The Conventional Contact Center QA Is Dead.

Transforming Contact Center “Quality Assurance (QA)” into “Outcome Assurance (OA).”

 

Ray Naeini (CEO, Chairman – OnviSource)
Brian Severson (Chief Product Officer – OnviSource)
 

Executive Summary

 

The critical questions we should ask are:

• Do today’s contact center QA functions score interactions or score outcomes that matter?

• Do they just measure performance, or do they actually enable performance?

• Do they use AI as a bolted-on tool to just automate the process, or are they AI-Ready and AI-Native that learns to evolve?

 

Today’s contact centers operate in complex, high-stakes environments where customer expectations, regulatory pressure, and operational efficiency must be balanced in real time. They require a paradigm shift in how quality and performance are managed.

 

However, traditional contact center Quality Assurance (QA) programs were designed for a different era, not for the AI era. They are focused on compliance checklists and retrospective scoring. They rely on a single composite scorecard to collectively evaluate diverse aspects of quality, such as compliance, agent performance, and customer experience. While convenient, this model introduces structural flaws that limit its effectiveness, as the traditional QA scorecards:

 

• Mask root causes by blending unrelated signals

• Distort incentives, encouraging agents to optimize for scores rather than outcomes that matter

• Reduce QA to a grading exercise instead of a learning performance enabler

• Limit the impact of coaching, training, and process improvement

• Provide lagging and limited insights into what to fix, when, or why

• Measure performance, not enabling performance

• Allow distorted results, such as great agent scoring, but bad outcomes

 

As a result, organizations invest heavily in QA without achieving sustained improvements in compliance, performance, experience, or efficiency.

 

Introducing Outcome Assurance (OA)

To address today’s complex contact center challenges, this paper introduces Outcome Assurance (OA): an AI-native, multilayer, closed-loop learning system for managing compliance, performance, customer satisfaction, and operational efficiency. Outcome Assurance produces critical business outcomes rather than lagging indicators that require additional manual actions and processes to assess scores, plan training, and conduct training and retraining, often failing to produce critical outcomes. 

 

Outcome Assurance, in addition to offering post-processing capabilities, operates in real time and provides real-time analytics, automation, and guidance to comply, improve performance, and produce outcomes during the interaction - eliminating missed opportunities, failed adherences, policy violations, laborious post-processing functions, manual QA score reviews, and training and retraining. Additionally, it acts as a virtual employee, learning from every interaction, growing and evolving, and improving its performance over time.  

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Outcome Assurance reframes quality as a system designed to enable performance, not simply measure it. The core principle of Outcome Assurance is that quality programs should ensure compliance, improve agent effectiveness, and drive measurable outcomes—each independently but connected through a continuous learning system.

 

Outcome Assurance is AI-Ready

Effective use of AI requires reviewing and optimizing all conventional processes and functions for AI Readiness before applying AI solutions. Furthermore, AI-driven solutions need to be AI-native, not bolt-on tools, to leverage autonomy, reasoning, and learning capabilities, much like hiring qualified employees who learn, evolve, and grow with the company.

 

Outcome Assurance, first and foremost, uses the AI Readiness methodology to transform the conventional contact center quality assurance into a multilayer system of compliance, performance, and outcome management. Next, it uses AI-Native technology to power multilayer systems with real-time analytics and guidance. Finally, it leverages AI-native technology to add a continuous learning system that connects the layers, measures outcomes, and learns and evolves with every interaction.

 

Outcome Assurance, thus, replaces composite QA scores with a system that assures:

1. Compliance,

2. Improves agent effectiveness and performance, 

3. Drives outcomes and delivers business objectives, and

4. Learns and grows with every interaction.

 

Bottomline

Organizations adopting Outcome Assurance achieve:

• Clear separation of risk, quality, and performance

• Faster agent ramp-up and improved consistency

• Reduced QA effort with higher insight density and better outcomes

• Improved CSAT, FCR, and operational efficiency

• Stronger governance and audit readiness

 

Quality is no longer a score. It is a system that learns, guides, and improves outcomes in real time.

 

Result: Quality becomes a Real-Time Compliance-Improvement-Outcome-Learning Engine, not a lagging report.

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Layered and Learning Quality Model (LLQM) of Outcome Assurance

Outcome Assurance replaces monolithic QA scorecards with a Layered and Learning Quality Model (LLQM) supported by AI-Native analytics, Agentic-AI, real-time guidance, and a learning system. LLQM consists of 4 separate yet connected functional layers: Quality and Compliance Management, Interaction Performance Management (for both agent and customer satisfaction), Outcome Management, and Learning System Management. Layers 1, 2, and 3 are equipped with real-time interaction analytics and agent guidance to analyze and act in real time.