Office Machine Operators, Except Computer
43-9071.00Median wage $40,960/yr25,130 employed (US)Rank #154 of 923 scored · top 17% by substitution
Operate one or more of a variety of office machines, such as photocopying, photographic, and duplicating machines, or other office machines.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution41
Exposure35
Augmentation38
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
18 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
11%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%38
panel mean rating 2.5/5 → substitution pressure 38/100
Technical feasibility todayw 20%28
panel mean rating 2.1/5 → substitution pressure 28/100
Cost vs. human wagew 15%34
panel mean rating 2.4/5 → substitution pressure 34/100
Adoption barriersw 20%inverted — strong barriers lower the score70
panel mean rating 2.2/5 (barrier strength) → substitution pressure 70/100
Sector adoption velocityw 10%29
panel mean rating 2.1/5 → substitution pressure 29/100
Task breakdown (18 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Complete records of production, including work volumes and outputs, materials used, and any backlogs.
72CI 65–79 · exposure 70 · augmentation 63 · importance 4.2/5 · click for rater detail
Complete records of production, including work volumes and outputs, materials used, and any backlogs.
72| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Manufacturing and logistics sectors have actively deployed RPA and process automation for inventory and production tracking. Digital transformation in these fields is well underway, with measurable adoption of automated logging systems in factories and warehouses. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Office machine operation is a lower-digitization, often small-scale clerical/production hybrid role where automated tracking adoption is moderate but not fast-moving compared to finance or professional services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist operators by auto-populating forms from sensor data or photographs, flagging anomalies, and summarizing backlogs, improving speed and accuracy of reporting. However, the task itself remains relatively routine with limited scope for transformative human-AI collaboration. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI and automated systems can significantly ease record compilation, flagging discrepancies and auto-populating logs, greatly aiding the human operator even if not fully replacing oversight. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | This task involves straightforward data entry and record-keeping with clear, structured inputs (volumes, outputs, materials, backlogs). Current AI systems with OCR and form-filling capabilities can automate most of the data collection, aggregation, and logging steps with minimal setup, achieving >50% time savings at equal accuracy. |
| Task automatability | claude-sonnet-5 | 4/5 | Recording production volumes, materials, and backlogs is structured data entry/reporting that can largely be automated via integrated tracking systems, scanners, or software connected to office machines, meeting the time-saving threshold with modest setup. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few hard barriers exist; production records are rarely legally required to be manually completed by a licensed operator. Integration with existing production systems and worker resistance represent minor friction, but no regulatory or liability walls prevent substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates human record-keeping; main friction is organizational inertia and legacy systems rather than regulatory or liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven data entry and logging cost a small fraction of human operator wage (minutes of inference + integration overhead vs. hours of manual work). The cost asymmetry strongly favors automation for this routine, high-volume task. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated logging via sensors/software is far cheaper per record than manual paperwork once integrated, though initial setup and machine integration add some cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products including RPA platforms, document processing APIs, and data logging systems already perform analogous record-completion tasks reliably in manufacturing and office environments. Error rates are low for structured data entry, though some variance exists in unstructured handwriting or non-standard formats. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Many production tracking and ERP/MES systems already automate this logging in production environments, but for office-machine operations specifically, deployment is uneven and often still supplemented by manual entry. |
Maintain stock of supplies, and requisition any needed items.
72CI 64–80 · exposure 67 · augmentation 63 · importance 3.9/5 · click for rater detail
Maintain stock of supplies, and requisition any needed items.
72| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Inventory management and supply chain automation are widely adopted across retail, healthcare, manufacturing, and office environments; many organizations already use digital stock tracking and automated reordering systems. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Office/administrative functions have moderate AI and software adoption for inventory and procurement automation, with pilots and partial deployment common but not universal, especially in smaller organizations still using manual methods. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted inventory systems can help humans by providing real-time stock visibility, predictive reorder suggestions, and alerts, improving their decision-making efficiency, though the core task remains relatively transactional and benefits moderately from augmentation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-enabled inventory systems significantly reduce the manual burden of tracking stock levels and generating requisitions, letting a human focus on exceptions and approvals rather than routine monitoring. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | Inventory tracking and reorder logic can be partially automated with inventory management systems that monitor stock levels and generate requisition alerts, but physical inspection, dynamic decision-making about quantities, and vendor selection still require human judgment, placing this at roughly half-automatable. |
| Task automatability | claude-sonnet-5 | 4/5 | Inventory tracking and requisition generation is largely rule-based (threshold monitoring, reorder triggers, form submission) and is well-suited to existing inventory management software and AI-driven procurement tools, though physical stock checks still require some human involvement. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or regulatory barriers prevent automation; typical friction involves organizational preference to retain human oversight for high-value items and integration with existing procurement workflows, but nothing fundamentally blocks substitution. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There is no licensing, regulatory, or liability barrier to automating supply tracking and requisitioning; it's a low-stakes administrative task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated inventory and requisition systems (including software and periodic monitoring) cost substantially less than full-time human labor for the same output, especially at scale; the ratio favors automation significantly. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated inventory/requisition software has low marginal cost per transaction compared to a human manually tracking and ordering supplies, though initial setup and integration carry some cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Inventory management and requisition systems are mature, deployed at scale in many organizations, and reliably perform stock tracking and automated reorder functionality, though integration with existing supply chains varies and some manual oversight remains typical. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Inventory management systems with automated reorder points and requisition workflows are mature, widely deployed products in office and warehouse settings, though full end-to-end automation without any human verification is less common for smaller offices. |
Compute prices for services and receive payment, or provide supervisors with billing information.
