Mathematical Technicians
Overall Exposure
2025 vs 2023
Theoretical Exposure
91What AI could do
Observed Exposure
61What AI actually does
Automation Risk Score
70Displacement risk
3-Year Outlook (2025 โ 2028)
Projected changes in AI automation metrics over the next 3 years based on estimated data.
Overall Exposure
2025 โ 2028 (estimated)
Theoretical Exposure
2025 โ 2028 (estimated)
Observed Exposure
2025 โ 2028 (estimated)
Automation Risk
2025 โ 2028 (estimated)
Exposure Metrics (2023 - 2028)
Detailed Metrics Table
| Year | Overall | Theoretical | Observed | Risk | Data Type |
|---|---|---|---|---|---|
| 2024 | 72 | 88 | 56 | 65 | actual |
| 2025 | 76 | 91 | 61 | 70 | estimated |
| 2026 | 80 | 93 | 67 | 74 | estimated |
| 2027 | 83 | 95 | 71 | 78 | estimated |
| 2028 | 86 | 96 | 76 | 81 | estimated |
Task Breakdown
About This Occupation
If you work as a Mathematical Technician, AI is automating the majority of your routine computational tasks. With an automation risk of 70/100 and overall exposure at 76%, this role faces very high transformation. Data computation and tabulation sees the highest automation at 88%. BLS projects -8% decline through 2034.
Frequently Asked Questions
With an automation risk score of 70%, Mathematical Technicians faces a significant risk of AI-driven displacement. Many core tasks in this role can be automated by current AI systems. However, full replacement is unlikely in the near term -- AI will more likely transform the role rather than eliminate it entirely.
The AI automation risk score for Mathematical Technicians is 70% (2025 data). Overall AI exposure is 76%, with 91% theoretical exposure and 61% observed exposure. The risk trend from 2023 to 2025 is 0 points.
The tasks with the highest automation potential for Mathematical Technicians are: Compute and tabulate numerical data (88%), Verify accuracy of computational results (82%), Prepare statistical charts and visualizations (76%). These rates reflect how much of each task current AI systems can handle, based on research data from Anthropic and academic sources.
The BLS projects -8% employment change for Mathematical Technicians from 2024 to 2034. Combined with an overall AI exposure of 76%, this occupation is experiencing both traditional labor market shifts and AI-driven transformation. Workers should monitor both employment trends and AI capability growth.
Since AI primarily automates tasks in this role, professionals in Mathematical Technicians should focus on developing skills that complement AI rather than compete with it. Consider learning AI tool management, shifting toward supervisory and quality-control tasks, and building expertise in areas where human judgment remains essential.