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Imagine that you are locked inside a windowless room. You do not know Chinese, not a single word. Through a slot, you are handed sheets of paper covered in Chinese symbols and, inside the room, you have a rulebook that tells you exactly which symbols to return based on the ones you receive. You follow the instructions carefully and provide responses that, from the outside, are flawless. To the person on the other side, it seems obvious that there is someone inside who understands Chinese. But you know the truth: you understand nothing. You are simply applying rules mechanically. This thought experiment, known as the Chinese room, was proposed by philosopher John Searle in the 1980s and serves to challenge a widely held idea: that if a machine behaves as though it understands, then it genuinely understands. Searle argues that it does not. Just like you in the room, a computer can manipulate symbols, produce correct responses and engage in coherent conversations without understanding their meaning. There is no understanding or lived experience behind any of it: it does not understand what it does or what it says. It simply strings symbols together without knowing what they mean. That is why this experiment is a key metaphor when we talk about artificial intelligence, consciousness and what it really means to understand. But it also helps us appreciate the importance of human oversight. A machine can produce flawless responses without understanding a single thing it says. And what about creativity? Can generative AI create, or does it simply simulate creation? If we apply this idea to the realm of creativity, a similar question emerges. Some writers argue that AI, particularly generative AI, helps them create by enabling them to come up with better ideas. But is that really creation? In the same way that simulating understanding is not the same as understanding, simulating creativity is not the same as creating. The raw material of human creativity is experience, but not experience understood as the accumulation of data or optimisation through trial and error. Rather, it is lived experience: experience that carries meaning for the person who goes through it and leaves a mark on the way they understand the world. When we transform experience into understanding, we learn. And creation emerges when that learning is allowed to combine in new ways to produce something that was not anticipated. This is why there can be no creation without prior experience and without learning. AI expands the space of possibilities; human judgement determines which ones make sense. Types of creativity and the limits of AI: from combinatorial to transformational Computational creativity is a growing field of research. However, its development still faces fundamental challenges. It is very difficult, for example, to define creativity in objective terms. According to the classic classification developed by Margaret A. Boden, widely used in science communication and cognitive science, there are three types of creativity: The first is combinatorial creativity, which brings together ideas or elements that are already known in a new way. This type of creativity has played a well-documented role in scientific discovery, technological innovation and artistic activity throughout history. The act of connecting previously unrelated concepts has long been a cornerstone of progress. Second, exploratory creativity, which explores an already defined space of possibilities and pushes it to its limits. Third, transformational creativity, which changes the rules of the game and redefines the very space of possibilities. The last of these is the most radical: it requires agency, its own criteria and an understanding of the framework being transformed. Creativity does not arise solely from combining data, but from transforming experience into meaning. What generative AI can do and why it does not redefine the problem From this perspective, current AI can achieve forms of combinatorial creativity and, in some cases, exploratory creativity, albeit with nuances that fall beyond the scope of this article. What it does not achieve is transformational creativity in the stronger sense. If you ask an AI to write a poem in a style that blends Federico García Lorca and Gata Cattana, it will do so. If you ask it, for example, to create a horror novel with multiple endings, it will also be able to do that while respecting the conventions of the genre. What it will not do, however, is change the framework of the problem: it will not redefine what the problem is or what counts as a good solution. Human judgement and human intention, within a framework of responsible technology use, remain essential to finding a solution. Artificial Intelligence and dialogue: why there is no understanding, yet creativity can still emerge Creativity, moreover, rarely emerges in complete silence. It is usually born through dialogue. But can we really speak of dialogue when the other party, the machine, does not understand? When we interact with AI, there is no dialogue in the strict sense. What exists instead is a functional and asymmetrical exchange: the machine does not understand, but the interaction can spark reflection and creative thinking in the person using it. It is we who, faced with this conversational interface, as though standing before a mirror, must formulate good questions, interpret the answers and find meaning in them. AI should be understood as a creative collaborator rather than an autonomous creator. It is essential to avoid anthropomorphising computer systems (the well-known ELIZA effect). Technology can generate multiple possibilities, but meaning, judgement and intention and, therefore, creation itself remain fundamentally human responsibilities. Cloud Diary of an AI-augmented employee May 22, 2025

