. Smart city AI use cases are growing as officials deploy sensors and other devices that comprise the IoT, tap citizen data from smartphones and other devices, and deploy advanced analytics.