Within its Digital Hub, the transportation and mobility multinational has gone all in on an AI-powered center of excellence that allows technology knowledge, ideas, and projects to develop for all business units.
With the aim to accelerate innovation and transform its digital infrastructures and services, Ferrovial created its Digital Hub to serve as a meeting point where research and experimentation with digital strategies could, for example, provide new sources of income and improve company operations.
Since its creation over five years ago, the Digital Hub has included a team of experts in innovation, technologies, and trends — such as IoT, big data, AI, drones, 3D printing, or advances in customer experience — who work in concert with other business units to identify and execute new opportunities. There’s no defined hierarchical structure, but the Hub is based on a transversal and collaborative model where groups are created, and employees from all areas work on specific projects along with external experts.
One of the main strategic transversal areas is AI. According to Luis Carlos Prieto Fernández, head of Ferrovial’s AI center of excellence (AI CoE), its value is demonstrated by the work the company has been doing with all business units. “They have AI-based services that either make processes automatic, more efficient, or provide the business with services that give competitive advantages,” he says.
With this in mind, the AI CoE acts along two clear lines at a global level. The first is the development of AI models and projects for business units that create digital solutions together with the rest of the Digital Hub team. The second, given Ferrovial’s global nature, is its continuous contact with all units, acting as an AI knowledge hub. “This allows us to contribute ideas and solutions we’ve already been working on, and avoid duplicating proofs of concept and projects,” he adds.
Getting AI off the ground
After several years of innovating in AI, Ferrovial can cite many examples of projects and services benefitting from it. “If we look at what we could call classic AI, an example would be our Herbicide Train project, where front cameras have been installed in several trains throughout Spain that don’t go below 50 km/h,” Prieto says. “And combined with the help of our computer vision models, we’re able to detect when there’s vegetation between the rails and shoot herbicide up to seven cars behind.” Being able to do this has economic as well as ecological impacts — reduction in the cost of glyphosate by more than 80%, regarding the former, and only irrigating where necessary, for the latter.
Plus, to provide all its businesses with the ability to obtain information in record time, the company has been working with gen AI and LLMs, specifically Azure OpenAI, since December 2022. So projects to analyze huge amounts of data in multiple formats already in production are now considerably more efficient and cost effective.
“Being able to provide the business with solutions that accelerate processes and prioritize the highest value tasks for our colleagues are very clear advantages, which allow us to increase the value of our work,” says Prieto.
Incremental AI advancement
Despite all these advanced deployments of technology, Prieto believes there’s still a lot of work to do. As a result, Ferrovial is a founding partner of IndesIA, the association to promote AI in Spanish industry. “It’s true AI has demonstrated to C-suites that its easy to use and the value obtained can be incredible,” he says. “But we start from a situation where data scientists were misunderstood and could only add value if provided with funds that the business preferred to invest in other areas. It’s beginning to be clear now this is no longer the case, and we can demonstrate marked cost reduction and positive ROI on these investments.”
There’s still a long way to go, however, given that not all companies have AI teams, but it’s becoming easier and more affordable to obtain AI services. “And with the help of initiatives such as IndesIA, the industry can have these services available in a simple way,” adds Prieto.
The AI basis of digital transformation
Digital transformation is one of the most important catalysts for companies in any sector to add greater value and remain competitive, and AI plays a crucial role, says Prieto.
In addition to the Herbicide Train project and the use of computer vision to inspect power lines, there are many others AI applications at Ferrovial developed from the AI CoE that impact all its businesses. But AI also poses some challenges and risks when it comes to privacy, security, ethics, transparency, and accountability. “It’s necessary to regulate this technology, to guarantee it’s used appropriately, safely, and beneficial to society,” Prieto says. “Of course, regulation must be proportionate, flexible and adaptable, and promote trust, innovation and competitiveness.”
In addition to AI, other technologies are also being explored to transform Ferrovial’s way of working to create value. “For example, we use cloud computing to store and process data in a scalable and efficient way, and IoT to monitor and control highway, airport, and field op infrastructure,” he says. “We also use augmented and virtual reality to train and assist workers, big data to analyze and visualize large volumes of information, machine learning to automate and optimize tasks, and deep learning to extract knowledge from images, texts, sounds and other types of data.”
A future fixed in AI
The business landscape continues to radically change due to AI and other disruptive technologies. “More processes and tasks are going to be automated, optimized, and personalized with the help of AI, which will mean an increase in productivity, efficiency, and customer and employee satisfaction,” says Prieto. In addition, AI will also accelerate innovation, facilitate the creation of new products and services, and make adaptation to changes in the market and society more efficient. Specifically at Ferrovial, AI is a key to its digital transformation. “We’ve been able to see it very clearly because it allows us to extract value from data, improve decision-making, and generate knowledge,” he says.
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