67CI 52–81 · exposure 62 · augmentation 63 · importance 4.2/5 · click for rater detail
Compute prices for services and receive payment, or provide supervisors with billing information.
67| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Retail, hospitality, healthcare, and professional services have already deployed automated billing and payment systems at scale; this represents some of the deepest and fastest AI adoption in business operations. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Office machine operator roles are in a shrinking, less digitized niche; while billing software is common, this specific job category has seen slow, incremental rather than deep AI-driven adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI billing systems augment human supervisors by handling routine computation and generating structured reports, freeing them to focus on dispute resolution, adjustments, and customer exceptions. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Billing software and calculators substantially speed up price computation and record-keeping, meaningfully aiding the worker even though payment collection and supervisor reporting still need human execution. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI can reliably compute prices based on rules, rates, and service parameters, and can process payment information or generate billing reports with minimal human intervention. The task is mostly rule-based calculation and data entry, achieving well over 50% time savings with existing systems. |
| Task automatability | claude-sonnet-5 | 3/5 | Price computation and billing generation can be automated via POS/billing software, but receiving payment often involves physical or in-person interaction and handling exceptions, limiting full end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While payment processing has compliance and PCI-DSS requirements, these primarily govern system architecture rather than preventing automation. No licensing requirement mandates human involvement in price computation or billing data collection. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement typically applies, though cash handling and payment security (PCI compliance) create some procedural friction, and customer preference for human interaction in payment settings is a minor barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven billing automation costs a fraction of human operator wages; a single payment-processing system handles hundreds or thousands of transactions at near-zero marginal cost per transaction. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Automated billing software is cheap per transaction, but the human task also includes payment handling and supervisor communication, which still require paid staff time, keeping costs roughly comparable in many small operations. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed billing and point-of-sale systems routinely perform price computation and payment processing in production environments at scale. However, edge cases (discounts, adjustments, exceptions) often require human oversight, preventing a perfect 5. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Billing and payment systems are widely deployed and reliable for standard transactions, but integration with supervisor reporting workflows and edge cases still requires human involvement in many settings. |
File and store completed documents.
61CI 52–70 · exposure 58 · augmentation 63 · importance 3.9/5 · click for rater detail
File and store completed documents.
61| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Document automation has seen widespread adoption across finance, healthcare, legal, and corporate sectors over the past decade. Digital filing and document management systems are now standard practice in most information-intensive organizations. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Clerical/office-support roles in smaller and administrative-heavy organizations have adopted digital document management unevenly, with physical filing still common in many settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by automatically suggesting file categories, detecting misfiled documents through OCR and metadata analysis, and flagging documents requiring special handling, improving human operator speed and accuracy without replacing judgment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered document management tools significantly speed up sorting, tagging, and retrieval, meaningfully boosting productivity for workers still handling filing tasks. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Document filing and storage is highly routine and repetitive, involving classification, labeling, and placement in predefined locations. Modern AI systems with computer vision and robotic process automation can handle physical document movement and digital filing at scale, though real-world variability in document formats and organizational schemes requires some setup. |
| Task automatability | claude-sonnet-5 | 3/5 | Digital filing and storage (indexing, naming, routing to folders/systems) can be automated with document management systems and AI classification, but physical document filing and mixed physical/digital workflows still require human handling.atability |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or licensing barriers exist for automating document filing. Primary barriers are organizational inertia and preference for human verification of sensitive documents, but these are soft constraints rather than regulatory requirements. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational inertia, legacy physical archives, and internal data-handling policies create moderate friction to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated document filing through robotic process automation and document management software is substantially cheaper than human operators once deployed, with minimal per-document marginal cost and no wage-related overhead, though initial integration costs are non-trivial. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Software-based automated filing is cheap at scale, but integration, digitization of physical documents, and oversight for exceptions add cost that narrows the gap versus a low-wage clerical worker. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Robotic document handling systems and digital filing automation exist in production (e.g., document management platforms, scanning + OCR + auto-filing), but performance is inconsistent with heavily damaged documents, non-standard formats, or complex organizational hierarchies. Most deployments require human oversight and exception handling. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Document management systems with AI-assisted tagging and OCR-based filing exist and are deployed, but many office-machine-operator roles still involve physical paperwork or legacy systems not fully integrated with automated filing. |
Read job orders to determine the type of work to be done, the quantities to be produced, and the materials needed.
