AI Agent for Customer Relations: Best Practices

Customer relations are one of the areas where agentic AI produces the results that are quickest to measure. At Logista, a European distributor with 2,500 employees, a voice AI now automatically handles a large share of the 20,000 monthly calls received by support, without degrading the experience on complex cases. At EcoPark, an agent answers 100% of Google reviews in the brand's tone. Here is how these two projects unfolded, what an AI agent can handle in customer relations, the cases where humans must stay on the front line, and how to deploy this type of solution without degrading your customers' experience or overloading your teams.

The Logista case: filtering 20,000 calls a month without losing quality

Of the 20,000 monthly calls handled by Logista's teams, a large share concerned recurring, predictable requests: order status, delivery information, procedural questions. After a workshop to identify use cases, Qolaig deployed a voice AI able to understand, qualify and handle these frequent requests in real time. For requests beyond its scope, the AI identifies the level of complexity and transfers the call to the right human contact, with the context already captured.

The solution was plugged into the existing telephone infrastructure, with no change of number and no heavy migration. Result: 20,000 calls a month now filtered and qualified automatically, zero advisors tied up on repetitive requests, and shorter handling times on complex cases thanks to contextualized routing.

The EcoPark case: answering 100% of customer reviews, authentically

EcoPark, a network of leisure parks with 40 employees, welcomes thousands of visitors every season, and almost every visit leaves a trace online: a Google review. Answering each review with the warmth and consistency the brand requires took time the teams simply did not have.

Qolaig designed an AI agent connected directly to EcoPark's Google Business Profile, trained on the brand's voice and values. As soon as a review is published, whatever its rating, the system picks it up in real time, analyzes the context (rating, presence or absence of text, content of the feedback), and writes an answer suited to the situation, never a recycled generic template. Result: 100% of reviews handled automatically, answers published in real time, and zero hours spent by the teams on this task.

What an AI agent can handle in customer relations

End-to-end resolution of routine requests, as the Logista case shows for order status and delivery information.

Qualification and intelligent routing, by collecting the necessary information even before a human steps in.

Follow-up and proactive responses, as at EcoPark, where every new review receives an answer consistent with the brand identity, without manual monitoring.

Automating the handling of customer returns is another frequent example: the agent checks the order, applies the return policy and only escalates the cases that fall outside the rules.

The channels to cover

Customer relations play out across several channels, and each has its own codes. The phone requires an immediate answer and a natural voice, as at Logista. Email tolerates a delay, but requires a complete, reviewed answer. Chat expects speed and short answers. Online reviews are public: the tone must reflect the brand, as at EcoPark. Start with the channel that concentrates the most repetitive requests, then extend to the others once the results have been measured.

An example of a first scope

For a first project, a reasonable scope covers three or four types of requests: for example order tracking, opening hours, changing an appointment and requesting an invoice. You record the requests actually received over a month, write for each one the expected answer and the information to check, then define what triggers a transfer to an advisor. Everything else is passed to a person from the start. This narrow scope makes it possible to measure results quickly, then to widen it one type of request at a time.

The cases where humans must stay on the front line

Conflictual or sensitive situations. A complaint with a strong emotional charge requires human judgment. At Logista as at EcoPark, the agent remains calibrated to transfer or flag whatever falls outside its scope rather than forcing an unsuitable answer.

Exceptional decisions, such as a significant commercial gesture or an exemption, which remain a human prerogative.

How to deploy an AI agent in customer relations without degrading the experience

1. Start with a workshop to identify use cases. This is the method applied at Logista before any development.

2. Always provide a way out to a human. Logista's contextualized routing illustrates this principle: advisors never join a conversation cold.

3. Calibrate the tone on the brand identity, not on a generic template. At EcoPark, the goal was not for the answers to sound like AI, but for them to sound like EcoPark.

4. Measure what matters. Resolution rate without human intervention, response time, transfer rate: these indicators make it possible to adjust the scope over time.

Our article on the enterprise chatbot details the technical principles that make this kind of natural interaction possible.

The mistakes to avoid

Deploying without informing the advisors. Advisors who discover the agent on launch day perceive it as a threat. Present the scope to them, explain what changes for them and involve them in the settings.

Hiding that it is an AI. Customers readily accept an automated assistant when they know who they are talking to and how to reach a person.

Not reviewing the first conversations. During the first weeks, regularly listening to samples of calls or reading samples of answers quickly reveals clumsy wording or forgotten cases.

Measuring only volume. An agent that handles many requests but leaves customers dissatisfied improves nothing. Track satisfaction as much as volume.

According to the Liferay 2026 Agentic AI Adoption and Governance Report, customer service accounts for 32% of agentic AI uses in companies, the second most cited category after data analysis.

FAQ

Does an AI agent in customer relations lower customer satisfaction?
The Logista and EcoPark cases show the opposite on a well-chosen scope: speed of resolution and responsiveness improve perception rather than degrading it.

Do you need an AI agent or a chatbot for customer relations?
A chatbot is enough for simply answering questions. An AI agent becomes relevant as soon as resolution requires acting on several systems or channels, like the voice routing at Logista. Our article on custom AI agents explains when a tailored development is worth it, and our article on agentic AI governance covers how to connect an agent to your tools safely.

At Qolaig, our Workshop → Build → Run methodology frames this type of project before any technical investment. Let's talk about your project.