Generative artificial intelligence (GenAI) in business

Generative artificial intelligence (GenAI) is currently often seen in everyday life as a chat window, but in companies it represents a broader change: a new way of processing text, information and content so that it can be integrated into processes and systems.

When generative AI is implemented in a controlled manner, it reduces manual work, speeds up decision-making and improves the findability of information. At the same time, it brings with it new technical and organisational requirements, such as information security, quality assurance and regulatory compliance.

This article provides an overview of what generative AI means, how it works technically, how it is used in companies, and how it can be implemented in production-level solutions.

What does generative artificial intelligence mean?

Generative AI refers to artificial intelligence that produces new content based on existing data and learned models. The content can be text, images, code, sound, or structures such as tables and classifications. In practice, generative AI does something that feels like ‘creation’ to humans: it forms new sentences, new drafts, new proposals, new solutions and new summaries.

In a business context, the value of generative AI is often linked to the fact that a large part of the work involves working with text and information: documents, contracts, instructions, customer service messages, meeting notes, orders and reporting.

Generative artificial intelligence can process this material faster than humans and produce useful results from it.

Generative artificial intelligence typically produces:

• text: answers, summaries, reports, instructions, drafts
• images: illustrations, concept images, variants
• code: functions, tests, refactoring suggestions
• voice: speech to text, text to speech
• structure: classification, keywords, excerpts, field filling

A practical definition of generative AI is this: generative AI produces new content using a probability model, and in businesses it should be understood as a user interface and tool for converting information from one form to another.

How does generative artificial intelligence work technically?

In business use, it is useful to understand how generative AI works at a basic level. Generative AI is based on a model that converts input, such as text, images or data, into new content by calculating probabilities based on a learned model.

In practice, the model does not retrieve ready-made answers from a database, but rather forms the answer step by step by predicting which content unit is most likely to follow in a given context. This mechanism is the same regardless of whether text, images or code is being produced, but the structure and training of the model differ depending on the intended use.

Model types: language models, image models, and multimodal models

Model types describe the type of input and content that the model processes, not necessarily completely separate technologies. Modern models can belong to several categories at the same time, depending on the use case.

• Large language models (LLMs) generate and interpret text, produce summaries and answer questions, such as GPT, Gemini and Claude. The basic capability of these models is based on language comprehension and production, which is why they form the starting point for the use of generative AI in most companies.
• Image models generate images based on text descriptions or edit existing images, such as DALL·E (also integrated into GPT), Midjourney, Adobe Firefly, and Stable Diffusion. They are specifically trained to generate visual content and utilise different architectures than purely text-based models.
• Multimodal models understand and produce multiple formats, such as text and images together, sometimes also sound, such as GPT and Gemini. This means that the same model can function both as a language model and a multimodal model, depending on the type of input and task it serves.

In business use, the most common starting point is a language model, as most business information and process descriptions are text documents. Multimodality extends this capability to include, for example, the joint processing of images and other materials.

Tokens and probability – what does the model actually do?

Language models do not process text as words, but as tokens, which are parts of words or sometimes whole words. The model receives a sequence of tokens as input and calculates the probabilities for the next tokens. When the model ‘writes’, it selects the next token based on these probabilities and continues this step by step.

Key technical concepts:

• tokenisation: breaking down text into a form that the model can understand
• probability distribution: an estimate of which token is most likely to follow
• generation: producing tokens sequentially using a selection method
• temperature and sampling: adjustments that affect creativity and determinism

Transformer architecture and context processing

Modern language models are based on transformer architecture. Put simply, a transformer processes input in such a way that each token can ‘pay attention’ to other tokens. This is called the attention mechanism.

This is what makes the model good:

• summarising long texts
• question-and-answer situations
• understanding documents
• in identifying structures and dependencies
• Prompts, context and guidance

In business use, a single question is not enough to reliably guide generative artificial intelligence. The model’s operation is controlled on several levels, which can be described as context engineering. In this case, the focus is not only on a single prompt, but on the entire environment in which the model operates.

The model is typically controlled on three levels:

• prompt: instructions and assignment
• context: necessary background information (documents, system information, customer data)
• limitations and rules: what the model can do, what it cannot do, what sources it can rely on

When the context is high-quality and correctly defined, the results improve significantly and the model works more reliably.

“Why doesn’t the model invent,” but rather generates

The model does not have its own ‘database of truth’. It generates content based on what is probable in a given context. For this reason, business use requires practices that link the model’s output to reliable information and processes. This is particularly evident in RAG solutions and agent structures, which will be discussed later.

Generative AI vs. traditional AI

In companies, artificial intelligence is often perceived as a single entity, when in reality it is a continuum. Understanding this makes it easier to choose the right type of solution.

Rule-based automation

Rule-based solutions operate on if/then logic. They are excellent when the process is standardised and the rules are clear. Typical examples include integrations, workflows and form validations.

Predictive artificial intelligence and machine learning

Predictive models assess probabilities, classify, identify anomalies, and make predictions, such as forecasting demand or failure. They work well when the data is structured and the objective is measurable.

Generative AI is particularly powerful when:

• data is in text form and scattered across documents
• the task requires natural language processing
• want to summarise, reformulate or transform content
• the work involves a lot of ‘interpretation’ and communication

In practice, generative AI performs less well in situations that require 100% determinism or precise mathematical calculations without a verified source. For this reason, in production solutions, it is linked to processes, validations and system data.

How is generative AI used in businesses?

