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AI is likely to automate some routine call-center work, but current evidence does not show that it will replace all call center agents. The clearest pressure is on repetitive requests that can be handled by standard rules. Customer service employment is projected to decline in the United States, but that forecast does not attribute the entire decline to AI.
Which call-center tasks are most exposed to AI?
Tasks are more exposed when they are repetitive, follow a predictable process, and need little individual judgment. The U.S. Bureau of Labor Statistics (BLS) says advances in speech and language processing and AI allow computers to handle customer interactions more often. A World Bank contact-center case study gives password resets and profile updates as examples of basic tasks already being automated. It also describes automated call summaries as a way to make agents’ work more efficient.
The World Bank uses three service levels to illustrate how work can differ within a contact center. These levels come from its case study; they are not a universal classification for every employer.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problems- Level 1: basic service. Routine requests such as password resets and profile updates are the clearest candidates for automation.
- Level 2: specialized subject-matter work. Cases requiring specific product or service knowledge involve more complexity than basic service.
- Level 3: higher-trust work. The case study includes fraud prevention, executive relations, and product specialists in this category.
More complex or high-trust work may be less straightforward to automate, but it is not immune to AI’s effects. AI may assist an agent rather than replace the interaction—for example, by preparing a call summary—so task automation and job replacement are not the same thing.
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What do the job and AI-exposure numbers actually show?
These figures measure different things. A projected change in an occupation, a company’s staffing plan, a count of exposed workers, observed employment trends, and a workplace productivity result cannot be treated as interchangeable estimates of jobs AI will eliminate.
| Evidence | Reported result | What it does—and does not—establish |
|---|---|---|
| U.S. occupational projection, BLS, 2026 | Customer service representative employment is projected to fall 5.5 percent, or 153,700 jobs, from 2024 to 2034. | BLS connects the decline to limited demand as AI adoption and productivity gains expand. It is a forecast for the occupation, not a count of jobs AI alone will eliminate. |
| Contact-center case study, World Bank, 2025 report | Companies in the case-study context planned reductions of up to 10 percent in Level 1 contact-center jobs in 2025. | This reports plans, not independently verified realized cuts or a representative industry-wide result. |
| Global occupational exposure, ILO, 20 May 2025 | One in four workers globally are in occupations with some generative-AI exposure. | Exposure does not mean every exposed job will be automated. The ILO says most jobs are more likely to be transformed than made redundant because human input remains necessary. |
| Canadian employment trends, Statistics Canada, 2026 | From November 2022 through December 2025, employment growth was generally similar across groups with different potential AI exposure and complementarity. | The analysis found no clear persistent decline in the high-exposure, low-complementarity group. It covers broad occupational groups in Canada, not call-center jobs alone. |
| Workplace study, Brynjolfsson, Li, and Raymond, 2025 | A study followed 5,172 customer-support agents at one business-process-software firm using an AI assistant and found productivity gains. | The study was not designed to establish economy-wide employment or wage effects. |
Taken together, the evidence points to pressure on routine work and the possibility of workforce changes, not a reliable estimate of how many agents AI will replace. In particular, a productivity gain at one company cannot by itself show whether the employer, the industry, or the overall economy will need fewer workers.
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What can call-center agents do as the work changes?
For workers, the practical distinction is between handling only repeatable requests and building capability in work that needs context, specialized knowledge, or sound judgment. The World Bank case study discusses upskilling as part of the sector’s adjustment, but it does not establish a particular course or credential as a route to job security.
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Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →- Build subject-matter knowledge. Deeper familiarity with a product, service, or customer problem can prepare you for work beyond basic scripted requests.
- Develop judgment and communication. Complex cases and interactions where trust matters can call for interpretation and human responsiveness, even when AI supports the process.
- Learn to work with AI-assisted processes. If your workplace uses tools for tasks such as call summaries, understanding how to check and apply their output can help you adapt to a changed workflow.
- Track what is changing at your employer. Ask which tasks are being automated, which roles are expanding, and what training is available. A general employment projection cannot tell you what will happen to a particular team.
These steps may improve adaptability, but they cannot guarantee that a specific role will remain unchanged or that an employer will preserve its staffing levels.
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