59CI 47–70 · exposure 50 · augmentation 63 · importance 4.5/5 · click for rater detail
Read job orders to determine the type of work to be done, the quantities to be produced, and the materials needed.
59| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Manufacturing, print, and logistics firms—where this task is prevalent—have been actively adopting document automation and RPA solutions for order processing for several years; production deployments are increasingly common in mid-to-large operations. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | This occupation sits in physical, lower-digitization production/office-support environments where AI adoption for such micro-tasks remains slow and mostly manual. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI systems can assist by pre-parsing job orders, highlighting key fields (quantity, material, deadline), and flagging unusual requests, significantly speeding human review and reducing transcription errors while keeping humans in control of final validation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist by quickly summarizing or extracting key details from job orders, helping the operator plan work, though human judgment is still needed to interpret ambiguous specifications. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | Job orders are typically structured documents (paper or digital) containing explicit instructions about work type, quantities, and materials. Current OCR, document parsing, and LLM systems can reliably extract this information, though complex handwritten orders or ambiguous specifications may require clarification—achieving 50% time saving on average but not end-to-end autonomy. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can readily read and extract structured information (job type, quantities, materials) from job orders if digitized, but integration with actual machine setup and physical production workflow limits full end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | This is a clerical, information-extraction task with no licensing or regulatory requirement, no legal liability for minor misreads, and minimal customer-contact friction. Organizational systems may require some integration, but no hard barriers prevent substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates human review of job orders, though organizational habits and need for accuracy on materials/quantities create some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Inference cost for document parsing and extraction is minimal (pennies per order), while human reading and data entry commands loaded wages of $20–40/hour; AI is orders of magnitude cheaper for this clerical task. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Automated document parsing is cheap per transaction, but system setup, integration with legacy job-order formats, and oversight for accuracy add cost comparable to a low-wage worker performing this quick task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Document processing and data extraction products are mature and widely deployed; systems can parse job orders from PDFs, images, and databases with high accuracy. Some edge cases (poor handwriting, non-standard formats) introduce error, but production deployments in manufacturing and print shops demonstrate reliable performance. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Document parsing and OCR/NLP products exist and can extract order details, but few deployed systems are integrated specifically into office machine operator workflows for this exact task at scale. |
Operate office machines such as high speed business photocopiers, readers, scanners, addressing machines, stencil-cutting machines, microfilm readers or printers, folding and inserting machines, bursters, and binder machines.
55CI 44–66 · exposure 53 · augmentation 25 · importance 4.3/5 · click for rater detail
Operate office machines such as high speed business photocopiers, readers, scanners, addressing machines, stencil-cutting machines, microfilm readers or printers, folding and inserting machines, bursters, and binder machines.
55| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Many office environments and print/document services already deploy automated scanning, copying, and mail handling systems; larger organizations have shifted significantly away from manual operators. Digital-first workflows and cloud-based document management accelerate displacement in information-intensive sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | This is a physical, equipment-operation task in a declining occupational category; adoption of full automation is slow due to low digitization pressure and shrinking demand rather than fast AI-driven transformation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers limited augmentation potential because the task is primarily mechanical operation rather than decision-making or judgment. AI could assist with error detection or job queuing, but does not materially amplify human capability in the core activity. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with digital workflow routing, OCR, and job scheduling, but offers limited direct assistance to the manual, mechanical aspects of loading and operating these physical machines. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Most office machine operations involve straightforward mechanical tasks (feeding paper, setting parameters, pressing buttons) that modern document processing pipelines and robotic process automation can handle end-to-end. High-volume copying, scanning, and sorting routines already have substantial automation solutions deployed, achieving >50% time savings in many workflows. |
| Task automatability | claude-sonnet-5 | 3/5 | Some of this task (scanning, printing, binding) can be handled by automated equipment or AI-driven document workflow systems, but physical setup, machine loading, jam-clearing, and troubleshooting still require human presence and dexterity. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or regulatory barriers protect office machine operation; no licensing requirement or mandatory human sign-off exists. Organizational friction is modest—workplace mail and document handling still require coordination, but nothing prevents automation procurement. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but physical machine operation, maintenance, and paper handling create practical friction that AI software alone cannot overcome without robotic automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | Robotic document processing and scanning automation can be cost-competitive with manual operators for high-volume, repetitive tasks, but setup, integration, and per-page processing costs are comparable to modest operator wages when overhead is included. Not yet an order of magnitude cheaper for all machine types. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Capital investment in automated high-throughput equipment can be costly relative to a single operator's wage, especially for smaller offices, though large-scale operations may see savings from equipment upgrades rather than AI per se. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Document scanning and processing automation products exist in production (e.g., intelligent document capture, automated scanning workflows), but their scope is often narrow and error rates on complex jobs (mixed-batch scanning, unusual media) remain material. Physical handling of stencil-cutting, binding, and microfilm equipment remains difficult for current robotics. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Modern smart copiers/scanners with automated feeders and digital workflow integration exist, but fully unattended operation of the diverse machine set listed (stencil-cutters, bursters, binders) is not standard in production environments. |
Sort, assemble, and proof completed work.
