Over the years, artificial intelligence has continuously evolved and spread to more industries. Its scope expects only to grow with time. According to a Verified Market Research survey, the AI Software Market size was valued at USD 53.54 Billion in 2021 and is projected to reach USD 850.62 Billion by 2030, growing at a CAGR of 41.30% from 2022 to 2030.
People use AI in their daily life even without realizing it. We receive recommendations on which video to watch on YouTube, which shows to choose from on Netflix, and have a pre-created playlist on Spotify. Search engines give us the suggested predictions for the inquiry we are typing, and sort the results. Our phone detects and recognizes our identity by scanning our faces, checks grammar when we text messages, and groups photos based on the people depicted in them.
In its simplest form, Artificial Intelligence is a simulation of human cognition in machines programmed to think like humans and mimic their actions. It also refers to any device that exhibits signs associated with a human mind, such as learning and problem-solving. To achieve such functions, AI technology uses specially designed algorithms, which are complex mathematical formulas and operations. Artificial intelligence is not one technology but rather a collection of such technologies as machine learning (ML), natural language processing (NLP), and deep learning (DL).
The applications of artificial intelligence in different industries and platforms are endless. According to the World Economic Forum Report, 85 million jobs may be displaced by a shift in the division of labor between humans and machines. At the same time, 97 million new roles may emerge due to the new division of labor between humans, machines, and algorithms.
According to Grand View Research, AI finds its use in the following sectors (listed by their market share in 2021): Advertisement and Marketing, BFSI, Healthcare, Retail, Automotive and Transportation, Agriculture, Manufacturing, and Law.
The advertising and media segment is leading the market. The revenue share of the segment reached more than 20.0% in 2021. AI helps build more effective marketing strategies, improve customer experience, and change how businesses attract prospects and convert them into customers. Some platforms, like social media companies, are working towards fully automated advertising systems that can target markets, detect audiences, generate content, execute ad bidding and position the ads in an independent manner. The primary use cases where AI has been most impactful within the advertising and media industry are:
Personalization. Most consumers prefer dealing with businesses that recognize, remember, and provide relevant offers and recommendations. It can be personalized emails, dynamic websites changing based on user preferences, or customer-tailored suggestions. But delivering a truly personalized experience means collecting and analyzing a ton of data which could hardly be possible without AI. AI helps gain a personalized experience - AI tools gather, process, and analyze vast amounts of behavioral data, giving real-time insights regarding consumers’ purchasing intents, interests, demographic information, behavior patterns, etc. AI-based personalization allows brands to improve customer loyalty, increase sales and give them a deeper insight into what their customers want in real-time. AI also helps monitor ad spending, sales, and consumer behavior.
Effective audience segmentation and targeting the right audiences. AI helps to properly segment the audience regarding the kind of content that a specific group would prefer to see, providing more relevant advertising. Such an approach results in higher engagement rates and more conversions. Proper audience segmentation is possible by using all the data about a particular individual, like his online behavior and preferences.
Forecasting. AI helps to predict what customers will do in the future. AI-enabled tools analyze massive amounts of data to predict future trends and insights. The more data AI tools collect, the more accurate those predictions will be. Predictive marketing helps better forecast which products are most likely to sell, optimizes future campaigns, and experiments with personalized experiences. It is essential for budget allocation and improving ROI.
Social Media Listening. Using AI for social listening lets you effectively monitor and analyze customer feedback and direct mentions of your brand or discussions regarding specific keywords, topics, competitors, or industries. Businesses can gain insights and act on those opportunities. Thanks to AI, you can collect this data in real time. You will be able to come up with brilliant solutions and offer an immediate resolution to your customer’s problems if you listen to what your customers and prospects say about you.
One of the examples of AI-powered marketing personalization software is Human. It reveals products and services people are most interested in and helps marketers build marketing campaigns that resonate with each individual. Another outstanding example is Albert, an artificial intelligence marketing platform for the enterprise, driving fully autonomous digital marketing campaigns. Albert autonomously plans, executes, tests, optimizes, and evolves paid search, social, and programmatic campaigns 24x7.
AI is transforming the advertising and media world, easing its strategies and revolutionizing approaches from delivering ads and enhancing ad relevance to monitoring ad quality. AI allows advertisers to get the maximum benefits for their ad spend.
