Market Intelligence
The days of general-use large language models (LLMs) in companies could be coming to an end. The new reality: the use of enterprise LLMs and/or small language models (SLMs) is on the rise, as businesses realize that that impactful, efficient AI must be based on tailored models that have been customized to their own data and use cases.
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Artificial intelligence (AI) is transforming industries—but how can businesses leverage large language models for real results?
Everywhere you look, AI technologies are reshaping business operations. At the core of this transformation are large language models (LLMs), which can analyze huge amounts of data, understand natural language, and generate human-like responses to user queries.
But as promising as LLMs are, enterprises face challenges like handling sensitive data, integrating with complex systems, and ensuring that solutions provide relevant and up-to-date information tailored to specific industries. Luckily, enterprise LLMs offer the customization and scalability businesses need to turn cutting-edge AI into practical solutions.
In this article, we’ll explore what makes enterprise LLMs unique, their real-world applications, and how transitioning to Small Language Models (SLMs) can bring additional value to your AI strategy.
An enterprise LLM is a large language model tailored to meet the needs of enterprise systems. Unlike off-the-shelf LLMs, these models can:
By customizing their training data and workflows, enterprise LLMs can provide more relevant information, adapt to unique business contexts, and support decision-making at scale. These models empower organizations to use generative AI for practical, impactful applications.
For example, Ricoh, a leading printing company, implemented a specialized LLM to assist their technicians in troubleshooting equipment issues. This AI-driven approach reduced the onboarding period for new agents from 10 weeks to just two.
Implementing LLMs seems like a massive and costly project, but the benefits they bring can far outweigh the initial investment. Here’s how Enterprise LLMs can make a difference in industries like healthcare, finance, retail, and manufacturing:
Effective decision-making is a critical challenge for organizations, often hindered by the overwhelming volume of data and the complexity of extracting actionable insights. Enterprise LLMs address this challenge by processing vast datasets with natural language understanding. This capability enables leaders to make data-driven decisions confidently, from identifying market trends to pinpointing operational inefficiencies.
For instance, Bloomberg developed BloombergGPT, a finance-focused LLM. This model improves financial tasks such as sentiment analysis and question-answering, allowing Bloomberg's analysts to make more informed decisions based on comprehensive data insights.
With generative AI, Enterprise LLMs can analyze and respond to a user's query in real time, enabling personalized customer interactions through chatbots and support systems.
Enterprise LLMs free up valuable time for teams to focus on strategic priorities by automating repetitive tasks like data entry and reporting. They integrate seamlessly into enterprise systems to optimize workflows and reduce human error.
While the initial investment might be significant, the long-term savings are undeniable. Enterprise LLMs streamline operations, minimize errors, and eliminate redundant processes, thereby delivering a strong return on investment.
For instance, a study by researchers at the University of Michigan found that replacing proprietary LLMs with open-source SLMs in production environments led to cost reductions ranging from 5x to 29x, while maintaining competitive performance levels.
Enterprise LLMs shine when applied to specific business needs.
These benefits demonstrate how large language models can move beyond theory into transformative real-world results.
Enterprise LLMs address complex challenges and unlock new opportunities for innovation. Here are some of the most impactful use cases for Enterprise LLMs across key sectors:
Enterprise LLMs are reshaping healthcare by simplifying patient record analysis and enabling personalized treatment plans. The AI in healthcare market has seen exponential growth, expanding from $1.1 billion in 2016 to $22.4 billion by 2023, reflecting the growing reliance on AI-driven solutions. It was also reported that AI-assisted surgeries could shorten hospital stays by over 20%, with potential savings of $40 billion annually.
In retail, LLMs are used to improve inventory management and provide tailored customer recommendations. For instance, Amazon uses LLMs to enhance product recommendations by understanding "common sense" relationships, ensuring that suggestions align more closely with customer queries.
The use of AI in retail is anticipated to grow at an impressive compound annual growth rate (CAGR) of 30.5% between 2021 and 2028.
The finance industry leverages LLMs for tasks like fraud detection, compliance verification, and financial forecasting. Analysts predict that generative AI could contribute an additional $340 billion to the operating profits of the U.S. banking sector.
Manufacturing benefits from LLM-powered predictive maintenance and streamlined operational planning. The global market for AI in computer vision, a critical tool for manufacturing automation, is projected to reach $100 billion by 2032.