Data management is key to business competitiveness, and data governance is a fundamental element for building trust, driving innovation, and creating value. It's not just about collecting information, but about establishing principles, rules, and roles that ensure its quality, integrity, and security across the entire organisation. When properly governed, data becomes a strategic asset that drives decision-making, fosters collaboration, and enables the company to evolve into a system where information flows and sustains the entire organisational ecosystem. The organisation as a living organism: data and systems in balance Systems thinking is a way of understanding reality as an interconnected whole, where each part influences the whole and vice versa. It encourages us to see reality not as a sum of isolated parts, but as a set of interdependent, connected components. Instead of tackling problems in a linear or isolated way, this approach seeks to understand the dynamics, flows, and relationships that keep a complex system running. An excellent example of a complex system is the human body. It doesn't behave like a machine made up of separate parts, but rather like a living, adaptive, coordinated, and resilient system in which each organ performs a specific function, yet all are interrelated: blood, for example, transports oxygen, nutrients, and information; veins and arteries enable this flow; the immune system protects against external threats, while metabolism regulates balance and energy availability. Systems thinking teaches us that, in an organisation, balance and interconnection between all parts are essential for its health and resilience. All of this happens in dynamic balance: when one part of the body fails, others try to compensate, but that balance can only be maintained up to a point. If blood stops circulating, if information doesn't reach its destination, if the immune system doesn't respond... the entire organism is affected. The company as an interconnected system: the vital flow of data A company operates in a similar way. It's not just a group of people, departments, goals, tools, or systems. In this analogy: Functional areas are the organs that perform specific tasks. Processes are the veins that connect decisions, people, and technology. Data is the blood that brings the system to life: it flows, nourishes, and alerts. Leadership and control systems act as the brain. And Cybersecurity, operations, or compliance play the immune system's role, maintaining business integrity and continuity. When we see the company as a system, we understand that data is not a by-product, but a strategic asset. Without data flowing reliably through well-defined processes, the business organism loses its ability to respond and make clear decisions. Applying systems thinking helps us understand that a local decision can have global effects, that visible symptoms often have invisible causes, and that a system's health depends on balance, not on perfection in each part. Data is not a by-product, but the strategic asset that brings life and purpose to the business system. Data governance: key to organisational health Just as a blood test reveals the state of the body’s health, data reveals the state of the business. But for this information to be useful, it must be governed rigorously. Data governance is the framework that ensures the data we manage is reliable, secure, accessible, and valuable for business management. Data governance is the set of processes, policies, roles, and technologies that ensure data is managed consistently, efficiently, and securely throughout the organisation. It's not just about 'monitoring' or 'limiting', but about structuring, contextualising, and facilitating reuse, and, above all, ensuring the value of data throughout its entire lifecycle: from the moment it is generated to the moment it is consumed for decision-making. It travels through the system while also following its own journey: from information to knowledge. One of the reference frameworks is the one proposed by DAMA (Data Management Association International), an approach that allows data to be tackled in a structured and cross-functional way, helping different areas to speak a common language when it comes to sharing, consuming, or protecting data. Two key roles: Data Owner and Data Steward For this model to work, it is essential to define who does what. Two fundamental roles are: The Data Owner is ultimately responsible for a dataset: they define how it is used, authorise its sharing, and ensure it complies with internal and external (regulatory, legal, and ethical) policies. The Data Steward ensures the data’s quality, definition, and consistency. They handle day-to-day tasks: identifying errors, applying validation rules, maintaining metadata, and collaborating with other areas to ensure the data is useful. A Data Steward may be someone from a functional team who, although not in BI, has deep knowledge of their area’s data and validates it regularly. Data governance is not just about control, it's the foundation that ensures data is reliable, useful, and value-generating across the organisation. Data governance, a key pillar for Telefónica Tech At Telefónica Tech, data governance is a fundamental pillar: we manage critical and large volumes of data, which demands a structured and rigorous approach. Our priority is to establish clear processes and roles that ensure the quality, integrity, and security of information at all levels of the organisation. This fosters cross-functional collaboration and guarantees that every piece of data is reliable, accessible, and secure, supporting strategic decision-making and delivering value to both clients and the company. This cross-cutting and responsible vision makes data management part of our way of working, driving trust, innovation, and operational excellence. Some key aspects that support this approach include: We manage massive volumes of critical data. We operate in sectors where trust, traceability, and information protection are essential. Our services require well-defined and governed data to deliver real value. We aim for every decision to be backed by reliable, accessible, and secure data. Our commitment to data is not a one-off project, but a way of working, with responsibility, efficiency, and future vision. The goal is clear: to improve decisions, reduce risk, accelerate innovation, and increase the value we deliver to our clients and the entire organisation. Applying a solid model, with clear roles and a collaborative approach (not just from the Business Intelligence team, data is everyone's responsibility) is what will enable us to keep leading with purpose and impact. ■ At Telefónica Tech, we help you implement a Data Governance Office to drive knowledge and innovation in your business. Find out more → AI & Data From suspicion to trust: the real journey to better Data Governance May 20, 2025 Updated: 04.21, 2026