For decades, coaching in the contact center has been relatively straightforward.
Managers listened to calls, reviewed QA scorecards, identified coaching opportunities, and worked with agents to improve performance. While the tools and processes evolved over time, the fundamental model remained the same: humans coached humans.
That model is beginning to change.
As organizations deploy AI agents, virtual assistants, and increasingly autonomous customer service systems, a new challenge is emerging. Someone needs to monitor those systems, identify where they succeed, understand where they fail, and continuously improve their performance.
In other words, someone needs to coach the AI.
The Rise of AI Agents Changes the Equation
The contact center industry has spent the last several years focused on AI copilots and agent-assist technologies that help human employees perform more effectively.
Now the conversation is shifting.
Many organizations are beginning to deploy AI agents capable of handling entire customer interactions, resolving requests, and completing workflows with limited human involvement. Major investments across the industry suggest this trend is only accelerating.
But while AI agents can perform work, they still require oversight.
An AI agent may misunderstand customer intent. It may provide incomplete information. It may escalate too quickly, or not quickly enough. It may struggle with edge cases that fall outside the patterns it was trained to handle.
Just like a new employee, an AI agent needs feedback.
The difference is that the feedback loop looks very different.
You Can't Improve What You Can't Measure
One of the biggest challenges organizations will face is understanding how their AI agents are actually performing.
It's not enough to know that an interaction was completed.
Leaders need visibility into:
where conversations break down
which responses create confusion
where customers become frustrated
when escalation should have occurred
how outcomes compare to human agents
Without that visibility, organizations risk making assumptions about AI performance instead of understanding it. And that creates a dangerous situation.
An AI agent may appear successful based on high-level metrics while quietly creating customer frustration, compliance risk, or operational inefficiencies underneath the surface.
The organizations that succeed will be the ones that treat AI performance with the same rigor they apply to human performance.
Transcription Quality Matters More Than Ever
This is where many organizations underestimate the challenge. The effectiveness of AI coaching depends entirely on the quality of the information being analyzed. If transcripts are inaccurate, insights become unreliable... if customer intent is misunderstood, recommendations become misleading... if conversation data is incomplete, organizations lose visibility into what actually happened.
As AI becomes responsible for more customer interactions, transcription quality stops being a nice-to-have feature and becomes foundational infrastructure.
The better the transcription, the better the analysis.
The better the analysis, the better the coaching.
And the better the coaching, the better both humans and AI perform over time.
QA Is Expanding Beyond Human Performance
Traditionally, quality assurance focused on evaluating human agents.
Did the agent follow the process? Did they use the correct language? Did they handle the interaction appropriately?
Those same questions increasingly apply to AI systems.
Organizations need to understand:
whether AI responses are accurate
whether compliance requirements are being met
whether customers are receiving consistent experiences
whether escalation decisions are appropriate
whether outcomes align with organizational goals
This is where automated QA becomes particularly valuable.
Rather than reviewing isolated examples, organizations can continuously evaluate performance across thousands of interactions and identify patterns that would otherwise remain invisible.
The future QA team may spend as much time evaluating AI performance as human performance.
The Best Systems Create Fast Feedback Loops
Perhaps the most important lesson from high-performing contact centers is that improvement depends on feedback.
The faster organizations can identify issues, the faster they can improve.
That principle applies equally to humans and AI.
When managers discover a coaching opportunity for an agent, they want to act quickly.
The same is true for AI.
If an AI agent is repeatedly mishandling a particular type of interaction, organizations need the ability to identify the issue, adjust guidance, refine workflows, and measure the impact of those changes quickly.
This is why usability matters.
Insights have little value if they remain trapped inside dashboards, reports, or complex systems that are difficult to update.
The organizations that gain the most value from AI will be the ones that can turn insights into action quickly and continuously.
The Future Contact Center Manager Looks Different
Historically, managers focused on developing people. In the future, they may be responsible for developing both people and AI.
That doesn't mean leadership becomes less important. It means leadership becomes more important.
Managers will still coach empathy, judgment, communication, and problem-solving skills in human agents. But they will also need visibility into how AI systems are performing, where improvements are needed, and how customer expectations are evolving.
The role becomes less about monitoring activity and more about improving performance across an increasingly hybrid workforce.
The Bottom Line
As AI agents become more capable, organizations face a new challenge: ensuring those systems continue to improve.
The future belongs to organizations that can effectively coach both humans and AI.
That requires accurate data, high-quality transcription, automated QA, actionable insights, and systems that make continuous improvement easy.
Because in the end, the question isn't whether AI will need coaching.
It's who will be coaching it.