Artificial intelligence is transforming the workforce at an unprecedented pace. While AI creates new opportunities and enhances productivity in many fields, it also poses a significant threat to millions of workers whose jobs involve routine, repetitive tasks that AI can perform more efficiently.

This report analyzes 784 occupations to identify the 50 jobs most vulnerable to AI automation. The analysis reveals that office and administrative support roles dominate the high-risk category, with exposure levels ranging from 77.67% to 96.25%.

The data shows that these high-risk occupations have nearly 2.9× the automation exposure of the average job. Telemarketers face the highest exposure—meaning about 96 out of every 100 tasks could potentially be automated.

However, the story isn't entirely bleak: some high-exposure occupations are actually growing because AI augments worker productivity rather than replacing workers entirely. This report provides workers, policymakers, and educators with actionable insights to navigate the AI transition, including which occupations are most at risk, what makes them vulnerable, and what workers can do to adapt.

What Does "Exposure" Mean?

"Exposure to AI Automation" is the percentage of tasks within an occupation that could potentially be automated using current or near-future AI technology. For example, 90% exposure means 90 out of every 100 tasks could be automated.

96.25%
of Telemarketers' tasks can be done by AI

86.3%
average share of tasks AI can do in the top 50 occupations

2.9×
higher automation exposure than the average job

77.67%
of tasks AI can do in the "lowest-risk" job in the top 50

54%
of high-risk jobs that are office/admin roles

6.4%
share of all occupations in the extreme high-risk group

Key Takeaways

  • 96.25% of Telemarketers' tasks can be done by AI—the highest of any job analyzed.
  • 86.3% of tasks in the top 50 high-risk jobs can be done by AI.
  • These 50 jobs are nearly 2.9× more automatable than the average job (86.3% vs. 29.84% exposure).
  • Even the "lowest-risk" job in the top 50 still has 77.67% of its tasks automatable.
  • 54% of the top 50 high-risk jobs are office and administrative roles.
  • Only 6.4% of all occupations fall into this extreme high-risk group.

Expert Insights: Industry Leaders on AI Automation Risk

Industry experts and business leaders share their perspectives on which jobs are most vulnerable to AI automation and when displacement is likely to occur.

"The next wave of AI disruption won't target people — it will target patterns. Jobs built on repetition, compliance, and predictable logic are standing on the fault line. Think payroll clerks, data entry keyers, medical records specialists, credit authorizers, and insurance underwriters — roles where accuracy and volume once equaled value. Within 2–5 years, automation and large language models will absorb these transactional tasks faster than most leaders expect."
Dr. Amanda Main
Chief Innovation Officer, University of Central Florida College of Business
Industrial-Organizational Psychologist

"Accounts clerks have the most vulnerable jobs because their job involves the maximum repetition, such as invoice processing and payment scheduling. Even today, most of their jobs are AI-automated. In 2 to 5 years, I see almost all their duties entirely taken over by AI. In 5 to 10 years, I expect human oversight to be reduced to exception handling alone."
Ravi Parikh
CFO and Managing Director, Parikh Financial
Economics and Finance graduate, University of Texas at Austin

"Accounts Payable Clerks spend as much as 70% of their time on invoice matching, 3-way verification, and data entry, which makes the job susceptible to software bots and large language models that can process documents and execute transactions faster and with almost zero error. The financial efficiency gain is too large to be ignored. These sorts of roles are facing automation in the two to five year timeline."
Jesse Singh
Founder, Maadho
Former Investment Banker specializing in logistics

"Data Entry Clerks and Junior SEO Content Writers will be immediately impacted because AI models are able to scrape, format and enter data much faster than humans and can generate basic SEO-driven content and marketing copy from a few prompts, a task that previously required an entry level writer. In fact, these types of tasks are not only at risk of being replaced but are already being replaced today. Payroll Clerks and Insurance Underwriters will also experience significant disruption within the next 2-5 years as both roles require verification of information against established rule sets, a function which is perfectly suited to AI processing."
Michael Pedrotti
Co-founder, GhostCap
Over a decade in SEO and digital marketing

These expert perspectives align with the data analysis: routine, repetitive, and rule-based occupations face the highest automation risk. The consensus timeline suggests significant displacement within 2-5 years for the most vulnerable roles, with complete automation of routine tasks occurring within 5-10 years.

Interactive Tool: Explore High-Risk and Low-Risk Occupations

See how at-risk your job is in seconds.

