How to Implement Large Language Models in Your Business

Large language models (LLMs), advanced artificial intelligence (AI) models trained on vast amounts of text data, are designed to understand and generate human-like text using large-scale natural language processing (NLP) technologies like Generative Pre-trained Transformers (GPTs). LLMs have become a significant part of the AI landscape due to their ability to revolutionize various business processes, with ChatGPT spearheading the change in the tech landscape. Brainy Insights estimates that the generative AI market will reach $188.62 billion USD by 2032, with North America expected to have the largest market share in the generative AI market.  

The market for AI technologies is projected to reach approximately $244 billion USD by 2025 and is expected to grow significantly beyond that, surpassing $800 billion USD by 2030, according to Statista.

By leveraging the power of LLMs and generative AI, businesses can automate tasks, improve decision-making and uncover valuable insights that lead to greater efficiency and competitive advantages. Despite their recent popularity, language models have been a part of the tech world for several years. 

The biggest breakthrough since social media

LLMs never would have advanced so quickly without transformer models, a novel deep learning architecture introduced in 2017 in Transformer Model Architecture (by Ashish Vaswani and others) and which revolutionized NLP and the understanding of tasks. Transformers significantly improved NLP and enabled the development of more powerful and versatile AI models. 

With the “AI Arms Race,” as the media and industry calls it, in full swing, companies are racing to develop and implement AI-driven solutions.


Read also: Large Language Model Training

Examples of Large Language Models

As of late 2025, the latest versions of flagship LLMs have moved beyond pure text generation, focusing on native multi-modality, verifiable reasoning, and autonomous “agentic” capabilities to perform complex tasks. 

GPT-5

The successor to GPT-4, GPT-5’s most significant leap is its native multi-modality, allowing it to seamlessly understand, reason across, and generate a mix of text, images, audio, and simple video formats within a single output. It features vastly improved reasoning abilities, significantly reducing hallucinations. Its agentic framework has also matured, enabling it to autonomously execute multi-step tasks requiring web browsing, external tools, and interacting with APIs to achieve a specified goal.

Claude 4 

The model by Anthropic Claude 4 introduced verifiable reasoning. It can now cite sources for its claims in real-time and explain its thought process, adhering to its advanced Constitutional AI framework. This makes it a trusted tool for enterprise and research applications where accuracy and transparency are non-negotiable. Its context window has also expanded dramatically, capable of analyzing and synthesizing information from entire books or complex codebases in a single prompt. 

Notion AI

A versatile AI partner created by Notion Labs for tasks such as summarizing content, brainstorming ideas, drafting text, correcting spelling, improving grammar, and translating content across languages. 

The Pathways Language Model (PaLM) 

A 540-billion parameter transformer model developed by Google Research that showcases advanced few-shot performance in language tasks. Utilizing the Pathways system for efficient distributed computation, PaLM achieves 57.8% hardware floating point operations per second (FLOPS) utilization, the highest for large language models at this scale. 

Presently most companies embracing LLMs can be described as early adopters. It’s worth looking at real-world business use cases of LLM implementation and analyzing their outcomes. 

Read also: What are LLM hallucinations?

Read also: LlamaIndex vs LangChain: key differences

LLM use case in the food processing industry

A company from the food processing industry Software Mind worked with wanted to eliminate the time-consuming task of analyzing a vast amount of research papers. A labor-intensive endeavor became solvable by turning to the GPT-3 LLM. After successfully fine-tuning the GPT-3 model, building an internal API and developing a user interface (UI) tool, the company’s scientists had seamless access to the model for research analysis. What were the direct results of leveraging the GTP-3 model? 

Improved efficiency: By quickly accessing and examining research papers, the company’s scientists were able  to make informed decisions promptly. 

Reduced workload: The model’s ability to extract and summarize essential information from the research papers significantly reduced the manual effort required by the team. 

Enhanced innovation: By streamlining access to research papers, the company’s scientists were able to identify new opportunities and trends, leading to the development of innovative products and processes. 

Reduced costs: The company saved on resources and time previously spent on manual research analysis, which cut costs. 

If you want to see more real-world use cases of LLM implementations and learn what’s next for LLMs, sign up for an on-demand video on How to enhance your business with AI and Large Language Models.

LLMs offer endless possibilities across industries, but which methods of implementation are the most effective for using AI in business? 

Examples of use cases for LLMs in companies 

BlackRock  

The world’s largest asset manager, BlackRock, has integrated a powerful LLM co-pilot into its Aladdin risk management platform. Portfolio managers can now use natural language to ask complex, real-time questions. The Aladdin Co-Pilot synthesizes terabytes of market data, geopolitical news, and internal research in seconds to provide an actionable summary. This task previously took hours for a team of analysts. This dramatically accelerates data-driven investment decisions. 

Roche 

Pharmaceutical giant Roche uses a specialized “Bio-LLM” as the core of its RACTI project to streamline clinical trials. The model analyzes a new drug candidate’s molecular profile and cross-references it with global patient databases and genomic information to identify the ideal patient cohort for a trial. It then automatically drafts large portions of the complex regulatory submission documents required by the FDA and EMA. The project has reduced patient recruitment timelines by over 30% and cut documentation overhead by months, accelerating the delivery of new therapies. 