54CI 44–64 · exposure 53 · augmentation 63 · importance 4.3/5 · click for rater detail
Sort, assemble, and proof completed work.
54| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Adoption is mixed: large organizations in finance and government have deployed document automation, but many mid-market and small offices still rely on manual operators. Pilots are common but full replacement remains uneven across sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Office machine operation is a declining, moderately digitized occupation with limited reported AI/robotics adoption for physical sorting and assembly tasks compared to faster-moving white-collar sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-assisted proofing and document flagging tools can significantly boost a human operator's productivity by highlighting anomalies or errors, reducing manual inspection time while the operator retains final sign-off authority. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-powered proofing and quality-check tools can meaningfully speed up the proofing portion of this task, though it offers little assistance for the physical sorting and assembling components. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Sorting and assembling physical documents can be partially automated with document imaging and optical character recognition systems, though proofing for quality typically requires human judgment. Current robotic process automation and document handling systems can achieve substantial time savings on the mechanical sorting and assembly portions. |
| Task automatability | claude-sonnet-5 | 3/5 | Sorting and assembling physical documents/materials requires physical manipulation that AI cannot do alone, but proofing (checking for errors) is well within current AI capabilities like OCR and text comparison tools; overall partial automation is feasible with robotics/software integration for digital-only workflows. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are few hard regulatory or legal barriers to automating office machine operation. Main friction comes from organizational transition costs, existing equipment investments, and customer or stakeholder preferences for human quality control on sensitive documents. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates human sorting/assembly/proofing, though quality control and accountability for completed work may create some organizational friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While sorting and assembly machinery has dropped in cost, the integration, maintenance, and oversight required to handle proofing tasks—where errors are costly—keeps total system cost roughly comparable to or slightly higher than employing human operators for mixed workloads. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | For digital proofing, AI tools are cheap and fast, but the physical sorting/assembly component still requires human labor or expensive automation hardware, making blended cost roughly comparable to human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Document sorting and assembly equipment exists in production environments, but fully automated proofing systems with high accuracy remain limited. Hybrid human-AI systems are deployed, but end-to-end reliable automation without human oversight is not yet standard practice at scale. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Deployed products can proof digital text/documents reliably, but sorting and assembling completed work often involves physical materials handling, which lacks mature commercial robotic solutions in typical office machine operator settings. |
Operate auxiliary machines such as collators, pad and tablet making machines, staplers, and paper punching, folding, cutting, and perforating machines.
46CI 24–67 · exposure 41 · augmentation 25 · importance 3.9/5 · click for rater detail
Operate auxiliary machines such as collators, pad and tablet making machines, staplers, and paper punching, folding, cutting, and perforating machines.
46| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Larger commercial print shops and mail centers have adopted automation for collating and finishing; however, many small to mid-sized offices still rely on manual operation. Adoption is growing but remains uneven across sector and firm size, falling short of the deep, fast penetration of information-intensive sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | This occupation sits in a low-digitization, physical-equipment-operation sector with minimal AI/robotics adoption reported to date. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | These machines are already semi-automated; AI could assist with job scheduling, material queue management, or error detection, but the operator's role is primarily monitoring and feeding material. Augmentation opportunity is modest because the task is already narrow and mechanized. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with scheduling, job routing, or predictive maintenance, but offers little direct assistance to the physical act of operating these machines. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Most of these auxiliary machines can be programmed or retrofitted with automation (collators, folding/cutting/perforating machines are routinely automated in modern facilities). Vision-guided robotic systems can handle material feeding, stacking, and output collection, achieving substantial time savings, though integration complexity and setup costs limit the 5 rating. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a physical machine-operation task requiring manual setup, feeding, and monitoring of mechanical equipment; current AI (software/LLM-based) cannot physically operate collators or paper cutters end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal regulatory or licensing barriers exist for automating these machines. Safety interlocks and guarding are required, but no legal mandate requires a human operator. The main friction is organizational inertia and capital budget constraints rather than hard legal barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human operator, but physical presence and manual handling of paper/materials creates practical friction against remote AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated collators and finishing machine solutions have declining costs; once installed, the per-task cost is very low. However, initial capital investment and integration overhead are non-trivial, so the ratio is favorable but not yet at 5:1 magnitude for all machine types. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic automation of this niche task would require expensive custom hardware integration exceeding the cost of a human operator for the same throughput, making AI not cost-competitive. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Industrial automation for collating and finishing machines exists in production settings, but integration varies widely by machine type and facility. Deployed solutions work reliably for high-volume repetitive tasks but often require custom engineering; off-the-shelf drop-in automation is less mature than for other office tasks. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed general-purpose AI or robotic products that reliably replace human operation of these auxiliary office finishing machines today; this remains a manual/mechanical task. |
Prepare and process papers for use in scanning, microfilming, and microfiche.
41CI 35–46 · exposure 33 · augmentation 50 · importance 3.8/5 · click for rater detail
Prepare and process papers for use in scanning, microfilming, and microfiche.
41| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Large document management and records services firms have adopted scanning and digitization at scale, but uptake is concentrated in high-volume, standardized contexts (banks, government). Smaller operations and specialized records still rely on manual prep work. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | This work occurs in records management and administrative sectors with moderate digitization; adoption of full automation for physical prep is slow compared to office information tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered document analysis (OCR, quality-checking, categorization) can assist operators in organizing and validating papers for scanning, speeding inspection and error detection. However, the physical manipulation piece remains human-driven. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-enabled scanning and OCR software assists workers by automating classification, quality checks, and metadata tagging, improving throughput even though physical prep remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical preparation and arrangement of papers (removing staples, organizing, collating) requires robotic manipulation that current AI systems struggle with reliably. While document scanning itself is mature, the upstream physical prep work is labor-intensive and contextual, preventing end-to-end automation with 50% time savings today. |
| Task automatability | claude-sonnet-5 | 3/5 | Document preparation (removing staples, sorting, aligning) is physical and hard to automate, but the sorting/indexing logic can be partially automated with scanning software; still requires physical manipulation of paper.dupath |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or licensing barriers exist for automating document preparation itself. The main friction is organizational (institutional inertia, preference for human verification of sensitive records) and technical (robotics integration complexity), not regulatory or authorization-based. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but physical handling of original documents (sensitive records, legal or medical materials) can create some procedural and chain-of-custody constraints. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current automation (scanners, OCR) is cost-effective for high-volume operations, but the labor-intensive physical prep phase and quality-control checkpoints mean total automation cost per document remains comparable to or higher than human labor for many workflows. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Physical handling of paper still requires human labor or specialized equipment; AI-driven software alone doesn't replace the manual prep cost, though scanning automation reduces some downstream costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Document scanning and microfilm production systems exist, but the physical preparation step—sorting, checking for damage, removing fasteners—remains largely manual in deployed workflows. No integrated system reliably handles the full prep-to-digitization pipeline without human oversight. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Scanning software and automated document feeders exist, but the manual prep work of physically organizing and readying diverse paper documents is still largely done by humans in production settings. |
Monitor machine operation, and make adjustments as necessary to ensure proper operation.
30CI 23–38 · exposure 20 · augmentation 50 · importance 4.1/5 · click for rater detail
Monitor machine operation, and make adjustments as necessary to ensure proper operation.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Manufacturing and office-equipment sectors show growing adoption of predictive maintenance and IoT monitoring, but actual autonomous adjustment remains pilot-stage; most deployments are sensor-based alerting rather than closed-loop automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | This occupation is a low-digitization, physical-equipment role with minimal reported AI adoption or investment in autonomous machine monitoring for office equipment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-powered dashboards, anomaly detection, and predictive alerts meaningfully enhance an operator's ability to spot problems early and plan interventions, substantially raising their productivity in monitoring and preventive action. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-based predictive maintenance and simple sensor alerts could help flag anomalies, but for most office machines this augmentation is minimal or absent in practice today. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Monitoring discrete, well-defined machines can be partially automated with sensors and simple rule-based triggers, but real-world adjustments require contextual judgment, physical intervention, and problem-solving that current AI systems cannot reliably perform end-to-end without significant human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical presence to observe and manually adjust a machine, which current AI cannot perform end-to-end without robotic embodiment and sensors integrated into legacy office equipment.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Many industrial machines have safety interlocks and regulatory compliance requirements that may require human sign-off on adjustments; operational risk and liability concerns create moderate friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No strict licensing requirement exists, but the physical, hands-on nature of the task and reliance on human presence to catch mechanical issues creates practical friction to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Sensor networks and monitoring software have upfront capital costs and ongoing integration overhead that often exceed the wage of a single operator, especially for smaller installations or lower-utilization machines. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Retrofitting older office machinery with sensors, actuators and AI monitoring would likely cost more than the modest wages of an office machine operator, making AI substitution currently more expensive. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Computer vision and IoT sensors can detect basic operational anomalies, but deployed systems remain limited to flagging alerts rather than autonomously diagnosing and adjusting complex machines; most solutions require human technicians to implement fixes. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists that autonomously monitors and physically adjusts general office machines like copiers, mail-sorting or printing equipment; this remains a manual task performed by a human operator on-site.' |
Cut copies apart and write identifying information, such as page numbers or titles, on copies.