The value of generative AI in companies comes from concrete applications that support everyday work and business processes. Organisations are looking for answers to questions such as: in which tasks does the technology bring real benefits, to which areas is it best suited, and how do the effects manifest themselves in terms of time savings, improved quality or better decision-making? The following examples illustrate the most typical ways of utilising generative AI in business.

Generative artificial intelligence in content production

Generative AI helps to quickly produce and edit content. In companies, this often means two different uses: internal content and external content.

For internal use, the focus is on efficiency and structuring information. For external use, the emphasis is on brand, consistency and quality.

Useful destinations include:

• summaries of lengthy documents and meetings
• drafting guidelines and internal process descriptions
• translation and localisation
• first versions and variants of marketing texts

Quality management in companies is often based on guidelines, approval practices and feeding brand-compliant examples into the model.

Generative artificial intelligence in customer service

In customer service, generative AI manifests itself in the form of chatbots, AI agents and assistants that search for answers in documentation and instructions. Companies particularly value the reduction in response times and the lightening of the workload for experts.

Typical solutions:

• frequently asked questions and instructions in natural language
• classification and summarisation of support requests
• draft replies approved by the expert

The division of labour between humans and artificial intelligence should be structured so that artificial intelligence handles repetitive and clearly defined tasks, while humans make decisions and deal with exceptions.

Generative artificial intelligence in software development

In software development, generative AI can speed up work, especially when the task involves a lot of mechanical production and conversion.

Applications include:

• drafting code and proposing alternatives
• support for refactoring
• creation of tests and test data
• producing and updating documentation
• comparison of technical solutions and drafting of justifications

The productivity impact is greatest when usage is integrated into the development process and team practices such as code reviews and the CI pipeline.

Generative artificial intelligence in business processes

In business processes, generative AI works well in situations involving messages, documents and data between systems.

Typical destinations:

• interpretation of reports and production of summaries
• processing of orders and messages
• document extraction and field filling
• Workflows connected to ERP and CRM environments

A good example of this is the order processing solution we have developed, in which orders received by email are read and interpreted using generative artificial intelligence and converted into structured data for the ERP system. When business rules, validations and approval steps are added to the mix, the solution forms a controlled and production-ready process that reduces manual work and improves data quality.

AI Sandbox concretises the use of generative artificial intelligence

For many organisations, the biggest challenge in utilising generative artificial intelligence is understanding the big picture. The question is not so much whether the technology can be implemented, but what it means in their own operating environment and existing processes.

To this end, Hurja uses an AI Sandbox demo environment, which illustrates the possibilities of generative artificial intelligence through concrete examples based on real business situations. The demos show how language models and related structures work as part of everyday systems and workflows.

AI Sandbox demos include, among other things:

• order processor that automates orders received by email into the ERP system
• Ask the document – a 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 format
• AI trainer that supports induction and training interactively
• trend analysis that identifies development trends from business data

The purpose of the demos is to demonstrate how generative artificial intelligence works in practice as part of business processes and what kind of structures are required for production-ready solutions.

Generative AI is not just a chat window

Chat is a user interface, but enterprise solutions require an entire structure around them. When generative AI is integrated into systems, practical benefits arise: information can be found, processes move forward, and employees do not need to copy things from one system to another. Generative AI is a practical way for companies to produce, edit and structure content when it is connected to data, integrations and access rights.

Typical building blocks for business use:

• integrations with ERP, CRM and document management systems
• AI agents that perform multi-step tasks
• RAG structures, where the model searches for answers in the source material from the company’s data
• automations that trigger workflows based on events (e.g. email, ticket, form)

Benefits and limitations in business use

The benefits of generative AI often arise from the sum of many small improvements. When repetitive and text-based tasks become faster, the impact is visible in terms of time and quality.

• efficiency and time savings in data processing
• scalability in content and customer encounters
• new operating models that make information more easily accessible

The limitations are particularly related to the fact that the model generates content based on probabilities and requires a good context.

• hallucinations and false details without sources
• quality variation if guidance and context are imprecise
• lack of context if the company’s data is not included or available
• data security and responsibility when handling sensitive information

Data security, accountability and the EU AI Act

In business use, generative AI requires information security and responsibility. When dealing with contracts, personal data, trade secrets or production data, the solution cannot be based on open processing without clear restrictions and controls.

Production-level solutions typically utilise:

• closed environments and controlled service models
• RAG structures where the model uses only limited source material
• access rights models that limit visibility based on role
• logging and monitoring to ensure that usage can be traced and audited

The EU AI Act affects companies in particular through risk-based thinking: the intended use, impact and control of the system determine what requirements must be met. In practice, this guides companies to document use cases, governance models and oversight in a way that withstands both internal review and potential external requirements.

How should a company get started with generative artificial intelligence?

The implementation of generative artificial intelligence should be carried out in stages, first identifying the processes and points where value is created, and then proceeding with a limited set of use cases from pilots to production.

Good starting points include the accumulation of manual work, information that is difficult to utilize, and situations where decision-making is slowed down by fragmented or unclear material. Identifying these helps to target the use of artificial intelligence where it will have the greatest impact.

When solutions are integrated into everyday life, generative artificial intelligence begins to shift the focus of work around information and communication. Guidance, verification and decision-making are emphasised, while drafting and content conversion can be automated.

In practice, information is easier to find and discuss, documentation and instructions are updated more quickly, experts can focus their time on exceptions and valuable decisions, and processes are shortened as messages and documents are handled more automatically.

That is why the choices made in the early stages are important. Usage patterns, data structures and management models remain in place and expand into new use cases.

We help companies identify these areas and integrate generative AI into existing processes and systems in a controlled manner.