Will AI Replace Tellers?
A study in contrasts: the GPT-based study rates over half of teller tasks as AI-exposed, yet Anthropic's real-usage data shows almost none — branch transactions were automated by ATMs and apps long before AI arrived. What remains is advice, trust and problem-solving.
The exposure evidence — three sources, side by side
There is no official "AI risk score" for any job, and any site that gives you one number without a source is making it up. Here are the three serious research measures that exist for this occupation, each with what it actually counts:
| Research source | Exposure figure | What it actually measures |
|---|---|---|
| Anthropic Economic Index — observed exposure | 2% of tasks | Share of this occupation's tasks where AI use was actually observed in real Claude conversation data (Anthropic, "Labor market impacts of AI", March 2026 dataset). This is measured use, not a prediction. |
| GPTs are GPTs (OpenAI / University of Pennsylvania) — GPT-4 rating, β measure | 54% of tasks | Share of tasks rated exposed to large language models, counting direct exposure in full and assisted exposure at half weight (the paper's β measure). Human expert raters scored the same occupation at 33%. |
| Microsoft "Working with AI" — AI applicability score | 0.25 / 1.00 | A 0–1 score built from 200,000 real Bing Copilot conversations, weighting how often AI was used for this occupation's tasks and how well it completed them. It is a score, not a percentage; the median occupation scores about 0.14. |
The three sources measure different things — observed use, rated capability and Copilot-based applicability — so they rarely agree exactly. Where they disagree, that disagreement itself is information: it usually means a job is being changed by specialist tools, or in ways general chatbots do not show. All three agree on the framing: exposure is not replacement.
The work itself: what AI can help with, and what stays human
These are this occupation's real tasks, from the US Department of Labor's O*NET database (version 30.0). The split is our editorial analysis — the tasks on the left are the routine, screen-based ones current AI tools demonstrably assist with; the ones on the right involve physical presence, licensed responsibility, or judgement about people.
Where AI can already help
- Entering customer transactions and issuing computer-generated receipts
- Counting, sorting and preparing cash for deposit or shipment
- Balancing cash drawers and calculating daily transactions
- Processing routine deposits, withdrawals and payments
What stays human
- Verifying signatures, endorsements and the legality of documents — fraud judgement
- Resolving problems and discrepancies in customers' accounts
- Explaining and recommending bank products matched to a customer's situation
- Carrying out special services for customers who need a person, not an app
Pay, outlook and entry requirements (BLS)
| Median annual pay, 2024 | $39,340 |
|---|---|
| Employment change, 2024–34 | BLS projects employment to shrink by 13% from 2024 to 2034 (347,400 → 302,500 jobs, a change of -44,900). |
| Typical entry-level education | High school diploma or equivalent |
Basis: BLS Occupational Outlook Handbook, 2024–25 edition profile (2024 pay, 2024–34 outlook). A median is the midpoint of what all workers in the occupation earn — it is not a starting wage, and it is not a forecast.
What to learn next
A roadmap only helps if it starts from the skills this job already builds. These three moves do — each is chosen because the destination work is growing, harder to automate, or carries a credential software cannot hold:
- Personal banking and relationship banking → a stronger position in the same field. Branches increasingly exist for advice and complex needs. Tellers who move into account opening, lending support and small-business banking do the work apps cannot.
- Fraud and compliance awareness → a stronger position in the same field. As routine transactions automate, the human value shifts to spotting fraud, scams and money-laundering red flags — a growing, regulated responsibility.
- Financial services qualifications → Accountants and Auditors. Banking experience plus bookkeeping or accounting credentials opens a much larger occupation projected to grow 5% by 2034. See: Accountants and Auditors — projected +5% 2024–34 (BLS).
AI tools in this job today
Mobile banking, smart ATMs and AI document checks handle the transaction layer. Inside branches, the tools to learn are the CRM and account-opening systems your bank uses — the work is moving from processing transactions to advising the customers who still walk in.
Frequently asked questions
Will AI replace tellers?
Not on any schedule the research supports. The strongest exposure figure for this occupation is high — about 54% of its tasks are within AI's reach — but exposure measures tasks, not whole jobs. BLS projects this occupation shrinking 13% between 2024 and 2034, and Anthropic's own labour-market research found no systematic rise in unemployment among workers in highly exposed occupations. The realistic outcome is a changed job, not an abolished one.
Which parts of a job are most exposed?
The tasks listed under “Where AI can already help” above — mostly the routine, text- and data-handling parts of the role. They come from this occupation's real O*NET task list, and they are the parts to stop competing on and start supervising.
How much do tellers earn?
The BLS median annual pay was $39,340 in 2024 — half earned more, half earned less. It is a national median, not a starting salary or a promise; pay varies widely by state, employer and experience.
What should I learn next?
The roadmap above is built from this occupation's actual skills: Personal banking and relationship banking; Fraud and compliance awareness; Financial services qualifications. Each points at work that is either growing, harder to automate, or both — with the BLS outlook for the target occupation stated next to it.
Where do these numbers come from?
Exposure figures come from three published research datasets — Anthropic's Economic Index, the OpenAI/University of Pennsylvania GPTs-are-GPTs study, and Microsoft's Working with AI paper. Pay, outlook and education come from the US Bureau of Labor Statistics (Employment Projections and Occupational Outlook Handbook, 2024–34 cycle). Every figure on this page is dated and sourced in the Sources section below, and our full method is on the Methodology page.
Sources
- Anthropic, "Labor market impacts of AI" (Massenkoff & McCrory, March 2026) and the Economic Index job-exposure dataset (observed exposure by occupation). Accessed 11 November 2026.
- Eloundou, Manning, Mishkin & Rock, "GPTs are GPTs" (OpenAI / University of Pennsylvania, 2023), occupation-level exposure data (β measure), official dataset. Accessed 11 November 2026.
- Microsoft Research, "Working with AI: Measuring the Applicability of Generative AI to Occupations" (2025), AI applicability scores (CC BY 4.0). Accessed 11 November 2026.
- US Bureau of Labor Statistics, Employment Projections Table 1.2 and Occupational Outlook Handbook (2024–34 cycle): pay, outlook, education. Public domain. Accessed 11 November 2026.
- US Department of Labor, O*NET Database 30.0 (CC BY 4.0): occupation tasks and skills. Accessed 11 November 2026.
Data checked November 2026. Figures are national US figures. Exposure scores describe tasks, not people, and none of the sources above predicts that this occupation will be eliminated.