What is Logical Reasoning in AI Quality Assurance?

What is Logical Reasoning in AI Quality Assurance? A young woman and two young men work at computers and talk on headsets.

Contact centers are increasingly looking to incorporate AI into their Quality Assurance programs, but what is logical reasoning in AI Quality Assurance? And should you be using it?

The answer for many contact centers is a resounding “yes.”

Logical reasoning is a powerful methodology used in Artificial Intelligence Quality Assurance (AIQA) to evaluate and improve customer interactions within contact centers. Keep reading to learn more about this highly valuable and effective approach and how to use it in your contact center.

What is Logical Reasoning in AIQA?

Logical reasoning involves using rules-based algorithms — including if/then statements, logical operators (such as and/or), and other formulas — to analyze complex scenarios and human interactions.

This is different from pattern matching, another type of AI system that detects recurring patterns, similarities, and anomalies in the data.

The goal of logical reasoning in AIQA is to emulate human logic and decision-making processes by looking for context and understanding relationships within the data generated by contact with your customers.

The result is that you get better insights and actionable feedback that your contact center can then use to improve agent performance and customer satisfaction.

Scenarios Suited for AIQA Based on Logical Reasoning

Here are some examples of when using logical reasoning parameters within AI Quality Assurance makes good sense:

  • Compliance Monitoring:

    Logical reasoning AI can assess customer interactions to ensure compliance with regulatory requirements and company policies. For example, it can identify data privacy violations or ensure adherence to script guidelines during sales calls.

  • Issue Resolution:

    When addressing customer inquiries or complaints, logical reasoning AI can analyze conversation flow and identify potential resolutions based on predefined rules and criteria. For instance, it can recognize common troubleshooting steps for technical support issues and guide agents accordingly.

  • Script Adherence:

    Logical reasoning AI can evaluate agent performance by monitoring adherence to scripted responses and guidelines. It can identify deviations from established protocols and provide feedback to improve consistency and compliance.

  • Fraud Detection:

    In financial services or e-commerce settings, logical reasoning AI can detect suspicious patterns or behaviors indicative of fraudulent activity. By analyzing transaction data and user interactions, it can flag potential fraud cases for further investigation.

Best Practices for Using Logical Reasoning in AIQA

When implementing a system that uses logical reasoning in AI Quality Assurance, you’ll need to consider the following best practices:

1. Clear Rules and Criteria 

You’ll need to define clear rules and criteria for evaluating customer interactions based on business objectives, regulatory requirements, and customer expectations.

2. Regular Updates and Calibration 

Continuously update and calibrate logical reasoning algorithms to adapt to evolving business needs, industry regulations, and customer preferences.

3. Use of Other AI Techniques 

Combine logical reasoning with other AI techniques, such as pattern matching and natural language processing, to enhance accuracy and effectiveness.

4. Human Oversight and Intervention 

Maintain human oversight and intervention to validate AI-generated assessments, address edge cases, and ensure evaluation fairness and accuracy.

Benefits of Quality Assurance that Uses Logical Reasoning AI

Some of the benefits of implementing logical reasoning methodology in AIQA for your contact center include the following:

  • Consistency and Standardization: Logical reasoning AI ensures consistent and standardized evaluations of customer interactions. This minimizes variability and ensures compliance with predefined criteria.
  • Efficiency and Scalability: By automating the assessment process, logical reasoning AI enhances efficiency and scalability, enabling contact centers to handle large volumes of interactions effectively.
  • Actionable Insights: Logical reasoning AI provides actionable insights and feedback to agents, enabling targeted coaching and training initiatives to improve performance and customer satisfaction.

Contact centers can enhance performance and improve customer satisfaction by implementing an AIQA program that uses logical reasoning to assess and evaluate data.

ARC’s AIQA solution is based on logical reasoning. We invite you to book a meeting with the experts at ARC to discuss in further detail what logical reasoning in AIQA could do for your contact center.

To get in touch, use this form to book a call, or you can reach us directly at 1-866-798-0488. If you prefer email, you can contact us at info@arcqs.com.