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By TJ SoftUpdated:

Does Every Company Need Artificial Intelligence? Implementing AI in Business.

Czy każda firma potrzebuje sztucznej inteligencji? Wdrażanie AI w biznesie.

In answer to the question: yes, in 2025 practically every company will need artificial intelligence. However, this does not mean that every company must create its own dedicated solutions or spend significant amounts on training and specialized systems.

In reality, awareness is key—both of the threats and of the current capabilities of AI. It is enough for employees to be able to devote part of their time to learning about artificial intelligence. It is also worth supporting this process with additional training and ensuring that managers, supervisors, the management board, and the company owner expand their own knowledge in this area.

Thanks to knowledge and experience, better decisions can be made regarding the implementation of advanced artificial intelligence. This helps avoid the trap of implementing AI simply because it is fashionable, just as with “fruit Thursdays” or meditation sessions at the company.

Table of contents

What exactly does a basic AI implementation in a company mean?

The foundation for implementing AI in any company is ensuring that a sufficient number of people at every level understand the capabilities of artificial intelligence. Every person in the company should also be aware of the associated risks and know what not to do.

  • Familiarity with various language models (Claude, Grok, ChatGPT, Gemini, Bielik, Plumm, Llama)

  • Knowledge of models that can be run locally (Llama, Bielik, DeepSeek, Mistral)

  • Familiarity with useful features in AI clients (e.g. projects in ChatGPT), as well as knowledge of interesting tools, such as NotebookLM

  • AI capabilities and applications, e.g. creating website prototypes, generating photos and videos, writing schematic texts, proofreading, data analysis, writing Excel formulas

  • Practical experience with how it works. What kinds of images can be created easily, when AI-generated texts, videos, and graphics are useful and when they do not make sense; not everything can be done with AI, and traditional methods are often still useful.

  • AI as an aid in research and learning

  • Basics of task automation

  • A general understanding of how AI works (tokenization, vectors, temperature)

  • Prompting principles and providing language models with the appropriate context

  • Principles for verifying what AI creates.

  • Knowledge of how important good data is

  • Security fundamentals (do not upload sensitive data to language models that are not deployed locally.

  • Legal aspects

Shadow AI – why AI in some form should be implemented in every company.

The phenomenon of shadow AI, meaning employees using artificial intelligence independently, means that artificial intelligence must be implemented in some form in every company. Companies that fail to do so are exposed to losses, for example if customer data is leaked because employees entered it into language models. The foundation, therefore, is to establish rules and specify what not to do. Another problem is publishing poor-quality content created by AI (which looks good at first glance). Or sending such things to a client.

One could simply ban the use of AI, but apart from the obvious losses this would entail, it could have the opposite effect, leading employees to use AI secretly. The temptation to speed up their work is too great, and it is hard to blame them for it. Employees who nowadays had to use abacuses instead of computers would understandably feel frustrated.

AI Security Fundamentals in the Company.

  • Do not upload sensitive data, passwords, or confidential information to public language models.
  • Content created by AI must be verified. AI can hallucinate, and AI-generated images may contain glaring errors.
  • In some cases, content created by AI should be labeled. Even if it is not legally required, this is good practice. The extent of AI’s involvement in the process is important (e.g., whether it was used only for proofreading or research). It is also important whether an AI-generated graphic is pretending to be a real photograph.

How can AI be implemented in any company?

The list below facilitates planning the implementation of AI when starting from scratch. In some cases, you can move directly to later steps. These points are expanded on in greater detail later in the article.

  • Step 0. Security fundamentals. Defining what not to do is already a certain stage of AI implementation.
  • Step 1. Learning and playing with AI – testing tools at work and after hours, sharing links, experimenting.
  • Step 2. Purchasing tools – taking costs, team licenses, and security rules into account.
  • Step 3. Simple internal solutions – automatic note-taking, searching for procedures, implementation support, local models for sensitive data. Creating internal solutions is much easier.
  • Step 4. Advanced solutions – systems for clients or larger projects, which only make sense after the earlier stages and with the appropriate team expertise.

