Explore AI-Driven Evaluation and Quality Roles
Learning Objectives
After completing this unit, you’ll be able to:
- Differentiate between AI and manual evaluations.
- Explain the hybrid approach to evaluations and why it's the most effective.
- Identify the key roles in Quality Management and what each person does.
- Describe what Quality Management can and can't do.
AI-Driven Versus Manual Evaluation
AI-driven scoring doesn't replace human judgment, it amplifies it. Let’s dig deeper into these two evaluation areas.
Manual Evaluation
You have a quality analyst listen to a call and score it against your form. They provide written feedback. The rep reads it and discusses it with their supervisor. It takes time and requires experienced people, but it's nuanced, contextual, and thorough.
Manual evaluation is like having a master chef taste every dish. They catch things a recipe can't capture like the subtle balance of flavors, the perfect texture, the moment something needs adjusting. But a master chef can only taste so many dishes in a day.
AI-Driven Evaluation
AI evaluates each call automatically, applying a large language model to score the interaction transcript against your evaluation form.
It's fast and scales to 100% of calls, but the results are only as good as the questions and answers you’ve defined in the form. Vague questions produce vague scores. Specific, well-defined questions and answers produce reliable ones.
AI-driven evaluation is like having a sous chef who can prep hundreds of dishes consistently when you hand them a clear recipe. The recipe is your evaluation form, but the sous chef can only tell you whether the recipe was followed, not whether the dish tastes right. It’s fast, reliable, and scalable, but the master chef still needs to spot-check the results.
The Hybrid Approach: The Best of Both Worlds
The best approach? Combine them.
- Use AI to score 100% of customer interactions and flag the outliers (the really great calls and the ones that need attention).
- Have managers and quality analysts review the flagged interactions and spot-check the AI scores.
- Use the manual evaluations to continuously train and improve the model.
- Have managers and quality analysts coach people, not just score them.
You’re not choosing between AI and people. You're giving people superpowers. Here's what that looks like in practice.
Before Quality Management
- Chris, Jordan, and Pat manually review 150 calls a week (4% of total).
- They spend 8 hours a week just scoring.
- Coaching happens when there's time (rarely or never).
- Nobody knows what's happening in the other 96% of calls.
After Quality Management (Hybrid Approach)
- AI scores 100% of calls overnight.
- Chris, Jordan, and Pat spend 2 hours reviewing flagged calls and spot-checking scores.
- They spend 6 hours coaching reps with specific, data-backed feedback.
- The AI gets more effective every week as it fine-tunes the form based on evaluation results.
Same three people. 25-times more coverage. More time for coaching. Better results.
Key Roles in Quality Management
So who does what? Let’s meet the predefined quality roles for the team.
Role |
Description of Tasks |
Typical Role in Organization |
|---|---|---|
Manager |
This is your program leader. They’re the architect who designs the quality system.
|
Think of them as the head coach. They set the game plan, call the plays, and make sure everyone's working together toward the same win. |
Quality analyst (sometimes called a QM evaluator) |
This is your evaluator. They listen to calls, score interactions, and provide feedback.
|
Think of them as the talent scouts. They watch the game, spot what's working and what's not, and help players level up. |
Rep (service rep) |
This is the person being evaluated. They’re on the front lines, working cases and helping customers every day. Reps only need to see evaluation results for themselves. There’s no need for reps to edit results or create forms.
|
Think of them as athletes. They're in the game, and Quality Management is their training program and playbook. |
Quality viewer |
This is a read-only role for people who need to see Quality Management data but don't evaluate or create forms.
|
Think of them as the front office. They review the stats, spot trends, and make strategic decisions based on what they see. |
AI (the machine-learning model) |
Don't forget the AI! It plays a key role.
|
It's the teammate who never gets tired, never plays favorites, and never has a bad day. AI doesn't have judgment or intuition, but it has consistency and scale. |
The AI model scores at scale, but humans stay in the loop for coaching, discernment, and continuous improvement.
Quality Management lightens your quality team's workload, but it doesn't replace their experience or intuition.
What Quality Management Doesn’t Do
Let's set some expectations. Quality Management is powerful, but it's not magic. Here's what it doesn't do.
-
It doesn't automatically coach reps. It gives you the data and insights to coach reps well, but you still need humans in the loop to do the coaching.
-
It doesn't replace supervisor judgment. The AI scores consistently, but supervisors decide what to do with those scores.
-
It's not a surveillance tool. Quality Management is about development, not catching people making mistakes. The goal is to make everyone better, not to punish underperformers.
Think of Quality Management as your copilot, not your autopilot.
Wrap Up
Quality Management closes the gap between incomplete, inconsistent manual evaluation and real-time, data-driven coaching. The four-step loop—Create, Evaluate, Coach, Improve—runs continuously. The best approach blends AI-driven scoring with manager or quality analyst experience and coaching.
And it's not a replacement for your quality team: It's a multiplier. You're taking experienced people and giving them tools to evaluate more, coach better, and help every rep improve.
Ready to set up Quality Management in your org? Check out the Salesforce Help documentation to get started: Configure Quality Management for Service Interactions.
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