Concern 663 · Working draft · editorial review in progress
Customer support and call centers
Customer-support displacement risk arises when companies use AI to handle enquiries and change how many human support workers they need or what those workers do.
Understanding the concern
Some customer questions have predictable answers, such as how to reset a setting. Others involve distress, unclear records or an exception to policy. AI can answer routine questions or help an agent find information. If it handles simpler cases, the remaining human work may become more complex and demanding.
A workplace study found productivity gains when support workers received AI assistance, especially for less experienced staff. It did not show that every call-centre job should be removed. The choice is partly organizational: a company can serve more people, improve support or cut staffing. Customers also need a clear route to someone empowered to resolve problems the bot cannot.
An example
Human agents receive only difficult escalations after a bot takes the routine enquiries, but their targets are not adjusted.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
- Agent
- An AI system that can choose and carry out several steps toward a goal, using the tools and access people give it.
Evidence and its limits
This study concerns assisted workers in a particular service setting. It does not establish a universal displacement rate for call centres.
Brynjolfsson, Li and Raymond · Quarterly Journal of Economics 2025 · Workplace study
Generative AI at Work ↗ (opens in a new tab)Staggered rollout of an assistant to customer-support workers; heterogeneous productivity effects. Not an economy-wide displacement estimate.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
How should staffing, training and performance targets change when AI handles the simpler cases?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Seeing potential
Seeing potential means recognizing people's capacity to learn, contribute, and grow. It resists reducing someone to a score or to the tasks a machine can perform faster.
Use tools to support people's development. Give learners time, feedback, and real opportunities to practice. A prediction about someone's performance should not close off their future.
Ask together: How could this help people develop abilities they value?
Solidarity
Solidarity means standing with people whose lives are affected, especially when they have less power to protect their interests. It turns concern into a willingness to share responsibility.
Include affected people in decisions and support practical remedies when something goes wrong. Do not leave the cost of learning or repair entirely with those who were harmed.
Ask together: What would it mean to stand with the people carrying the greatest burden?
Dignity
Dignity means recognizing each person's worth regardless of usefulness, status, or a system's score. People deserve respect and a voice in matters that affect them.
Explain important decisions, avoid humiliating treatment, and keep room for a person's story. Do not treat a prediction as a complete account of who someone is.
Ask together: Does this use treat each person as someone whose experience matters?
Editorial ratings
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