Artificial intelligence (AI) has become a mass phenomenon and in the case of companies has ceased to be a futuristic promise to become a strategic tool. In the field of customer service, its implementation advances at high speed with the objective of automating processes, improving operational efficiency and offering more agile responses. We can observe it in any industry: motor, logistics … However, the incorporation of generative models should not become a career for the latest technology. What is at stake goes beyond technical performance: we talk about confidence, corporate reputation and, above all, the link between company and client. Not everything is worth. It is not just adapting. We must understand.
From our experience as a strategic consultant at AI, we know that one of the keys to a successful deployment lies in developing flexible solutions and adapted to the specific context of each company. Automatize without understanding the operational reality of the company or without knowing in depth the behavior and expectations of its customers is a recipe for failure.
Ia in customer service
Customer service is not homogeneous. An insurer, a banking entity or an e-commerce company have different needs, protocols and framework frames. Generic solutions, designed without an adequate customization layer, tend to offer inaccurate, too generic responses or generate automated decisions that, in the absence of human supervision, can be difficult to justify.
From our experience as a strategic consultant at AI, we know that one of the keys to a successful deployment lies in developing flexible solutions and adapted to the specific context of each company
In addition, risks are not only technical. One of the great challenges of generative AI is the governance of the data: where does the model be nourished? What kind of information do you use? Are you biased or incomplete? If a clear control and supervision framework on the data that feeds the systems is not established, we run the risk of building tools that perpetuate stereotypes or even errors. This fact can degenerate in the loss of client’s confidence and subsequently reputational damage that could be difficult to repair. It is for this reason that we must evaluate biases from the beginning of a project, in the research and design phase to ensure that we will implement a safe and appropriate A -IA to the needs of the client and, what is most relevant, of its customers.
That is why we insist that the responsible implementation of AI in customer service must be based on three principles: solid data structures, flexible and human models capable of validating and correcting the results. Because automation cannot be synonyms for dehumanization, on the contrary: human supervision is what allows meaning and sensitivity to what algorithms generate. Having human teams throughout the development and implementation process is essential to achieve a specialized solution that solves real and effective real tasks. Know the business model of each company, detect friction points, analyze cases and determine classification systems … are just some of the functions that must have the human vision for the design of a solution of customer service, without forgetting, of course of a big question: What do the company’s customers expect?
In short, we must keep in mind that, if we want to achieve success with the application of AI in any type of industry, we will only be victorious if we are able to understand in depth each context and bet on an adaptable and safe the AI devised by and for people.
