Five AI Use Cases for Quality Monitoring

AI is revolutionizing quality monitoring in contact centres, helping organizations improve agent performance, customer satisfaction, and operational efficiency.

Across various industries, companies are harnessing AI to streamline quality monitoring processes, providing faster, more accurate insights while saving significant time and resources.

Below, we explore five compelling use cases, showing how companies are leveraging AI for better quality monitoring.

1. Games24x7 Boosts Efficiency With Scorebuddy

Games24x7, a leading gaming company in India, sought a more advanced system to move away from their outdated spreadsheet-based processes.

By partnering with Scorebuddy, they implemented AI-powered quality sampling to automate the selection of customer service interactions for evaluation.

This shifted their focus from reactive analysis to a more proactive, consistent review of agent performance across all channels.

Thanks to this approach, Games24x7 saw a 20% increase in productivity, as their team could now evaluate more interactions in less time while ensuring greater consistency across reviews.

The AI-powered system also enabled deeper insights into agent performance and customer satisfaction, allowing for better root cause analysis of service issues.

Read the full case study on how Games24x7 Boosts Efficiency with Scorebuddy

2. Hexaware Elevates QM and Training With Genesys

Hexaware, a global leader in technology and business process services, embarked on a transformation journey to revolutionize its customer service.

Facing challenges with disparate systems and inefficient quality monitoring processes in its previous on-premises contact centre set-up, Hexaware turned to Genesys Cloud for an integrated, AI-driven solution.

The shift introduced advanced speech and text analytics that automated agent performance evaluations and enabled targeted training.

This strategic move not only streamlined the training process – leading to a 90% seamless transition for agents – but also improved service levels by 7–9% and boosted agent productivity by 12–15%.

The robust post-interaction analytics provided managers with actionable insights to monitor customer sentiment and identify performance trends, optimizing both service quality and agent engagement.

Read the full case study on how Hexaware Elevates QM and Training

3. Maps Credit Union Utilizes NICE

Maps Credit Union revitalized its quality monitoring by implementing a suite of NICE solutions, including Interaction Recording, Interaction Analytics, and Quality Management.

Previously limited by an outdated system with no post-call review or effective call recording, Maps CU now benefits from robust call evaluations, integrated feedback mechanisms, and real-time dashboards that flag service issues promptly.

These tools empower supervisors to deliver detailed coaching and improve agent performance. The use of NICE Enlighten AutoSummary significantly reduced after-call work in the Loss Prevention department, cutting down note-taking time from 2–3 minutes to just 20–30 seconds.

This comprehensive approach to quality management has not only boosted agent efficiency but has also aligned member feedback with service enhancements, maintaining consistent CSAT scores and fostering a seamless experience across all contact channels.

Read the full case study on how Maps Credit Union Utilises NICE

4. CIPS Modernizes With AI Suite From Scorebuddy

Citizens Information Phone Service (CIPS), an Irish national helpline, was struggling with a manual, Excel-based system that was time-consuming and inefficient.

To modernize their process, CIPS turned to Scorebuddy’s AIdriven suite of tools. These tools streamlined the entire process, offering customizable scorecards for more detailed performance evaluations and an advanced reporting system that provided deeper visibility into quality trends.

The introduction of AI not only improved quality scores by 2%, but also allowed supervisors and agents to receive real-time feedback through personalized dashboards. This helped CIPS stay ahead of emerging issues and continuously improve service delivery.

Read the full case study on how CIPS modernizes with AI Suite

5. O.phon GmbH Strengthens Oversight With Genesys

O.phon GmbH, renowned for providing customized marketing and customer service solutions, faced limitations with its outdated on-premises system, impacting quality monitoring and operational transparency.

Partnering with NTT, O.phon adopted Genesys Cloud CX to centralize communication channels and improve quality oversight. This unified platform enabled more efficient monitoring of service levels and project quality, supporting up to a fivefold increase in traffic without compromising performance.

Enhanced transparency allowed O.phon to better evaluate SLAs and ensure consistent quality, while AI-driven insights facilitated proactive issue resolution.

The result was a comprehensive solution that improved response times by 64% and boosted employee satisfaction through seamless, efficient workflows.

Read the full case study on how O.phon GmbH Strengthens Oversight with Genesys

These real-world case studies demonstrate how AI is transforming quality monitoring in contact centres, allowing organizations to not only improve performance but also deliver superior customer experiences.

By automating time-consuming tasks, providing real-time sentiment analysis, and offering actionable insights, AI driven quality monitoring solutions are helping businesses stay competitive in an increasingly demanding service landscape.

Looking for more real-world examples for your contact centre applications? Check out Call Centre Helper’s collection of over 150 case studies

If you are looking for more on AI and contact centre use cases, read these articles next:

• Our Top Use Cases for AI in Customer Service
• Game On! 12 Use Cases for Gamification
• The AI Revolution – How Contact Centres Are Adapting

Author: James Groves
Reviewed by: Jo Robinson