Search 784 occupations by exposure level, job category, or risk type. Filter and sort to find specific occupations or explore patterns across industries.









Rank Occupation Category AI Exposure

Deep Dive: What These Numbers Mean

Telemarketers at 96.25% (Highest Exposure)

Telemarketers face the highest AI automation risk—about 96 out of every 100 tasks could be automated. This is 3.2× higher than the average occupation (29.84% exposure).

The 70% "Very High Risk" Line

Occupations with exposure above 70% are considered "very high risk" and are most vulnerable to displacement as AI technology advances. All 50 occupations in this report exceed this threshold, with exposure levels ranging from 77.67% to 96.25%. Even the lowest exposure (77.67%) is still 7.67 percentage points above the critical risk line, indicating uniform vulnerability across this group.

The Risk Gap vs All Occupations

The top 50 occupations average 86.3% exposure—nearly 2.9× higher than all 784 occupations analyzed (29.84% average). These 50 jobs represent just 6.4% of all occupations but account for a disproportionate share of extreme risk. The range of exposure (77.67% to 96.25%) spans 18.58 percentage points, suggesting these occupations share similar vulnerability characteristics—highly structured, rule-based, and data-intensive tasks that AI systems can increasingly perform more efficiently than humans.

The Scale of Risk: Putting These Numbers in Context

Compared to All Occupations
2.9×

Average exposure: 86.3% vs. 29.84% across all 784 occupations. This represents a massive concentration of risk in a relatively small number of job types.

Above Critical Threshold
100%

All 50 occupations exceed the 70% "very high risk" threshold, with exposure levels ranging from 77.67% to 96.25%.

Range Compression
18.58

The gap between highest (96.25%) and lowest (77.67%) is 18.58 percentage points.

High-Risk vs. Overall Workforce

Compared to the broader workforce, these 50 jobs occupy a distinctly higher risk band.

Metric Top 50 High-Risk All 784 Occupations Difference
Average Exposure 86.3% 29.84% +56.5 percentage points
Median Exposure 85.0% 23.52% +61.5 percentage points
Minimum Exposure 77.67% 0.0% +77.67 percentage points
Maximum Exposure 96.25% 96.25% Same (this list includes the maximum)
Occupations Above 70% 50 (100%) 77 (9.8%) 10.2× higher concentration

Risk by Occupational Category

The high-risk occupations are not evenly distributed across all job categories. Some sectors are dramatically overrepresented, revealing patterns in which types of work are most vulnerable to AI automation.

Share of Top 50 High-Risk Jobs by Occupational Category

Administrative workflows are the easiest to automate because they involve structured data and rule-based decisions. Office and administrative support roles account for 54% of the top 50 high-risk jobs (27 out of 50), and just three categories—Office/Admin, Business/Financial, and Computer/Math—make up 82% of all high-risk occupations. Nearly all categories in this group have average exposure above 80%, suggesting that once an occupation is in this group, risk is uniformly high rather than scattered.

What Makes These Occupations Vulnerable?

Analysis of the top 50 high-risk occupations reveals common characteristics that make them susceptible to AI automation. Understanding these factors can help workers identify their own risk level and plan for transitions.

1. Routine, Repetitive Tasks

Jobs involving the same tasks performed repeatedly with little variation are highly automatable. Examples: Data entry, order processing, bookkeeping. AI excels at consistent, pattern-based work.

2. Rule-Based Decision Making

Occupations that follow clear rules, procedures, or algorithms are vulnerable. Examples: Insurance underwriting, credit checking, tax preparation. AI can apply rules more consistently than humans.

3. Data Processing Focus

Jobs primarily involving collecting, organizing, or processing structured data are at high risk. Examples: Medical records, statistical analysis, database management. AI systems are designed for data manipulation.

4. Limited Human Interaction

Roles requiring minimal interpersonal communication or emotional intelligence are more automatable. Examples: Telemarketing (ironically), switchboard operations, correspondence clerks. AI can handle scripted interactions.

5. Information Retrieval

Jobs that primarily involve finding, organizing, or presenting existing information are vulnerable. Examples: Travel agents, title searchers, proofreaders. AI search and retrieval capabilities are rapidly improving.

6. Structured Work Environment

Occupations with standardized processes, clear inputs/outputs, and measurable outcomes are easier to automate. Examples: Payroll processing, billing, order fulfillment. Predictable workflows enable AI implementation.

The Augmentation Paradox: When High Exposure Doesn't Mean Job Loss

Exposure vs. Job Growth

High exposure doesn't always mean job loss—some high-exposure roles are growing due to AI augmentation. Hover over points to see details.

