What is Artificial Intelligence, really? The mechanics, the history and the facts

1. What is Artificial Intelligence?

⚡ In one sentence

Artificial Intelligence is a field of computer science in which algorithms solve tasks that previously required human involvement — e.g. recognizing text, classifying documents or predicting trends.

💡 In plain terms

Imagine an employee who can read 10,000 invoices in an hour, extract the data from them and sort it without a single mistake. Not because it "thinks" — but because it has learned patterns from thousands of earlier examples. That's exactly what AI does: it detects recurring patterns in data and applies them to new situations.

AI doesn't replace people in making strategic decisions. It replaces them in tasks that are repetitive, tedious and error-prone — so that people can focus on what truly requires creativity and experience.

🔧 Deep dive

The European Parliament defines AI as the ability of machines to display skills such as reasoning, planning and processing data. In practice this means systems that take in data (text, image, sound), process it using mathematical models and generate an output — an answer, a classification, a forecast.

2. How does AI differ from Machine Learning and Deep Learning?

⚡ In one sentence

AI is the whole field. Machine Learning is its main method. Deep Learning is an advanced variant of ML. Generative AI is the newest layer, capable of creating new content.

💡 In plain terms

Picture a pyramid — a wide base at the bottom, a narrow peak at the top. Each layer builds on the previous one:

Artificial Intelligence (AI) — the base of the pyramid, the broadest concept. Any program that does something "intelligent" (even a simple spam filter) is AI.

Machine Learning (ML) — the layer above. Programs that learn from data instead of operating by rigid rules. Example: a system that, after analyzing 50,000 orders, predicts which products will be popular next month.

Deep Learning (DL) — higher still. Advanced neural networks inspired by the structure of the brain. They can analyze images (e.g. recognizing product defects on a production line) and sound (e.g. transcribing phone calls).

Generative AI (GenAI) — the peak of the pyramid. Models that not only analyze but create: they write text, generate code, produce reports. This is where tools like ChatGPT, Claude and Gemini operate.

Agentic AI — the newest layer, built on top of GenAI. Systems that don't just generate content but independently carry out multi-step tasks: they search for information, compare documents, and perform actions in company systems. We write more about AI agents in the article What is RAG and an AI agent?.

The higher up the pyramid, the more advanced the technology — but also the more it depends on the soundness of the layers below.

🔧 Deep dive

Each layer builds on the previous one. ML uses algorithms that optimize a cost function on training data. Deep Learning adds multi-layer neural networks (CNNs for images, RNNs/Transformers for sequences). GenAI consists of generative models (LLMs, diffusion models) trained on enormous datasets, capable of generating new data with a distribution similar to the training data. Agentic AI is the layer in which generative models gain access to tools, contextual memory and autonomy in carrying out multi-step tasks.

In a business context: you don't always need GenAI. Sometimes a simple ML model (e.g. logistic regression) will solve your problem faster, more cheaply and more predictably than a large language model.

"Before you start looking for an AI vendor, ask yourself one question: what specific problem do I want to solve? Not 'I want to implement AI' — but 'I'm losing 20 hours a month manually re-keying data from invoices'. AI isn't a single solution, it's a whole toolbox. If you define the problem well, the right tool almost picks itself. If you define it badly — no technology will help."

— Michael Jan Rogocki, AI Engineer & Data Scientist, cm-opti

3. The history of Artificial Intelligence: from the 1950s to today

⚡ In one sentence

The foundations of AI were laid in the 1950s — but only now do we have enough data and computing power to fully exploit them.

💡 In plain terms

AI didn't appear out of nowhere. Today's "boom" is the result of an evolution lasting more than 70 years:

1950–1960: Alan Turing publishes the paper "Computing Machinery and Intelligence" (1950), proposing a test — can a machine, in conversation, pass for a human? Six years later, at the Dartmouth conference (1956), the term "artificial intelligence" is coined for the first time. The first programs play chess and solve logic problems.

1970–1990: The so-called "AI winter" — progress slows, because computers are too slow and the data too scarce. Even so, the mathematical foundations of neural networks and machine learning develop.

2000–2015: The internet generates vast amounts of data (Big Data). Graphics processing units (GPUs), originally designed for games, turn out to be ideal for training neural networks. Cloud computing gives companies access to computing power without buying servers.

