What are the applications of AI?
AI has entered a wide variety of industry sectors and research areas. The following are several of the most notable examples.
AI in healthcare
AI is applied to a range of tasks in the healthcare domain, with the overarching goals of improving patient outcomes and reducing systemic costs. One major application is the use of machine learning models trained on large medical data sets to assist healthcare professionals in making better and faster diagnoses. For example, AI-powered software can analyze CT scans and alert neurologists to suspected strokes.
On the patient side, online virtual health assistants and chatbots can provide general medical information, schedule appointments, explain billing processes and complete other administrative tasks. Predictive modeling AI algorithms can also be used to combat the spread of pandemics such as COVID-19.
AI in business
AI is increasingly integrated into various business functions and industries, aiming to improve efficiency, customer experience, strategic planning and decision-making. For example, machine learning models power many of today's data analytics and customer relationship management (CRM) platforms, helping companies understand how to best serve customers through personalizing offerings and delivering better-tailored marketing.
Virtual assistants and chatbots are also deployed on corporate websites and in mobile applications to provide round-the-clock customer service and answer common questions. In addition, more and more companies are exploring the capabilities of generative AI tools such as ChatGPT for automating tasks such as document drafting and summarization, product design and ideation, and computer programming.
AI in education
AI has a number of potential applications in education technology. It can automate aspects of grading processes, giving educators more time for other tasks. AI tools can also assess students' performance and adapt to their individual needs, facilitating more personalized learning experiences that enable students to work at their own pace. AI tutors could also provide additional support to students, ensuring they stay on track. The technology could also change where and how students learn, perhaps altering the traditional role of educators.
As the capabilities of LLMs such as ChatGPT and Google Gemini grow, such tools could help educators craft teaching materials and engage students in new ways. However, the advent of these tools also forces educators to reconsider homework and testing practices and revise plagiarism policies, especially given that AI detection and AI watermarking tools are currently unreliable.
AI in finance and banking
Banks and other financial organizations use AI to improve their decision-making for tasks such as granting loans, setting credit limits and identifying investment opportunities. In addition, algorithmic trading powered by advanced AI and machine learning has transformed financial markets, executing trades at speeds and efficiencies far surpassing what human traders could do manually.
AI and machine learning have also entered the realm of consumer finance. For example, banks use AI chatbots to inform customers about services and offerings and to handle transactions and questions that don't require human intervention. Similarly, Intuit offers generative AI features within its TurboTax e-filing product that provide users with personalized advice based on data such as the user's tax profile and the tax code for their location.
AI in law
AI is changing the legal sector by automating labor-intensive tasks such as document review and discovery response, which can be tedious and time consuming for attorneys and paralegals. Law firms today use AI and machine learning for a variety of tasks, including analytics and predictive AI to analyze data and case law, computer vision to classify and extract information from documents, and NLP to interpret and respond to discovery requests.
In addition to improving efficiency and productivity, this integration of AI frees up human legal professionals to spend more time with clients and focus on more creative, strategic work that AI is less well suited to handle. With the rise of generative AI in law, firms are also exploring using LLMs to draft common documents, such as boilerplate contracts.
AI in entertainment and media
The entertainment and media business uses AI techniques in targeted advertising, content recommendations, distribution and fraud detection. The technology enables companies to personalize audience members' experiences and optimize delivery of content.
Generative AI is also a hot topic in the area of content creation. Advertising professionals are already using these tools to create marketing collateral and edit advertising images. However, their use is more controversial in areas such as film and TV scriptwriting and visual effects, where they offer increased efficiency but also threaten the livelihoods and intellectual property of humans in creative roles.
AI in journalism
In journalism, AI can streamline workflows by automating routine tasks, such as data entry and proofreading. Investigative journalists and data journalists also use AI to find and research stories by sifting through large data sets using machine learning models, thereby uncovering trends and hidden connections that would be time consuming to identify manually. For example, five finalists for the 2024 Pulitzer Prizes for journalism disclosed using AI in their reporting to perform tasks such as analyzing massive volumes of police records. While the use of traditional AI tools is increasingly common, the use of generative AI to write journalistic content is open to question, as it raises concerns around reliability, accuracy and ethics.
AI in software development and IT
AI is used to automate many processes in software development, DevOps and IT. For example, AIOps tools enable predictive maintenance of IT environments by analyzing system data to forecast potential issues before they occur, and AI-powered monitoring tools can help flag potential anomalies in real time based on historical system data. Generative AI tools such as GitHub Copilot and Tabnine are also increasingly used to produce application code based on natural-language prompts. While these tools have shown early promise and interest among developers, they are unlikely to fully replace software engineers. Instead, they serve as useful productivity aids, automating repetitive tasks and boilerplate code writing.
AI in security
AI and machine learning are prominent buzzwords in security vendor marketing, so buyers should take a cautious approach. Still, AI is indeed a useful technology in multiple aspects of cybersecurity, including anomaly detection, reducing false positives and conducting behavioral threat analytics. For example, organizations use machine learning in security information and event management (SIEM) software to detect suspicious activity and potential threats. By analyzing vast amounts of data and recognizing patterns that resemble known malicious code, AI tools can alert security teams to new and emerging attacks, often much sooner than human employees and previous technologies could.
AI in manufacturing
Manufacturing has been at the forefront of incorporating robots into workflows, with recent advancements focusing on collaborative robots, or cobots. Unlike traditional industrial robots, which were programmed to perform single tasks and operated separately from human workers, cobots are smaller, more versatile and designed to work alongside humans. These multitasking robots can take on responsibility for more tasks in warehouses, on factory floors and in other workspaces, including assembly, packaging and quality control. In particular, using robots to perform or assist with repetitive and physically demanding tasks can improve safety and efficiency for human workers.
AI in transportation
In addition to AI's fundamental role in operating autonomous vehicles, AI technologies are used in automotive transportation to manage traffic, reduce congestion and enhance road safety. In air travel, AI can predict flight delays by analyzing data points such as weather and air traffic conditions. In overseas shipping, AI can enhance safety and efficiency by optimizing routes and automatically monitoring vessel conditions.
In supply chains, AI is replacing traditional methods of demand forecasting and improving the accuracy of predictions about potential disruptions and bottlenecks. The COVID-19 pandemic highlighted the importance of these capabilities, as many companies were caught off guard by the effects of a global pandemic on the supply and demand of goods.