The creation of value for businesses from Artificial Intelligence (AI) has a fundamental basis that cannot be overlooked: what fuels AI. The revolution of this technology has brought unimaginable benefits and completely transformed the way companies view data in their strategies. However, there is still an important path to be taken for this absolutely transformative innovation to be truly relevant to companies. Many Artificial Intelligences are still fueled by wrong or very low-quality information. And, as a consequence, they only deliver results at the same level. The well-known concept of “garbage in, garbage out” has never been more true.
With advancements in Generative AI and increased computational power, we are witnessing the generation of information and contexts in an extraordinary volume. To harness all this potential, using accurate and reliable data to underpin AI is key. After all, they are the fuel that nurtures 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.
In order for AI to produce accurate and useful results, the data that supports it needs to reflect the reality of the market and the company without errors or distortions. This requires diversity in data, collected from various sources, to reduce biases and ensure that applications are less likely to make unfair decisions. Additionally, constant updates and accuracy of the information must be considered because when it is outdated or incorrect, it leads to inaccurate responses, compromising its reliability. Updated data allows AI models to keep up with trends, adapt to multiple scenarios, and deliver the best possible results.
In the financial market, for example, incorrect databases can result in inappropriate credit risk analysis and forecasts, leading to approval of loans for defaulting customers or denial for good payers. In the logistics sector, outdated and poor quality information generates distribution problems with out-of-stock product sales, causing delivery delays. Consequently, it results in loss of customers.
Data security is also paramount. Leaving data vulnerable in AI applications is like leaving the door of a safe open, exposing them to information theft or system manipulation to generate biases. Only through security is it possible to protect privacy, maintain model integrity, and ensure responsible development.
Data ready for AI also needs to be identifiable and accessible in the system, or it will be the equivalent of a library full of locked books. Knowledge exists, but it cannot be used. However, it is important to grant access to the right people and areas. The same data could be accessed in its entirety by one area, that is, complete and detailed. In another area, only access to the data summarization may be released. Not always will a specific set of data be accessible to everyone in the same way. Identifiable information, made possible through the use of business and technical metadata, reveals the true potential of machine learning and Generative AI, so that these tools can learn, adapt, and produce innovative insights.
Finally, 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 it smoothly and turn it into intelligent and creative actions.
The path to maximizing the potential of Artificial Intelligence in business inevitably goes through the quality of the data that feeds it. Companies and organizations that understand the importance of a robust, secure, and up-to-date database are ahead of the competition, turning AI into a strategic ally and a market differentiator. This new era of innovation we live in demands that companies invest in the right ingredient — their data — to steer the AI machine in the right direction, bringing a new perspective to business.
