Large language models (LLMs) in business use – where they bring the most value
Large language models (LLMs) have taken center stage in the AI debate in recent years. Services such as ChatGPT in particular have made language models easily accessible, and many organizations have already tried them out for writing texts, brainstorming or searching for information.
From a business perspective, this is just scratching the surface. Language models are not just tools for content creation, but technology that changes the way people interact with systems, data and processes. When LLM is integrated into core business functions, it acts as an interpreter between humans and complex systems.
For companies, the essential aspect of artificial intelligence is not whether the model can write good text, but how it helps to speed up work, reduce manual workload and improve decision-making.
- What are large language models?
- LLM is not the same thing as ChatGPT
- Why are LLMs of interest to companies right now?
- Where do large language models work best in business use?
- LLM + agents – language models as part of operations
- Case: Odoo AI order processor
- Case: AI security agent
- Context is key, which is why a language model alone is not enough
- AI Sandbox makes the possibilities of language models visible
- Information security and responsibility in corporate use of LLM
- How should a company get started?
What are large language models?
A large language model (LLM) is an artificial intelligence model that has been trained on vast amounts of text data to understand and generate natural language. The models learn language structures, meanings and contexts through statistical connections, rather than through rules or manually defined logic.
This enables LLM models to handle language flexibly and adapt to different situations. In business use, this means that the same model can be used to support reporting, customer service and system user interfaces.
In practice, LLMs are particularly suitable for tasks where:
- the information is in text form or semi-structured
- a lot of content is created quickly
- people spend time searching for and interpreting information
The capability of models is based on how well they can use the correct context and limitations.
LLM is not the same thing as ChatGPT
In many organizations, large language models are still perceived as individual tools, such as ChatGPT. This is largely because ChatGPT has been the first point of contact with LLM technology for many people.
In business use, however, ChatGPT is only a user interface for a language model. The real benefit only comes when the model is not a separate conversation partner, but part of a system as a whole.
In a corporate environment, LLM is typically connected to:
- the company’s own data
- background systems and interfaces
- business processes
- rights of use and rules
In this case, the language model can retrieve information, combine sources, make limited updates, and produce summaries to support decision-making. This distinguishes it from production use trials.
Why are LLMs of interest to companies right now?
The operating environment for companies has changed significantly in recent years. There are more systems, data is constantly accumulating, and decision-making is increasingly based on up-to-date information. At the same time, in many organizations, information is scattered across different systems, documents and communication channels.
Large language models offer a new solution to this problem. They enable natural language to be used as an interface to complex information. The user does not need to know where the information is located in the system or how it is stored.
LLM can act as a layer that:
- compiles information from multiple sources
- summarize the essentials
- present the answer in an understandable format
The capabilities of language models significantly change the usability of systems and lower the threshold for using existing data.
Where do large language models work best in business use?
In our experience, LLMs generate the most value in situations where the work is already knowledge-intensive and manual. It is not a question of individual automations, but of entities where information moves slowly or requires a lot of interpretation.
LLMs are particularly well suited to situations where:
- processes large amounts of text or semi-structured data
- information is scattered across multiple sources
- expert work is spent on routine tasks
- decision-making requires the combination of information
Typical business applications include customer service, ERP and CRM systems, reporting, and document processing. In these situations, the language model works in collaboration with humans and supports them by doing the heavy lifting.
LLM + agents – language models as part of operations
In business use, language models rarely work alone. They are part of a broader agent architecture in which the language model is combined with rules, integrations and user interfaces.
An AI agent is essentially a programmatic entity that uses a language model as a layer of thought. The agent makes decisions according to a predefined process.
For example, an agent may:
- read emails
- identify essential information
- update the ERP or CRM system
- generate reports
- propose further measures
The language model brings flexibility and understanding to the process, but the actual operation remains controlled.
Case: Odoo AI order processor
One concrete example of the use of LLM in business is the Odoo AI order processor we have built. In many organizations, orders still arrive by email in various formats: as messages, attachments or even images.
The AI agent reads incoming messages, recognises order details and converts them into a uniform structure. Before saving, the user is offered a preview, after which the information is transferred directly to Odoo ERP as sales orders.
The solution uses a large language model specifically for interpreting information. The actual business logic and integration are clearly defined, which makes the solution reliable and scalable.
Case: AI security agent
Another example of a solution we have developed is an AI security agent that continuously monitors multiple security sources. Security threats arise every day, and identifying what is essential requires expert work.
The agent reads data from various sources, identifies relevant observations and compiles them into a comprehensible summary. Reporting is done automatically, for example to Slack or another selected channel.
In this case, the language model acts as an analytical layer. It does not make decisions for you, but highlights what experts should pay attention to.
Context is key, which is why a language model alone is not enough
One of the most common reasons for unsuccessful LLM experiments is a lack of context. Without the right background information, the language model will produce answers, but they will easily remain at a general level.
In business use, this is solved with context engineering thinking. In practice, this means that the language model is precisely defined:
- what it is allowed to do
- what data it can access
- what kind of answers are expected
- within what limits it works
When the context is right, the content produced by the language model becomes significantly more useful.
AI Sandbox makes the possibilities of language models visible
For many organizations, the biggest challenge is not technical implementation, but conceptualization. What could artificial intelligence do in our particular environment?
To this end, Hurja uses an AI Sandbox demo environment, which presents concrete solutions based on real business challenges.
AI Sandbox demos include, among other things:
- Order processor that automates email orders into the ERP system
- Ask about the document solution, where the language model searches for answers directly from technical instructions or agreements.
- Speech recognition that converts meeting recordings and maintenance reports into text
- AI trainer who supports induction and training
- Trend analysis that identifies trends from business data
The purpose of the demos is to help you understand how language models work as part of real business processes.