AI technologies significantly impact the Banking, Financial Services, and Insurance (BFSI) segment by making banking processes faster, money transfers safer, and back-end operations more efficient. Here are some use cases where AI has been most impactful within the BFSI industry:
Chatbots. Live chat software engages and interacts with customers 24/7 and enhances online conversations. Chatbots are essentially AI programs that work based on preset rules. They provide typical responses to customers' questions to help them work through their account details. They can also help open an account, deposit and withdraw money from an account, transfer funds between accounts, or between accounts at different banks, etc. They can also direct complaints to appropriate customer service units. Chatbots allow for sending notifications to consumers, providing balance data, suggesting how to save money, providing credit report updates, and paying bills.
Fraud Detection & Prevention. With customer preferences changing and technology advancing, banks started offering such services as e-banking, mobile banking, and real-time money transfer services, helping customers use these services whenever they want. But increased digitization also gives space to more fraudulent activities. AI in banking helps to detect risks associated with fraudulent activities - enhanced AI components are being added to the existing systems to identify previously undetected transactional patterns, data anomalies, and suspicious relationships between individuals and entities. It allows for a more proactive approach, where AI is used to prevent fraud before it happens.
Predictive Analytics: AI has opened the door to accurate forecasting and prediction. Data Analytics and AI allow for revenue forecasting, stock price predictions, risk monitoring, and case management. The exponential increase in the data collected has been pivotal in improving the performance of the models, resulting in a gradual decline in the level of human intervention required.
Credit Risk Management: The use of AI in credit risk management is gaining more popularity as it can help lenders assess a borrower's risk more accurately and improve the accuracy of credit decisions. It is achieved by analyzing data not included in a traditional credit score, like whether the borrower spends their capital on necessities or luxuries. AI could also help lenders identify risk factors that may not be obvious, like whether a borrower is using too much of their available credit. As a result, the market is moving towards insights-driven lending rather than expert judgment, which helps maximize the rejection of high-risk customers, minimize the rejection of creditworthy customers, and reduce credit losses incurred by financial institutions.
One of the examples of an AI chatbot in banking applications is Erica, a virtual assistant from the Bank of America. This AI chatbot can help customers manage their bank accounts, keep track of their spending habits, and handle tasks like credit card debt reduction and card security updates. Another prominent example of AI software in the BFSI field is a DreamQuark AI solution called Brain. Brain covers all the main activities of banks and insurance companies, such as segmentation, targeting, underwriting, credit scoring, asset management, compliance, anti-money laundering, fraud, dunning, satisfaction, and customer retention. It also provides outstanding prediction performance and unique decision interpretability within a single platform.
So, implementing AI in the BFSI domain results in better customer analysis and service, cleaner practices, a quicker turnaround of essential services, greater customization of services, better maintenance of documents, cash, and inventory, safer transactions, and a customer-friendly online experience.
One of AI's most prominent potential benefits in the healthcare sector is to help people stay healthy, so they don't need a doctor, or at least not as often. AI finds diverse applications in the industry and is anticipated to gain a leading share by 2030. AI can transform the medical sector from patient care to the development of drug discovery methodologies. Here are some use cases where AI has been most impactful within the healthcare industry:
Virtual nursing assistants and preliminary diagnosis. Virtual nursing assistants and chatbots are now available 24/7. They may help to make an initial disease diagnosis by asking a series of questions verbally or by chatting and capturing the information about the patient's symptoms. They can also answer questions and provide quick answers. Then, based on cognitive abilities, they can assist in self-diagnosis and prevent unnecessary hospital visits, saving the time of doctors and patients.
Smart Assistance. AI can analyze medical images. Patients, for instance, can send pictures of rashes, cuts, or bruises, and AI will determine necessary care. Or, AI can also analyze clinical research trials and identify patterns and insights often undetectable by manual human skill sets, helping doctors make more precise diagnoses. AI-based image recognition can see far more details on MRI and X-ray images than can human eyes. With the help of AI algorithms, radiologists can improve the speed and accuracy of interpreting X-rays, computed tomography scans (CT scans), magnetic resonance imaging (MRI), etc.
Remote Patient Monitoring. Remote patient monitoring allows the medical professional to monitor certain aspects of a patient's health from outside hospitals, cutting down on patients' travel costs and infection risk. Remote patient monitoring allows for managing acute and chronic conditions. It enables continuous collection and analysis of patient data, ensuring that providers can make the best possible treatment decisions for their patients. In addition to making more effective treatment decisions, providers can also keep a close eye on their patient's conditions and make more timely interventions to avoid readmissions and emergency care.