2017–today: The Transformer architecture revolutionizes language processing. Large language models emerge (GPT, Claude, Gemini). GenAI goes mainstream.

The takeaway: today's breakthrough is not a sudden discovery — it's the moment when three elements (algorithms + data + computing power) finally came together.

🔧 Deep dive

Key breakthroughs in chronological order: the popularization of the backpropagation algorithm by Rumelhart, Hinton and Williams (Nature, 1986), the LeNet convolutional networks (LeCun et al., 1998), deep learning on GPUs — AlexNet (Krizhevsky et al., 2012), the Transformer architecture "Attention Is All You Need" (Vaswani et al., 2017), the scaling of language models (GPT-3, 2020). Each of these steps built on the previous ones — none arose in a vacuum.

4. How does AI actually work? The mechanics in plain words

⚡ In one sentence

AI doesn't think — it calculates the probability of which words, images or decisions best fit the context, based on patterns from the training data.

💡 In plain terms

When you ask an AI model a question (e.g. "write a summary of this report"), the model doesn't read the report the way a human does. Instead, it:

1. Converts the text into numbers (so-called tokenization).
2. Compares those numbers with the billions of patterns it learned during training.
3. Calculates which words are most likely to appear in the answer.
4. Generates the text word by word, each time choosing the statistically best continuation.

That's why AI can write fluent, grammatically correct text — but sometimes "hallucinates", i.e. generates information that sounds convincing but is untrue. The model doesn't distinguish truth from falsehood. It only distinguishes "likely" from "unlikely" in a linguistic context.

🔧 Deep dive

Modern LLMs (Large Language Models) are based on the Transformer architecture with an attention mechanism, which lets the model "weigh" the importance of different parts of the input. The training process consists of optimizing billions of model parameters to minimize a loss function over an enormous text corpus. The resulting "knowledge" of the model is not a database — it's a probability distribution over possible sequences of tokens.

"If AI doesn't understand but only predicts — can you trust it? That's the question I hear most often. The answer: yes, but with a safeguard. AI works brilliantly where the result can be verified — e.g. you compare the data extracted from an invoice with the original, and a person reviews the generated report. The problem starts when a company treats AI as an oracle and switches off critical thinking. The tool is only as good as the person supervising it."

— Michael Jan Rogocki, AI Engineer & Data Scientist, cm-opti

5. Is AI a bubble and hype?

⚡ In one sentence

No — AI is a proven technology with a 70-year history, but some of the marketing promises really do run ahead of current capabilities.

💡 In plain terms

After every technological "boom" comes the question: is this a bubble? In the case of AI, the answer is more nuanced than "yes" or "no".

What is real: AI can analyze text, recognize images, automate documents, detect anomalies in data and generate content. These aren't promises — they're tools that, in 2025, one in five companies in the EU already uses.

What is exaggerated: the promises that AI will "replace all employees", "solve every problem" or "think like a human". AI doesn't think. It doesn't understand the business context without well-organized data. And it isn't suited to every task — sometimes a spreadsheet is enough.

What the real risk is: not that AI is a "bubble", but that companies will deploy it without preparing their processes and data — and then conclude that "AI doesn't work". An experiment described in MIT Sloan Management Review (2026) confirms this emphatically: researchers from Harvard and MIT gave hundreds of entrepreneurs access to a GPT-4-based AI advisor. The result? Companies that already had well-organized processes increased their profits by 15%. Companies with problems — lost 10%. The same AI model, the same advice — the difference lay in human judgement and the company's readiness to act on the recommendations.

AI works — but it requires foundations.

🔧 Deep dive

The technology hype cycle (Gartner Hype Cycle) describes the current AI situation well: after the Peak of Inflated Expectations comes the Trough of Disillusionment, and then the Plateau of Productivity — stable deployments. GenAI is probably in the transition between the first and second phases. The fundamental AI technologies (ML, Computer Vision, NLP) are already on the plateau — they work in production in thousands of companies.

In its analysis of the opportunities and threats of AI, the European Parliament points out that under-using AI is just as great a risk as overusing it — companies that don't deploy AI may lose competitiveness to those that do.

