Spain’s Customer Service Act, commonly known as the Ley SAC, is prompting many companies to rethink how they monitor their customer service channels. The question is no longer simply whether customers are being served properly, but whether the organization can prove it when required.
For a company handling thousands of calls, complaints, and inquiries every day, this presents a significant challenge. Information is often spread across the CRM, contact center platform, call recording system, and other internal applications. The data exists, but it is not always connected or easy to interpret.
For example, a company may retain all its calls and still struggle to answer seemingly straightforward questions: What percentage of interactions follow the established quality guidelines? Where are most complaints occurring? Which teams show the highest number of deviations? What evidence supports each metric?
Artificial intelligence can help address this problem. Not because it eliminates the need for human oversight, but because it makes it possible to analyze large volumes of conversations, identify patterns, and direct managers’ attention to the cases that genuinely require review.
Manual Sampling Is No Longer Enough
Traditionally, compliance and quality teams have worked by listening to a selection of calls. A supervisor checks whether the agent followed the correct procedure, provided the required information, and responded appropriately to the customer’s request.
This work remains necessary. The problem is that a small sample does not always reflect what is happening across the entire operation (and therefore cannot provide a complete picture of whether the organization is complying with the law).
A significant incident may not be included in the calls selected for review. Likewise, certain behaviors only become visible when hundreds or thousands of conversations are analyzed, such as unnecessary transfers, unclear explanations, repeated requests, or customers calling several times about the same issue.
AI can significantly expand the scope of these evaluations. Conversations can be transcribed, classified, and analyzed according to criteria defined by the organization itself.
This makes it possible to determine whether a protocol was followed, whether specific information was provided, whether the customer was served in the appropriate language, or whether there are signs of dissatisfaction, abandonment, or repeated contact.
Turning Calls Into Actionable Information
Storing a recording is not the same as having evidence that is easy to access and use.
When a company needs to investigate an incident, its teams should not have to listen to an entire call or search across multiple platforms. Ideally, they should be able to access the recording, transcript, relevant excerpt, and the criterion that triggered the alert from a single environment.
AI can also extract a summary of each conversation, the main topics discussed, the actions agreed upon, the commitments made, and any potential customer objections.
This information supports quality and compliance teams, but it is also valuable to operations managers. Instead of reviewing calls one by one, they can receive periodic reports containing compliance alerts, trends, anomalies, recurring issues, and cases requiring immediate action.
This is where conversation analytics delivers the greatest value: it enables organizations to move beyond isolated incidents and identify the underlying causes that keep recurring.
A Dashboard That Leads Directly to the Evidence
A compliance dashboard should do more than display percentages. Its value depends on whether users can understand what lies behind each figure.
Senior management needs an overall view of service performance. Quality, compliance, and operations teams need more detail: where a deviation occurred, which team it affected, how it has evolved, and which conversation supports the finding.
The dashboard should therefore allow users to move from a high-level metric to the details of the individual interaction. A compliance percentage means very little on its own if it cannot be linked to specific calls, agents, incidents, or supporting documents.
Some of the most useful information includes:
- Overall compliance levels and how they change over time.
- Results by criterion, team, campaign, service provider, channel, or language.
- The most significant deviations and their level of risk.
- The conversations and specific excerpts supporting each alert.
Combining an organization-wide view with operational detail supports both day-to-day monitoring and the preparation of internal or external audits.
Technology Helps, but It Requires Governance
Analyzing conversations involves processing personal data and, in some cases, sensitive information. Appropriate access controls, encryption, retention policies, and activity logs must therefore be in place.
It is also important to document the evaluation criteria. The organization must know what is being measured, how each metric is calculated, and when the rules were last changed. A result is no longer reliable when no one can explain where it came from.
Automating compliance monitoring under the Ley SAC is not simply a matter of adding AI to a call recording system. It requires connecting conversations to metrics, metrics to supporting evidence, and evidence to specific actions.
The goal is not to replace compliance and quality teams, but to give them a much broader view of what is happening across the service operation. Less time spent searching for calls and preparing reports, and more time devoted to understanding root causes, correcting deviations, and improving customer service.
Because in this new regulatory environment, serving customers properly remains essential. But being able to prove it through clear, traceable, and accessible information is just as important.
Learn more about AI solutions to ensure compliance with the SAC Act by clicking here.