Today, artificial intelligence is perceived as a revolutionary force that will change the rules of the game or as a Leviathan overvalued by some and undervalued by others. Personally, I consider it another milestone of the innovations that have driven the advancement of humanity, similar to the industrial or scientific revolution and, like them, capable of being used for good or to unleash chaos.
The potential of AI for business is unquestionable. Predictive AI is already being used to recognize patterns, achieve dramatic efficiency improvements, or solve business and social problems more quickly and effectively. For example, it allows us to predict how proteins fold in medical research or protect users and companies with the detection of financial fraud, and its power to process enormous amounts of data helps in decision making and is transforming the way we face change. climate, the greatest threat to humanity.
Even better, generative AI not only recognizes but generates new patterns, making software developers more productive, content creators delivering even more immersive experiences to their audiences, and customers, employees, citizens, and students finding the information they need in a much easier way. And all this mountain of possibilities is made of a single material: data.
Yes, Artificial Intelligence is nourished by data and, therefore, its storage, security and accessibility are crucial for the analyzes provided by AI. Eminent computer scientist Peter Norvig summed it up elegantly: ” More data beats smart algorithms, but better data beats more data .” In this way, generative AI deployment projects will only reach as far as the quality of the data that feeds them reaches. And, as AI evolves and becomes an integral part of businesses, the importance of an intelligent data infrastructure becomes paramount.
Data Integration, Performance, and Trust Needs
To put their data “to work,” companies must manage multiple versions of their models and keep them up to date with the most recent data sets. This requires a free flow of data, regardless of whether it is the organization’s own data or other relevant sets used to improve AI systems. And this is not as easy as turning on a tap, since we are talking about a considerable and incessant volume of data, with data dispersed, unstructured and requiring protection.
Complex technologies and organizational silos are the main obstacles to launching AI projects. Therefore, if they want to advance in this area, organizations must equip themselves with a modern, intelligent and integrated cloud data infrastructure that provides them with the most complete, powerful and sustainable solutions, without information silos.
Any company, large or small, looking to optimize its data engine to reap the benefits of generative AI must address four key issues. First, ensure alignment between your data and your AI organization. Many companies today have data analysts and engineers with a deep understanding of data, data scientists who can apply modern analytics tools, and business analysts who understand how to use data and AI recommendations to drive business results. But these different functions in each organization must collaborate as one team to accelerate the impact of AI.
The second fundamental aspect would be the analysis and consolidation of unstructured data . For years, companies have invested in tools to extract value from structured data. Generative AI, however, provides a powerful engine to extract value from the fastest growing part of data today, which is unstructured data. Therefore, each organization needs an updated view of its unstructured data landscape and its relevant applications, to be ready to use it with AI applications.
To prepare for AI, businesses also can’t forget to integrate their workloads and data with an intelligent multi-cloud hybrid infrastructure . The volume, types and velocity of data are growing inexorably, and with massive amounts of data to process, simplicity and integration are crucial.
The last thing, and not least, is to strengthen data security and governance. With every great power comes great responsibility and this is especially relevant in the case of AI, where private data is much more valuable, but can also be a source of errors, biases or inaccuracies in the models, which is why They must protect and govern.
Ultimately, when an organization invests in optimizing its data engine, it is building a solid foundation to unleash the full potential of artificial intelligence in a responsible, secure and accessible way. Over the past year, AI has kept many of us IT market leaders awake at night, not out of concern, but because it has transformed our reality with its potential to reshape industries and redefine human experiences.
Despite the enthusiasm for technology, we must face real challenges in this area, such as bias, transparency and control. And our focus, going forward, must go beyond pushing technical limits, harmonizing the transformative capabilities of AI with human ingenuity, ethical considerations, and a clear vision of the future we want to build.