The demand for large language models is at an all-time high. Organizations from all industries show great interest in this technology, finding many applications for it in different settings. LLMs are quickly becoming a crucial component in chatbots and AI virtual assistants, with organizations spending considerable sums on their acquisition.
In 2024, the market for this product shows no signs of slowing down as large language models continue to become better at working with human language input. In this article, we’ve gathered the latest information about the large language model market, the hype surrounding this technology, its effects on different industries, and the future of adoption.
LLM Statistics: Overview
Large language models have been around since 2017. With each version, they became better at their tasks and delivered faster language processing. Starting with 2022, these models became more robust and accurate in their results, as shown by LLaMA, Bloom, and GPT-3.5. Their popularity has caused a boom in the large language model market, which has led to the following results.
- The global LLM market is projected to grow from $1,590 million in 2023 to $259,8 million in 2030. During the 2023-2030 period, the CAGR will be at 79,80%.
- The market will reach $105,545 million in North America by 2030, with a CAGR of 72,17%.
- In 2023, the world’s top five LLM developers acquired around 88,22% of the market revenue.
- By 2025, it's estimated that there will be 750 million apps using LLMs.
- In 2025, 50% of digital work is estimated to be automated through apps using these language models.
Is The LLM Hype Worth It?
As the demand for LLM solutions gains momentum, so do the expectations about their capabilities. People have come to view them as truly thinking machines capable of doing everything. These high expectations have led to a degree of dissolution with the technology as these models require improvement.
While there’s a certain degree of truth concerning the versatility of this technology, otherwise, there wouldn’t be all the large language model statistics going around, it still has a long way to go. It’s easy to forget this fact when looking at products like OpenAI’s Sora, but large language models still suffer from hallucinations, biases, and inaccuracies.
For example, when working with real business data used by insurance companies, LLM products show only 22% accuracy. This number drops to zero when the models have to process mid and expert-level requests. In addition to the inner workings of LLM, there are ethical concerns involved with many cases of deep fakes.
Politicians like Donald Trump, Kamala Harris, and Joe Biden have been subjects of these artificial videos, portraying them in a less than favorable light. While these factors are concerning, they can’t fully negate the impact LLM products have already had on different sectors of the economy. The next part of our guide is all about them.
Industries Using LLM Solutions
Products that use large language models are highly popular among organizations working in retail, e-commerce, marketing, education, finance, and healthcare. Here’s how enterprises in these industries utilize LLM products in their daily work.
- E-commerce & retail
Large language models are highly popular among enterprises working in retail and e-commerce. Usually, these components are added to chatbots that enhance the customer experience. The capabilities of large language models are most prominent in the following areas.
Buying assistance
Software products using this component help customers select the items by asking additional questions about their needs. They aid in adding products to carts, selecting payment methods, and completing the checkout process. This lowers cart abandonment and increases higher sales.
Product descriptions
E-commerce and retail businesses use LLM-based chatbots to make engaging and informative product descriptions quickly. This is an important selling point, as 76% of customers check item descriptions before making decisions.
Personalized recommendations
Through customer data analysis, large language models provide tailored product suggestions, increasing the up-sale and cross-sale capacities of enterprises. Personalized recommendations are highly important to 91% of clients.
- Education
Learning providers use LLMs to tailor personal academic paths for students. They integrate the models into different types of edtech, including learning management systems and administrative chatbots. Here, large language models help in several areas.
Automated grading
LLM-based solutions help educators automatically grade assignments, tests, and quizzes. They offer feedback, highlighting areas where students must improve their understanding of subjects. This saves time and effort for learning providers.
Performance tracking
These models track the academic performance of students. By analyzing their grades, sophisticated products identify potentially failing learners and notify educators to take swift action.
Personalized tutoring
Students often use LLMs to revise their knowledge of different disciplines. The models can be fine-tuned to produce tailored tests and quizzes. Using this approach, students improve their test scores by 62%, according to Knewton.
