Using Artificial Intelligence in Business: How AI Becomes Part of Everyday Company Life
Using artificial intelligence at companies delivers results fastest in repetitive processes involving a lot of text work, where AI prepares the groundwork and a human carries out the final review. This can include summarising documents, translation, drafting minutes and report outlines, preparing customer replies, or supporting marketing communication.
However, successful adoption takes more than subscribing to an AI programme. You need to define a concrete use case, a measurable goal, an approved tool, data handling rules and points of human review. In this article, we show how you can effectively build artificial intelligence into your company’s operations.
What Can Artificial Intelligence Be Used For in Everyday Work?
AI is most useful for preparatory, organising and pattern recognition tasks. The best starting point is a frequent, measurable and reversible process whose output is reviewed by a knowledgeable member of staff.
| Area | What can AI be used for? | Human review |
|---|---|---|
| Project management | Preparing minutes, status reports and risk registers | The project manager validates decisions, owners and deadlines |
| Marketing | Creating campaign ideas, copy drafts and variants | Checking brand voice, facts, legal claims and originality |
| Customer service | Draft replies and topic categorisation | Staff handle sensitive, complaint-related or unique cases |
| Administration | Summarising, and categorising emails and invoices | Approving exceptions and uncertain matches |
| HR | Job ad drafts and internal communication | Ranking CVs can be high-risk; legal and professional control is required |
IMPORTANT: Leading AI tools can also be used in Hungarian, but performance varies by task and provider. Fact-checking is necessary even for general business text; for legal, financial, technical or organisation-specific terminology, professional proofreading is recommended.
Where Does AI Use in Companies Stand Today?
According to the K&H Innovation Index’s first-half 2026 survey of 360 Hungarian companies, 58% of companies with annual revenue of at least HUF 300 million had already used some form of AI solution, up from 48% six months earlier. This is a usage rate, not proof of daily or organisation-wide integration.
- Development: the share of companies carrying out AI development rose from 24% to 36% within six months.
- Most common tasks: translation 39%, text processing 33%, support for marketing communication 22%.
- Differences between companies: 66% of companies in the services sector used AI, compared with 72% among companies with revenue above HUF 4 billion.
- Innovation support: 80% of companies that received innovation support used AI.
According to Eurostat’s 2025 data, 20.0% of EU enterprises with at least 10 employees used AI technology, up from 13.5% in 2024. Denmark led with 42.0%, followed by Finland with 37.8% and Sweden with 35.0%; the rate was 5.2% in Romania, 8.4% in Poland and 8.5% in Bulgaria. Analysing written language was the most common use, at 11.8%.
The K&H and Eurostat figures cannot be compared directly, because of differences in sampling, company size and methodology. Together, they show that AI use is spreading quickly, while organisational maturity and actual integration vary considerably from company to company.
Which Business Task Is Worth Starting With?
Start by choosing a task that takes up a lot of working time, recurs frequently, is easy to measure, is low-risk, and has a clear human approver.
- Frequency: a process that occurs at least weekly has an advantage.
- Measurability: the current effort, turnaround time or error rate should be known.
- Data security: the organisation should approve the use of the data needed for the pilot in advance.
- Verifiability: an expert should be able to quickly judge whether the output is correct and usable.
- Reversibility: an error should not automatically create legal, financial or personal consequences.
A simple rule of priority: start with a task that is high-frequency and time-consuming but low-risk. Only bring in high-impact or regulated decisions after a separate risk assessment.
How Should You Start Using Artificial Intelligence at Your Company?
Successful adoption starts with a limited pilot, not a company-wide licence purchase. The process can be structured in six steps.
- Choose 2-3 pain points. Gather common, text-intensive, time-consuming tasks, then rank them by risk and measurability.
- Appoint a pilot team. Include a business process owner, a professional reviewer, an IT or data protection contact, and a clear decision-maker.
- Set the ground rules. Define the approved tools, the scope of data that may be entered, logging, review points and incident handling.
- Measure a baseline. Record current working time, turnaround time, error rate and rework cycles, so the change can be demonstrated later.
- Train your staff. Alongside prompting, teach data handling, source checking, recognising bias, and the limits of handing off a task.
- Evaluate and scale. At the end of the pilot, expand, adjust or discontinue the solution based on the measured results and the risks.
Without an approved tool list, a designated owner and clear rules, staff can easily start using AI tools in their own personal accounts, outside the company’s oversight.
Which AI Programs Can You Choose From?
Artificial intelligence also works well in Hungarian. The right AI programme should be chosen primarily based on the task, the existing office environment, the data handling terms and the administrative controls.