Information security and responsibility in corporate use of LLM
The use of large language models in business requires a careful approach to data security, responsibility and regulation. The data being processed is often critical to business operations and may include personal data, contracts or other sensitive material.
For this reason, language models are only used in production environments in clearly defined and controlled ways. LLM solutions suitable for production use are built into controlled entities where data processing is transparent and predictable. The key principles are:
- Closed environments where company data is not transferred to model training material and is not accessible to external parties
- RAG (retrieval augmented generation) structures, in which the language model retrieves its answers from the company’s own, limited and up-to-date information sources
- Access control models that ensure users can only access information that belongs to them
- Logging and monitoring, which enable the use of language models to be traced and deviations to be addressed in a timely manner
These solutions improve the reliability of responses, reduce the risk of errors and hallucinations, and ensure that artificial intelligence is used in accordance with the company’s operating models.
Information security and responsible implementation are also key requirements in EU AI regulation. The EU AI Act requires that the use of AI systems be transparent, controlled and risk-aware, especially in business environments where AI has an impact on decision-making or personal data.
A carefully designed LLM solution supports companies in meeting these requirements and building trust in the long-term use of artificial intelligence as part of everyday operations.
How should a company get started?
It is advisable for a company to start by identifying the actual needs of its business and key areas for development. Large language models are most effective when you understand which processes generate the most manual work, where information is difficult to use, and where decision-making is slowed down by fragmented information.
These observations form use cases where language models can bring genuine and measurable benefits. Large language models are an excellent way to use systems and data as part of everyday activities. In business use, their value is created when they are integrated in a controlled manner into processes, systems and decision-making.
When the context is carefully defined, use cases are clearly defined, and information security is taken into account from the outset, language models can effectively and sustainably support business operations.
This development is supported, for example, by Hurja’s AI Sprint, which offers a concise and practical way to identify suitable applications and build a realistic roadmap. In this way, the use of artificial intelligence becomes a natural part of a company’s development and strengthening of its competitiveness.
A large language model (LLM) is a neural network-based model trained to process and generate natural language by predicting the most probable next token in a given context.
The model typically has billions of parameters, the training data is enormous, and the model learns broad linguistic and conceptual regularities without a task-specific model for each problem.
The basic idea: probability distribution for language
The language model models language in the form:
P(w₁, w₂, …, wₙ)
where w₁ … wₙ are words or characters. In practice, this is broken down into a chained prediction:
P(wₙ | w₁, w₂, …, wₙ₋₁) The task of the model is therefore to answer the question: which word or token is most likely to follow in this context.
This applies regardless of whether it is a matter of text generation, question answering, translation or summarisation. All these tasks boil down to the same core function: predicting the next token.
Tokens
Modern language models do not usually deal with whole words, but with tokens. A token can be: a whole word, part of a word, a single character, a punctuation mark or a special character.
Tokenisation enables the model to work with different languages and different word forms without the vocabulary becoming unmanageable. From the model’s perspective, language is a sequence of tokens, not a grammatical structure in the human sense.
Classical language models
Early language models were based on n-grams. An n-gram model examines a fixed-length string of words, for example the previous two or three words, and predicts the next one.
Example:
bigram model: P(wₙ | wₙ₋₁)
trigram model: P(wₙ | wₙ₋₂, wₙ₋₁) The
limitation of these models was the shortness of the context and poor generalisability. The model did not understand long dependencies or the meanings of language, only frequencies of occurrence.
Neural network-based language models
Modern language models are based on neural networks, specifically transformer architecture. The transformer model consists of layers in which the key mechanism is self-attention.
Self-attention enables the model to: examine the entire context simultaneously, emphasise the significance of different tokens in relation to each other, and learn long dependencies without a fixed context boundary.
This solved many of the limitations of previous models and enabled the development of large language models.
What a language model actually learns
A language model does not explicitly learn facts, rules or meanings. It learns statistical patterns: – what kinds of words occur together – in what contexts certain concepts are used – how sentences and longer structures are constructed
Meaning emerges emergently from these patterns. The model does not ‘understand’ the world, but it is capable of producing language that follows the structures of language and knowledge with astonishing accuracy.
Large language model (LLM) A
large language model or large language model refers to a language model in which: the number of parameters is very large (billions or hundreds of billions), the training data covers huge amounts of text, and the model is capable of generalising to multiple tasks without separate training.
LLMs often function as foundation models that can be guided to different tasks through prompting, fine-tuning or context (RAG).
Training process
Language model training usually takes place in two stages:
Pre-training: The model is taught to predict the next token from a huge text corpus. At this stage, the model learns the general structures of the language.
Fine-tuning/alignment: The model is adapted using human feedback, instructions and tasks. This stage makes the model usable in conversation and business applications.
In business use, domain-specific fine-tuning or a RAG structure can be added to this.
Language model in business systems
Technically, the language model functions as a component in a business environment, not as an independent system. It is connected to application logic, databases and document repositories, access control systems, integrations and API interfaces.
In this case, the language model acts as an interpretation and reasoning layer, not as a source of information. This distinguishes production use from experimental chat use.
Limitations and hallucinations
Because the language model predicts probable tokens, it can produce incorrect but plausible-sounding content, a phenomenon known as hallucination. This is not a bug but a consequence of the fundamental nature of the model.
Technical solutions to this include context limitation, RAG models, rule-based validation and human approval at critical points.
A language model is, so to speak, a probability-based system that models the structure of language by predicting the next tokens based on context. Modern large language models are based on transformer architecture and are capable of generalising widely across different linguistic tasks.
In business use, the language model is not an intelligent agent in itself, but rather an effective component that brings flexibility, automation and a new type of user interface to data and systems.
Shall we get started?
"*" indicates required fields
Artificial intelligence is not a divine being, but a tool built by humans