Two of the biggest technology development booms in the past year are large language models (LLM) and similar artificial intelligence (AI) systems. With popular generative AI tools like ChatGPT quickly becoming commonplace for users worldwide, AI is transforming how people and organizations collect and manage data. This includes the industrial marketplace and enterprise analytics, especially in healthcare, financial services, customer service, and education.
According to recent market reports, the AI market is expected to grow in value from $11.3 billion in 2023 to $51.8 billion by 2028. With ChatGPT setting a record as the fastest-growing internet app in history, it is safe to assume that AI and LLMs are here to stay and will continue to develop at an exponential rate. To fully realize the impact LLMs are having on the marketplace, it is crucial to have a comprehensive understanding of the LLM market and how it is transforming industries worldwide.
The industrial impact of the large language model market
Understanding the scope of the overall marketplace and how AI technologies continue to impact major industries is essential before implementing LLMs into an organization’s operational workflow. With the value of the LLM market estimated to quadruple by 2029, the potential for innovation, progression, and expansion is increasing. LLMs provide global industries with the flexibility to automate cognitive tasks, engage customers around the clock, and uncover integral insights from critical data and analytics. They aid enterprises in anticipating shifts in the market while reducing the time, effort, and costs of doing so.
In November 2022, LLMs made a huge splash with the launch of ChatGPT. Trained by OpenAI, the model quickly grabbed attention from the public and business professionals. In March 2023, a more advanced version, known as GPT-4, was introduced into the marketplace. This development impressed industry leaders with its expanded and disciplined applications to specific industries, including healthcare, education, and finance, while vastly improving the customer experience. Because these models are trained on domain-specific data, they produce accurate and trusted results for industries requiring complex domain knowledge. By establishing the parameters, LLMs have the potential to learn extensive text datasets along with intricate architectures and infrastructures. This gives organizations more reliable insights and allows them to focus their efforts elsewhere and maximize the potential for business growth.
Transforming healthcare, education, financial services, and the customer experience
While LLMs are adaptable for several larger-scale industries, they have mainly impacted key industries, including:
- Healthcare. The introduction of these models immensely impacts patient care and medical research. Patient-caregiver rapport is an essential part of ensuring patient satisfaction. LLMs help provide accurate and communicable reports and diagnostics that allow caregivers to communicate precisely and comprehensively with patients. These models also affect the overall decision-making process of medical professionals by providing the latest medical evidence to ensure the proper actions are taken for efficient patient care.
- Education. The integration of comprehensive LLMs into education can deliver significant benefits to educators, including personalized learning, automated tasks, innovative educational tools, and constructive feedback. Models can customize lesson plans for individual students while monitoring real-time progress. They impact the quality of the student’s experience by delivering the latest learning technologies and interactive resources. Models offer helpful feedback and support to students by providing in-depth answers to important inquiries via a chatbot functionality to further assist students 24/7. For instructors, models effectively and efficiently automate processes, including grading and lesson planning.
- Financial services. Because of the nature and sensitivity of the information necessary for this industry, financial service professionals need to have an accurate view of the data and reliable security to protect their customers’ funds and information. Some of the ways in which LLMs are applied include fraud detection and prevention, risk assessment/management, and providing financial advice. Models analyze higher volumes of financial data in real time to quickly identify fraudulent behavior. They can also aid professionals in avoiding potential risks in loans and investments by analyzing data sources through machine learning (ML) algorithms that improve decision-making. From there, human advisors can give customers financial advice and personalized recommendations powered by analytical guidance.
One common benefit LLMs provide most industries is an improved customer experience. Through automated chatbots and interactive customer capabilities, models offer consumers quicker and simpler communication tools to ensure all inquiries and needs are properly addressed. Enterprises develop higher customer satisfaction, loyalty, and retention by providing customers with around-the-clock rapid response and support.
Understanding limitations and weighing the options
Like any technology, there are risks and limitations to integrating an LLM into an organization. Optimizing the value of an LLM means training it according to the established standards and parameters that best suit a unique industry. They can spread harmful information or expose sensitive customer data without guidelines. While these models deliver advanced reporting and analytics, it is imperative the behavior remains predictable and controlled without any sense of bias in its data output. The LLM model must also be domain-specific to maximize its value. Ensuring a model meets specified needs through established rules is necessary for success.
Specific use cases and business goals are required for an enterprise to optimize an LLM. As such, it is important for technology teams to train and fine-tune a model to coincide with the final endgame. It may also be best to start small and build from there, such as incorporating a customer chatbot to test customer satisfaction. Regardless of the level of implementation, it’s best to balance LLM automation and analysis with human oversight and monitoring to ensure accuracy and accountability.
Innovation and the future of large language models
AI and LLMs have fundamentally altered how people and organizations interact with technology. While they drive innovation and automation across multiple sectors simultaneously, they also change how professionals make decisions and communicate with customers. They have redefined industry-specific domains while enhancing industrial growth and innovation potential. With further development and research, it is only a matter of time before these AI-driven models can replicate the qualities of human speech and interaction.
There is no certainty as to the extent of AI developments and capabilities. While the potential for innovation and development seems endless, AI’s rapid growth in business and industry proves that developers have only reached the tip of the iceberg. As AI functionalities become faster and more proficient, the healthcare, education, and financial service industries will thrive further and deliver trustworthy, reliable care and services for patients, students, and customers worldwide. Because LLMs offer operational support in data and analytics, there will be cost savings as professionals transfer their time and efforts elsewhere. It is an exciting time for technological innovation as users and developers navigate where it will take business in the future.
About the Author
Siddhant Raman is a software engineer and subject matter expert in software development, artificial intelligence, database design, and cloud computing. He drives innovation through data-driven solutions that empower businesses in the digital landscape. Siddhant holds a bachelor’s degree in computer science from the University of South Florida, Tampa. Connect with Siddhant on LinkedIn.
Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.