Drug Discovery. AI could assist pharma companies in getting medicines to market faster. In the drug discovery stage, researchers go through various processes like gaining new insights into a disease, finding new compounds, testing these molecular compounds, etc. It can predict the properties of molecular compounds that are candidates for drugs more accurately than humans. Using AI technology, researchers can identify suitable patients to participate in clinical trials and monitor their medical responses more efficiently and accurately.
Robotics Applications. The potential for both AI and robotics in healthcare is vast. Robots help doctors perform everything from minimally-invasive procedures to open heart surgery complex procedures with precision, flexibility, and control that go beyond human capabilities. Robots are equipped with cameras, mechanical arms, and surgical instruments to augment doctors' experience, skills and knowledge to create a new kind of surgery. These robots help surgeons achieve new levels of speed and accuracy while performing complex operations with AI- and computer vision‒capable technologies. Some surgical robots may even be able to complete tasks autonomously, allowing surgeons to oversee procedures from a console.
Buoy Health is an example of AI software in the healthcare industry. It is an AI-based symptom and cure checker that uses algorithms to diagnose and treat illness. The software chatbot listens to a patient's symptoms and health concerns, then guides that patient to the correct care based on its diagnosis. Another example is a da Vinci robotic surgical system, with robotic arms, precise movement, and magnetized vision, which enables doctors to perform precision surgery that would not have been possible with a completely manual approach.
The future of AI in health care could include tasks that range from simple to complex. It can be anything from answering the phone to medical record review, population health trending and analytics, radiology image reading, clinical diagnosing, and treatment planning. It can even be talking with patients. Implementing AI solutions increase convenience and efficiency, reducing costs and errors and making it easier for more patients to receive the health care they need.
Retail is anticipated to witness a substantial rise owing to the increasing focus on providing an enhanced shopping experience. AI technologies will bring significant improvements to inventory and supply chain management. Robot assistants will share shopping information with customers and perform many repetitive tasks in retail stores. Store automation can shorten queues, reduce human personnel, and save operating costs. Here are some use cases where AI has been most impactful within the retail industry:
Automation. AI is transforming the retail industry by automating tasks previously performed by in-person workers. Autonomous robots can move pallets of goods or pick up purchases for individual customers. Checkouts at shops and supermarkets can be carried out without cashiers but automatically when exiting the store. Automating trucks, ships, delivery robots, etc. boosts efficiency, allowing particular levels of a supply chain to operate autonomously.
Supply Chain Optimization. AI can help the retail supply chain with restocking. It reviews sales history, location, weather, trends, promotions, and past customers' buying patterns and calculates the demand for a particular product.
Enhancing Customer Experience. AI enables customized recommendations, visual search, augmented reality (AR)/visual reality (VR) and detects the mood of customers during the shopping process. For example, the AI technology of chatbots can help customers with store navigation, send notifications about new collections, suggest similar products, and provide personalized product suggestions. Visual search allows people to upload images of the item they are interested in and get the products they are looking for. Augmented reality applications allow people to place true-to-scale digital furniture models in their homes with smartphones. With virtual fitting rooms of clothing retailers, customers don't have to leave their homes to find a perfect item or outfit. The AI technology places the item on the customer's image to better understand the size, style, and color.
Price adjustments and predictions. AI reads and interprets the statistics according to demand, seasonal trends, characteristics, customer choices, other products, promotional activities, sales figures, etc., and makes the most accurate price forecasting based on collected data.
One of the examples of AI retail applications is Macy's On Call app. It is tailored with answers for each specific store. Customers open the app when they're in the store and can chat with an AI bot to get directions to a particular item or check if something is in stock. Another example is Olay's Skin Advisor. The application can analyze a customer's selfie, assess the skin health, determine a customer's actual age and recommend medication for the problem areas.
The future of AI in retail looks promising. By processing vast amounts of data, AI will help create enhanced customer experiences, whether online or in physical stores.