AI should be treated like the internet. In most cases, implementing the internet in a company is both obvious and simple.

The comparison to the Internet aptly illustrates the significance of artificial intelligence. By analyzing the process of the Internet and computers becoming widespread in companies, many useful conclusions can be drawn about the relationship between AI and business.

In this case, I am referring to the general implementation of the use of artificial intelligence, because implementing more complex AI systems, such as knowledge systems within a company (RAG), or other analytical mechanisms, is more difficult.

The Internet in business is now a given. Even small companies whose core activities are not technology-based use the Internet every day. Nowadays, implementing the Internet in a company involves choosing a provider, paying invoices, connecting computers to the network, and setting up Wi-Fi routers. The larger the company, the more complex this becomes; the creation of local networks, VPNs, and so on also comes into play. In smaller, non-technology companies, everything is based on privately owned equipment and employees’ devices (for example, when someone manages social media from their own phone).

Nowadays (I am deliberately overusing this word), everyone knows how to use the internet in a basic way. Employers know that every employee has a camera, a search engine, and maps on their phone. 30 years ago, when meetings were held in less obvious locations, it was often necessary, for example, to provide employees with maps.

For a while, say from 1995 to 2010, there were many courses on how to use the internet.

http://3drupal.pl/podstawowy-kurs-komputerowy

Nowadays, knowledge of the basics of the Internet is taken for granted. People who have had access to the Internet since early childhood are now 20–40 years old, and virtually everyone uses the Internet on a daily basis, for example for communication, entertainment, and so on.

When it comes to AI, we are at a similar stage as we were back then. Courses and free guides on the general use of AI will not be of much use in a dozen or so years, because by then everyone will know these things. In addition, using AI will be simpler and cheaper. The same was true of the internet—it was initially complicated and expensive, and even for simple tasks, costs had to be calculated carefully.

However, gaining knowledge about how artificial intelligence works is currently very important. Companies that slept through the development of the internet (and operated in industries where the internet proved important) often went under.

Implementing AI: Start by Investing in Skills and Knowledge. The AI-Ready Company Model.

The foundation for implementing AI in a company is knowledge of how artificial intelligence works and what can be achieved with it. Awareness of the risks is also important—for example, the potential leakage of data resulting from providing sensitive information to language models.

Why Should Every Company Implement Artificial Intelligence? The False Analogy to a Hammer.

Some people compare AI to a specific tool, such as a hammer. Companies do not massively deploy, for example, hammers in the hope that they will eventually pay off. The typical approach is to analyze and define the problem (e.g., driving nails), and then look for a solution (using a hammer). The more expensive the tool, the more complex the analyses required.

Your analogy works when we are talking about implementing AI that performs a specific task. In such a case, especially when it involves greater expenses, analysis and searching for a specific solution are necessary.

But artificial intelligence has a much broader range of applications. We can therefore compare it to an entire set of tools. In many situations, it is worthwhile to have a toolbox, even if you do not use it every day. People who know how to use basic tools are better able to assess the value of more expensive and advanced tools. While everyone has an idea of how a hammer works, there are many types of tools whose operation and applications need to be learned.

That’s why implementing AI—for example, to support research and text summarization or to assist with learning—makes sense in practically every company.

The previously mentioned phenomenon of shadow AI also comes into play here. There is a strong chance that employees will use artificial intelligence in some way regardless, despite the lack of top-down guidelines.

Stages of Implementing AI in a Company

Step 0. Safety Basics

Before a company begins experimenting with artificial intelligence, it is worth defining boundaries and rules. Simply identifying what not to do is itself part of the implementation process. This will help you avoid mistakes that could cost time, data, or money.

Why Is Taking Care of AI Security Important, Even in Small Businesses?

Some people might think that, for example, OpenAI would gain nothing from reading chats from a small company. Additionally, service providers often have some access to data stored on “cloud” drives. However, there is a major difference between data stored on a drive and LLM chats. In the first case, a given company would have to read the documents manually and find confidential information in them. In the case of language models, there is a strong temptation to use users’ data to train the models.