6. How many companies in Europe use AI? Eurostat 2025 data

⚡ In one sentence

According to Eurostat (December 2025), only 8.4% of Polish companies use AI — against an EU average of 20%. That's a gap, but also an enormous opportunity for competitive advantage.

💡 In plain terms

Data from Eurostat's 2025 survey of companies employing 10 or more people show clear differences in AI adoption across Europe. The table below illustrates the gap between the leaders and our region:

Source: Eurostat, EU survey on ICT usage and e-commerce in enterprises, December 2025 (dataset: isoc_eb_ai). Data for companies with 10+ employees. Norway included as an EFTA country covered by the survey. Full data: interactive Eurostat database.

Denmark also recorded the largest annual increase: +14.5 percentage points in a single year. AI adoption in European companies is clearly accelerating — the EU average jumped from 13.5% to 20.0% (+6.5 pp) in just one year.

What do EU companies do with AI? Most often they analyze written text (11.8% of companies), generate images, video and audio (9.5%), generate text or speech (8.8%) and convert speech to text (7.2%). These aren't futuristic projects — they're everyday tools for working with documents, content and communication.

For a Polish business, this gap is not a reason to panic — it's a window of opportunity. Companies that deploy AI now will have an edge over competitors who are still waiting. And deployment doesn't have to mean million-euro budgets — it often starts with a single process, a single problem, a single measurable goal.

🔧 Deep dive

Source: Eurostat, EU survey on ICT usage and e-commerce in enterprises 2025, published on 11 December 2025 (dataset: isoc_eb_ai). The data cover companies with 10+ employees, NACE Rev. 2, sections C–J, L–N and group 95.1. It's worth noting that the category "generating images, video, audio" is new — it didn't exist in the 2024 questionnaire, so there are no comparative figures.

Regulatory context: the EU AI Act (Regulation 2024/1689) introduces a classification of AI systems by risk level — unacceptable, high, limited and minimal. The rules on prohibited AI practices have applied since February 2025. For companies, this means that an AI deployment should account for regulatory requirements from the outset — which, in practice, rewards working with a consultant who knows both the technology and the legal environment.

We work with companies in the Polish and European markets. We see these differences in digital maturity first-hand. A company in Scandinavia treats AI as part of its everyday operations. A company in Poland is often still considering where to start. Our role is to help them start — from diagnosing processes, through choosing tools, to deployment with measurable results.

— The cm-opti perspective

7. Why does implementing AI require well-organized processes?

⚡ In one sentence

Implementing AI without well-organized processes deepens the chaos instead of resolving it.

💡 In plain terms

Artificial Intelligence can be compared to a high-speed train. It can get a company to its destination in record time. But no train will run at 300 km/h on crooked, rusty tracks.

Those tracks are the operational processes in your company: document flow, the flow of information between departments, the way customers are served, reporting. If they're disorganized — AI won't fix the problem. It will accelerate it.

That's why an effective AI implementation starts not with technology, but with process optimization:

1. Diagnosing the current state (process mapping).
2. Identifying bottlenecks and time losses.
3. Organizing flows and data — including systems integration, so that data doesn't get stuck in "digital islands".
4. Only then — deploying AI where it will deliver a measurable effect.

🔧 Deep dive

In Lean Management terminology this is the "fix the process, then automate" approach. In the AI context: garbage in, garbage out — the quality of a model's results is directly proportional to the quality of the input data and the organization of the process that generates that data.

Methodologies such as Six Sigma (DMAIC: Define, Measure, Analyze, Improve, Control) and Kaizen (continuous improvement) provide ready-made tools for mapping and improving processes before deploying AI. The key is measuring the "before" and "after" state — without KPIs there's no way to prove that AI delivered an effect. We write more about these methodologies in the article What is process optimization? and in the Glossary.

"Before you spend the first euro on technology, answer one question: are the processes you want to automate well-organized and repeatable? Because automating a mess is simply a faster mess. And if, on top of that, the people who work in the process aren't included in the change — the result will be that the system 'sort of works', but the team works around it. Technology reinforces standards — but it doesn't replace them."

— Karol Jurewicz, Business Process Architect, cm-opti

8. Where to start with implementing AI in a company?

⚡ In one sentence

Start with a single process that is repetitive, data-based and time-consuming — that's where AI will deliver the fastest, measurable effect.

