ARTIFICIAL INTELLIGENCE AND THE AMSTRAD GENERATION

Artificial intelligence is today more human than it seems. We are the ones who program it, generate the inputs, and use its outputs, responses, and results. In the early eighties, when I was six years old, I started playing handball. Learning and perfecting technique, managing pressure and emotions, I became Spanish champion ten years later. That curiosity and interest in discovering new things that children have led me also to get involved with programming and the use of "machines". At the end of the eighties my parents bought me an Amstrad 6128 and I learned to program in Basic and later in Pascal. From that time, I remember the generation of iterations and the endless "if then" or "for next". It is a time when mankind learns to explain to the machine exactly what he wants it to do, and the machine applies it. Forty years later we have moved to the next level: it is no longer necessary to explain to it how to do things, but the machine is now also capable of learning it for itself.

The constant growth of computing power and the absence of emotions give machines an advantage in data processing and decision-making processes, which has led to the exponential growth of Artificial Intelligence, which does not need ten years to learn, like children. Learning that is generated through data analysis, the search for patterns, the establishment of connections and the drawing of conclusions. We are providing machines with copies of the neural systems that humans have through infinite data matrices and vectors, and now the AI needs only a few seconds to process billions of operations. Deep learning ingests and processes unstructured data, such as text and images, and automates the extraction of features that allow it, for example, to recognize images (animals, things, etc.). Images, colors, features are converted into data and processed. In this way it learns, for example, to make sales predictions by generating iterations of the evolution in recent years, taking data from the system (sales of past years by reference, country, and customer) and also generating correlations with relevant data outside the system, such as demographic development, temperature or other variables that condition the sale and use of drugs, vaccines or consumer products.

As we can imagine, AI can be used for many things, to automate, to lower costs, to be more efficient, to sell more by customizing solutions to customers, etc. And it is very important that senior managers understand that this whole game is not about technology, because it already exists and will be polished over time. In addition, companies such as Microsoft, Oracle, Google, OpenAI and others provide us with the applications and technology today, so there is no need to hire data science experts. This is not about IT departments either, this is about "what for"; in short, it is about general management and strategy.

Executives, managers, and strategy managers must understand how new technological advances and neural networks work, understand their potential and what problems these new technologies can solve. In other words, what they want to get out of these new organizational capabilities that are available to them at the click of a button. They must determine whether they want them to reinforce existing competitive advantages in production, distribution, or sales, or to create new competitive advantages and change the company's business model.

Entrepreneurs and managers need to determine what profiles they need in the company to make the most of AI. Profiles that understand how neural networks work, their data vectors, and iterations, which will allow them to better interact with it. Those young, adaptable, and curious people can tomorrow be more efficient and effective working with AI than classic expert profiles. Those of us who started our relationship with computers in the late 80s with the Amstrad, full of curiosity and desire to do different things, had parents who looked at us with suspicion and much less curiosity. Let's learn from our parents' mistake and not do the same! Companies need the managers of the "Amstrad generation" to maintain their curiosity forty years later, and to know how to give responsibility to this new generation that will change the operating model of companies in the future.

Boards will have to renew themselves, and executive committees will have to renew themselves. AI will change the world, our companies, and the way we work, and it will once again be the human that creates winners and losers. And this will be especially true for small and medium-sized companies because of the opportunity of cheap and affordable technology. Perhaps this will also be a solution in the transition of family businesses because it will allow new generations to take the lead in specific areas such as automation and simplification of processes through AI. A great opportunity that should be attacked with humility, curiosity, and interest, just like the Amstrad child we all have hidden inside.