Augmentation (High Exposure, Growing)

Displacement (High Exposure, Declining)

Mixed (Moderate Growth)

High exposure can mean augmentation when roles require strategic thinking, creativity, or human interaction—making workers more productive rather than replaceable. For example, Data Scientists (82.28% exposure) are projected to grow 33.5% over the next decade, and Software Developers (52.11% exposure) are growing 15.8%. The key distinction is whether a job is built on strategic judgment and human interaction (augmentation likely) or pure routine execution (displacement likely).

What Workers in High-Risk Occupations Should Do

High exposure doesn't necessarily mean immediate job loss, but it does signal the need for proactive career planning. Here are evidence-based recommendations:

Your Career Transition Roadmap

1
Now (0-2 years)
Assess your role + build AI fluency

2
Next (2-5 years)
Develop transferable skills + explore adjacent roles

3
Future (5+ years)
Complete transitions + move into lower-risk / augmented roles

Action Steps

  • Build AI Fluency: Learn to work alongside AI tools rather than competing with them.
  • Develop Transferable Skills: Focus on critical thinking, problem-solving, emotional intelligence, creativity, and complex communication.
  • Explore Adjacent Roles: Many high-risk occupations have related roles with lower exposure (e.g., data entry clerks → data analysts).

Long-Term Strategy

  • Pursue Targeted Education: Consider certificates or degrees in fields with lower AI exposure (healthcare, skilled trades, certain technology roles).
  • Plan for Transitions: Start researching and preparing for career changes now, before displacement occurs.
  • Build Financial Resilience: Prioritize building emergency savings and reducing debt to provide a buffer during potential career transitions.

Timeline and Urgency: When Will Displacement Occur?

The risk is already here; the disruption will unfold over the next decade.

Now
AI can already handle 73-96% of tasks in high-risk occupations

2-5 Years
Workplace adoption ramp-up as organizations integrate AI tools

5-10 Years
Likely peak of job displacement in highest-risk occupations

The "Last Mile Problem" and the Window for Adaptation

While AI technology capable of automating these tasks already exists, actual workplace adoption typically lags behind technological capability by 2-5 years.

This creates a critical window for workers to adapt—but only if they act proactively.

Research from the Brookings Institution and NBER studies shows that organizational inertia, regulatory barriers, and implementation costs slow AI adoption.

However, once adoption begins in an industry, it tends to accelerate rapidly.

Technology Readiness
Now

AI systems capable of performing 73-96% of tasks in these occupations already exist.

The technology is ready.

Workplace Adoption
2-5 Years

Organizational adoption typically lags technology by 2-5 years.

This creates a window for workers to adapt.

Peak Displacement
5-10 Years

Once adoption accelerates, peak displacement is likely within 5-10 years.

This timeline applies to the highest-risk occupations.

Early Warning Signs

Workers should watch for these indicators that displacement may be accelerating in their field:

  • Job Postings Decline: Fewer openings in your occupation, especially entry-level positions
  • Wage Stagnation: Salaries not keeping pace with inflation despite high demand
  • Employer Investment in Automation: Companies in your industry investing heavily in AI tools
  • Job Descriptions Change: New roles requiring "AI fluency" or "working with AI tools"
  • Industry Consolidation: Mergers and acquisitions reducing total employment

Policy Implications: What Needs to Happen

The Role of Policy in Managing AI Displacement

The concentration of risk in these 50 occupations—representing millions of workers—requires proactive policy intervention to manage the transition and prevent widespread economic disruption.

Reskilling Programs

Government-funded training programs targeting high-risk occupations can help workers transition to growing fields. Programs should be accessible, affordable, and aligned with labor market needs.

Employer Incentives

Tax credits and grants for employers who invest in upskilling existing workers rather than replacing them. This encourages augmentation over pure automation.

Safety Net Expansion

Enhanced unemployment benefits, healthcare coverage during transitions, and wage insurance programs can provide security for displaced workers during career transitions.

Regional Workforce Development

Targeted investments in regions with high concentrations of at-risk workers, focusing on creating opportunities in low-exposure fields like healthcare and skilled trades.

Education System Reform

K-12 and higher education must prioritize durable human skills (adaptability, critical thinking, creativity) alongside technical skills to prepare students for an AI-augmented economy.

Labor Market Transparency

Public reporting on AI adoption rates, displacement numbers, and transition outcomes can help workers make informed decisions and hold policymakers accountable.

