Most companies in the world are adopting artificial intelligence in their operations. There are certain business structures that are independent of the company's area of operation, such as having a marketing department focused on creating campaigns that ensure more clients, more satisfied clients, advertising, etc. It is not and will not be different with AI. It is safe to say that virtually every organization will have, within itself, in some process or even an entire department, AI applied to different levels of problems and solutions.
A very current area of this adoption is happening through AI agents, created to be co-pilots for various activities, especially those that require interaction with the customer, in order to ensure a better experience. But, implementing AI is not enough. Like any technology, solution, or system, AI requires a certain infrastructure.
A coherent and cohesive data platform is extremely necessary, as it can be used to train AI with all the information the company already has, whether about its customers or any other detail involving its operation. This training is complex and largely depends on primary data about interactions carried out over years of transactions. This is essential for creating effective marketing strategies.
While 81% of brands claim to be "good" or "excellent" at providing positive customer engagement, only 62% of consumers agree. Only 16% of brands strongly agree that they have the data they need to understand their customers, and only 19% of companies strongly agree that they have a comprehensive profile of their customers (Twilio Customer Engagement Report 2024). It's all about the data gap!
It is crucial to fill in the data gaps. In fact, many companies are merging to gain deeper insights about their customers by combining their databases. Any AI is and will always be as good as the data that feeds it. Without knowing how to act better, she will be working with gaps that make all the difference.
You have probably encountered this situation. For example, if you are buying shoes online and ask an AI chatbot about a new shoe model that has not yet been announced. A mistaken AI can provide false information based on rumors, inventing data about the comfort, versatility, and usability of the product.
This happens because the lack of data is what truly limits this technology. Data is the greatest resource we have today. Companies cannot afford to have hallucinating AI or one without relevant data, harming their customers' experience or even critical systems.
With the correct data, what would happen in this situation is that the AI would inform the consumer about the non-existence of the product they are looking for, and as a complement, it could also provide information about options that are already being sold and match the consumer's profile; explain why the sneakers they are looking for are currently just a rumor originating from unreliable sources; and even offer to contact the consumer when new models that fit their preferences become available.
The need for processed, unified, verified, and reliable data, available in real time, is constant. Databases are more important than ever because, even to advance AI competitiveness, they remain the cornerstone of the entire process. That's why the first step to take is to fill the data gap. Only then will the true potential of AI be unleashed.