This article is part of our series on the 70:20:10 Learning Model, exploring how organizations can blend experiential, social, and formal learning to enhance agent development.

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In a perfect world, every new and experienced agent would have a coach by their side, offering real-time feedback, modeling best practices, and helping navigate tricky customer calls. But in today’s distributed contact centers, with agents spread across multiple locations or working remotely, coaching can’t rely just on passing conversations or call reviews. The challenge is how you keep social learning alive when your team is rarely in the same room.

What is the 70:20:10 Learning Model?

The 70:20:10 framework suggests that employee learning and development is most effective when it’s broken down into:

• 70% experiential learning: on the job experiences, decision-making, and problem-solving.
• 20% social learning: feedback, mentoring, and collaboration.
• 10% formal learning: structured courses, workshops, or classroom training.

Why Social Learning Matters

The 20% of the 70:20:10 model dedicated to social learning relies on feedback, mentoring, and collaboration. It’s what turns knowledge into instinct.

In a remote or hybrid setting, the absence of in-person observation can cause:

• Slower skill development for new hires
• Missed opportunities for real time corrections
• Inconsistent coaching quality across the team

Without intentional systems for feedback and mentorship, the performance gap between top and average agents can grow wider.

How AI-Powered Practice Makes a Difference

AI-powered simulation tools allow managers to bring that 20% social learning into the digital space:

1. Simulated Scenarios Accessible AnywhereAgents, regardless of location, can participate in realistic, interactive customer conversations designed to mirror actual challenges.
2. Built-in Feedback LoopsManagers can review recorded practice sessions, annotate key moments, and score performance within the same platform.
3. Data-Driven Coaching PlansInstead of relying on personal feedback, managers can pull objective performance data to personalize coaching for each agent
4. Peer to Peer LearningPractice recordings can be shared among teams to highlight best practices, creative problem solving, and real-life examples

Scaling Quality Feedback

One of the biggest benefits for distributed teams is that coaching becomes asynchronous and consistent:

• Agents complete practice sessions on their own time
• Managers provide targeted feedback without needing to schedule live shadowing
• High quality coaching reaches everyone, not just the agents who happen to get the most floor time with supervisors

This means leaders can maintain coaching quality across hundreds of agents without burning out their leadership team.

Keeping the “Human” in Digital Coaching

It’s important to remember that AI simulation doesn’t replace the human connection in coaching, it amplifies it. The role of the manager becomes more strategic by:

• Curating the right scenarios for each agent’s growth
• Providing thoughtful, personalized feedback
• Encouraging collaboration between agents to share lessons learned

The technology handles the logistics and scalability, leaving leaders to focus on mentorship and culture.

In a distributed workforce, coaching doesn’t have to be difficult, it can improve. By embedding AI-powered practice into your 70:20:10 learning strategy, you can ensure that every agent, no matter where they are, gets consistent, actionable feedback that drives real performance gains.

This is part 3 of a 3-part series on the 70:20:10 Learning Model. Read the rest of the series:

Post 1: The 70:20:10 Learning Model: How AI-Powered Practice Drives Real Performance
Post 2: The 70:20:10 Learning Model: Reducing Ramp Time Without Sacrificing Quality

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