When AI Gets Smarter, Agents Get Burnout

AI was meant to lighten the load on contact centers by automating routine tasks, speeding up resolutions, and freeing agents to focus on empathy. In reality, automation often reduces overall contact volume but increases task complexity, leaving human agents to handle only the most difficult, emotionally charged interactions. By siphoning off the easy questions, AI has inadvertently pushed agents into a perpetual “escalation” role, which many agents report is draining and stressful. It’s a classic unintended consequence: smarter customer service tech but more cognitive load on the people behind the screens. The result? Rising agent burnout and disengagement, with frontline staff feeling increasingly emotionally fatigued after back-to-back tough cases.

Burnout and frustration don’t just hurt agents; they hurt the bottom line. Contact center attrition is notoriously high, and it’s getting worse in many regions. And with an average cost to replace a single contact center agent at more than $20,000, the cumulative impact on operational budgets is substantial. Globally, benchmarks vary, but it’s not uncommon for contact centers to see attrition in the 30 percent to 45 percent range, especially in high-stress sectors. Every resignation sets off a chain reaction: understaffed shifts, higher workloads on remaining team members (which can lead to their burnout), and often a dip in service quality as new hires move through the learning curve.

Why hasn’t AI delivered the agent “relief” that was promised? Many organizations set themselves up for disappointment by skipping the hard foundational work—too many companies try to layer AI onto a broken foundation. Key systems aren’t integrated, data remains trapped in silos, and tacit frontline knowledge is never captured or fed into AI training. In other words, the basic plumbing of a smart service system is not put into place. When critical customer context is scattered across five applications that don’t talk to each other, or tacit knowledge lives only in seasoned agents’ heads, even the best AI will struggle.

Another critical factor is how AI tools are designed and embedded into agent workflows. The design of AI interfaces determines whether they truly help agents or just get in the way. When AI pops up in a clunky manner—say, interrupting an agent with noncontextual suggestions or forcing extra clicks—it erodes the workflow and even the empathy that an agent can show. Embedding explainability into AI prompts and workflows is key. For example, if an AI assistant suggests a response or a next best action, it should briefly note why. When agents can see the logic, they’re more likely to follow the advice, and over time they’ll see AI as a partner rather than a nuisance.

For managers, using the right metrics can ensure that AI actually reduces cognitive load instead of adding to it. Contact center KPIs often ignore the mental effort required of agents. Forward-thinking teams are starting to track metrics such as agent reaction time to AI prompts, the frequency of task switching or screen toggles during a case, and self-reported fatigue levels at the end of a shift. Spikes in these metrics can indicate that agents are overwhelmed—perhaps the AI system is throwing too many suggestions at once, or maybe the agent is juggling too many apps. By monitoring such signals, customer service leaders can tune the AI interface and workflows to minimize disruptions.

Recently, after rewatching Ridley Scott’s Alien with my son, I was reminded of the crew aboard the Nostromo and their uneasy relationship with “Mother,” the ship’s AI system. Designed to guide and protect, Mother ultimately prioritizes corporate directives over crew safety. In one chilling moment, Ripley pleads: “Mother! I’ve turned the cooling unit back on. Mother!” only to be met with the cold reply: “The ship will automatically destruct in T-minus 5 minutes.” This moment captures the emotional dissonance agents feel when AI systems, meant to support them, instead act with opaque logic and little empathy.

What does this all mean? Treat AI as a crew member, not the commander. Build systems that empower agents to make judgment calls, reduce cognitive load, and maintain emotional resilience. Get your house in order, choose the right use cases, and push vendors for pragmatic road maps. Organizations should restructure customer service operations to include AI talent as team members, roles that mirror human service functions, from onboarding and coaching to real-time supervision and optimization. This shift underscores a critical mindset change: AI is not a plug-and-play solution but a dynamic colleague requiring enablement, governance, and accountability. 

Riccardo Pasto is a principal analyst at Forrester Research.

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