Creating business value from Artificial Intelligence (AI) has a fundamental foundation that cannot be overlooked: what fuels AI. The revolution of this technology has brought unimaginable benefits and completely transformed how companies view data in their strategies. However, there is still an important path to tread for this absolutely transformative innovation to become truly relevant for businesses. Many Artificial Intelligences are still fed with incorrect or very low-quality information. And, as a consequence, they deliver only results at the same level. The well-known concept of “garbage in, garbage out” has never been more true.
With advances in Generative AI and increased computational power, we are witnessing the generation of information and contexts at an extraordinary volume. To harness this full potential, using accurate and reliable data to ground AI is key. After all, they are the fuel that nourishes AI algorithms, and therefore, companies and organizations that do not invest in a solid data foundation may take longer to implement these solutions. Or worse. They may adopt the technology incorrectly and turn this initiative into a major problem.
For AI to produce accurate and useful results, the data supporting it must reflect the reality of the market and the company without errors or distortions. This requires that the data be diverse, collected from different sources, to reduce biases and ensure that applications are less prone to making unfair decisions. Additionally, it is necessary to consider the constant updating of information and its accuracy, as outdated or incorrect data produces inaccurate responses, compromising their reliability. Updated data allows AI models to follow trends, adapt to multiple scenarios, and deliver the best possible results.
In the financial market, for example, incorrect databases can result in inadequate credit risk analyses and predictions, leading to loan approvals for delinquent customers or denials for good payers. In the logistics sector, outdated and low-quality information generates distribution problems with sales of out-of-stock products, causing delivery delays. And, consequently, loss of customers.
Data security is also paramount. Leaving data vulnerable in AI applications is like leaving a vault door open, exposing them to theft of sensitive information or system manipulation to generate biases. Only through security is it possible to protect privacy, maintain model integrity, and ensure its responsible development.
AI-ready data must also be identifiable and accessible in the system, or they will be equivalent to a library full of locked books. The knowledge exists, but it cannot be used. However, it is important to highlight here the importance of granting access to the right people and areas. The same data can be accessed in its entirety by one area, i.e., complete and detailed. In another, only access to summarized data totals may be granted. Not always will a given data be accessible to everyone in the same way. Identifiable information, made possible with the use of business and technical metadata, reveals the true potential of machine learning and Generative AI, so these tools can learn, adapt, and produce innovative insights.
Lastly, the data must be in the right format for machine learning experiments or Large Language Models (LLM) applications. Facilitating the consumption of information helps unlock the potential of these AI systems, enabling them to ingest and process data smoothly and transform it into intelligent and creative actions.
The path to maximizing the potential of Artificial Intelligence in business inevitably passes through the quality of the data that fuels it. Companies and organizations that understand the importance of a robust, secure, and updated data foundation stay ahead of the competition, turning AI into a strategic ally and a market differentiator. This new era of innovation we live in requires companies to invest in the right ingredient—their data—to move the AI machine in the right direction, bringing a new perspective to business.