29CI 24–35 · exposure 20 · augmentation 25 · importance 3.5/5 · click for rater detail
Cut copies apart and write identifying information, such as page numbers or titles, on copies.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task remains in low-digitization, small-firm, physical-work contexts where adoption of office automation is slow. Modern workflows increasingly bypass physical copying and labeling, rather than automating it. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Office machine operation is a low-digitization, declining occupational category with minimal AI adoption momentum; automation here is mechanical rather than AI-driven. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist in automatically reading and suggesting page numbers or titles for labeling, but the core tasks—cutting and writing—remain primarily manual. Augmentation is limited to the information-extraction portion. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help generate titles or numbering schemes to be applied, but offers little assistance for the core physical cutting and manual writing actions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Cutting copies apart is mechanically feasible but requires physical manipulation that most desktop robots cannot reliably perform today. Writing identifying information could be automated with OCR and printing, but the combination—precise cutting plus accurate labeling on physical documents—lacks a practical end-to-end solution with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical cutting and manual annotation of copies is a hands-on task that current AI systems cannot perform end-to-end; only the software-side labeling logic could be automated, not the physical handling. , |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There is no legal requirement for a human to perform this task, but organizational inertia and the need for flexible, small-batch handling create practical friction against adoption of specialized automation equipment. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or regulatory barriers exist, but the physical nature of the task (cutting, sorting, handling paper) creates practical friction against pure software automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The equipment required for reliable automated cutting and labeling (precision cutters, robotic arms, vision systems) is expensive and requires significant integration. Human operators remain cheaper for this task in most office settings. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Robotic or AI-guided physical cutting/labeling would require specialized machinery and integration costing more than simply having a low-wage worker do this manual task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No commercially deployed product reliably performs both the cutting and identifying-information tasks on diverse physical documents. While document handling exists in specialized contexts (mailrooms, print services), the full integration with labeling is not a standard production solution. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | There are no widely deployed AI products that physically cut paper copies and mark them; automated document-finishing equipment exists but is mechanical, not AI-driven, and page numbering software is a separate, well-established non-AI tool. |
Place original copies in feed trays, feed originals into feed rolls, or position originals on tables beneath camera lenses.
27CI 19–35 · exposure 20 · augmentation 25 · importance 4.3/5 · click for rater detail
Place original copies in feed trays, feed originals into feed rolls, or position originals on tables beneath camera lenses.
27| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Document management automation remains concentrated in large enterprises and specialized scanning services; small to medium offices and many sectors still rely on manual operators, indicating slower, uneven adoption. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Office machine operation is a low-digitization, physically manual occupation with minimal AI or robotic adoption for document handling tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Automated document feeders and positioning systems can assist operators by reducing manual handling time and improving consistency, though the core task remains largely manual and human-directed. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for the physical act of placing or feeding documents into machines. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Only narrow portions of this physical task (placing and positioning originals) can be partially automated with specialized equipment, but end-to-end automation with 50% time savings at equal quality requires reliable vision systems and robotic manipulation that struggles with variable paper sizes, conditions, and orientations in unstructured office environments. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a physical manipulation task (loading paper, positioning documents) that requires embodied action, which current AI systems cannot perform without robotic hardware that is not deployed for this purpose.'}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Modest organizational friction exists around equipment investment and workflow redesign, but no licensing or legal requirement mandates human performance of this task, and error consequences are manageable. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human for this task, but the physical nature of the work and lack of automation infrastructure create practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic document handling systems are capital-intensive and require integration and maintenance costs that typically exceed the loaded wage of a single office machine operator, making the all-in cost higher for most organizations. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based substitute for this physical handling task, so any comparison would require robotics investment far exceeding the cost of a human operator for this simple action. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While document scanning robots and automated feed systems exist in specialized industrial settings, they rarely perform reliably across the full range of original documents and office conditions at scale; most deployed systems require significant manual intervention and setup. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product physically places documents into feed trays or positions them under camera lenses; this remains a manual physical task in essentially all workplaces. |
Set up and adjust machines, regulating factors such as speed, ink flow, focus, and number of copies.