Widely used solutions include OpenAI ChatGPT, Microsoft 365 Copilot, Google Gemini and Anthropic Claude, alongside numerous specialised AI programmes.
- Existing infrastructure: Copilot may integrate more closely with Microsoft 365, and Gemini with Google Workspace. Test the integration on a specific workflow.
- Data handling: Many business packages exclude customer data from model training by default, but always check this in the provider’s terms, the contract and the settings.
- Access and administration: Examine permission management, logging, data retention, single sign-on, and the process for removing users.
- Integration: Only connect the AI tool to company documents, email or other systems with approved permissions.
- Hungarian language: Measure terminology accuracy and the amount of post-editing needed using your own sample tasks.
How Much Does Using Artificial Intelligence Cost?
The public US list price for individual AI subscriptions is typically $19.99-20 a month, while common business packages cost around $14-25 per user per month; enterprise arrangements often require a custom quote. Besides the licence, the total cost should also include the effort spent on rollout, integration, training, governance and human review.
| Solution | Public price (August 2026) | Note |
|---|---|---|
| ChatGPT Plus | $20 / month | Individual plan |
| ChatGPT Business | $20 / user / month annual; $25 with monthly billing | Minimum 2 users |
| Microsoft 365 Premium | $19.99 / month | Individual plan, with Copilot features |
| Microsoft 365 Copilot Business | $18/user/month with annual billing (promotional price), original price $21 | Requires an eligible Microsoft 365 plan |
| Google AI Pro | $19.99 / month | Individual plan |
| Google Workspace Business Standard | $14 / user / month with an annual commitment | Gemini is included in the plan |
| ChatGPT Enterprise | Custom pricing | Quote required |
These are US list prices, reflecting publicly available information as of 12 August 2026. They may vary by region, VAT, exchange rate, promotion, billing cycle and existing base licence. Always check the current offer.
Sources: ChatGPT pricing; Microsoft 365 Copilot business pricing; Microsoft 365 individual plans; Google Workspace pricing; Google One AI plans.
Is Using Artificial Intelligence Free?
Yes, several providers offer a free plan, which can be suitable for trying the tool out with public, non-sensitive content. However, only enter customer data, personal data, contractual secrets, internal documents or source code into an environment that has been explicitly approved for business use.
The data handling terms, default settings and administrative controls of free and consumer-oriented plans may differ from those of business plans. Before business use, check whether the provider uses the content for model development, where it processes the data, how long it retains it, and what logging or permission options it provides.
What Risks Does Using Artificial Intelligence Involve?
The main risks can be reduced with proper governance and review, if the organisation defines the tools, the data, the intended uses and human approval in advance. The most important areas are set out below.
- Data protection and confidentiality: Business or personal data entered into an unapproved tool can create GDPR, contractual and information security risks. Mitigation: a tool list, data classification, permission management and, where necessary, a data protection impact assessment.
- Factual inaccuracy: The model can confidently state something untrue or outdated. Mitigation: checking sources, double-checking critical data, and using approved company knowledge sources.
- Bias and discrimination: Imbalances in the input data or in the data used to train the model can show up in the output. Mitigation: testing, documentation and human review; especially strict control for decisions about people.
- Copyright and confidentiality: The origin and usability of generated content is not always clear. Mitigation: internal guidance, originality checks and, where necessary, legal advice.
- Cybersecurity: External content or a connected system can mislead the AI, or the AI may use overly broad permissions. Mitigation: minimal permissions, isolated testing, logging and an incident process.
- Loss of competence and vendor dependency: professional knowledge and workflows should not become dependent on a single tool. Mitigation: human decision-making authority, documented procedures and portable templates.
What Does the EU AI Act Require Regarding the Use of Artificial Intelligence?
The EU AI Act is a risk-based piece of regulation: the obligation is determined by the intended purpose and actual use of the AI system, not simply by the name of the tool. Most general office text preparation is not, by itself, high-risk, but the same technology built into a regulated purpose can fall into a different category.
The Digital Omnibus was published in the EU Official Journal on 24 July 2026 and entered into force on 27 July. Certain rules for purpose-based high-risk systems under Annex III apply from 2 December 2027, while those for systems subject to product safety regulation under Annex I apply from 2 August 2028.
Systems that may be high-risk include, among others:
- certain biometric, critical infrastructure-related, educational and employment-related uses of AI, and the assessment of individuals’ creditworthiness
- risk assessment and pricing for life and health insurance
- certain law enforcement and judicial applications
Automatically ranking CVs therefore cannot be treated as a simple office convenience feature.
