Applications of large language models (LLMs) in industry: Benefits and examples by sector

LLM applications are reshaping how humans interact with information, solve problems, and create value. From business and education to research and creativity, this form of artificial intelligence (AI) automates complex tasks, streamlines processes, and enhances user and organizational experiences. The adaptability of LLM applications across multiple contexts makes them strategic, versatile tools for all kinds of industries.

In this post, we explore LLM applications across different industrial sectors, their advantages and limitations, and their growing role in logistics and warehouse management.

What are LLMs and how are they used in industry?

LLM stands for large language model, which is an AI system trained on vast amounts of text using machine learning techniques, specifically deep neural networks. The goal of LLM applications is to perform a wide range of tasks, from recognizing patterns and understanding language to producing coherent responses.

These models represent a major leap forward in AI because they process language much like humans do: answering questions, translating, summarizing information, writing long texts, or even generating code. Today, users and companies alike can access LLM applications through tools such as ChatGPT, Claude, and Gemini. Their versatility explains why organizations in many sectors are eager to integrate them into daily operations.

In business and industry, applications of LLMs support everything from customer service (through round-the-clock automated assistance) to content creation. They also enhance corporate training by simulating real-life scenarios to improve communication and problem-solving skills. In e-commerce, they personalize the customer experience with tailored recommendations. Meanwhile, in manufacturing, they enable predictive maintenance and process optimization by interpreting data from sensors and equipment.

Benefits of using LLMs in industry

LLM applications offer clear advantages for organizations looking to automate language-related tasks such as content generation, document analysis, and customer communications.

Key benefits include:

• Decision-making support. LLMs help analyze massive datasets and summarize them into actionable insights, allowing companies to identify trends, risks, and opportunities. While they don’t replace human judgment, they strengthen strategic decisions with richer, more accessible data.
• Process optimization. When combined with sensor data, technical reports, or operational records, LLMs interpret information, uncovering patterns and anomalies. This supports early issue detection and process planning, leading to faster, more cost-efficient operations.
• Higher productivity. By generating text and code, LLM applications free professionals from repetitive tasks so they can focus on solving complex challenges and driving innovation.
• Scalability and efficiency. LLMs process large volumes of information and lengthy documents simultaneously, helping organizations handle workload peaks and grow sustainably.

Beyond process optimization and cost reduction, LLM applications are redefining how companies innovate. By automating repetitive tasks, they enable professionals to concentrate on higher-value, creative work.

Limitations of LLM applications

While industrial applications of large language models unlock enormous potential across sectors, understanding their limits is essential for using them responsibly:

• Hallucinations. LLM applications may “see” patterns or connections that don’t exist, generating inaccurate or fabricated information that may sound convincing but lacks factual basis.
• Bias. Because they learn from real-world data, LLMs can inherit existing biases, leading to outputs that reinforce stereotypes or exclude certain social groups.
• Privacy. There’s a risk that applications of large language models might reproduce fragments of confidential data used during training, unintentionally revealing sensitive information.
• Limited reasoning. LLMs are not autonomous thinkers. They generate text based on patterns, requiring human oversight to validate outputs and ensure they align with organizational goals.

These challenges affect logistics and supply chain management, in particular. A study from the University of Cambridge found that general-purpose models like ChatGPT aren’t always effective in business contexts. Since they lack company-specific knowledge, their answers can be incomplete or irrelevant. The researchers suggest developing custom models trained on internal data for more accurate results. However, this requires significant data volumes and computing power. Despite the challenges, ongoing research and specialized tools show that industrial LLM integration is becoming increasingly feasible.

Common applications of LLMs

Large language models have countless practical uses that are already changing how people and organizations work, learn, and communicate. To harness their full potential, it’s critical to understand how to use artificial intelligence:

• Data analysis and insights. LLM applications can adapt to different types of language and formats. They extract relevant information from unstructured data — even from loosely formatted documents — recognize key entities, and take into account the context in which they appear.
• Automated internal communication. LLM applications streamline queries in HR (leave policies and payroll) and IT (troubleshooting and account setup). They also help standardize templates and newsletters, maintaining a consistent tone across internal communications.
• Programming assistance. LLMs support software developers by suggesting code snippets, detecting errors, and proposing solutions. They adapt to different programming languages and offer contextualized recommendations, speeding up development.
• Virtual assistance. These systems power conversational assistants that answer customer questions, perform internal tasks, and deliver clear, natural-language information. Their integration into self-service solutions enhances the customer experience.
• Technical documentation and content generation. Through user prompts, LLM applications can draft texts in multiple formats and styles, improving contextual relevance and reducing repetitive work.
• Translation and localization. LLMs translate between languages and analyze large text datasets — such as customer feedback and internal documentation — fostering faster, more efficient global communication.

