BRANDLEX Newsletter · April 2026
Artificial intelligence is no longer a futuristic promise or a distant technology. Today, it is already embedded in our professional and business ecosystem: in Microsoft 365, Google Workspace, search engines, CRMs, customer service platforms, design tools, marketing solutions, data analytics systems, and virtual assistants.
This means that many companies are already using AI, even before having defined an internal policy, reviewed their technology contracts, or assessed their legal risks.
According to recent studies, 88% of surveyed organizations reported using AI regularly in at least one business function, although most had not yet fully scaled their AI programs at an enterprise level. Source: McKinsey 2025.
Adoption is also happening at the individual level: Microsoft and LinkedIn’s Work Trend Index reported that 75% of “knowledge workers” — people whose main work consists of processing information, analyzing data, making decisions, creating content, solving problems, or generating knowledge, rather than performing physical or manual tasks — were already using AI at work in 2024.
AI is present in emails that autocomplete themselves, meetings that are automatically transcribed, documents that are summarized in seconds, and tools that allow us to draft text, create images, write code, or make better decisions based on data.
But although we use it more and more, we often still talk about “AI” as if it were just one single thing.
…and it is not.
In these ten questions and answers, we want to explain in simple terms what artificial intelligence is, what types exist, how it works, and why understanding it has become an essential skill for any company that wants to innovate, grow, and compete in the digital environment.
Today, AI literacy is not just an advantage: it is a necessary competence to use technology with judgment, protect business assets, and reduce legal, operational, and reputational risks. In Europe, moreover, the Artificial Intelligence Regulation already requires providers and business users of AI systems to adopt measures to ensure a sufficient level of AI literacy among the people who operate these systems on their behalf.
1. What is artificial intelligence?
Artificial intelligence is a technology that enables a machine to perform tasks that we normally associate with human intelligence.
For example: recognizing an image, understanding a question, translating text, detecting patterns, classifying information, recommending a movie, identifying potential fraud, or generating a draft contract.
Put simply: AI allows a computer system to observe data, find patterns, and produce a useful response.
It is not magic. It is not consciousness. It is not a person inside the computer. It is a combination of data, mathematical models, computing power, and operating rules.
2. Does AI think like a human being?
No.
AI does not think like a person. It has no consciousness, emotions, intent of its own, or moral judgment. Although it may respond in a very convincing way, it does not “understand” the world as humans do.
In the case of generative AI, such as ChatGPT, Claude, Gemini, or Copilot, what it does is process large volumes of information, identify patterns, and generate a probable response based on the instruction received.
For example, if a model has seen thousands of phrases such as “the sky is blue”, when someone writes “the sky is…”, it will probably respond “blue”. Not because it knows what the sky is, but because it detected a statistical relationship between those words.
That is why it is so important not to confuse a well-written response with a necessarily correct response.
Generative AI can be very useful for summarizing, drafting, comparing, or ideating, but it can also make mistakes, invent information, or present something as certain when it is not.
3. What types of AI exist?
A simple way to understand artificial intelligence is to look at what it does.
Some systems analyze information and predict scenarios. Others create new content. And the most recent ones not only respond, but can also execute tasks by following instructions.
Predictive AI: analyzes data and anticipates outcomes. For example, detecting a potential fraudulent transaction, forecasting product demand, or recommending content.
Generative AI: creates new content. For example, texts, images, music, code, summaries, presentations, or conversational responses.
AI agents: can perform actions within a workflow. For example, reviewing emails, searching for information, completing forms, coordinating tasks, or interacting with different tools.
Put simply: predictive AI anticipates. Generative AI creates. AI agents anticipate, create, and also execute.
4. What is generative AI and why is everyone talking about it?
Generative AI is the type of AI that can create new content from an instruction.
If you write: “summarize this contract”, “create an image of a futuristic city”, “draft a warmer commercial email”, or “compare these two versions of a document”, AI generates a response.
