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 activity, such as having a marketing department focused on creating campaigns that guarantee 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 it, in some process or even in an entire department, it was applied to different levels of problems and solutions.
A very current field of this adoption is taking place through AI agents, created to be co-pilots of different activities, especially those that require interaction with the customer, in order to ensure a better experience. But, it is not enough to implement AI. Like any technology, solution, system, AI requires a certain infrastructure.
A coherent and coherent data platform is extremely necessary, as it can be used to train AI with all the information that the company already has, whether about its customers or about any other detail involving its operation. This training is complex and largely depends on primary data on the interactions carried out over years of transactions. This is essential for creating efficient marketing strategies.
While 81% of brands claim to be “good” or “excellent” in 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’s 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 into their customers by merging their databases. Any AI is and will always be as good as the data that feeds it. Without the knowledge of how to act better, she will be working with gaps that make all the difference.
You must have already come across this situation. For example, if you are buying shoes online and ask a AI chatbot about a new model of footwear that has not yet been announced. A misguided AI can provide false information based on rumors, inventing data about comfort, versatility and usability of the product.
This is because the lack of data is what really limits this technology. Data is the biggest resource we have today. Companies cannot afford to have an AI hallucinating or without relevant data, harming the experience of their customers, or even critical systems.
With the correct data, what would happen in this situation would be that the AI would inform the consumer about the non-existence of the product he is looking for, and as a complement, he could also offer information about options that are already sold and that correspond to the consumer’s profile; Explain why the sneakers he seeks, for now, are just a rumor originated from unreliable sources; And even offering to contact the consumer when new models that fit your preferences are 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 are still the cornerstone of the whole process. This is why the first step to be taken is to fill in the data gap. Only then will the true potential of AI be released.
