Workforce Engagement Management

Over the past months, we've implemented Virtual Supervisor (AI Scoring) in a large-scale operation, fundamentally transforming how quality evaluations are performed.

Before AI Scoring, the quality process was entirely manual and fragmented. Customer interactions were handled in one platform, while quality evaluations were conducted in another, with no native integration between them. This model limited scalability, increased operational effort, and significantly delayed feedback to agents.

From manual sampling to scalable quality

With the adoption of Genesys Quality Management, Policies, and AI Scoring, we redesigned the end-to-end quality process:

• Genesys Policies were used to automatically distribute evaluation forms, ensuring consistent and unbiased sampling

• AI Scoring was applied directly to evaluation questions, pre-filling answers and accelerating the scoring process

• AI Insights enabled fast understanding of interaction context, highlights, and improvement opportunities

This allowed the quality team to move away from manual scoring and focus on calibration, coaching, and quality governance.

Real, measurable impact

The results were immediate and measurable:

• Quality evaluations scaled from ~2,000 forms per month to over 15,000 forms per month

• The number of quality analysts was reduced from ~30 to ~15, while overall coverage increased

• Average evaluation time dropped from ~18m30s per form to ~4m30s, even with human validation still in place

• The quality process became able to keep pace with the actual volume of customer interactions, something previously impossible with manual workflows

Key learnings and best practices

Along the journey, some critical factors became clear:

• Evaluation form design is essential: clear, objective, and well-structured questions directly impact AI Scoring accuracy

• Initial calibration and continuous tuning are mandatory, especially during early adoption

• AI Scoring should be positioned as an accelerator for quality teams, not a replacement, to build trust and adoption

From operational burden to strategic capability

By combining automation, AI Scoring, and human expertise, the quality process evolved from a manual, sample-based model into a scalable, data-driven quality strategy.

AI Scoring is no longer just about speed - it's about enabling broader visibility, faster feedback cycles, and more consistent performance improvement across the operation.

I'd love to hear from the community:
How have you structured your quality forms and calibration process to maximize AI Scoring accuracy?
What lessons have you learned while scaling AI-driven quality monitoring?

Good morning Mateus.

My organisation has been testing out the AI Scoring, but have not yet had the same levels of success that you seem to be getting. It would be very interesting to see what you are doing, the range of questions you have within your forms and how you are structuring your evaluations to get the best output

Over the past months, we've implemented Virtual Supervisor (AI Scoring) in a large-scale operation, fundamentally transforming how quality evaluations are performed.

Before AI Scoring, the quality process was entirely manual and fragmented. Customer interactions were handled in one platform, while quality evaluations were conducted in another, with no native integration between them. This model limited scalability, increased operational effort, and significantly delayed feedback to agents.

From manual sampling to scalable quality

With the adoption of Genesys Quality Management, Policies, and AI Scoring, we redesigned the end-to-end quality process:

• Genesys Policies were used to automatically distribute evaluation forms, ensuring consistent and unbiased sampling

• AI Scoring was applied directly to evaluation questions, pre-filling answers and accelerating the scoring process

• AI Insights enabled fast understanding of interaction context, highlights, and improvement opportunities

This allowed the quality team to move away from manual scoring and focus on calibration, coaching, and quality governance.

Real, measurable impact

The results were immediate and measurable:

• Quality evaluations scaled from ~2,000 forms per month to over 15,000 forms per month

• The number of quality analysts was reduced from ~30 to ~15, while overall coverage increased

• Average evaluation time dropped from ~18m30s per form to ~4m30s, even with human validation still in place

• The quality process became able to keep pace with the actual volume of customer interactions, something previously impossible with manual workflows

Key learnings and best practices

Along the journey, some critical factors became clear:

• Evaluation form design is essential: clear, objective, and well-structured questions directly impact AI Scoring accuracy

• Initial calibration and continuous tuning are mandatory, especially during early adoption

• AI Scoring should be positioned as an accelerator for quality teams, not a replacement, to build trust and adoption

From operational burden to strategic capability

By combining automation, AI Scoring, and human expertise, the quality process evolved from a manual, sample-based model into a scalable, data-driven quality strategy.

AI Scoring is no longer just about speed - it's about enabling broader visibility, faster feedback cycles, and more consistent performance improvement across the operation.

I'd love to hear from the community:
How have you structured your quality forms and calibration process to maximize AI Scoring accuracy?
What lessons have you learned while scaling AI-driven quality monitoring?

Over the past months, we've implemented Virtual Supervisor (AI Scoring) in a large-scale operation, fundamentally transforming how quality evaluations are performed.

Before AI Scoring, the quality process was entirely manual and fragmented. Customer interactions were handled in one platform, while quality evaluations were conducted in another, with no native integration between them. This model limited scalability, increased operational effort, and significantly delayed feedback to agents.

From manual sampling to scalable quality

With the adoption of Genesys Quality Management, Policies, and AI Scoring, we redesigned the end-to-end quality process:

• Genesys Policies were used to automatically distribute evaluation forms, ensuring consistent and unbiased sampling

• AI Scoring was applied directly to evaluation questions, pre-filling answers and accelerating the scoring process

• AI Insights enabled fast understanding of interaction context, highlights, and improvement opportunities

This allowed the quality team to move away from manual scoring and focus on calibration, coaching, and quality governance.

Real, measurable impact

The results were immediate and measurable:

• Quality evaluations scaled from ~2,000 forms per month to over 15,000 forms per month

• The number of quality analysts was reduced from ~30 to ~15, while overall coverage increased

• Average evaluation time dropped from ~18m30s per form to ~4m30s, even with human validation still in place

• The quality process became able to keep pace with the actual volume of customer interactions, something previously impossible with manual workflows

Key learnings and best practices

Along the journey, some critical factors became clear:

• Evaluation form design is essential: clear, objective, and well-structured questions directly impact AI Scoring accuracy

• Initial calibration and continuous tuning are mandatory, especially during early adoption

• AI Scoring should be positioned as an accelerator for quality teams, not a replacement, to build trust and adoption

From operational burden to strategic capability

By combining automation, AI Scoring, and human expertise, the quality process evolved from a manual, sample-based model into a scalable, data-driven quality strategy.

AI Scoring is no longer just about speed - it's about enabling broader visibility, faster feedback cycles, and more consistent performance improvement across the operation.

I'd love to hear from the community:
How have you structured your quality forms and calibration process to maximize AI Scoring accuracy?
What lessons have you learned while scaling AI-driven quality monitoring?