- Finance
Large language models used in banking and financial services make them more accurate, effective, and transparent. There are several areas where LLM-based solutions enhance daily operations.
Compliance support
Financial organizations use these models to keep up-to-date with the latest rules and regulations. They offer real-time updates on changes in laws and rules of conducting business, address compliance-related requests, and assist with the necessary documentation.
Fraud detection
Additionally, the LLMs help financial institutions identify various types of fraudulent activities. By analyzing payment history and patterns, they find anomalies in credit card use and transactions, notifying account owners instantly.
Financial guidance
Large language models play the role of financial advisors, helping customers make informed decisions. They offer guidance on investments, insurance, and retirement plans. Almost 60% of Bank Of America’s clients use LLM products for these purposes.
- Healthcare
Advanced large language models are often embedded into chatbots used by organizations to handle daily patient flow, make appointments, and provide information about their services. There are several more areas where LLMs help healthcare providers tremendously.
Clinical documentation
Solutions using these models aid healthcare organizations in patient data gathering, storage, and access. LLMs also analyze this information, structure, and categorize it. With this approach, organizations save time and effort on documentation.
Diagnostics
Large language models assist healthcare professionals by analyzing patient data. This leads to more accurate diagnostics and better treatment outcomes. They achieve 83,3% accuracy by analyzing historical data and similar cases.
Patient assistance
Healthcare organizations can improve their daily work through patient assistants. Programs built on LLMs allow individuals to tell their symptoms and receive treatment recommendations. Should this fail, people can always make appointments.
- Marketing
Businesses in advertising and marketing have been using LLMs to streamline various processes. There are several areas where advanced models are most effective.
Content creation
Large language models used in marketing help companies create compelling articles, social media posts, and blog posts. These materials benefit product promotion and customer engagement. LLMs also help experts with real-time style, spelling, and grammar suggestions.
Personalization
Marketing agencies use LLMs to create customized content based on an individual’s behavior, interests, and background. This is used to make tailored email campaigns and product suggestions. Businesses using LLMs see higher conversion rates and client interaction.
Sentiment analysis
Large language models also help assess the effectiveness of marketing campaigns. With their help, marketers get insights into customer opinions and satisfaction. This allows companies to identify the best strategies for different demographics and improve their effectiveness.
How Many Companies Will Integrate Large Language Models In 2024?
Currently, there are over 300 million companies in the world. The latest LLM statistics published by Iopex show that almost 67% of organizations use generative AI products that rely on LLMs to work with human language and produce content. Since nonprofits also fall under this category, the number of potential adopters grows even higher.
Despite the apparent LLM hype, businesses still have second thoughts about adoption. In a survey conducted in August 2023 by Datanami, it was discovered that while 58% of companies work LLMs, in most cases, they’re just experimenting. Only 23% of respondents planned to deploy commercial models or have already done so.
The main factors for the slow adoption are privacy and ethical concerns. Companies aren’t too happy about the prospects of sharing information with the large language models. Some of it is highly sensitive, including financial, medical, and biometric information. Until businesses are sure about the safety of this information, adoption rates will go at a snail’s pace.
While companies like OpenAI and Meta research the large language models, there’s a growing market for paid and open-source solutions like Lakera Guard, Lasso Security, Gaeak, and Vigil. Their adoption can strain enterprise budgets even further, as building LLM-based products can be quite costly to produce.
Final Thoughts
Integrating LLMs into business solutions offers various perks. Models like Llama 2, MPT-7B, Alpaca.cpp, and Falcon-40B-Instruct show developers are trying their hardest to make this technology private and secure. The faster fully secured LLMs become available for commercial use, the more inclined enterprises will be to adopt these models in their daily work.
AI solutions help educational providers automate student evaluation through instant grading and performance tracking. This, combined with text-to-speech and speech-to-text solutions creates an environment with equal access to education and better academic outcomes.
