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Categories : Contact Center, Customer Experience CX

Bluewave | April 14, 2025

Automated Quality Management without Coaching

Peanut Butter Without Jelly: The Disappointment of Automated Quality Management without Coaching

by Aaron Portz

I don’t know about you, but I love a peanut butter & jelly sandwich. That perfect balance of textures and flavors just works—and can quickly turn my hangry, non-productive state of mind into a pleasantly satisfied mood ready for a productive afternoon.

When it comes to turning around poor performance in a contact center, it takes that same sort of balance to get the job done effectively. You need to know what the problem is and how to fix it. Quality Management (QM) tells you there’s a problem, but Performance Management—coaching—is how you take action to fix it. It’s peanut butter and jelly.

QM vs. Coaching

AI-driven Automated Quality Management (AQM) has flooded contact centers with an unprecedented volume of quality data, scoring nearly all interactions instead of just a small sample. At this point, AQM is almost table stakes for any CCaaS platform. While this data explosion provides incredible visibility into performance trends, it also exposes a critical gap: the lack of robust coaching capabilities within many CCaaS solutions.

QM ensures consistency in customer interactions by monitoring, scoring, and evaluating calls or chats based on predefined criteria. With AI-driven automation, QM now delivers vast amounts of performance data, enabling large-scale quality evaluations.

However, QM alone does not drive improvement—it only identifies issues.

Coaching turns QM insights into action. Targeted interventions help agents improve through:

  • Personalized development plans to address specific gaps
  • Behavioral changes to adopt better practices
  • Real-time reinforcement of good habits and quick correction of mistakes
  • Ongoing progress monitoring and refining of coaching strategies

Without coaching, your QM data remains just that—data. Data on a shelf. Data without real impact. QM is measurement. Coaching is action.

The Data Abundance & Actionability Gap

While AQM can now highlight issues at scale more successfully than ever, CCaaS platforms often fall short in operationalizing that data. This results in:

  • Data Overload Without Insights – Organizations are drowning in QM data but struggle to translate it into actionable coaching insights.
  • Manual Coaching Processes – Supervisors must sift through large data sets to identify true coaching opportunities, making the process inefficient and inconsistent.

Why waste your time with peanut butter and no jelly?

If I tell you there’s a performance problem in your company but can’t tell you what to do to fix it, I haven’t helped you.

Even with all the data from an AQM tool, supervisors may be able to point to poor performance but still lack a strategy—or the tools—to help agents improve. That’s especially challenging when one agent’s poor performance may look just like that of ten or a hundred others. But each agent likely has different specific challenges and will almost certainly respond differently to the same intervention.

Your supervisors would need superhuman abilities to consistently and effectively coach every agent based on their unique needs.

And when those agents fail to improve, you may terminate them—only to restart the costly cycle of churn.

To systematically and consistently improve performance, you need more than data. You need agent-specific insights and recommended best actions—actions you can measure and link to real, successful outcomes.

Bridging the Gap: Unlocking the Full Potential of QM Data

To truly leverage the benefits of AI and your shiny new AQM, organizations must invest in coaching tools that transform raw data into structured, actionable programs.

Key elements include:

  • Automated Coaching Triggers – Systems that automatically recommend coaching sessions based on QM insights
  • Integrated Performance Dashboards – Real-time visibility into QM scores and agent trends (both improvement and decline)
  • AI-Driven Next Best Coaching Actions – AI that goes beyond scoring to offer personalized coaching suggestions based on agent behavior patterns
  • A Continuous Learning Culture – Shifting from periodic evaluations to ongoing, dynamic coaching that evolves with customer expectations

Conclusion

The contact center industry has successfully harnessed AI to produce vast amounts of high-quality data—the peanut butter. But without equivalent advancements in application, measurement, and refinement—the jelly—the true potential of that dry peanut butter sandwich remains as unfulfilled as your performance improvement goals.

Coaching is the bridge that transforms QM insights into real performance improvements—driving better customer experience, operational efficiency, and growth.

To fully realize the potential of AI-powered QM, organizations must prioritize investment in performance management tools that automate, streamline, and scale coaching efforts.

Only then can contact centers turn data into action—and elevate agent performance to new heights.

Now, where did I put that jelly…?