💡 In plain terms

You don't have to deploy AI across the whole company at once. The best implementations start with one specific problem:

Examples of good first AI projects:

• Automated processing of invoices and documents (OCR + NLP) — instead of manually re-keying data.
• Classification of customer inquiries — AI routes emails to the right department before anyone even reads them.
• Data analysis of sales data — an ML model detects trends and anomalies faster than a spreadsheet.
• Visual quality control — a camera on the production line detects defects faster and more accurately than a tired inspector.
• Report generation — AI creates drafts of reports from raw data, an employee verifies and approves them.

What to avoid at the start:

• Projects without a clear business goal ("let's deploy AI because the competition is").
• Deployments without well-organized data.
• The expectation that AI will solve problems that are actually organizational problems.

An example from cm-opti's practice:

For a company operating in the construction sector in the German market, we designed an intelligent assistant that, in seconds, finds answers within company regulations and legal documentation — instead of hours of manual searching or a phone call to a lawyer. How does it work on the technical side? We cover that in the article What is RAG and an AI agent?

🔧 Deep dive

A proven approach is the CRISP-DM methodology (Cross-Industry Standard Process for Data Mining): business understanding → data understanding → data preparation → modeling → evaluation → deployment. Each step has clear success criteria and requires the involvement of both the technical and the business side.

Our process always starts with a diagnosis — not with technology. We talk to people, map processes, look for the places where AI will deliver a measurable return on investment. Only then do we design the solution. Because AI should serve your company's goal — not be a trendy buzzword in a board presentation.

— The cm-opti perspective

Frequently asked questions (FAQ)

Will AI replace people at work?

AI takes over repetitive and mechanical tasks — it does not replace creativity, experience or customer relationships. In practice it changes the nature of work: instead of re-keying data, people verify results and make decisions.

How much does implementing AI in a company cost?

It depends on scale and complexity. A first implementation — e.g. automated invoice processing or classification of customer inquiries — is a project that takes weeks, not months. The key question is not "how much does AI cost?" but "how much does the absence of automation cost?" — count the team hours spent on repetitive tasks.

Is AI safe for my company's data?

It depends on the implementation architecture. In a well-designed system, company data stays in a controlled environment and does not reach public AI models. Security is an architectural decision that has to be made at the start of the project — not at the end.

How does AI differ from automation?

Automation is a broad concept — from simple rules to intelligent systems. AI is one of the tools of automation, useful where text, images or unstructured data need to be interpreted. More in the article What is automation?.

Can a small company implement AI?

Yes. Implementing AI does not require corporate budgets. It starts with a single process and a single measurable goal — e.g. reducing document-processing time. Company size doesn't matter; what counts is data quality and a well-organized process.

Summary

Artificial Intelligence is neither magic nor a threat. It's a precise statistical tool that detects patterns in data faster than a human, automates repetitive tasks and generates content based on training data. It doesn't think, doesn't understand, doesn't make decisions — it calculates probability.

Poland, with 8.4% AI adoption among companies, lags behind the European average (20%). But that means companies that start now have a real chance to build a competitive advantage — provided they start by organizing their processes, not by buying technology.

Not sure where to start? Let's talk about your processes — a diagnosis is the first step, and the first step costs nothing.

Related articles in the cm-opti Knowledge base

• What is process optimization?
• What is automation?
• What is OCR, NLP and how does AI read documents?
• What is RAG and an AI agent?
• What is Computer Vision?
• What is systems integration?
• What is data analysis and BI?

Concepts explained in this article → Glossary

AI, Machine Learning (ML), Deep Learning (DL), Generative AI (GenAI), Agentic AI, Big Data, GPU, Transformer, LLM, AI hallucinations, tokenization, CRISP-DM, EU AI Act, Gartner Hype Cycle

Sources

• Eurostat, "20% of EU enterprises use AI technologies", 11 December 2025 — link
• European Parliament, "Artificial intelligence: what it is and how it is used" — link
• European Parliament, "Artificial intelligence: opportunities and risks" — link
• European Parliament, "EU AI Act: first regulation on artificial intelligence" — link
• MIT Sloan Management Review, Koning R., Holtz D., Clarke R., Otis N., "How AI Can Help Top Performers and Hurt the Rest", April 2026 — link