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.
For managers, that means clearer sight lines. They can spot issues like poor handoffs between AI and human agents sooner, and they don’t have to rebuild their quality programs every time a new platform gets added.
In environments where coaching already demands more from supervisors, that kind of continuity can make a significant impact.
Understanding Customer Intent: A New Lens for Coaching
Beyond quality evaluations, AI is also transforming how leaders understand the ‘why’ behind customer interactions.
By analyzing intent across the entire contact volume, organizations can pinpoint friction points, training needs, and systemic issues much earlier.
Creasey emphasizes the central role that intent analysis plays, claiming that “everything leads from that.”
Whether the goal is reducing effort, improving routing, or refining process guidance, intent intelligence shapes the steps that follow.
For managers, this creates a foundation for more strategic coaching. Instead of reacting to isolated issues, they can address the themes driving customer dissatisfaction or repeat contacts.
This results in more effective conversations and more confident agents.
AI-Supported Coaching is Making Performance More Transparent
Coaching has traditionally depended on the experience and intuition of individual team leaders.
Now, performance management platforms consolidate data from scheduling, quality, coaching, and sentiment analysis into a single view.
This enables managers to identify trends, monitor progress, and refine strategies with precision.
Creasey says:
“It makes it very easy to see performance across all of these tools and measure the effectiveness of your coaching.”
For enterprise operations teams, this means coaching no longer hinges on subjective interpretation or inconsistent documentation.
Instead, leaders gain a real-time understanding of agent progress and can prove the impact of coaching activities – something that’s becoming increasingly important as organizations look to justify investments in both people and technology.
Why Managers Need New Skills to Maximize AI Investments
Creasey is quick to emphasize that AI does not replace coaching; it reshapes it.
Through this lens, the role of the manager is crucial in making the technology valuable.
They must learn how to interpret new data sources, guide agents through unfamiliar tools, and ensure AI insights translate into positive behavioral change.
This is a significant shift. Quality leaders, workforce planners, trainers, and supervisors must now grow into hybrid roles: part analyst, part strategist, part coach.
And they must do so at a time when contact centers are embracing AI faster than ever before.
For Creasey, the priority is clear: education must match the pace of adoption.
Without that foundation, the most advanced tools risk becoming underused, misunderstood, or disconnected from the coaching strategies meant to support them.
A Turning Point for Enterprise Contact Centers
There is no denying that AI is delivering tremendous opportunities for the customer experience and service industry, but only if coaching and performance management evolve alongside it.
The organizations gaining an advantage are those that pair advanced intelligence tools with managers who know how to convert insights into actionable improvements.
As Creasey’s observations show, when deployed correctly, AI-powered coaching can usher in a wider shift in how contact centers evaluate performance, support agents, and align teams around emerging expectations.
Leaders who modernize their coaching frameworks now will see a direct impact on quality, efficiency, and customer outcomes. Those who don’t, may find that the tools themselves are not enough.
The next stage of contact center transformation won’t be defined by what AI can do, but by how effectively managers help their teams use it.
You can find more insights into Calabrio’s Voice of the Agent report by checking out this article.
You can also download the full report here.