The automotive and transportation industry has undergone multiple changes and revolutions over the last few hundred years, developing from animal-drawn carts to airplanes. Now we are at the point where significant breakthroughs are achieved with the help of AI when vehicles can navigate and move without human intervention. Here are some use cases where AI has been most impactful within the automotive and transportation industry:
Autonomous vehicles. To develop a self-driving car, AI uses a complex combination of a vehicle’s camera, radar, cloud services, GPS, and control signals. Technology companies like Lift and Waymo and automakers like Tesla, Toyota, BMW, and General Motors have spent billions of dollars developing self-driving cars. Despite concerns around the technology and its ability to safeguard passengers from harm, a report published by the accounting giant KMPG predicts that by 2050, the adoption of self-driving vehicle technology could reduce the frequency of accidents by approximately 90%. AI in transportation is not limited to roads. Remote-controlled ships, air drones, and driverless trains are also currently in their development stage.
Traffic Management. AI can process historical, real-time, and predictive data using high-resolution cameras, automated traffic signals, automated plate recognition systems, and other traffic details. This data can then be sent to the cloud for analysis of traffic patterns with big data analytics and an AI-powered system. The system can predict optimal bus schedules, plan logistics, and notify the users of the quickest routes to their destinations.
Parking management. AI can effectively provide parking management solutions. With automated number plate reading, time tracking, and billing possibilities, it can notify when the empty parking space is available, suggest queue time estimations, detect unauthorized parking, enhance parking security, and many more.
Vehicle Remote Inspections. AI allows inspecting and evaluating vehicle damage remotely to assess the cost of the repairs and which parts are needed, thereby saving time, fuel, and miles on cars. Remote inspections can also replace other off-site reviews, such as those required for insurance and license plate renewals. Generally, AI-powered checks can be considered more objective and consistent than human inspections and can therefore be more accurate. It can also help detect mechanical problems and send alerts when routine maintenance is needed.
InspektLabs is one example of automotive vehicle AI-driven inspection services offering to automate photo- or video-based inspection of any physical asset such as cars, bikes, properties, etc. Parquery AI is an example of an AI-powered parking management system.
Using AI in transportation can assist the industry in improving passenger safety, improving traffic efficiency, relieving traffic congestion and reducing accidents, freeing up driver time, cutting carbon emissions, making parking more convenient, and lower overall costs.
The agriculture industry is turning to AI technologies to help yield healthier crops, control pests, monitor soil and growing conditions, organize data for farmers and help with the workload. Here are some use cases where AI has been most impactful within the agricultural industry:
Disease and weeds detection and prevention: Agricultural AI techniques detect diseases, pests, and nutritional deficiencies. AI sensors can identify weeds and then, based on their identification, select the appropriate product to control detected weeds and spray the exact amount of the product in that area. What’s more, some AI techniques, for instance, AI-powered robots, allow removing weeds mechanically. These robots can be taught to detect and remove plants that show signs of disease, to minimize the impact on the crop. Both approaches help limit or even eliminate the unnecessary use of toxic substances in agriculture that adversely affect human health and nature.
Growth requirements monitoring and adjustment. The ideal requirements necessary for the growth of a given crop, such as the amount of water, herbicides, and nutrients, can change depending on weather conditions, time of year and soil characteristics, etc. AI allows receiving such data in real-time every day and making better decisions by identifying problems. Even in controlled greenhouse environments, some requirements can change. AI can optimize various aspects of the indoor farming environment, such as climate, temperature, humidity, moisture, and sunlight.
Labor resources issue solving. There is a shortage of workforce in the agriculture industry these days. Humanity is moving away from being an agrarian society with large quantities of people living in cities. AI robotic machines can harvest crops in more significant amounts and faster than human workers. They can identify and remove weeds more accurately and with fewer errors, and their use significantly reduces labor costs.
Plantix is one of the plant diagnostic applications proving how valuable this AI agriculture innovation can be. It provides ecological information and advice to farmers, researchers, and stakeholders. The developers behind the app have created a database of 100,000 photos of sick plants, which is used by AI-powered image recognition to identify over 60 diseases. An example of an AI-powered robot is a strawberry harvesting robot called Harvest CROO. The robotic machine provides a strawberry harvesting service that automates crop management, harvesting, and packing of specialty crops.
AI technologies help in the production of healthier crops, pest and weed control, managing workload, monitoring soil health, and various other aspects related to the farm value chain. With the integration of the latest technological innovation in agriculture, agriculture production and efficiency are growing significantly.
