Below-Average Risk (35–42%)
Industry occupation-mix grade — shared by every Miscellaneous Electrical Machinery, Equipment & Supplies employer tracked
EnerSys
Workforce AI-exposure profile for EnerSys: industry grade B (37.6/100), plus SEC headcount and BLS occupation mix.
Weighted average O*NET AI exposure score for this industry's occupation mix
EnerSys's workforce carries a grade B AI-risk profile (37.6% weighted exposure), 4.3 points below the 41.9% average across 1,965 tracked employers. That is broadly typical of the industries tracked here; it describes the industry, not this company's own staffing.
Where EnerSys sits among tracked employers
Industry occupation-mix AI exposure across the 1,965 public employers tracked. The marker is Miscellaneous Electrical Machinery, Equipment & Supplies's value, which every employer we track in that sector shares — it is not a measurement of EnerSys specifically.
Where EnerSys's industry AI exposure sits
Industry occupation-mix AI exposure across tracked public employers
37.6% Lower than most lower than 60% of 1,965 employers
Employers, banded by industry AI-exposure mix
Each bar is a band; taller bars hold more employers. The dashed line + filled bar mark this entry. Hover or tap any bar for its full count and share, and where it sits relative to this entry.
Source O*NET / PlainWorkforce employer industry mix · 2026-05-15
AI Displacement Risk Analysis
Companies in Miscellaneous Electrical Machinery, Equipment & Supplies sit below the typical industry AI-exposure band. Most roles still need dexterity, interpersonal skill, or on-site presence that current AI systems struggle with.
Industry exposure standing
Signature: 37.6/100 (grade B) · industry AI-exposure score (corpus extremity among disclosed peers)
As of May 2026, EnerSys sits near the middle on both disclosed headcount and three-year change. Industry grade B (37.6/100) is shared by every tracked employer in Miscellaneous Electrical Machinery, Equipment & Supplies.
SIC 3690 · NAICS 33 · 6 estimated roles; largest share Molders, shapers, and casters, except metal and plastic · sources SEC EDGAR · O*NET 30.0 · BLS industry-occupation distributions.
Estimated Workforce Occupation Mix (Industry Average)
EnerSys does not publish its own occupation breakdown, so this reflects the typical occupation mix for other employers in Miscellaneous Electrical Machinery, Equipment & Supplies (BLS industry-occupation employment distributions), not company-specific data. AI exposure scores from O*NET Database 30.0.
| Occupation | Est. Workforce Share | AI Exposure |
|---|---|---|
| Molders, shapers, and casters, except metal and plastic SOC 51-9195 | | |
| Civil engineering technologists and technicians SOC 17-3022 | | |
| Security and fire alarm systems installers SOC 49-2098 | | |
| Shipping, receiving, and inventory clerks SOC 43-5071 | | |
| Funeral home managers SOC 11-9171 | | |
| Gas compressor and gas pumping station operators SOC 53-7071 | | |
Nationwide employers with similar workforce scale
Two data-derived peer sets for EnerSys: headcount neighbors and three-year workforce trajectory neighbors (distinct comparison neighborhoods).
Similar reported headcount
Nearest SEC-reported employee counts nationwide (9,200 here).
- Visteon Corp · 10,000 employees
- Peabody Energy Corp · 6,600 employees
- Factset Research Systems Inc · 6,258 employees
- Packaging Corp Of America · 13,600 employees
Peers are nearest-neighbor matches on SEC EDGAR workforce fields among tracked public employers with ≥5,000 reported employees.
Methodology and scoring detail live on our how we score AI exposure page; occupation hubs sit under Browse all occupations.
Frequently Asked Questions
What is the AI risk grade for EnerSys's industry?
EnerSys (ENS) maps to Miscellaneous Electrical Machinery, Equipment & Supplies with a mid-pack industry AI grade B (37.6/100). That score is identical for every tracked employer in the sector. Companies in Miscellaneous Electrical Machinery, Equipment & Supplies sit below the typical industry AI-exposure band. Most roles still need dexterity, interpersonal skill, or on-site presence that current AI systems struggle with.
How is the AI risk score calculated?
The score uses the company's primary industry (SIC 3690: Miscellaneous Electrical Machinery, Equipment & Supplies), mapped to BLS occupational employment shares. Each occupation gets an O*NET task-level AI exposure weight; the industry mix is the weighted average. 0 = no exposure; 100 = maximum.
Which occupations at EnerSys face the most AI risk?
Largest estimated shares in the Miscellaneous Electrical Machinery, Equipment & Supplies mix: Molders, shapers, and casters, except metal and plastic (34% AI exposure); Civil engineering technologists and technicians (39% AI exposure); Security and fire alarm systems installers (26% AI exposure). Treat these as industry-typical roles, not EnerSys's published org chart.
How many employees does EnerSys have?
EnerSys reports about 9,200 employees in its latest SEC 10-K - mid-pack among disclosed headcounts.
Has EnerSys had layoffs?
WARN Act layoff notices for EnerSys are published by state labor agencies and the U.S. Department of Labor when a covered mass layoff hits the 100-worker threshold. Many quieter workforce changes never appear in WARN.
What industry is EnerSys in?
EnerSys is classified under SIC 3690 (Miscellaneous Electrical Machinery, Equipment & Supplies), mapping to NAICS sector 33. That classification selects the BLS occupation mix behind the AI exposure grade.
What to do with this
Use EnerSys' risk profile above to plan next steps, not just react to the grade.
- Compare this employer against another company on AI risk, headcount, and trend using the interactive tool. Workforce comparison tool
- See exactly how the AI risk score above is calculated from occupation-level exposure data. Reading AI exposure scores
- See how every tracked employer ranks nationwide by AI displacement risk. Employer AI risk rankings
AI risk scores are estimates based on industry-level occupation distributions and may not reflect individual employer hiring practices; this is not financial advice.
AI risk scores are estimates based on industry-level occupation distributions and may not reflect individual employer hiring practices. Data sources: SEC EDGAR (company SIC codes) · O*NET Database 30.0 (AI exposure scores) · BLS Employment Projections 2024–2034. This is not financial advice.