LLM examples by industry

LLMs are making waves in diverse sectors, adapting to each one’s unique challenges and workflows. These examples of LLMs in different industries show how AI is transforming how we work, learn, produce, and consume.

• Automotive. A study by the University of Stuttgart’s Institute of Automotive Engineering, in collaboration with Mercedes-Benz AG, shows how LLM applications can play a crucial role in software development for vehicle manufacturing. They help anticipate the impact of updates and shorter release cycles on assembly lines, maintaining product quality and improving coordination between software and production teams. While manual validation and sophisticated evaluation methods are still necessary, this approach promises greater process stability and tighter integration between digital innovation and manufacturing.
• Ecommerce. A paper by Brandeis University’s Mitchom School of Computer Science and eBay indicates that LLMs are already employed for tasks such as generating product descriptions, summarizing reviews, expanding search queries, and managing buyer-seller inquiries. They also detect consumer trends, fine-tune merchandise management, and offer personalized recommendations, improving both the customer experience and operational efficiency.
• Manufacturing. Research by MIT, in collaboration with Harvard and the University of Washington, highlights how LLM applications can support the entire product design and manufacturing process. These tools can transform ideas expressed in natural language into technical specifications, generate design variations, translate them into machine instructions, and even estimate product performance. The study revealed that LLM applications can accelerate the creation of prototypes and shorten the gap between the initial idea and the physical product. Nevertheless, the MIT experts caution that there are still limitations — as in the case of geometrically complex designs — so LLMs should be combined with human expertise and specialized tools.
• Chemicals. Carnegie Mellon University researchers demonstrated how LLM technology can optimize chemical processes. Traditionally, this required complex calculations as well as full knowledge of operating conditions, which isn’t always possible. The new approach uses multiple LLM-based agents that collaborate to propose operating limits and automatically identify optimal parameters. This opens the door to applying these techniques in existing chemical plants and new processes, reducing costs and accelerating innovation.

LLM applications in logistics

Logistics is a natural fit for LLMs: wherever data and decisions converge, these models can add value. Implementing AI in warehouse management helps optimize workflows, prevent incidents, and support real-time decision-making.

An article study from MIT’s Sloan School of Management points out that digital technologies are changing how supply chain decisions are made. Still, logistics professionals often need expert support to interpret system recommendations or run what-if analyses. With LLMs, that process can happen in seconds instead of hours or days, boosting operational efficiency.

Companies can implement LLM technology to enhance logistics processes through:

• Data analysis. LLM applications allow natural-language queries such as “What happens if demand changes?” or “Which contracts should I review?” The system pulls from internal data and returns clear answers without disclosing sensitive information.
• Scenario simulation. Supply chain planning often calls for evaluating hypothetical situations — demand surges, production interruptions, or price shifts. LLMs convert these queries into calculations and generate easy-to-understand explanations that support decision-making.
• Real-time management. When an issue arises — for example, deciding where to allocate inventory — users simply describe the situation in plain language. The LLM tool adjusts the calculations and proposes alternative plans in a format accessible to non-technical teams.

A practical example of an LLM use case in logistics is Interlake Mecalux’s Easy AI, integrated into the Easy WMS warehouse management system. Through its chat interface, users can ask complex questions in seven languages. They can query warehouse data in natural language and receive responses in various formats (figures, tables, or charts), create custom KPI dashboards, generate downloadable reports, design on-demand data views, and perform specific actions such as releasing rush orders and unlocking aisles. This integration helps companies speed up their facility operations and processes and access information more efficiently.

LLM applications in industry: Building smarter operations

The rapid growth in LLM usage reflects how organizations are embracing these tools to change how they access, interpret, and utilize information. Their ability to turn data into practical knowledge is enabling faster, more accurate operations. But successful adoption requires awareness of LLM limitations — such as potential bias or factual errors — and careful human oversight.

In logistics, LLM applications have tremendous potential in everything from inventory optimization to real-time reporting that supports better decisions. Solutions like Interlake Mecalux’s Easy AI show that the key lies in purpose-built tools designed for warehouse needs. The future points toward an industrial landscape where AI complements — rather than replaces — human teams, driving productivity and resilience across the board.