AI in the manufacturing industry radically transforms how companies run their production lines to optimize yields and maximize quality. Some potential benefits are greater efficiencies, lower costs, improved quality, and reduced downtime. The most important AI use cases in the manufacturing industry are the following:
Predictive maintenance. Using AI, predictive maintenance practice helps to foresee and avoid machine failure, minimize disruption, and resolve problems quickly. The approach allows manufacturers to avoid costly repairs and loss of productivity caused by breakdowns. Deloitte report states that predictive maintenance increases productivity by 25%, reduces breakdowns by 70%, and lowers maintenance costs by 25%.
Supply chain and inventory management. AI technologies make better predictions of the complex interactions between each production unit and automates requests for parts, labor, tools, and repairs. Thus, with AI technologies, manufacturers of complex products consisting of diverse components can decrease delays, breakdowns, or mistakes, resulting in shutting down a product assembly point. AI can also improve inventory management by analyzing inventory levels and usage trends, providing insights for organization and ordering. Digital warehouse management systems can provide far better accuracy over inventory records than traditional systems.
Quality testing. American Society for Quality states that many organizations have actual quality-related costs as high as 15-20% of sales revenue, some going as high as 40% of total operations. AI allows new quality testing procedures that result in significant savings. For instance, AI-powered image recognition systems make visual inspection and fault detection of products that must be eliminated from a production line.
An example of AI-powered quality testing software is Google Cloud Visual Inspection. It automates visual inspection tasks using a set of AI and computer vision technologies that enable manufacturers to transform quality control processes by automatically detecting product defects. Navio predictive maintenance solution is designed to help manufacturers reduce equipment failures, machine downtimes, and costs.
AI in manufacturing can transform the industry, making it more effective and productive. It can help enhance record-keeping, inventory management, and supply chain flow. Machine data analysis can diagnose existing problems and improve machine health, thereby saving manufacturers time and money on maintenance and repairs.
The legal industry has been very cautious about technology. With many of the activities conducted by lawyers being repetitive and rule-based, AI potentially represents the greatest threat to the legal profession. A study by Deloitte estimated that AI will automate about 114,000 jobs in the legal sector by 2036. However, AI is not yet ready to replace humans in the legal profession. It is more of an opportunity than a threat. Its main aim is to reduce the time spent on manual tasks. And early adopters are likely to provide more efficient and cost-effective legal services to existing and potential clients. The most important AI use cases in the law industry are the following:
Documents Review and Legal Research: AI streamlines document analysis for legal use as machine review of documents is much more efficient than human review. AI allows lawyers to scan documents using search terms or specific parameters. Once a particular type of document is denoted as relevant, AI algorithms can work to find other similarly relevant documents. As a result, lawyers get almost instant responses—which is significantly faster than scanning hard copies.
Litigation prediction. AI can provide lawyers with recommendations on making decisions and actions based on access to previous case laws, a judge’s history, or win/loss rates. Lawyers can quickly review those precedents and assess the likelihood of a successful outcome for a lawsuit.
Document management and automation. AI-powered document management software stores and organizes contracts, case files, notes, emails, etc. This method of storing and organizing digital files, along with full-text search, makes documents much easier to find. Document automation helps law firms save time and effort by creating documents using intelligent templates of letters, agreements, bills, invoices, and other legal documents. The lawyer must only fill form fields directly from case records into the templates and receive the ready document. Many technology companies and law firms are partnering to create programs that can assist with specific practice areas, including transactional and due diligence, bankruptcy, litigation research and preparation, real estate, and many others.
Lex Machina is an example of legal analytics software that provides accurate, valuable data that combines the knowledge of legal experts with AI to predict the behavior of judges, attorneys, and parties in U.S. courts. Lawgeex is an example of an AI-powered platform that automates and accelerates the review and approval of everyday business agreements and does it more accurately than humans. Lawgeex understands the contractual context as well as user position.
AI can increase attorney productivity, speed up research, and avoid costly mistakes. Incorporating AI into a law firm’s systems and operations is gradual, so early adopters will have a significant advantage over firms that lag in adopting the technology.
AI is rapidly being applied to all major sectors of the economy and society, revolutionizing industries with its applications and helping solve complex problems. It will displace some jobs but will also create new opportunities instead. Artificial intelligence shall automate many kinds of work, making life easier and more comfortable.
Do you agree with the use cases that we selected? Or is there something important that we should add? Feel free to send us your comments on this article. We are excited to hear from you!
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