Strategic decision making in professional soccer is no longer a matter of the gut feelings of coaches and managers. “Go out and play soccer,” iconic player Franz Beckenbauer, dubbed in Germany as the “emperor,” once said during the 1990 FIFA World Cup. Things are no longer that simple. Today, GPS vests equipped with sensors provide real-time data about speed, pulse rate, and distances run – soccer players become a source of data-driven practice control. Smart insoles or other high-tech wearables issue warnings before calves start hurting. Artificial intelligence predicts the course of matches, analyzes opponent behaviors, and recommends tactical adjustments – digital co-coaches have become a reality.

That was the case with former Champions League winner Liverpool FC too, where they want to use technology to boost the soccer wisdom of “corner kicks having high potential for goals.” Together with Google, the Brits are working on an AI model that analyzes corners and proposes optimal strategies. Based on historic data, “TacticAI” recommends which player should run where and where the ball needs to be placed. “As a manager, I could then modify my players’ tactics to take advantage of the weaknesses of my opponents,” said developer Petar Veličković from Google DeepMind.

However, technology in soccer isn’t only a tool of tactics and controlling practice – it’s also a strategic partner for squad planning. Today, algorithms are taking the place of what used to be a manager’s gut feelings as mentioned above: clubs like tradition-steeped FC Sevilla in Spain are planning their squads using “IBM watsonx” software – making data-based choices about transfers to the tune of millions. The platform analyzes not only their performance data but also histories of injuries, psychological profiles, and development potential.

Even smaller clubs benefit from a platform called “Plaier” because it analyzes more than 100,000 players worldwide – including those from less noticed divisions in Africa or Asia – based on more than 200 parameters. Based on this, the platform calculates an AI score showing how well a player fits a club’s playing philosophy and squad. In this way, Big Data turns into smart transfers.

Sensitive grapes, smartly irrigated – modern winegrowers rely on precision technology instead of on deeply-rooted habits. Drones take off above grapes, fly autonomously, and deliver high-resolution images. In a parallel process, connected soil sensors continuously measure moisture, temperature, or nutrient content – resulting in data-based irrigation recommendations with milliliter accuracy.

Technologies such as drones with multi-spectral cameras measure reflected light in various wave length ranges. Near-infrared reflections, for instance, are an important indicator of vitality. AI-supported platforms like “VineSignal” from France combine these image data with soil moisture measurements down to a depth of 1.5 meters (5 feet), providing predictions about optimal harvesting and precision irrigation.

In Spain, the Pago Aylés wine estate has installed more than 100 IoT sensor stations – measuring soil moisture, temperature, wind, and rain, sending data into the cloud and thus providing the basis for decisions regarding irrigation, plant protection, and quality. That has resulted in up to 50 percent savings in production costs and quality increases of 25 percent in the first project year. “We urgently need information enabling us to make early choices about production strategy. That makes it possible for us to standardize quality and quantity,” explains agricultural scientist Julio Prieto.

When it comes to spraying technology in vineyards, drones have by now evolved into more than just experimental toys: equipped with LiDAR sensors, AI-based disease detection, and variable drop size, drones reduce the use of pesticides by 20 to 60 percent, prevent soil compaction, and reduce labor.

Artificial intelligence (AI) is increasingly revolutionizing the restaurant business – not only in the kitchen and service areas but also in terms of strategic menu design. Data-driven menu engineering enables restaurants to systematically optimize their choices and to introduce new meals with a higher prospect of success.

Traditionally, decisions about what to offer on menus used to often be based on subjective estimates and experiences. Today, restaurant owners are using AI tools to make well-founded, data-based decisions. The collaboration between Metro and Dish Digital Solutions is a case in point. Their AI-based menu analysis evaluates not only the popularity and profitability of individual meals but also considers commodity consumption, HR costs, and seasonal trends. In that way, restaurant owners can recognize which meals are profitable and which ones should potentially be deleted from menus. Metro CEO Dr. Steffen Greubel commented that “The sector should make greater use of this potential and deal with digital transformation to sustain its ability to manage their business successfully even under difficult overall conditions.”

An example from the field: Casa Lodato Da Giovanni, a restaurant in Essen, used AI to optimize its menu. The analysis revealed that certain choices such as fregola with artichokes were popular but not profitable. Consequently, the prices were adjusted, meals rearranged, and less profitable options removed. That resulted in higher profitability while patron satisfaction remained at the same level.

Other AI tools like Tastewise continuously analyze millions of social media posts, online ratings, and menus worldwide. In doing so, they identify emerging food trends and taste preferences in real time. Restaurants can use these insights to develop new dishes that meet the current preferences of their target groups.

Platforms such as RecipeGPT make it possible for restaurant owners to generate new recipes by entering ingredients and desired taste components. The AI then proposes creative ideas for recipes based on existing culinary data. That assists chefs in developing innovative dishes that are appealing in terms of taste as well as being economically feasible.

Strategic choices in the fashion industry have long ceased to follow instinctive impulses – they’re based on data, algorithms, and digital creativity. What used to be sketches on paper and manual selection of materials has been replaced by AI-supported processes today: from virtual show rooms to personalized avatars, to automatically generated campaign pictures.

Zalando, for example, uses generative AI, to dramatically accelerate image production. According to Matthias Haase, Vice-President, Content Solutions at Zalando, production times dropped from six to eight weeks to just three to four days – with costs being cut by as much as 90 percent. By now, some 70 percent of editorial pictures are generated by AI systems. Great creativity has suddenly become scalable.

Virtual fashion is booming too. The Doji app, for instance converts selfies into digital avatars, on which users try on clothes in the virtual world. The New York label Collina Strad has taken this even a step further: for a collection, chief designer Hillary Taymour had digital entities designed with the help of AI that were showcased on the virtual runway platform “The Fabricant.”

“The Fabricant” has established itself as a pioneer in the field of digital fashion by using non-fungible tokens (NFTs) to turn digital clothing items into tradeable assets. Designers, brands, and consumers can create, present, and acquire outfits there in the form of NFTs. That fashion can then be worn in various digital environments within the metaverse, which may well be a relevant topic for instance for gamers.

Back to the analog world where a lot has been changing behind the scenes too: According to Professor Ingo Rollwagen from the Academy of Fashion & Design Berlin, we’re currently seeing a “knowledge revolution in the fashion industry.” AI systems have long ceased to support designers only as digital assistants but are intervening deeply in creative and organizational processes. For instance, within fractions of a second, they analyze how sustainable materials like Tencel – an innovative cellulose fiber produced from wood – hemp or recycled fibers can be optimally combined. They automatically recognize harmonious color schemes, and help operationalize global trends in real time. That shifts the focus from pure intuition to data-driven decisions intended not to replace but to expand creativity.