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Manufacturing & Production · Updated Aug 2026

Mixing and Blending Machine Setters, Operators, and Tenders

Set up, operate, or tend machines to mix or blend materials, such as chemicals, tobacco, liquids, color pigments, or explosive ingredients.

JVS 2.0.0-phase4b
AI Exposure
55/100
Moderate

How much of this occupation’s work can be materially affected by current AI systems.

Replacement Risk
42/100
Moderate

How likely exposure is to translate into reduced human demand.

Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured

Evidence quality
Confidence80/100
Task coverage81%

Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.

Task-level evidence

What is driving the score?

Occupation scores are built from the task mix—not a single prediction about a job title.

JVS 2.0.0-phase4b
TaskImportanceAI impactExposure
Weigh or measure materials, ingredients, or products to ensure conformance to requirements.High
67
Mix or blend ingredients by starting machines and mixing for specified times.High
66
Read work orders to determine production specifications or information.High
67
Compound or process ingredients or dyes, according to formulas.High
68
Stop mixing or blending machines when specified product qualities are obtained and open valves and start pumps to transfer mixtures.High
65
Unload mixtures into containers or onto conveyors for further processing.High
68
Dump or pour specified amounts of materials into machinery or equipment.High
63
Operate or tend machines to mix or blend any of a wide variety of materials, such as spices, dough batter, tobacco, fruit juices, chemicals, livestock feed, food products, color pigments, or explosive ingredients.High
61
Add or mix chemicals or ingredients for processing, using hand tools or other devices.High
66
Transfer materials, supplies, or products between work areas, using moving equipment or hand tools.High
63
Collect samples of materials or products for laboratory testing.High
36
Examine materials, ingredients, or products visually or with hands to ensure conformance to established standards.High
35
Test samples of materials or products to ensure compliance with specifications, using test equipment.High
36
Observe production or monitor equipment to ensure safe and efficient operation.High
31
Dislodge and clear jammed materials or other items from machinery or equipment, using hand tools.Medium
59
Tend accessory equipment, such as pumps or conveyors, to move materials or ingredients through production processes.High
22
Most exposed

Where AI can do more

Routine, digitized, and highly repeatable tasks face the greatest pressure.

  1. Compound or process ingredients or dyes, according to formulas.68
  2. Unload mixtures into containers or onto conveyors for further processing.68
  3. Weigh or measure materials, ingredients, or products to ensure conformance to requirements.67
  4. Read work orders to determine production specifications or information.67
Hardest to automate

Where people still matter

These tasks score lowest on automation feasibility—physical presence, judgement, accountability and real-world variability all resist end-to-end automation.

  1. Tend accessory equipment, such as pumps or conveyors, to move materials or ingredients through production processes.01
  2. Observe production or monitor equipment to ensure safe and efficient operation.02
  3. Weigh or measure materials, ingredients, or products to ensure conformance to requirements.03
  4. Mix or blend ingredients by starting machines and mixing for specified times.04
  5. Read work orders to determine production specifications or information.05
Where else this work leads

Related occupations

Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.

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AI risk 49

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AI risk 49

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Beyond AI capability

Adoption and labour-market outlook

Structural factors are kept separate from raw capability so you can see what actually resists automation. Adoption pressure and labour-market resilience are still provisional models—25% of this occupation’s replacement-risk weight rests on them.

Human dependency59
Physical dependency76
Adoption pressure44
Labour-market resilience66
Methodology & sources

O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.

Confidence80/100
CalculatedAug 21, 2026
Read methodology →