The AI boom has meant that every company with access to large amounts of data is tempted to train AI on it, but content shared in Google Docs is still safer than content in Gemini.

Additionally, the conversation-sharing feature in ChatGPT caused chats to be indexed by internet search engines. Interestingly, after this minor scandal broke, it turned out that Grok had the same problem.

Besides that, people treat chats and language models more casually than documents, so they share more things there.

Another issue is verifying AI-generated content. Language models can generate coherent and convincing texts, but they sometimes hallucinate, that is, invent false information. Similarly, AI-generated graphics and videos may contain glaring errors—extra fingers, nonexistent elements, distorted text. Without a critical eye, it is easy to overlook such inaccuracies.

An important element of safety is also labeling content created with the assistance of AI. Even when not required by law, it is a good practice that builds transparency. It is important to indicate not only that artificial intelligence was used in the process, but also to what extent. Did AI merely help correct the text, or was it responsible for the entire research process? Is the graphic an original AI creation, or merely a lightly refined human sketch? And if an AI-generated image appears to be a realistic photograph, it is worth clearly indicating its source to avoid misleading the audience.

The fundamentals of security therefore come down to three pillars: do not share confidential data, always verify the results of AI’s work, and where necessary, label their origin. This allows the company to benefit from the capabilities of artificial intelligence without exposing itself to risk.

Step 1. Learning and First Experiments

The first stage is simply “getting used to” AI. Some work time can be allocated to testing, links to interesting tools and materials can be shared, and the team can be encouraged to experiment independently—even with less serious endeavors, such as generating funny images or videos. This is the stage of building curiosity and understanding the basics. It is at this stage that it is particularly important to learn the principles of prompting. Although it is worth remembering that prompts themselves account for only 10–30% of the skills and knowledge useful when working with AI.

Step 2: Purchasing Tools

When AI becomes part of everyday work, the need to purchase a license arises. It is worth remembering that team licenses differ from individual ones, and security rules must be clearly defined. Purchasing a single ChatGPT license and sharing one login with all employees is a risky practice that is best avoided.

At this stage, practical applications of AI should be sought—it is, after all, an investment that is expected to pay off.

Step 3. simple internal solutions

Internal, inexpensive, simple solutions. There are many possibilities here; the basic principle is that tools created for employees do not have to be as polished as those available publicly or intended for customers. This applies not only to artificial intelligence. Solutions created for customers or made publicly available must be much more secure and designed so that a given user cannot disrupt their operation.

AI can support the creation of meeting notes, searching for procedures, or helping with the onboarding of new employees. If the procedures are not confidential, it is enough to prepare them in an organized form and add such files, for example, to ChatGPT. In other cases, locally deployed models can be used.

At this stage, it is worth looking for external companies to implement these things, although many solutions can be developed in-house.

RAG

RAG, or Retrieval-Augmented Generation, is an AI operating method in which the model does not rely solely on its built-in knowledge, but first searches for the necessary information in a prepared database (e.g., documents, PDF files, or notes) and only then generates a response. Thanks to this solution, the system can use up-to-date data without being retrained, provide more precise answers, and indicate the sources on which it is based. In practice, this means that instead of teaching the model an entire company’s documentation from scratch, it is enough to load it into a knowledge base, and the AI will be able to answer questions using these materials.

Step 4: Advanced Implementations

The most demanding stage is creating proprietary internal systems or customer-facing tools. These are costly and risky solutions, so they only make sense once the company has completed the earlier stages and the team has gained sufficient experience using AI.

Summary

In summary, artificial intelligence in companies is no longer a matter of the distant future, but a real tool that is gradually becoming just as obvious as the internet. Implementing AI does not have to mean building costly systems or hiring an army of specialists. Much more important is gradually gaining experience, being aware of the risks, and taking a sensible approach to the opportunities these technologies offer. Companies that start with the basics and allow employees to learn through practice will gain an advantage and be ready for the next stages of development. As a result, instead of chasing trends, they will be able to use AI consciously and in a way tailored to their own needs.

The text was created with AI assistance, mainly for research and proofreading. Some paragraphs were written by AI, but they were created exactly according to my guidelines.