Bottom Line

AI automation risk is not evenly distributed. A small cluster of administrative, clerical, and data-processing occupations faces dramatically higher exposure—up to nearly 2.9× the average job.

While some roles may be augmented rather than displaced, millions of workers will need proactive adaptation, upskilling, or career transitions within the next 5–10 years.

The data is clear: routine, rule-based work is most vulnerable. The solution is equally clear: workers, employers, and policymakers must act now to prepare for an AI-augmented economy—one that will create new opportunities while displacing others.

For Journalists: Ready-to-Quote Statistics

Pre-written statistics and findings for immediate use in articles and reports.

Headline Options

  • "50 Occupations Face 78-96% AI Automation Risk, New Analysis Reveals"
  • "Top 50 High-Risk Jobs: 96.25% of Telemarketers' Tasks Can Be Automated"
  • "Office Workers Most Vulnerable: 54% of Highest-Risk Jobs Are Administrative Roles"
  • "AI Displacement Risk: 50 Occupations Face Nearly 2.9× Higher Exposure Than Average"
"77 occupations face very high AI risk (>70% exposure), with these top 50 representing the most vulnerable—exposure levels range from 77.67% to 96.25%."
Source: Wharton Budget Model Analysis (2025)

"Office and administrative support occupations account for 54% (27 out of 50) of the highest-risk jobs, revealing a concentration of vulnerability in routine white-collar work."
Source: Occupational Exposure Analysis

"The average exposure of these top 50 occupations (86.3%) is nearly 2.9× higher than the average across all 784 occupations analyzed (29.84%)."
Source: Wharton Budget Model (2025)

"Telemarketers face the highest AI automation risk at 96.25% exposure—meaning about 96 out of every 100 tasks could potentially be automated."
Source: Wharton Budget Model (2025)

Key Findings (Bullet Format)

  • 96.25% - Highest AI exposure (Telemarketers)
  • 86.3% - Average exposure across top 50 occupations
  • 77.67% - Lowest exposure in top 50 (about 3.3× higher than the median exposure across all occupations)
  • 54% - Office/admin support occupations in top 50
  • Nearly 2.9× higher - Average exposure compared to all occupations
  • 784 - Total occupations analyzed
  • 50 - Highest-risk occupations (top 6.4%)

Data Sources & Methodology

Overview

Primary Source: Wharton Budget Model - Occupational Exposure Analysis (2025)

Methodology: Analysis of 784 occupations calculating the percentage of tasks exposed to AI automation using current and near-future AI technology.

High-Risk Threshold: >70% exposure (very high risk)

How We Calculated AI Exposure

This report analyzes 784 occupations using data from the Wharton Budget Model, which calculates the percentage of tasks within each occupation that are exposed to AI automation.

The analysis is based on the 2025 AI exposure dataset.

What is "Exposure to AI Automation"?

Exposure to AI Automation is a metric that measures the percentage of tasks within an occupation that could potentially be automated using current or near-future AI technology.

This metric is calculated by:

  1. 1. Analyzing all tasks performed in each occupation (e.g., data entry, customer service, document processing, decision-making)
  2. 2. Evaluating each task against current and near-future AI capabilities to determine automation potential
  3. 3. Calculating the percentage of automatable tasks (e.g., 90% exposure = 90 out of 100 tasks can be automated)
  4. 4. Categorizing occupations by risk level (>70% = very high risk, 50-70% = high risk, 20-50% = moderate risk, <20% = low risk)

Example: If an occupation has 90% exposure, it means 90 out of every 100 tasks performed by workers in that occupation could potentially be automated by AI systems.

Key Distinction: The difference between "augmentation" (AI helps workers) and "displacement" (AI replaces workers) depends on whether the role requires strategic thinking, creativity, human interaction, or other skills that are difficult to automate.

Ranking Methodology

Occupations are ranked by their "Exposure to AI Automation" percentage, which represents the proportion of tasks that could potentially be automated using current and near-future AI technology.

The top 50 occupations shown in this report have the highest exposure percentages, ranging from 77.67% to 96.25%.

Data Sources

Primary Data Source:

Supporting Data Sources:

Key Metrics

Total Occupations Analyzed: 784

High-Risk Threshold: >70% exposure

Top 50 Criteria: Occupations ranked by exposure percentage, representing the highest-risk 6.4% of all occupations analyzed.

Data Collection Period: 2025

Projection Period: 2024-2034 (for growth data)