The AI literacy obligation has applied since 2 February 2025. Following the 2026 amendment, providers and deployers must still support the development of AI literacy through measures tailored to staff knowledge, role and the circumstances of use. The regulation does not prescribe a uniform level of knowledge, a set number of training hours, or a certificate.
- Create an AI register. Record which tool is used, in which process, with what data, and with what decision-making impact.
- Classify your use cases. Mark the prohibited, high-risk, transparency-related or general business categories.
- Record responsibilities. There should be a process owner, a professional approver, and a data protection and information security control.
- Ensure AI literacy. The training content should match the role, the tool and the risk involved.
How Does Artificial Intelligence Work?
In short, artificial intelligence means a collection of machine-based systems that draw conclusions from the inputs they receive and, with varying degrees of autonomy, produce a forecast, content, a recommendation or a decision output. Not every AI system is trained on huge volumes of data, and not every system operates without human involvement.
Large language models learn language patterns by breaking text down into smaller units known as tokens, then generate a response based on these. A modern enterprise solution, however, may also use a search function, a document repository, or retrieval-augmented generation (RAG), so the response does not always come purely from the model’s internal patterns. Connecting to sources can reduce, but not eliminate, the possibility of errors.
Common Types of Enterprise Solutions
- Machine learning: models trained on data that, for example, carry out forecasting or classification.
- Generative AI: models and assistants that produce text, images, audio or code.
- Agents and automation: solutions built on models and other systems that carry out multi-step tasks. This topic is covered in detail in our article What Is an AI Agent and How Does It Work?
It follows from how it works that AI is good for producing first drafts, summaries and suggestions, but professional and legal responsibility cannot be transferred to the tool. Any business, public, customer-facing or decision-influencing output must always be followed by human review appropriate to the risk involved.
Frequently Asked Questions About Using Artificial Intelligence
How Should You Give Instructions to an AI Tool?
A good instruction clearly sets out the goal, the necessary background information, the sources that may be used, and the expected output format. It’s also worth specifying which factors the tool should take into account, what it should avoid, and where it should flag uncertainty. For complex tasks, it’s better to refine the first answer rather than expect a single instruction to produce the final result.
How Can the Return on Using AI Be Measured?
Before rollout, you need to record the baseline: how much time, how many working hours and how much cost the process requires, and how often errors or rework occur. This data should then be compared with the results measured after applying AI. It’s also worth including the cost of licences, integration, training, review and maintenance in the calculation.
Is It Necessary to Create a Separate Corporate AI Policy?
A standalone policy isn’t necessarily required if your existing information security, data protection and IT rules can be properly extended. However, the regulation must clearly cover the approved tools, the scope of data that may be used, the review of outputs, areas of responsibility, and the reporting of irregularities.
Which Tasks Shouldn't Be Left Solely to Artificial Intelligence?
It’s not advisable to leave decisions with significant personal, legal, financial or security consequences solely to AI. AI can prepare and support such decisions, but the outcome must be reviewed by a person with the appropriate expertise. The same applies to any content the company uses as an official position, advice, or customer communication.
Does the EU AI Act Apply to Smaller Businesses Too?
Yes, it can apply. Company size alone doesn’t grant an exemption: obligations are determined primarily by the role in which the company uses AI, for what purpose, and in what risk level of process. The requirement to provide AI literacy, for example, can also affect organisations that deploy AI systems, though the measures can be tailored to the circumstances and risks of use.
Who Is Responsible for Content Created by Artificial Intelligence?
AI does not take over responsibility from the organisation or the member of staff. The company must determine who reviews and approves content, analysis or recommendations produced by AI. The precise legal liability depends on how it is used, the contractual relationships involved, and the applicable rules.
Does Artificial Intelligence Take Away People's Jobs?
AI typically doesn’t replace entire jobs, but transforms or automates individual tasks. The share of routine activities may decrease, while review, decision-making, professional judgement and customer relationships become more valuable. Because of this, successful adoption requires not only technological development but also preparing staff.
In Summary
In the K&H survey, most of the Hungarian medium and large companies studied had already used AI in at least one area, but this doesn’t mean the technology is a daily, integrated practice everywhere. Real business value is created by the combination of a well-chosen process, a measurable baseline, an approved tool, data handling rules, prepared staff and consistent human review.
Would you like artificial intelligence to become a corporate solution that delivers measurable results? ProMan Consulting’s AI consulting supports your company from selecting the right use cases, through building the pilot and internal governance, to organisation-wide rollout. Learn more about our AI consulting service and request a free consultation!