26CI 19–33 · exposure 20 · augmentation 25 · importance 4.2/5 · click for rater detail
Set up and adjust machines, regulating factors such as speed, ink flow, focus, and number of copies.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Office equipment operation is a declining, low-digitization function in small to mid-sized operations where AI adoption is minimal; the sector is heavily driven by legacy on-premises installations with little investment in automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Office machine operation is a declining, low-digitization occupation with modern equipment increasingly self-adjusting or automated by design rather than through AI-driven retrofits or agents. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by recommending parameter settings based on job specifications or by logging/tracking adjustments, but such assistance is limited in scope and marginal compared to the core manual adjustment work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some newer machines have digital interfaces or diagnostic software that can guide settings, but AI assistance for this specific physical calibration task is minimal and not widespread. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Setup and adjustment of office machines involve physical manipulation and real-time sensory feedback (visual inspection of output, tactile adjustment). While AI could theoretically guide or log parameters, current systems cannot reliably perform the mechanical adjustments or validate output quality end-to-end without human intervention. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical manipulation of machine controls and sensory judgment (visual focus, ink flow) that current AI cannot perform without robotic embodiment, which is not standard for office machine operation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While not a regulated professional service, office equipment ownership and liability for damage during setup creates moderate friction; many organizations prefer human operators for accountability, and equipment manufacturers may not warrant autonomous adjustment systems. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No strict licensing barrier exists, but physical presence and hands-on adjustment of machinery create a structural barrier to pure software-based AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of a robotic system with vision feedback, integrated software, and maintenance to autonomously adjust office machines significantly exceeds the loaded cost of a human operator, especially given the low wage for this role and the infrequent setup changes needed. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Without robotic automation, AI cannot replace the physical adjustment task, so a human operator remains necessary, making AI substitution costly to implement via robotics rather than software alone. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products can autonomously set up and adjust office machines at scale. Computer vision systems exist for quality control, but integrating them with robotic control for speed/ink/focus tuning remains research-grade; deployed solutions require human operators. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed consumer or industrial product autonomously sets up and adjusts physical office machines like copiers or printers end-to-end; this remains a manual task performed by operators. |
Clean machines, perform minor repairs, and report major repair needs.
21CI 10–33 · exposure 13 · augmentation 38 · importance 4.0/5 · click for rater detail
Clean machines, perform minor repairs, and report major repair needs.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Office machine operation is concentrated in small to mid-sized organizations with low digitization; physical automation of cleaning and repair has seen minimal real-world deployment in this sector. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | This occupation involves low-digitization physical maintenance work in a shrinking, low-tech sector with minimal AI adoption evidence. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI inspection tools and diagnostic systems can assist workers in identifying what needs repair and documenting issues, boosting efficiency in the triage and reporting phases while the operator performs physical tasks. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could offer minor assistance such as diagnostic guidance or repair manuals/troubleshooting chatbots, but it does not meaningfully transform the physical cleaning or repair work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can inspect machines and identify defects, the physical cleaning and repair work requires robotic dexterity and real-time manipulation in unstructured environments—well beyond current practical automation. Only the reporting/documentation aspect is readily automatable. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, hands-on task involving cleaning and mechanical repair of office equipment, which current AI systems cannot perform as they lack physical embodiment.//No off-the-shelf AI system can manipulate physical machines, clean components, or perform mechanical repairs. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Physical proximity requirements and liability for machine damage (error costs are asymmetric: a failed repair harms the organization) create moderate friction; no strict licensing barrier but organizational and risk considerations slow adoption. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this task, but the physical nature and need for hands-on dexterity create a practical barrier to automation beyond regulatory ones. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic systems capable of cleaning and minor repair remain capital-intensive and specialized. The integration cost and per-task inference overhead exceed the loaded wage of an office machine operator. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for the physical labor involved, so any AI-based approach (e.g., robotics) would be far more expensive than a human worker performing routine cleaning/repair. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Inspection and defect detection via computer vision have functional demos, but no deployed products reliably perform the full task of cleaning, minor repair, and assessment at scale in production office settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical cleaning or minor mechanical repair of office equipment; this remains firmly in the domain of human manual labor. |
Load machines with materials such as blank paper or film.
