AI and Customer Relations: Starting with Best Practices

AI and Customer Relations: Starting with Best Practices

AI has become an integral part of discussions about customer relations. It promises to provide faster responses, assist customer service representatives, automate certain tasks, make better use of data, and personalize customer journeys.

But AI should not be treated as some kind of magic layer added on top of existing tools.

To create value, it must address specific problems. It must be based on reliable data, clear processes, and robust control mechanisms. Without these, it risks producing results that look impressive in theory but are difficult to implement in practice.

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AI Does Not Replace Business Context

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Many companies want to integrate AI into their customer service operations. This demand makes sense: volumes are rising, customers want quick responses, teams are under pressure, and management is looking to improve efficiency.

But starting by “adding AI” is rarely the right approach.

The real question is simpler: What problem are we trying to solve?

This may involve reducing processing time, improving the quality of inquiries, providing better guidance to customers, assisting advisors during interactions, automatically summarizing conversations, or leveraging data to better manage operations.

Every use case has its own constraints, required data, risks, and success metrics. That is why AI must be approached as a business project, not just a technology project.

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Identify high-value use cases

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Not all AI use cases are created equal.

Some are visible but not very profitable. Others are less spectacular but much more useful in day-to-day operations. For example, an automated call summary can save time on a large scale. A response assistant can improve the quality and consistency of service. An analysis of contact reasons can help the company address the root causes of customer inquiries.

Priority should be given to use cases that meet three criteria: a real pain point, sufficient volume, and a measurable impact.

An effective AI project must be able to answer specific questions. How much time do we want to save? What resolution rate are we aiming for? What level of satisfaction do we want to improve? What percentage of requests can be automated without compromising the user experience? What human oversight must remain in place?

This approach avoids launching showcase projects and allows us to focus our efforts on use cases that can truly transform our business.

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Data remains the starting point

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AI depends heavily on the quality of the data.

If customer histories are incomplete, if contact reasons are misclassified, if knowledge bases are not maintained, or if tools do not communicate with one another, AI will struggle to produce reliable responses.

Before deploying a chatbot, an advisory assistant, or an analytics engine, it is therefore important to examine the data foundation: available sources, content quality, access rights, data freshness, governance rules, and traceability.

This work may seem less appealing than choosing an AI tool. However, it is often this work that determines the project’s success.

Useful AI isn't just a good model. It's a model powered by the right data, integrated into the right processes, and controlled in the right contexts.

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Deploying AI in a Methodical and Controlled Manner

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AI in customer relations should be rolled out gradually.

It is best to start with a clearly defined scope, including measurable objectives, an identified user group, and monitoring guidelines. This phase allows you to assess the quality of responses, operational impact, risks, and acceptance by the teams.

We also need to define the role of humans. Some requests can be automated, but others must continue to be handled by an agent, particularly when they are sensitive, complex, or high-value.

Finally, oversight must be ongoing. An AI use case is not deployed once and for all. It must be evaluated, refined, enhanced, and governed over time.

At IKATAN, we take a pragmatic approach: we start by addressing business pain points, identify high-impact use cases, verify data quality, and then develop a gradual and controlled roadmap.

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