What Is Generative Artificial Intelligence, and What Can Businesses Use It For?
Generative artificial intelligence does not only analyse or classify existing data, it also creates new content. Organisations can use it for writing, document processing, customer service support, analysis and software development, amongst other things, but the results require human review.
In this article, we explain how generative AI works, how it differs from predictive artificial intelligence, and which business tasks it can be applied to. We look at how widely it is used, its risks and the cost of adoption, as well as the requirements set out in the EU AI Act.
What Is Generative Artificial Intelligence?
„Generative” means „creating, producing”. Accordingly, generative AI is artificial intelligence that creates text, images, audio, video, programming code or other new content based on patterns learned during training. In professional usage, the abbreviation GenAI and the term generative AI are both widespread.
In Hungarian, artificial intelligence is abbreviated as MI, whilst internationally the abbreviation AI (artificial intelligence) is used. Both forms are common in Hungarian business communication.
Artificial intelligence is the broader concept: generative AI is one of its sub-fields. Put simply, the related concepts build on one another as follows:
- Artificial intelligence: a broad umbrella term for systems that can carry out tasks that typically require human intelligence.
- Machine learning: a field of artificial intelligence whose algorithms learn patterns from data without every rule having to be programmed into them individually in advance.
- Neural networks and deep learning: machine learning methods that process data using layers built on top of one another.
- Generative models: models that create new content or other types of output based on the patterns they have learned.
How Does Generative AI Work?
There is no single operating model that applies to every generative system. Text-based large language models (LLMs) estimate the probability of the next token (that is, a word, part of a word or a punctuation mark), then build the response from these step by step. Image, audio and video generation models use different technical methods, but what they have in common is that they create new output based on learned patterns.
Put simply, the process can be broken down into three main stages:
- Pre-training. The model does not primarily retrieve ready-made answers; instead, it describes the relationships between data in mathematical form. In some cases, however, it may also reproduce parts of its training data.
- Fine-tuning and feedback. The model can be developed further using targeted examples, human evaluation or other feedback methods, so that it gives responses that are more useful, safer and follow instructions more precisely. Not every generative model uses the same methods.
- Inference, that is, use. The user gives an instruction (this is called a prompt), and the model creates the output based on the patterns it has learned. The methodology of designing and refining good instructions is known as prompt engineering.
What Is a Large Language Model?
A large language model (LLM) is one of the most widespread model types in generative AI. It is trained on large volumes of text data so that it can process natural language and create new text.
The chat-based assistants ChatGPT, Gemini, Claude and Copilot are all built on LLMs, which can be complemented by search, document handling and other tool functions.
An LLM is not a finished product in itself but the technological foundation of one: it can run in a chat interface, be built into an email client, or support background processes in enterprise systems.
What Is the Difference Between Generative AI and Traditional Artificial Intelligence?
Predictive artificial intelligence, long used by organisations, mainly serves to analyse and classify existing data and to produce forecasts. Generative AI, by contrast, creates new content, such as text, images or programming code. There is not always a sharp boundary between the two categories, but they typically differ in their output, the way they make mistakes and the range of risks associated with adopting them.
| Aspect | Predictive AI | Generative AI |
|---|---|---|
| Primary output | Category, score or forecast | New text, images, audio, video or programming code |
| Typical question | „Which customer is likely to cancel their contract?” | „Write the reply to the customer!” |
| Typical operation | Recognises, classifies or forecasts based on historical data | Creates new content based on learned patterns |
| Customisation | Often built for a specific task and dataset | Often based on a general-purpose foundation model that can be customised afterwards |
| Evaluation | Performance can be tested with predefined metrics and test data | Output quality must be assessed against several criteria, often through human review |
| Typical error | Misclassification or inaccurate forecast | Plausible-looking but inaccurate or untrue content |
| Focus of adoption | Data quality, model evaluation and continuous monitoring | Usage frameworks, permissions, data protection and human review |
The practical conclusion is that adopting generative AI partly requires a different approach from predictive systems. Besides model performance, it also has to be defined how the tool fits into the organisation’s processes, who reviews the outputs, and who takes responsibility for them.
What Can Businesses Use Generative Artificial Intelligence For?
In enterprise use of generative AI, five areas stand out where results are relatively easy to measure.
Communication and Content Creation
Preparing first drafts of proposals, newsletters, internal briefings and presentations. Above all, research, drafting and putting together the first version of a text can be speeded up.
Customer Service
Drafting suggested replies, summarising customer conversations and searching for information in the knowledge base. In many cases, an assistant supporting the agent’s work can be introduced more safely than fully automated customer communication.
Document Processing
Summarising and comparing contracts, reports and minutes, or extracting data from them. Results can be measured, for example, by turnaround time, the number of documents processed and the need for corrections.
Software Development
Supporting the writing of code, tests and documentation, as well as identifying bugs. The impact can be assessed based on development time, error rate and completed tasks.
Analysis and Decision Support
Summarising market research material and internal data, and outlining possible scenarios. Generated analyses and the underlying data must be professionally reviewed before any decision is made.
