Will AI replace Adhesive Bonding Machine Operators and Tenders?
Most of the work in Adhesive Bonding Machine Operators and Tenders still leans on things AI struggles with — research rates its theoretical AI reach at only ~6%, and real-world use lower still.
O*NET-SOC 51-9191
How your 14 core tasks split
Top = what GPT-4 judged AI could speed up. Bottom = how much AI was actually used for these tasks (Anthropic's March 2026 report, usage from Aug & Nov 2025). The gap is the real story.
Back in 2023, GPT-4 judged AI could, in theory, assist with a relatively low share of this job's tasks (~6%). By late 2025, real-world AI use had reached about 0% of its task activity (still rare). The gap between that 2023 forecast and today is the real story.
Where this job sits among 738 jobs
Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.
The signals here line up
Theoretical reach (~6%), real-world use (~0%) and the task-level picture mostly agree — so this read is more reliable than for jobs where the signals contradict each other. Even so, AI-risk estimates shift by model (a 2026 study saw the "high-risk" share swing 2.7%–51.5%), so treat these as directional, not destiny.
See all 14 tasks, ratedBased on real task-level AI scores — click to collapse
- Maintain production records such as quantities, dimensions, and thicknesses of materials processed.
- No tasks in this middle tier.
- Align and position materials being joined to ensure accurate application of adhesive or heat sealing.
- Adjust machine components according to specifications such as widths, lengths, and thickness of materials and amounts of glue, cement, or adhesive required.
- Monitor machine operations to detect malfunctions and report or resolve problems.
- Start machines, and turn valves or move controls to feed, admit, apply, or transfer materials and adhesives, and to adjust temperature, pressure, and time settings.
- Fill machines with glue, cement, or adhesives.
- Perform test production runs and make adjustments as necessary to ensure that completed products meet standards and specifications.
- Examine and measure completed materials or products to verify conformance to specifications, using measuring devices such as tape measures, gauges, or calipers.
- Read work orders and communicate with coworkers to determine machine and equipment settings and adjustments and supply and product specifications.
- Remove and stack completed materials or products, and restock materials to be joined.
- Observe gauges, meters, and control panels to obtain information about equipment temperatures and pressures, or the speed of feeders or conveyors.
- Remove jammed materials from machines and readjust components as necessary to resume normal operations.
- Mount or load material such as paper, plastic, wood, or rubber in feeding mechanisms of cementing or gluing machines.
- Transport materials, supplies, and finished products between storage and work areas, using forklifts.
How we measured this — and how fresh it is
AI's theoretical reach data: 2023
From GPTs-are-GPTs (Eloundou et al.), where GPT-4 rated how much of each task an AI tool could meaningfully speed up. This is the most recent open, commercially-usable occupation-level potential dataset — it dates to 2023. Newer multi-model re-runs exist but swing wildly (one 2026 study saw "high-risk" jobs range 2.7%–51.5% by model) and aren't openly licensed, so we show the stable 2023 baseline and pair it with newer real-world data.
Real-world AI use 2026 report
From the Anthropic Economic Index, which observes how real Claude conversations map onto each occupation's tasks. Published in Anthropic's March 2026 labor-market report, based on usage measured in Aug & Nov 2025 (Sonnet 4 / 4.5).
Task list & ratings O*NET 30.3
Tasks come from O*NET 30.3. Each task's "AI can do / speeds up / still on you" tier uses the real task-level exposure scores from GPTs-are-GPTs (E1 / E2 / E0) — not a guess from keywords.
Sources: O*NET 30.3 (CC BY 4.0) · GPTs-are-GPTs (MIT, 2023) · Anthropic Economic Index (CC BY, Aug & Nov 2025). Page compiled June 2026. "O*NET" is a trademark of the U.S. Department of Labor.
This page is for general informational purposes only and is not career, financial, or employment advice. AI exposure reflects research estimates of task overlap, not predictions about any individual's job, employer, or future employment.