Dominican Republic, July 6, 2026. We have all seen how generative artificial intelligence has been widely adopted by people and, therefore, by companies, from simple questions about everyday life to the creation of images, presentations, articles, research, and more, simplifying tasks in a way that, until recently, was unimaginable for most of us.
This adoption, where we use AI as a commodity, in a sense allows for very fast adoption, changing models according to the needs of the moment and without integrations with each company's internal systems and processes. It gives us undeniable support, but not necessarily a market differentiator. Moving beyond seeing AI as a support tool and turning it into a competitive advantage implies using unique or proprietary data that must also be high quality and enriched.
In this case, artificial intelligence is not the star; data is. It becomes essential to have technological infrastructure where truth is centralized, information is reliable, and the business rules that give context to the data are properly documented and reflected in that source of truth.
A common case in the use of AI in business contexts where these requirements are ignored is so-called hallucination, where algorithms, which are not 100% deterministic, deliver false information or answers with great confidence, inventing data, mixing information incorrectly, and filling gaps with probabilities. This can result in financial losses, reputational damage, contractual liabilities, regulatory non-compliance, and structural mistrust, propagating errors that are later used by those same outputs to generate other answers.
In opposite scenarios, where quality data is used, impact is measured, and business processes are integrated, the result is admirable: automatic insights, reliable predictions, intelligent alerts, anomaly detection, and conversational BI, my favorite. This leads companies toward fully automated operational intelligence, once again showing that technology increasingly comes to accompany strategy, not the other way around, and that data remains the essential input for any innovation we aspire to develop.