Tools such as ChatGPT, Claude, Gemini, Copilot, Perplexity, Midjourney, or DALL·E are examples of solutions that use generative artificial intelligence or AI-based functionalities to assist with text, search, analysis, productivity, programming, image generation, or content creation tasks.
The reason why people talk so much about them is simple: for the first time, millions of people can interact with AI using natural language.
Before, AI seemed like something reserved for engineers, data scientists, or large companies. Today, anyone can write an instruction and obtain a result in seconds.
That has completely changed the way we work.
5. What is an AI model?
An AI model is the “engine” that allows the tool to work.
We can explain it like this:
The application is what you see.
The model is what processes the information.
The data is the material with which that model was trained.
The prompt is the instruction you provide.
For example, when you use ChatGPT, Claude, Gemini, or Copilot, you see a conversational interface. But behind it there are models capable of processing language, analyzing information, generating responses, writing code, summarizing documents, or supporting complex tasks.
And here comes an important point: today, AI is no longer limited to answering questions in a chat window.
Some tools are evolving into more specialized environments. For example, Claude Code is oriented toward software development and can work on codebases, edit files, and execute commands. Claude Cowork, on the other hand, is aimed at non-technical professional tasks, such as preparing documents, synthesizing research, or extracting information from unstructured sources.
But here is the critical point for companies: if the data is not properly selected, classified, or authorized, the result can be problematic.
That is why, when a company adopts AI, it should not only ask itself “what tool are we going to use?”, but also: what model is behind it, what data the tool processes, what data we are going to process through it, what tasks it can execute, what level of access it will have, and under what conditions it will be used.
This point is especially relevant because AI depends on data. If a company does not know what information it is entering — personal data, confidential information, trade secrets, code, internal documents, or content protected by intellectual property rights, among others — it may expose itself without realizing it.
That is why having data previously organized, classified, and governed is a plus. It allows AI to be adopted with greater security, traceability, and control. This logic is also present in the European AI Regulation, which, for certain high-risk systems, requires appropriate data governance and data management practices, including aspects such as the origin, preparation, relevance, representativeness, and quality of the data.
6. How does AI learn?
AI learns by identifying patterns in large volumes of data.
If it is trained with millions of texts, it learns language structures. If it is trained with images, it learns visual relationships. If it is trained with medical, financial, or legal data, it can learn patterns specific to those sectors.
But there is a key point here: learning does not mean understanding.
AI can identify highly sophisticated patterns, but it can also make mistakes, invent information, or reproduce biases present in the data with which it was trained.
That is why, in professional environments, AI should not be used as a “final authority”, but rather as a support tool, always with human supervision.
7. What is the difference between Machine Learning, Deep Learning, and generative AI?
These concepts are often confused, but they can be organized in a simple way.
Machine Learning means automated learning. It is a branch of AI in which the system learns from data without a person manually programming each rule.
Deep Learning is a more advanced form of Machine Learning, based on deep neural networks. It is especially useful for processing images, voice, language, and large volumes of information.
Generative AI is an application of these advances that allows new content to be created.
Machine Learning learns patterns. Deep Learning learns more complex patterns. Generative AI uses those patterns to produce new content.
8. What is a chatbot? Is it the same as AI?
Not necessarily. A chatbot is a tool designed to converse with users. Some chatbots are very simple and work with predefined responses. For example: “press 1 for sales, press 2 for support”.
Others use generative AI and can interpret open questions, respond with greater flexibility, and adapt to context.
That is why not every chatbot is advanced artificial intelligence. And not every artificial intelligence system is a chatbot.
AI may be behind a conversational assistant, but also behind a recommendation engine, a document analysis system, a fraud detection tool, productivity software, a customer service platform, or a technology solution integrated into internal processes.
9. Where is AI found within a company?
AI can be present in many areas of a company, as well as in the company’s own products, services, or technology solutions.
In marketing, to create campaigns, analyze audiences, or personalize messages. In sales, to prioritize leads, draft proposals, or automate follow-ups. In customer service, to answer frequently asked questions or assist users in real time. In human resources, to organize applications, design internal processes, or support talent management. In legal, to review contracts, detect risks, or analyze regulations. In finance, to detect anomalies, project scenarios, or automate reports. In operations, to optimize inventories, routes, times, or processes.