In these areas, generative AI primarily supports the workflow. The organisation remains responsible for reviewing the outputs and for their business use.
What Generative AI Tools Are Available?
When asking what artificial intelligence programmes exist, the right answer today is categories rather than a list of tools, because specific products and model versions change every few months.
| Category | What does it do? | Typical business use |
|---|---|---|
| General-purpose assistant | Writes, analyses and summarises text and answers questions | General day-to-day work support |
| Assistant built into office software | Works with documents, spreadsheets, presentations and email | Preparing reports, emails and meeting summaries |
| Image and video generator | Creates images and video from text or visual instructions | Producing marketing materials, illustrations and prototypes |
| Coding assistant | Supports coding, debugging, testing and documentation | Software development and operations |
| AI agent | Can plan and carry out multi-step tasks using various tools | Partial or full automation of workflows |
| Enterprise platform or API | Allows models to be built into your own systems and processes | Developing bespoke internal applications and integrations |
When selecting AI tools, three further aspects are worth examining alongside functionality: where the data entered is stored and processed, whether the provider may use it to develop its models, and how easily the solution can be integrated into the organisation’s existing software and workflows.
How Widely Has Generative AI Been Adopted by Businesses?
There are no data on the enterprise adoption of generative AI that consistently cover every type of use. Eurostat measures the use of technologies generating language or programming code separately from those generating images, video or audio. These shares cannot be added together, as the same enterprise may use several types of AI technology.
| Indicator | Value | Source and date |
|---|---|---|
| EU enterprises (10+ employees) using at least one AI technology | 20.0% | Eurostat, 2025 |
| EU enterprises using AI that generates images, video or audio | 9.5% | Eurostat, 2025 |
| EU enterprises using AI that generates written or spoken language or programming code | 8.8% | Eurostat, 2025 |
| Hungarian enterprises using AI-based software or applications | 32% | GKI, July 2025 survey, 390 responding companies |
| Enterprises with internal AI rules, among Hungarian companies using AI-based software | 17% | GKI, July 2025 survey |
The Eurostat figure of 20% and the GKI figure of 32% measure general AI use, so they show the wider business context in which generative AI is spreading. Specifically generative technology was used by 9.5% of EU enterprises to produce images, video or audio, and by 8.8% to produce language or programming code. There may be overlap between the two shares.
The results of different surveys cannot be compared directly, as the population of enterprises surveyed, the definition of AI use and the data collection method may differ. The business value of the technology is therefore best assessed not on the adoption rate alone, but on specific process metrics, such as turnaround time, error rate, need for corrections or freed-up working capacity.
What Are the Risks of Generative Artificial Intelligence?
It is worth distinguishing five types of risk, as each requires different preventive and control measures:
- Factual errors, that is, AI hallucination. The model may confidently state falsehoods and may also cite non-existent references, data or legal provisions. The risk can be reduced by drawing on credible sources, a verified knowledge base and mandatory review points, but it cannot be eliminated entirely.
- Data protection and confidentiality risk. Uploading a contract, customer data or source code to an external AI system may cause data protection, trade secret or contractual problems. Sensitive data may only be used in an approved environment, after checking the provider’s data processing terms and the organisation’s internal rules.
- Legal and copyright risk. The usability and copyright protection of generated content, and liability for any infringements, may depend on how it is used and on the applicable law. Content that is published or used for business purposes may therefore also require legal review.
- Bias and unfairness. Biases in the training data, in the way the model works or in the instructions given can also appear in the outputs. This can pose a particularly high risk in HR, customer management and other decision-making situations that affect people.
- Erosion of professional skills. If employees regularly rely on generative AI without review, their independent analytical, writing and problem-solving skills may decline over time. This can be mitigated through training, deliberate task allocation and mandatory human review.
These risks can be managed within an appropriate organisational framework, but this requires written usage rules, clear responsibilities and regular review.
What Does the EU AI Act Regulate Regarding Generative AI?
The EU AI Act takes a risk-based approach: it prohibits certain AI practices and imposes requirements on other systems depending on their risk and use. The Regulation entered into force on 1 August 2024, but its provisions become applicable in several stages. The most important milestones from a generative AI perspective are as follows:
| Date | Key milestone |
|---|---|
| 1 August 2024 | The EU AI Act entered into force. |
| 2 February 2025 | The ban on most prohibited AI practices and the AI literacy obligation took effect. |
| 2 August 2025 | The governance rules and the obligations for providers of general-purpose AI models (GPAI models) started to apply. |
| 2 August 2026 | Most of the AI Act’s provisions took effect, including the transparency rules for certain AI systems and for AI-generated or manipulated content. |
| 2 August 2027 | Providers of GPAI models placed on the market before 2 August 2025 must also meet the requirements that apply to them. |
| 2 December 2027 | The rules for high-risk AI systems used in certain sensitive areas, such as employment, education or migration management, become mandatory. |
| 2 August 2028 | The requirements for high-risk AI systems built into regulated products, such as machinery, lifts or toys, become applicable. |
Most Hungarian organisations do not develop or place AI models on the market; rather, they use systems from other providers. Obligations must therefore always be determined based on the organisation’s role, the AI system concerned and the purpose of use.