LLMs improve HR processes, including talent acquisition, onboarding, and analyzing employee sentiment. Notably, 75% of companies now use AI-driven tools for talent acquisition.
These trends highlight the expanding role of Enterprise LLMs across industries, paving the way for smarter, more efficient systems.
Organizations often face several hurdles when implementing enterprise LLMs. Here’s a breakdown of the most common challenges and actionable solutions:
Building or adopting Enterprise LLMs requires significant upfront costs, including infrastructure, licensing, and customization. For instance, OpenAI's ChatGPT model reportedly incurs operational costs of up to $700,000 daily due to expensive server requirements. However, these costs can be mitigated by starting with pilot projects. This approach allows organizations to test feasibility, measure return on investment (ROI), and scale up gradually based on proven results.
Handling sensitive data should be a top priority for enterprises, especially when deploying models interacting with proprietary or confidential information. In order to ensure security and to build trust, it's imperative that enterprises choose solutions with robust data encryption and compliance with industry standards (such as GDPR or HIPAA, to ensure security and build trust).
Integrating LLMs into existing enterprise systems can be challenging, especially for organizations without deep expertise in AI development. Partnering with experienced AI vendors like Arcee AI can ease this process by ensuring smooth integration and alignment with organizational goals.
Many organizations struggle with limited in-house expertise to manage and optimize AI technologies. Invest in training programs and adopt user-friendly tools to allow your team to use Enterprise LLMs effectively. This improves adoption rates and ensures long-term success.
By addressing these challenges, you will minimize risks and maximize the value of enterprise LLMs.
Enterprise LLMs offer vast capabilities to tackle complex problems. However, small language models (SLMs) are emerging as a better and more practical alternative for businesses looking to streamline their AI strategies. In terms of performance, SLMs can be just as good, if not better, than LLMs. Where LLMs bring power and generalization, SLMs deliver focus, efficiency, and precision—traits that are increasingly essential in agentic AI workflows. Agentic AI workflows refer to AI-driven processes designed to autonomously adapt, make decisions, and optimize outcomes in dynamic environments.
SLMs are streamlined versions of LLMs designed to handle specific tasks with high efficiency. While LLMs are trained on hundreds of billions or even trillions of tokens, SLMs operate with significantly fewer parameters, often under 72 billion. This substantial difference in size makes SLMs lightweight and far less demanding in terms of computational power.
These characteristics make SLMs ideal for applications where agility, cost-efficiency, and speed are priorities.
Arcee AI, the pioneer in small language models, has provided organizations with specialized AI solutions that deliver unmatched data security, resource efficiency, and performance. Dedicated to transforming the way companies operate in an increasingly digital landscape, Arcee AI helps teams handle complex tasks and solve operational problems with purpose-built SLMs tailored to their business, data and workflows. Our groundbreaking training techniques, developed in-house, differentiate our models, especially for organizations with domain-specific needs tied to impactful business results.
Enterprise LLMs offer customized solutions to improve decision-making, enhance customer experiences, and streamline workflows. While challenges like high initial costs and integration complexity exist, a strategic approach—defining goals, starting small, and involving stakeholders—can unlock their full potential.
As AI continues to evolve, incorporating tools like SLMs into agentic AI workflows adds even greater efficiency and focus to ensure your organization stays ahead.
Ready to transform your organization with AI? Book a demo with Arcee AI and take the first step today!
An enterprise LLM is a large language model customized for enterprise-level applications. It offers tailored solutions for decision-making, customer engagement, and operational efficiency.
In business, LLM stands for large language model, a type of AI that processes natural language to generate insights, automate tasks, and improve workflows.
More than 67% of organizations globally use generative AI products powered by LLMs. With over 300 million companies worldwide, this demonstrates the extensive adoption of LLMs, including by nonprofits, for tasks like content creation and language processing.
So you want to train a custom language model, and you do have the requisite large set of text data. But how do you know that the data is *really actually ready* for model training? Our researchers here at Arcee AI tell you what to look out for.
Why does the model or (models) used in your agentic AI workflows matter? In this article we explain why the choice of models is key to a successful agentic AI strategy, and why small language models (SLMs) are an ideal solution.
How can you use AI to deliver real business results fast? As we head into 2025, Generative AI alone won't cut it. Learn about how adding AI agents to your strategy is the new key to getting ROI from your AI.
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