Hi everyone,
We've been using Virtual Supervisor (AI Scoring) in a large retail operation in Brazil, and the impact on our Quality Management process has been very significant.
Before AI Scoring, the entire quality process was fully manual and fragmented. Customer interactions happened in one platform, while quality evaluations were performed in another, with no native integration between them. This resulted in high operational effort, limited scalability, and long evaluation times.
With the introduction of Genesys Quality Management, Policies, and AI Scoring, we were able to redesign the end-to-end process and achieve substantial gains:
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Scale quality evaluations from ~2,000 forms/month to over 15,000 forms/month, without increasing operational cost
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Reduce the number of quality analysts from ~30 to ~15, while significantly expanding evaluation coverage
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Use Genesys policies to automatically distribute evaluation forms, ensuring consistent and unbiased sampling
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Leverage AI Scoring to pre-fill and score form questions, accelerating the evaluation process
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Use AI Insights to quickly understand the interaction context, highlights, and opportunities for improvement
Even when human review and validation were still required, the efficiency gains were very expressive:
From a performance and management perspective, AI Scoring became a strong enabler for:
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Faster and more frequent feedback cycles for agents
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Greater consistency and standardization across evaluations, reducing subjectivity
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A shift in the QA team's role from manual scoring to calibration, coaching, and quality strategy
There were also important learnings and challenges along the way:
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Evaluation form design is critical. Clear, objective questions are essential for good AI Scoring accuracy.
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Initial calibration and continuous tuning are mandatory, especially during the early stages.
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Change management plays a big role: positioning AI Scoring as an accelerator for quality teams, not a replacement, was key for adoption.
Overall, Virtual Supervisor enabled a transition from a manual, sample-based quality model to a scalable, integrated, and data-driven quality strategy, which would not be feasible with traditional processes alone.
We're looking forward to reviewing the AI Scoring Best Practices Guide and continuing to evolve this model as the product matures.
Happy to exchange experiences with others who are also scaling AI Scoring in high-volume environments.
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Mateus Nunes
Pre sales
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