Working Alongside AI: How Humans and Machines Share the Workload

In many call centers, AI is no longer a future idea. It's already part of the team. AI systems answer some calls, route others, suggest answers during conversations, write notes, and analyze quality. People, meanwhile, handle complex calls, oversee automated conversations, and make the judgments machines can't.

This raises practical questions. Which work should go to AI, and which to people? How do they hand work to each other? How do people learn to trust AI the right amount, neither too much nor too little? And how can work be shared in a way that's good for customers and fair to staff? This article explores how humans and AI share the workload in modern call centers, and what makes the partnership work.

Thinking About Tasks, Not Just Calls

A useful starting point is to think about tasks rather than whole calls or jobs. A single call involves many tasks: greeting the caller, verifying identity, understanding the problem, finding information, making a decision, explaining the outcome, and recording what happened.

Some of these tasks suit AI well. Others suit people better. Sharing the workload effectively means matching each task to whoever does it best.

What AI Tends to Do Well

AI is generally strong at tasks that are:

• Repetitive and predictable, such as answering common questions or confirming appointments.
• Information-heavy, such as searching large knowledge bases quickly.
• High in volume, such as handling many simple calls at once.
• Time-sensitive around the clock, such as answering calls outside business hours.
• Administrative, such as transcribing calls and writing summaries.
• Pattern-based, such as spotting trends across thousands of calls.
• Consistency-dependent, such as delivering required statements exactly the same way every time.

What People Tend to Do Well

People are generally stronger at tasks that require:

• Empathy and emotional understanding, especially with upset, worried, or grieving callers.
• Judgment, such as deciding when to make an exception or how to balance competing needs.
• Handling the unexpected, such as unusual problems that don't fit standard answers.
• Building trust, particularly for significant decisions or long-term relationships.
• Authority and accountability, such as approving refunds or resolving serious complaints.
• Reading between the lines, noticing what a caller isn't saying, or recognizing signs of vulnerability.
• Ethical decisions, where fairness and context matter.

Ways of Dividing the Work

Call centers divide work between AI and people in several ways, often combining more than one.

By type of call

Simple, predictable call types go to AI, while complex or sensitive ones go straight to people. For example, appointment confirmations might be handled automatically, while complaints are routed to experienced staff.

By stage of the call

AI handles the early stages, such as greeting, identifying the caller, and understanding their need, then hands the call to a person for the stages that need human skills, such as resolving a complex issue or completing a significant decision.

By complexity in the moment

AI begins the call and continues as long as it can handle the conversation well. If the conversation becomes complex, emotional, or uncertain, it hands over to a person.

By time

AI handles calls during off-peak hours or overnight, while people handle calls during the busiest or most important times, or vice versa depending on the business.

AI as an assistant

People handle the call, while AI works in the background, transcribing, suggesting answers, retrieving information, and writing notes.

People as supervisors

AI handles calls, while people monitor them in real time and step in when needed.

What This Looks Like Day to Day

For people working alongside AI, a typical shift might include:

• Taking calls that AI has handed over, often with a summary of what's already been discussed.
• Using AI suggestions during calls, checking them against their own knowledge before sharing them with customers.
• Reviewing and correcting AI-written notes after calls.
• Monitoring automated conversations, stepping in when a caller needs a person.
• Flagging AI mistakes, so systems can be improved.
• Handling the complex and emotional calls that AI isn't suited to.

This can make work more varied and free people from repetitive tasks. It can also make it more demanding, since the calls that reach people are often the hardest ones.

Trusting AI the Right Amount

One of the most important skills in working with AI is knowing how much to trust it.

The risk of trusting too much

People can come to rely on automated systems so heavily that they stop questioning them, a tendency researchers call automation bias. In a call center, this might mean reading out an AI-suggested answer without checking whether it fits the situation, or accepting an AI-written summary without noticing a mistake. When AI is wrong, over-reliance means the error reaches the customer.

