As we start 2026, let’s play a game: how many times did you say ‘AI’ last year?
Let’s be honest, if your answer is anywhere under the 100,000 mark, you’re probably lying to yourself.
Here’s the real question: how often did you experiment with GenAI – but never turn the insight into real impact? If I’m honest, it happens to me more often than I’d like.
During a period where contact centers have invested heavily in automation, intelligence platforms, and AI-assisted workflows, the technology has been at the heart of almost every conversation.
Yet many leaders are now facing a different challenge: how to turn these tools into meaningful performance gains. AI may be advancing quickly, but the coaching models that support it haven’t kept the same pace.
As systems become more sophisticated, the pressure increases on managers to keep teams aligned, supported, and developing new competencies.
Traditional coaching methods, built for sample-based evaluations and manual intervention, no longer fit a world where interaction volumes are vast and customer expectations continue to rise.
This shift is forcing organizations to rethink how they measure performance and support agent growth.
It isn’t about replacing the human element. It’s about giving managers the tools to coach smarter, not harder.
The AI Knowledge Gap Is Holding Back Performance Gains
While agents work in increasingly AI-enabled environments, many still struggle to understand where AI is used and how it benefits them.
In fact, Calabrio’s recent Voice of the Agent report revealed that only 35% of agents know which tools use AI.
Despite this, 48% want more AI tools introduced, and 44% say AI is useful in their day-to-day tasks, suggesting that agents are feeling the benefits of AI even if they can’t always pinpoint the exact source.
This lack of clarity can limit adoption, and by extension, the value organizations get from their investments.
Ed Creasey, VP of Solution Engineering at Calabrio, has seen the problem across countless deployments.
“AI enablement has outpaced education,” he says, pointing to a widening divide between the sophistication of the tools and the training that surrounds them.
However, although this disconnect appears to be potentially problematic, Creasey sees the desire for more tools as a net positive:
“Nearly half of agents wanted more AI tools; that’s a clear sign of curiosity.”
Yet, without focused enablement, managers are left trying to coach teams in environments where expectations change faster than skills develop.
Automated Quality Management Is Redefining What ‘Good’ Looks Like
One of the areas in which AI is helping to introduce rapid evolution is quality management.
Historically, supervisors could only review a small portion of interactions, leaving blind spots that limited the accuracy of coaching.
However, AI-driven systems now allow leaders to analyze every conversation, surfacing behaviors and patterns that previously went unnoticed, as Creasey explains:
“I can take one question across 100% of an agent’s conversations… and that’s extremely powerful.”
Instead of broad, generic coaching sessions, managers can guide agents toward specific behaviors backed by concrete examples.
An advisor may receive support on resolution techniques, while another focuses on how they close contacts or manage complex explanations.
The process becomes more personal, more targeted, and considerably more effective.
For enterprise operations, this unlocks consistency at scale. Leaders can measure CX, improve brand value, or reinforce standards across thousands of interactions without overwhelming their teams with more manual review work.
Coaching Across Platforms: The Next Challenge for Quality Leaders
Automated quality management solves one problem but creates another. As organizations introduce AI agents alongside their human teams, often across multiple CCaaS or CRM systems, they're left with fragmented performance data.
Quality frameworks that were built for a single platform struggle to provide a complete picture, and managers lose the consistency they need to coach effectively.
Calabrio recently launched Omni Agent Intelligence to address this.
The feature sits within Calabrio ONE and applies a unified quality framework across human and AI agents, regardless of which platforms they operate on. Leaders can score and compare performance using consistent criteria, even when the underlying stack changes.