Why Quality Assurance in the Contact Center is Being Rethought

Traditional quality assurance in the contact center checks a handful of calls per agent per month — manually, based on gut feeling, and with a significant investment of time. The result is a sample that is hardly representative and often only makes risks visible late. AI-supported quality assurance turns this principle on its head: instead of fewer calls, all of them are evaluated.

For operators of customer service and contact center environments, this means a leap in expressiveness, fairness, and compliance — if the technology is used correctly.

From Sample to 100% Evaluation

The central shift in 2026 is complete coverage. AI-supported voice and text analysis no longer evaluates just one percent of calls, but every single interaction across all channels. This eliminates sampling bias: trends, training needs, and compliance risks become visible across the entire call base, not just in randomly drawn examples.

At the same time, manual effort drops drastically. The quality department no longer evaluates every call itself, but works with cases pre-evaluated by the AI, focusing on exceptions and coaching.

How AI Quality Assurance Works

The foundation is the combination of call recording, automatic transcription, and speech analysis. The AI detects topics, keywords, call phases, and anomalies, and compares them against defined evaluation criteria. This creates a comprehensible evaluation for every call — objective, consistent, and comparable across all agents.

Automatic Scorecards and Compliance Detection

The core of AI quality assurance is customizable scorecards: rules against which every call is automatically evaluated, such as greeting, identification, mandatory disclosures, resolution quality, and farewell. In addition, the AI detects compliance risks — missing mandatory announcements, risky phrasing, or escalation signals — in near-real-time or immediately after the call. This shortens response times from weeks to hours.

Coaching Instead of Mere Monitoring

Complete evaluation is not an end in itself. The actual added value lies in targeted coaching: the AI shows which agents need support with which types of calls and provides concrete examples. Monitoring turns into development — data-based and fair, because all calls are judged according to the same criteria.

Legal Framework: Transparency and Limits

AI quality assurance processes personal data of customers and employees and is therefore subject to clear rules. Information and transparency are central: employees and customers must be informed about the evaluation, and in Germany, co-determination under labor law must be observed. Another important limit comes from the EU AI Act: emotion recognition in the workplace directed at one's own employees is generally prohibited (Art. 5), with narrow exceptions. AI quality assurance should therefore target the content, structure, and compliance of calls — not the evaluation of agents' emotions.

Implementation in Practice

For a clean start, a clear process is recommended: first, define the evaluation criteria and scorecards together with the business department and works council. Next, ensure complete, audit-proof recording as a foundation and build the AI evaluation on top of it. After that, calibrate the AI evaluations against manual assessments on a sample basis, fulfill transparency requirements, and consistently use the results for coaching instead of mere monitoring.

How onsoft Supports This

onsoft combines call and screen recording, AI speech analysis, and quality management in an audit-proof system. This allows calls to be fully and consistently evaluated, compliance risks to be detected early, and coaching to be targeted — with comprehensible documentation that evaluation and recording are transparent and compliant with regulations.

Frequently Asked Questions

What is AI quality assurance in the contact center?

The automated evaluation of customer interactions using speech and text analysis. Instead of a manual sample, calls are automatically and consistently evaluated against defined criteria.

What does 100% evaluation mean?

It means that not just individual samples, but all interactions are evaluated. This eliminates sampling bias and makes trends and risks visible across the entire call base.

Does AI replace manual quality checks?

No. The AI takes over the comprehensive pre-evaluation; the quality department focuses on exceptions, calibration, and coaching. Human judgment remains part of the process.

Is the AI analysis of employees GDPR-compliant?

Only with transparency, a legal basis, and works council co-determination. Emotion recognition in the workplace is generally prohibited under the EU AI Act — evaluation should target content and compliance, not the emotions of employees.

What are the benefits of automated speech analysis?

Complete coverage, objective and comparable evaluation, early compliance detection, and targeted coaching with significantly reduced manual effort.