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, how to have a marketing department focused on creating campaigns that ensure more customers, most satisfied customers, advertising etc. It is not being 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, AI applied to different levels of problems and solutions
A very current field of this adoption is occurring through AI agents, created to be co-pilots of various activities, mainly 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 cohesive data platform is extremely necessary, because it can be used to train the AI with all the information that the company already has, whether about your clients or about any other detail involving your operation. This training is complex and depends, largely, of primary data on 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" 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 into their customers, merging your 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 encountered this situation. For example, if you are buying shoes online and ask an AI chatbot about a new shoe model that has not been announced yet. A misguided AI can provide false information based on rumors, inventing data about comfort, versatility and usability of the product
This happens because the lack of data is what really limits this technology. Data is the greatest resource we have today. Companies cannot afford to have an AI hallucinating or lacking relevant data, harming the experience of its customers, 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 sold and that match the consumer's profile; explain why the sneakers he is looking for, for now, they are just a rumor originating from unreliable sources; and even offer to get in touch with the consumer when new models that fit their preferences become available
The need for processed data, unified, verified and reliable, available in real time, is constant. Databases are more important than ever, because even to advance in AI competitiveness, they are still the cornerstone of the entire process. That is why the first step to be taken is to fill the data gap. Only then will the true potential of AI be unleashed
