Improving Service Quality and Process Accuracy with AI Studio

The service operation faced structural issues that directly affected quality and customer trust:

• Quality scores below target, with fluctuations driven by recurring process errors
• High volume of internal signaling and rework, indicating uncertainty during service interactions
• Process-related mistakes, especially around critical information (e.g., deadlines and authorization steps)
• Uneven agent knowledge, making outcomes dependent on individual experience rather than standard execution

Even when overall performance improved, specific gaps persisted, limiting sustainable gains in CSAT and quality.

Using Atento AI Studio, we connected quality data, operational signals, and agent performance into a continuous improvement loop focused on root cause, action, and adoption.

The process

1. Identify opportunities (where quality breaks)

• • Analyzed quality evaluations and signaling patterns to isolate the main drivers of score deflation
  • Distinguished people-related gaps (knowledge and execution) from process-related gaps (unclear or misapplied rules)

2. Create prompts (to uncover root cause)

• • Designed targeted prompts to explain why specific quality items failed
  • Identified recurring misunderstandings tied to critical service steps
  • Translated findings into clear operational language, ready for frontline use

3. Translate insights into actions (make it practical)

• • Defined focused improvement actions instead of broad retraining
  • Introduced knowledge validation mechanisms (quizzes and micro-learning) aligned to real errors
  • Updated service guidance to remove ambiguity at key moments of the interaction

4. Accompany applicability and results (ensure impact)

• • Worked with operations to apply actions where they mattered most
  • Monitored quality evolution and signaling reduction to validate effectiveness
  • Refined actions based on performance movement, not assumptions

What the client received was not static analysis, but a repeatable quality improvement system.

People (frontline execution)

• Targeted quizzes to assess real understanding of critical processes
• Focused micro-training for agents scoring below quality thresholds
• Clear behavioral guidance tied to quality criteria, not generic scripts
• Faster performance recovery without removing agents from production

Process (operational clarity)

• Identification of process steps generating the highest quality risk
• Clarification of critical rules that directly impact customer outcomes
• Reduction of signaling by eliminating uncertainty during service
• Alignment between quality evaluation, training, and daily execution

• Quality score increased to ~85, exceeding the operational target of 80
• Consistent week-over-week improvement, showing stability rather than short-term gains
• Significant reduction in signaling, driven by clearer processes and better agent confidence
• Fewer repeat errors, as knowledge gaps were addressed at the source