Mercedes-Benz 

Mercedes-Benz has rolled out an LLM-powered diagnostic tool for its service centers. When a vehicle is brought in for service, technicians interact with the MBUX Diagnostic Assistant. They describe the customer’s complaint and the vehicle’s symptoms in plain language. The LLM, trained on millions of historical repair logs and engineering schematics, instantly provides a ranked list of likely causes and the official, step-by-step repair protocols. This significantly reduces diagnostic time and improves the accuracy of first-time repairs for increasingly complex, software-defined vehicles. 

Eight best practices for implementing large language models

In many cases, businesses must take advantage of advanced AI models. Here are eight essential steps to start that journey. 

1. Identify the right use case: Assess your business operations to pinpoint areas where a LLM can add value, such as customer support, content generation, or data analysis. Identify tasks you can delegate to LLMs.

2. Select the appropriate model: Choose a suitable LLM based on your needs, taking into consideration task complexity, model capabilities and resource requirements.

3. Prepare and fine-tune the data: Gather and preprocess relevant data to fine-tune the chosen model, to ensure it aligns with your business context and produces accurate, domain-specific results.

4. Plan the integration with existing systems: Execute the integration of a LLM into your existing business processes and technology infrastructure to ensure seamless operation and minimal disruption.

5. Monitor and evaluate performance: Continuously track the performance of the implemented LLM, using metrics such as accuracy, response time and user satisfaction to identify areas for improvement.

6. Address ethical and privacy considerations: Be mindful of potential ethical and privacy concerns related to AI deployment, while ensuring compliance with data protection regulations and the responsible use of AI technologies.

7. Pay attention to scalability and maintenance: Prepare for the ongoing maintenance and potential scaling of your LLM implementation, keeping in mind aspects such as data storage, compute resources, and regular model updates.

8. Foster a culture of AI adoption: Encourage a company-wide understanding and acceptance of AI technologies by providing training and resources for employees to embrace and leverage LLMs.  

LLMs’ privacy-related regulations and ethical concerns 

Businesses adopting LLMs must now navigate a concrete and enforceable legal landscape, fundamentally shaping how these technologies are implemented, particularly in Europe and the United States. 

The European Union

The EU’s AI Act, now in its implementation phase, is the world’s most comprehensive AI regulation. It establishes a risk-based framework that directly impacts LLM adoption. Businesses face strict obligations if an LLM is used in a “high-risk” application, such as credit scoring, hiring, or medical diagnostics. They must conduct mandatory conformity assessments to prove the system is safe and unbiased, ensure robust data governance and quality, and guarantee meaningful human oversight. This regulation is forcing companies to invest heavily in AI governance and to be highly selective about deploying LLMs in critical decision-making processes, slowing down adoption in high-risk areas while demanding greater technical diligence. 

The United States

The U.S. lacks a single federal AI law, creating a complex patchwork of state-level and sector-specific regulations. States like California (CPRA) and Colorado now have rules governing automated decision-making, requiring businesses to provide consumers with the right to opt-out of AI-driven profiling. Simultaneously, federal agencies are enforcing existing laws in the context of AI. The Equal Employment Opportunity Commission (EEOC) is actively investigating algorithmic bias in hiring tools, while the Federal Trade Commission (FTC) is penalizing companies for “unfair or deceptive” AI claims. This fragmented approach requires businesses to navigate a complex compliance map, tailoring their LLM implementations to different rules across various states and industries.

Generative AI will lead the new age

Bill Gates named the model developed by OpenAI as the second demonstration of technology that struck him as revolutionary in his lifetime. The first was a graphical user interface he was introduced to in 1980. Bill Gates’ essay concludes with the words, “The Age of AI is filled with opportunities and responsibilities.” 

LLM application will undoubtedly be crucial in data analysis, content creation, translation, human resources, finance, accounting, compliance and software development. But the AI model will go even further, as, according to Gartner, generative AI will be crucial in drug development, medical diagnostics, material science and chip design.  

Read more: Generative AI in ecommerce 

As with every new type of technology, the earlier you start implementing LLM, the better your results. Experts believe that companies only have a time frame of 3 to 5 years to adapt solutions offered by the new wave of AI, if they want to stay competitive in their market. LLMs will only get better and embracing them will give companies a needed edge. At the same time, not taking advantage of that limited window of opportunity will only mean being left behind, when like Nokia – unwilling to make the necessary shift – lost ground in the smartphone market after the introduction of the iPhone. 

Implementing LLMs in any industry requires working with an experienced team who constantly keeps up to date with the latest developments regarding AI developments. If you’re interested in learning how LLMs can help your company, contact our experts using the form below.

FAQ 

What is the difference between GPT and LLM? 

The difference between GPT and LLM is one category versus a specific example; think of it as the difference between “car” and a particular car brand.  LLM (Large Language Model) is the general, descriptive term for any advanced artificial intelligence model designed to understand and generate human-like text. It’s a broad category of technology that includes many different models created by various companies, such as Google’s Gemini or Anthropic’s Claude. The term describes what the technology is: a large model that works with language. GPT (Generative Pre-trained Transformer) is the brand name for the series of LLMs created by OpenAI. Models like GPT-5 are famous instances within the broader LLM category. The “Transformer” part of its name refers to the underlying neural network architecture that has become the industry standard for building most modern LLMs. So, while every GPT is an LLM, not every LLM is a GPT. The widespread popularity of OpenAI’s models has led many people to use the term “GPT” colloquially to refer to all LLMs, but they are not the same thing.

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