Common Use Cases of Generative AI

Generative AI has transformed various businesses by automating manual administration, increasing efficiency, and simplifying business processes. Taking advantage of the skills of generative AI, human resources are released to work on strategic and innovative initiatives that lead to productivity and innovation in their companies. Public Generative AI technologies such as Large Language Models (LLMs) and their various use cases will be reviewed and examined in the following sections.

Healthcare

Generative AI is a key force in the healthcare industry, where it can be used to automate routine and time-intensive activities. It improves efficiency and precision in its operations, enabling medical workers to focus on enhancing patient care and outcomes. Personalized generation of patient care, prescriptions, clinical recommendations, and administration of administrative issues such as scheduling appointments for patients are improved. These are the main improvements which contribute a lot to the telemedicine industry, therefore creating the potential of visiting a doctor in the comfort of your own home. Generative AI models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) collaborate with each other to process medical images, identify medical abnormalities, and contribute to the research and development of novel medicines in the future.

Insurance

Likewise, the insurance sector uses Generative AI to process claims and automate the process by extracting data provided in forms, verifying claims, and helping in fraud prevention. The usage of Natural Language Processing (NLP) tasks, referred to as Optical Character Recognition (OCR) or Document AI, are both used to read, comprehend, and automate data entry processes. Insurance fraud can be identified through the adoption of pattern recognition on insurance claim information through Large Language Model (LLM) algorithms. Also, LLM-based chatbots aid in customer service, increasing the quality of customer service through the reception of customer requests and to handle policy requests to an extent automatically until the supervision of a human being is necessary.

Finance and Accounting

Generative AI is useful in the field of finance to process market trends and propose a portfolio with increased investment policies. Analytics of future trends, pricing of assets, and Generative AI models enable economic indicators by learning using previous financial data in order to determine intricate patterns and associations in the data. Portfolio management models can replicate a number of effects on portfolio performance, economic climates, market situations, and events. At that point, financial professionals are able to develop and refine their investment strategies to maximize risk-adjusted returns, improve portfolio management, and make better investment decisions consequently taking into account the following factors: risk tolerance, anticipated returns, and investment horizons.

Generative AI possesses the resources to automate tiresome and dull jobs in the accounting industry like: data entry, account reconciliation, and financial report generation. Special AI deep learning functions that are used in transformer models are designed to automate accounting data entry and account audit functions through the extraction of information in different documents offered, loading databases, and resolving inconsistencies to minimize created output errors related to manual input. Preparation of financial statements including balance sheets, income statements, etc., and cash flow statements can also be automated and made more efficient, through financial analysis and templates of financial experts.

Workflow Automation

Workflow Automation helps organizations in different sectors to automate and streamline processes. Part of the tremendous advantages is to increase productivity, boost user experiences, and operations of generative AI. Email automation will automate routine administrative functions such as the scheduling of appointments, information processing, businesses, and management. This frees up human resources to work on more strategic issues for the organization. Workflow bottlenecks are discovered, and recommendations are offered to improve general functions of an organization and utilize resources in the best way possible, thereby enhancing productivity. In order to enhance user experience, user tastes and past data are considered to give customized customer suggestions that can be made to improve the overall user experience with a platform of an organization. Thanks to large language models, customers can also interact with chatbots to seek their help by troubleshooting problems and to answer questions that could be raised by the customer.

Human Resources

Integration of generative AI in Human Resource Management (HRM) software has changed how the management of employee benefits can be streamlined to take on the time-consuming processes and improve operational efficiency on activities that are based on the handling of paperwork manually, disjointed systems, and disjointed communication into one. Generation of HR documents can be automated with the help of Optical Character Recognition (OCR) and Document AI technology such as employee contracts and on-boarding letters by filling in the proper templates with the proper data and merging the pertinent information about employees with payroll, benefits, and time-tracking systems into the HR database. Chatbots based on generative AI dedicated to HRM systems offer consolidated communication channels with real-time response to employee queries that give uniform answers by responding to questions on company policies, employees’ training processes, and employee perks. Workflow automation is commonly used to assist in quickening HR responsibilities such as approval procedures and leave requests for employees within an organization.

The ability of generative AI in these industries is only bound to grow as it develops, promoting creativity and productivity.

-Pradeep Arumugam