{"id":30457,"date":"2026-06-01T09:49:07","date_gmt":"2026-06-01T12:49:07","guid":{"rendered":"https:\/\/mpl.com.br\/?p=30457"},"modified":"2026-06-01T09:50:37","modified_gmt":"2026-06-01T12:50:37","slug":"ia-for-customer-service","status":"publish","type":"post","link":"https:\/\/mpl.com.br\/en\/ia-para-atendimento-ao-cliente\/","title":{"rendered":"AI for customer service: how can agents optimize operating costs?"},"content":{"rendered":"

Maintaining the quality of service while the operation grows is one of the biggest challenges facing companies today.<\/p>\n

For a long time, the solution to dealing with the volume of calls was to implement rigid triage layers. However, this can result in friction for the user and little effective resolution.<\/p>\n

THE artificial intelligence (AI) applied to customer service<\/strong> changed this scenario by creating a level capable of interpreting contexts and making operational decisions.<\/p>\n

The aim is to allow the technology to manage transactional processes and complex queries, ensuring that the budget for customer service does not grow at the same rate as the volume of interactions.<\/p>\n

Read on for MPL's view on the subject.<\/p>\n

What is AI for customer service?<\/h2>\n

THE AI for customer service <\/strong>uses Natural Language Processing (NLP) systems to automate and qualify the interaction between brands and consumers.<\/p>\n

In practice, it replaces fixed menus of options with autonomous agents capable of interpret the user's intention<\/strong>, You can consult internal databases and formulate resolutive responses in real time.<\/p>\n

These systems do not rely on isolated keywords, but analyze the context and history of each contact to decide on the best course of action.<\/p>\n

Second Gartner data<\/a>, 91% of service leaders report executive pressure to implement solutions of this size by the end of 2026.<\/p>\n

This urgency reflects a structural change, where almost 80% of organizations plan to restructure some functions of their human agents, delegating the automation of operational routines to artificial intelligence.<\/p>\n

How does AI for customer service work?<\/h2>\n

The application of the technology takes place in layers of complexity<\/strong> which are integrated into the company's existing infrastructure. Thus, rather than a stand-alone tool, AI can act in different ways, depending on the organization's objectives.<\/p>\n

Here are some examples.<\/p>\n

Screening and resolving dynamic queries<\/h3>\n

Unlike a static FAQ, the agent qualifies the user's profile and solves technical questions by consulting manuals and internal documents in real time.<\/p>\n

If a customer asks about the compatibility of a product, the system doesn't just send a link, it explains the solution based on the technical documentation.<\/p>\n

Transactional integration with ERP and CRM<\/h3>\n

Here, AI is no longer just informative, but executive.<\/p>\n

When integrated with management systems, it performs direct actions, such as issuing invoices, checking the status of an order in stock or updating registration data.<\/p>\n

The customer no longer needs wait for availability <\/strong>of an attendant to carry out simple tasks, and the company gains in data integrity, since automation reduces manual typing errors.<\/p>\n

Journey orchestration and proactive service<\/h3>\n

At a more advanced level, AI manages the relationship from end to end.<\/p>\n

In Customer Success, the system identifies usage patterns and suggests products in a predictive way.<\/p>\n

In the areas of Credit and Collection, technology automates debt negotiations<\/strong>, This is done by applying business rules to offer discounts or installments within permitted limits, referring only strictly sensitive cases to humans.<\/p>\n

Artificial intelligence becomes a productivity assistant<\/strong> who works alongside the teams, raising the standard of delivery without inflating operating costs.<\/p>\n

Why is AI for customer service replacing traditional chatbots?<\/h2>\n

The traditional chatbots<\/a>, The Oracle Digital Assistant (ODA), structured in models such as the Oracle Digital Assistant (ODA), is important for digitizing basic interactions. However, the market now demands more and more flexibility.<\/p>\n

This change directly affects financial and technical constraints<\/strong> of these models, as they use a single language model for all tasks, which generates unnecessary costs and often mechanical responses.<\/p>\n

The new architecture deals with this dependency.<\/p>\n

Nowadays, companies adopt strategies that allow them to switch between different language models<\/strong> depending on the complexity of the task.<\/p>\n

This avoids what we call Vendor Lock-in<\/em> and ensures that intelligence is not tied to a single supplier, allowing for much more accurate cost management through FinOps practices<\/strong>.<\/p>\n

What are the impacts of AI for customer service on costs and results?<\/h2>\n

The strategic use of artificial intelligence is projected to reduce operating costs by up to 30%, according to analysis by McKinsey.<\/p>\n

This impact is amplified by adoption of FinOps strategies<\/strong>, In this way, choosing smaller language models (SLMs) for simple tasks avoids spending too much on super-powerful and unnecessary models.<\/p>\n

Another McKinsey study (2025)<\/a> points out that generative AI could add between US$ 2.6 trillion and US$ 4.4 trillion annually to the global economy.<\/p>\n

Customer service stands out as one of the areas with the greatest value capture in this scenario.<\/p>\n

To ensure that this value is returned to the company's cash flow, MPL uses the concept of \u201cModel Benchmarking\u201d.<\/p>\n

This is a technical evaluation that identifies which AI engine offers the best performance for each type of interaction, balancing precision and cost.<\/p>\n

At the same time, the savings generated allow the company to reinvest in strategic areas of innovation, transforming the support sector from a cost center into a data generator for sales intelligence.<\/p>\n

How does the Intelligent Service Agent (AIA) optimize operations?<\/h2>\n

The EIA works as a platform low-code<\/em><\/a> which orchestrates artificial intelligence agents within the company's own cloud infrastructure.<\/p>\n

Unlike solutions that rely on external servers, MPL's technology operates in the customer's Oracle Cloud. This architecture ensures total sovereignty over data and allows agents to learn from manuals and internal databases without exposing sensitive information to public models.<\/p>\n

By keeping processing in this private environment, the company ensures that the flow of information respects compliance standards and the LGPD. To deliver efficiency and control, the platform is differentiated by three technical pillars:<\/p>\n