The end of productivity theater: What performance looks like in the AI era

The question leaders need to ask is not who appears busiest, but who is using their time, expertise and technology most effectively to move the business forward.

This represents one of the biggest workplace transformations organizations have faced in years.

Many customer experience leaders are among the first to navigate this shift through contact center workforce transformations. AI is no longer simply helping agents work faster; it is becoming an active participant in delivering customer experiences.

As work is shared across AI and people, traditional measures of productivity aren’t painting the full picture, creating an opportunity for leaders to rethink what high performance really looks like.

What does a high performer look like in the AI era?

AI is changing what it means to be a “good worker.” High-performing employees will certainly need to become comfortable using AI, but speeding up their work through AI is not the core differentiator.

As AI automates more routine work, employee roles evolve. People can use newly available capacity to increasingly apply judgment and empathy where technology falls short, solving more meaningful problems, and delivering greater value to customers.

In customer experience, this shift is already underway in many organizations. Human agents are spending less time answering routine questions that’s handled by AI and more time dealing with the complex or sensitive interactions where human expertise matters most. As a result, high performance is becoming more about impact than activity.

Customer experience is ground zero for hybrid workforce transformations.

Customer experience provides one of the clearest examples of this transformation because AI is already changing the nature of frontline work.

AI has evolved beyond just answering routine policy questions or checking order status. Increasingly, instead of just replying with information, AI is capable of resolving a customer’s problem—analyzing the issue and taking the necessary actions to fix it completely, without ever needing to hand them off to a human agent.

Rather than replacing human agents, AI is creating a hybrid workforce where AI and people each contribute different strengths. AI handles those routine, high-volume interactions while human agents focus on situations that require empathy, judgment, and complex problem-solving. Work is shared across both, with AI able to seamlessly hand-off a customer interaction with full context to a human agent, and assist that agent in real time. Digital and human labor each contribute where they create the greatest value.

Example of human-AI collaboration in customer experience.

Healthcare provides a good illustration of this shift.

In healthcare, AI can automate common needs like appointment scheduling, prescription refill requests, benefits questions and post-discharge follow-up. As a result, health system staff can spend less time on administrative tasks and more time supporting patients through their care journey.

As AI manages more of the routine work, the role of the agent will naturally shift. We may see agents stepping into a care guide role, using empathy, judgment, and expertise to help patients navigate complex and emotionally sensitive situations.

This is the hybrid CX workforce in action: human-AI collaboration in customer service where AI resolves the routine while people are elevated to focus on the moments where the human touch matters most.

In these hybrid workforces, AI and humans each contribute what they do best, creating better outcomes for customers and greater value for the business.

How is AI changing traditional contact center metrics?

As work evolves, leadership practices need to evolve with it.

Contact centers have relied on metrics such as average handle time (AHT) or number of calls handled to evaluate performance. Those measures remain useful operational indicators but may not reflect the broader impact in the AI contact center. And they no longer provide an accurate view of employee contribution.

Consider an agent who now spends most of their day resolving complex escalations because AI has already handled hundreds of routine inquiries. That agent may spend longer on each interaction and complete fewer conversations overall. Yet they may create significantly greater value by preserving customer relationships, resolving difficult issues on the first contact to reduce customer effort, and preventing future problems.

Traditional productivity metrics don’t always capture that impact.

As AI reshapes workforce productivity, leaders wondering how to measure hybrid CX performance should complement traditional efficiency metrics with outcome-based measures such as first contact resolution, resolution quality, and resolution rates.

What CX organizations are learning about the hybrid CX workforce.

Talkdesk customer InfoPay illustrates this shift. The company began by applying AI to high-volume, repeatable interactions, measuring success first through containment before expanding to additional use cases.

As AI integrated into day-to-day operations, taking on responsibility for routine customer requests like refunds, InfoPay also evolved how it measured performance. AI was evaluated using many of the same customer and operational outcomes as the human workforce, including containment, customer satisfaction, first-contact resolution, and speed.

The difference lay in how performance improved over time. AI performance was continuously monitored, refined, and optimized while human employees were coached and developed. The capacity created by AI enabled InfoPay to pivot the role of some of their contact center team members into that of an analyst. These members now take on very strategic work, doing deep research into customer behavior and usage insights which brings incredible value to the business.

Jessica Gupta, COO of InfoPay, has spoken about the importance of approaching AI adoption in the contact center intentionally. Leaders need to decide which work AI should own, which work should remain with people, how AI will be trained, what success looks like and how accountability and performance will be measured. With that operating model, organizations can redesign work in ways that strengthen both the customer experience and employee growth.

This shift also changes the technology organizations need. AI delivers its greatest value when it can work across customer journeys rather than isolated interactions, coordinating work between AI agents, human employees, and enterprise systems.

That’s the thinking behind Talkdesk Customer Experience Automation (CXA). Rather than simply helping organizations respond to customers faster, CXA orchestrates work across AI, people, and systems to resolve customer needs seamlessly from beginning to end.

Replace busyness with impact.

The AI era gives organizations an opportunity to move beyond productivity theater.
The organizations that benefit most won’t use AI simply to increase activity or expect employees to do more with less. They’ll use it to redesign work, automate repetitive tasks, and create more capacity for the uniquely human capabilities that technology cannot replace.

And the leaders who succeed won’t be the ones who reward the busiest employees. They’ll be the ones who cultivate and recognize the people creating the greatest impact.

Customer experience is ground zero for this shift. As AI increasingly handles routine interactions, human agents become more valuable because of their judgment, empathy, and ability to solve complex problems. Organizations that continue measuring activity will miss where value is being created.

Those that redesign work and redefine performance around the strengths of people and AI will be better positioned to accelerate the business with improved customer experience and strengthened employee engagement.