The risk of trusting too little

On the other hand, if people don't trust AI tools at all, they may ignore helpful suggestions, duplicate work AI has already done, or avoid using tools that could make their jobs easier.

Finding the balance

The aim is calibrated trust: using AI where it's reliable, while staying alert to its limits. This comes from:

• Understanding how the AI works and where it tends to make mistakes.
• Treating AI outputs as suggestions, not final answers.
• Checking important details, such as numbers, names, and policy information.
• Reporting errors so tools can be improved.
• Building experience over time, learning when AI can be relied on and when it can't.

The Human Role in Improving AI

AI systems improve through human feedback. People working alongside AI are often the first to notice when it misunderstands callers, gives wrong answers, or handles conversations awkwardly. Their feedback, whether through formal reporting, corrections, or conversations with AI teams, helps make the systems better over time.

This creates a valuable loop: people help AI improve, and better AI helps people do their jobs more effectively. Businesses that make it easy for staff to give feedback on AI, and that act on it, tend to see better results.

Planning the Workload

Sharing work between people and AI changes how call centers plan.

Forecasting needs to predict not just total call volume, but how many calls AI can handle and how many will need people.

Staffing needs to account for people handling escalations, supervising automated calls, and covering for times when AI systems have problems.

Contingency planning is essential. If an AI system fails or behaves unexpectedly, people need to be able to take over quickly.

Gradual change helps. Expanding what AI handles step by step, while monitoring results, reduces the risk of sudden problems for customers and staff.

Keeping the Work Fair and Sustainable

When AI takes over easier calls, people may spend their entire shift on complex, emotional, or difficult conversations. Without adjustment, this can increase stress and burnout.

Fair workload design includes:

• Adjusting targets, recognizing that calls reaching people are harder and may take longer.
• Building in recovery time, especially after difficult calls.
• Varying the work, mixing calls with other tasks such as reviewing AI conversations, training, or projects.
• Providing extra support, including coaching and wellbeing resources.
• Recognizing the value of the complex work people do.

Measuring Performance in a Shared Workload

Traditional metrics, such as calls handled per hour, become less meaningful when AI handles the simpler calls. Comparing people's productivity with AI's, or with how it looked before automation, can be misleading and unfair.

More useful measures often include:

• Resolution and quality of the calls people handle.
• Customer satisfaction, particularly after escalated calls.
• The quality of hand-offs, both from AI to people and back.
• Accuracy of AI-assisted work, such as how often AI suggestions are checked and corrected.
• Overall outcomes, looking at the combined performance of people and AI together.

Accountability

When AI and people share a call, it's important to be clear about responsibility. If an AI system gives a customer wrong information, or a person relies on an AI suggestion that turns out to be incorrect, who is accountable?

In general, the business remains responsible for every call, whether handled by AI, people, or both. Clear policies help, such as defining which decisions AI can make, which require human approval, and how errors are reviewed and corrected. Staff should feel supported, not blamed, when they catch and correct AI mistakes.

Keeping Customers Informed

Customers benefit from knowing who they're dealing with. Being clear when a caller is speaking with AI, explaining when a call is being passed to a person, and making sure customers don't have to repeat themselves all help the shared workload feel seamless from the customer's side.

Making the Partnership Work

Call centers that combine people and AI successfully tend to share some common practices:

• Involve staff in deciding how work is divided and how tools are designed.
• Train people to use AI tools well, including recognizing their limits.
• Design smooth hand-offs, with context passed in both directions.
• Encourage feedback on AI performance, and act on it.
• Adjust workloads and measures to reflect the new mix of work.
• Keep people in control of important decisions.
• Review the balance regularly, as AI capabilities and customer needs change.

A Team of People and Machines

The future of call center work isn't people or AI. It's people and AI, each doing what they do best. AI brings speed, scale, and consistency to routine work. People bring empathy, judgment, and the ability to handle the unexpected. When work is divided thoughtfully, hand-offs are smooth, trust is well calibrated, and workloads stay fair, the partnership can make calls better for customers and work better for the people doing it.

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