Most companies worldwide are adopting artificial intelligence in their operations. There are certain business structures that are independent of the company’s field of operation, such as having a marketing department focused on creating campaigns that ensure more customers, more satisfied customers, advertising, etc. It is not and will not be different with AI. It is safe to say that basically every organization will have, within some process or even an entire department, AI applied to different levels of problems and solutions.
A very current field of this adoption is happening through AI agents, created to be co-pilots for various activities, especially those requiring customer interaction, 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 operations. This training is complex and depends largely on primary data about interactions conducted over years of transactions. This is essential for creating efficient 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 the data gaps. In fact, many companies are merging to gain deeper insights into their customers by combining their databases. Any AI is and always will be only as good as the data that feeds it. Without knowledge of how to act better, it will be working with gaps that make all the difference.
You may have encountered this situation. For example, if you are buying shoes online and ask an AI chatbot about a new shoe model that hasn’t been announced yet. A misguided AI might provide false information based on rumors, inventing data about the product’s comfort, versatility, and usability.
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 AI hallucinating or lacking 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 offer information about options already available for sale that match the consumer’s profile; explain why the shoes they are looking for are, for now, just a rumor 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 is why the first step to take is to fill the data gap. Only then will AI’s true potential be unlocked.