The AI Act sets out separate transparency rules, for example, for deceptively realistic image, audio and video content, and for AI-generated or manipulated text published to inform the public on matters of public interest. For the latter, labelling is not required if the content has undergone human review or editorial control and a natural or legal person holds editorial responsibility for its publication.
Specific requirements must always be applied to the use case in question. On compliance matters, it is therefore advisable to involve a legal or data protection expert.
How Should a Company Introduce Generative AI?
Choosing a tool is not enough on its own to adopt generative AI successfully. The framework for use, data handling, review and measurement must be defined in advance. The process can consist of five main steps:
- Selecting use cases. First, it is worth identifying two or three clearly scoped tasks where current time spent, cost or error rate can be measured. Without baseline data, it will later be impossible to establish whether AI has genuinely improved the process.
- Setting data handling and access rules. It must be defined what data may be entered into the system, who can access it, where the data is stored and processed, and whether the provider may use it for model development.
- Establishing human review. It must be decided who reviews the generated output, who is responsible for its use, and how the review can be documented. The level of control must be matched to the risk of the task.
- Preparing employees. Besides how to use the tool, training must also cover which tasks can be entrusted to AI, how to recognise incorrect answers, and when expert review is needed.
- Measuring and scaling. The pilot results must be assessed against predefined metrics, such as turnaround time, error rate, need for corrections or cost. Use can be extended once the risks, as well as the results, can be kept at an acceptable level.
Adoption is therefore best started by defining the use cases and the operating rules. With these in place, the right tool and licensing model can be chosen on a sound basis.
How Much Does It Cost to Introduce Generative AI?
The total cost of introducing generative AI is not limited to the licence fee. The level of investment also depends on the number of users, the complexity of integration, data protection requirements and the chosen solution. The main cost items are as follows:
- Licence and usage fees. Providers may charge a per-user subscription, usage-based API fees or a combination of the two. Prices, eligibility conditions and available features may also differ by region and plan.
- Integration and customisation. Connecting the solution to the organisation’s existing systems, documents and permission structure, and designing bespoke workflows, may incur costs.
- Training and change management. Preparing employees, introducing new processes and supporting usage may require both one-off and ongoing investment.
- Governance and control. This includes drawing up internal policies, running review processes, documentation, security checks and regular compliance reviews.
Published list prices alone are therefore not enough to compare solutions. Besides price, data handling, security level, integration options, usage limits and implementation support must also be assessed.
Will Generative AI Replace Jobs?
Generative AI is expected to transform primarily the tasks within jobs rather than entire roles. It can, for example, speed up drafting, summarising, information searches and routine document work. Whether this leads to job losses, new roles or a reshuffling of tasks in a given organisation depends on the industry, the workflows and management decisions.
As the technology spreads, professional judgement, the review of AI-generated results and taking responsibility are likely to become more valuable. The impact therefore depends not only on the capabilities of the tool used, but also on how employees are prepared and how organisational processes are reshaped.
Frequently Asked Questions About Generative Artificial Intelligence
To close, let’s look at a few more useful questions and answers!
How can the business benefit of generative AI be measured?
Even before the pilot, it is advisable to record a baseline: for example, case handling time, turnaround time for completed documents, correction rate or customer service follow-up work. Success is measured not by the number of prompts, but by the time, quality and risk involved in the same task.
Who is responsible for content created by generative AI?
The organisation is primarily responsible for AI content it publishes or uses for business decisions. The internal policy should also set out who carries out the professional review, who approves its use, and how the review is documented.
Can company data be fed into generative AI?
Only under clearly defined conditions. First, it must be determined what data may be entered, where the provider processes it, whether it may be used for model development, and who can access it. For personal data, trade secrets or data subject to contractual restrictions, a data protection and legal review is also required.
What is the difference between generative AI and an AI agent?
Generative AI typically produces content or recommendations. An AI agent, by contrast, can organise several steps towards a goal, use tools and, depending on its permissions, also carry out actions. This is why access rights and approval points are especially important for agents.
Is a chat window enough, or do we need our own system?
Most organisations are best served by starting with a clearly scoped, approved tool. Your own integration is justified when the task relies on an internal knowledge base or a bespoke, recurring workflow, and the data handling, access and measurement conditions have already been clarified.
Summary
Generative artificial intelligence is a type of system that produces new content based on learned patterns. For this reason, in enterprise adoption, data handling, review and accountability matter as much as the quality of the tool itself.
Adoption is growing, but business value can only be judged through specific process metrics. The EU AI Act reinforces this approach as well: the relevant transparency obligations must be interpreted based on the role and risk of the use. Anyone considering introducing generative AI should start not with the licence, but by defining the use cases, the data and access rules, and the measurement plan.
We Help You Find the Solution That Best Fits Your Organisation
If your organisation is currently shaping how it uses generative AI (use cases, policy, roles, training, AI Act compliance), we can guide you through the rollout as part of our consulting services, and you will also find practical preparation in our training portfolio. Get in touch with us for a free consultation!