And especially in technology and productivity, where many companies are already integrating tools such as Copilot, ChatGPT, Claude, Gemini, or proprietary AI-based solutions into their workflows, platforms, applications, software, marketplaces, virtual assistants, recommendation systems, analytics tools, SaaS solutions, or digital products.
This means that a company may be using AI internally, but it may also be developing AI-based solutions for its clients, suppliers, or end users.
The question is no longer whether a company will use AI. The question is where it uses it, with what data, under what rules, with what providers, with what level of supervision, and with what controls.
Because using an AI tool to improve internal productivity is not the same as developing an AI-based product that directly impacts clients, users, or third parties. In both cases, the opportunity is enormous, but the risks and responsibilities must be properly identified from the outset.
10. Why is it important to understand AI from a legal and strategic perspective?
Because AI does not only create opportunities. It also creates risks.
Risks related to confidentiality, data protection, intellectual property, bias, discrimination, contractual liability, regulatory compliance, and reputation, among others.
For example, a company may be uploading confidential information to an external tool without realizing it. It may also be using images, texts, databases, or code without checking whether it has sufficient rights. Or it may adopt an AI solution without knowing who is responsible if the system makes a mistake.
That is why AI adoption should not be approached only as a technological decision, but as a responsible-use strategy. And if the company also develops AI-based solutions, that strategy must extend to the design, training, integration, commercialization, and supervision of those systems.
The question is not only “what tool do we use?”, but also: what do we use it for, with what data, under what rules, with what providers, with what human supervision, with what responsibilities, and with what internal controls.
AI can increase productivity, reduce time, and open new business opportunities. But to capture that value safely, it is not enough to use it.
It must be understood.
It must be organized.
It must be governed.
Conclusion
Artificial intelligence is not a passing trend. Nor is it a magic solution for every problem.
It is a powerful technology that is already changing the way companies create, sell, contract, make decisions, serve customers, and develop new products or services.
But precisely for that reason, its adoption cannot be left only to enthusiasm, speed, or trial and error.
Using AI involves making decisions about data, confidentiality, intellectual property, third-party rights, technology providers, bias, contractual responsibilities, and regulatory compliance. Every prompt, every integration, every dataset, and every tool can have legal, operational, commercial, and reputational effects.
That is why understanding AI is not only a technological competence. It is also a way to protect the business.
The key is not to stop innovation, but to build a clear AI-use strategy — and, where appropriate, a strategy for the development of AI-based solutions — that makes it possible to define which tools are used, for what purposes, with what information, under what conditions, with what controls, and who is responsible if something fails.
Because in the age of AI, the advantage will not only be technological. It will also be strategic, legal, and human.
AI can accelerate a company. But without strategy, clear rules, and adequate controls, it can also expose it.
How we can help at BRANDLEX
At BRANDLEX, we are a company dedicated to providing technology legal strategy, with a modern, flexible vision focused on the challenges of the digital environment.
We support our clients’ business models in an agile, scalable, and strategic way, helping them move forward with greater security in contexts where technology, data, intellectual property, artificial intelligence, and regulation are evolving rapidly.
If your company is adopting AI, developing AI-based solutions, or integrating artificial intelligence tools into its internal processes, it is essential to do so with an approach that combines innovation, compliance, and risk management.
Artificial intelligence can accelerate processes, reduce time, and open new business opportunities. But to use it properly, it is not enough to incorporate it: it must be integrated responsibly, securely, and in alignment with the company’s strategy.
At BRANDLEX, we help innovation move forward with structure, legal vision, and business sense.
Technology to search. Expertise to validate. Speed to decide.
If your company is exploring, adopting, or developing artificial intelligence solutions, let’s talk.
Contact us: info@brand-lex.com / www.brand-lex.com
Disclaimer: This content is for informational purposes only and does not constitute specific legal advice. For an analysis applied to your particular case, we recommend requesting professional review.