21CI 15–26 · exposure 8 · augmentation 13 · importance 4.1/5 · click for rater detail
Load machines with materials such as blank paper or film.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Office machine operation is declining in digitized organizations, and the sector shows minimal adoption of automation for routine loading tasks; most adoption occurs only in high-volume print centers rather than typical offices. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | This occupation involves physical, low-digitization work with limited AI agent penetration; adoption of AI for this specific physical subtask is minimal across the sector. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI systems offer minimal augmentation for material loading since the task is primarily physical manipulation with little cognitive component where AI could meaningfully assist a human operator. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI systems offer no meaningful assistance for the physical act of loading paper or film into a machine; this remains a manual, non-cognitive task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Loading blank materials into office machines requires physical manipulation and precise placement in varying machine configurations, which current robots and AI systems struggle with in unstructured office environments. While some aspects could theoretically be automated with specialized hardware, general-purpose AI systems cannot reliably perform this end-to-end task today at the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring hands to load paper or film into machines; no off-the-shelf AI system can perform this physical act, though robotics could theoretically do so with heavy customization.5-10% of time is negligible. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal regulatory or licensing barriers exist for automating material loading, though physical space constraints in typical offices and the need for ergonomic compatibility with existing workstations present practical friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or regulatory requirement exists for this task, but the physical nature of manual material loading creates a practical barrier to AI-only substitution absent robotics investment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of loading office machines are expensive to purchase, integrate, and maintain compared to the modest wage of an office machine operator, making the cost-benefit unfavorable at scale for routine loading tasks. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no generally available AI solution for this physical task, so comparing cost is moot; any robotic solution would require expensive custom hardware exceeding human wage costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products reliably automate material loading for diverse office machines in production environments. This task requires physical automation (robotics) rather than software AI, and such systems remain research-stage or highly specialized to single machine types. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical loading of materials into office machines; this remains firmly in the domain of human dexterity and simple mechanical automation, not AI. |
Clean and file master copies or plates.
19CI 15–24 · exposure 8 · augmentation 0 · importance 3.7/5 · click for rater detail
Clean and file master copies or plates.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task belongs to a legacy occupation (office machine operators except computer) that has been in steep decline due to digitization; automation adoption is minimal because the sector itself is contracting and largely unmechanized. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | This occupation involves low-digitization, physical equipment maintenance work in a declining sector (office machine operation), where AI/robotic adoption is minimal to nonexistent. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI cannot meaningfully assist with physically cleaning or filing tangible master copies, as the task requires manual dexterity and direct object handling with no informational or analytical component where AI could add value. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for the physical acts of cleaning and filing master copies or plates. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves physical manipulation of delicate master copies or printing plates in a mostly offline context. Current AI systems lack the embodied robotics, dexterity, and environmental perception needed to reliably clean and file physical objects without damage. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a physical cleaning and filing task involving physical materials (master copies/plates), which requires manual manipulation that current AI systems cannot perform; only robotics could address part of this, and general-purpose robots are not deployed for this niche task.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there is no formal licensing requirement, the physical nature of the work and organizational preference for human oversight of valuable masters/plates create modest adoption friction, though not substantial hard barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barriers exist, but the physical nature of the task and lack of economic incentive to automate such a narrow manual task create practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of handling delicate physical objects with the precision required for master copies would be significantly more expensive than paying a human operator to perform this maintenance task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical task, so any AI-based solution (e.g., custom robotics) would be far more costly than a human performing simple manual cleaning and filing. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can autonomously clean and file physical master copies or plates in an office setting. This remains a task requiring human hands and spatial reasoning that current AI systems cannot practically perform. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product performs physical cleaning and filing of printing plates or master copies in production settings today. |
Deliver completed work.
18CI 5–30 · exposure 8 · augmentation 13 · importance 4.3/5 · click for rater detail
Deliver completed work.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of automated delivery within traditional office environments remains slow. While some large firms experiment with internal logistics robots, most office settings still rely on human staff or contract messengers, indicating laggard adoption patterns in digitized but hierarchy-conscious organizational contexts. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Office machine operation is a low-digitization, physically-oriented occupation with minimal AI agent adoption for physical delivery tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance to the core task of physical delivery. Scheduling and routing software can help optimize delivery sequences, but the human still performs the actual work; the augmentation benefit is modest and indirect. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers little to no assistance for the physical act of delivering completed work, as this is a manual logistics step rather than a cognitive or digital task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical delivery of completed work requires transportation and placement in specific locations, which current AI systems cannot perform autonomously. While AI can manage task workflows and coordinate logistics, the actual delivery step involves physical manipulation and locational awareness beyond today's general-purpose robotic or autonomous systems. |
| Task automatability | claude-sonnet-5 | 1/5 | This task involves physically delivering completed work (documents, copies, etc.) to a location or person, which requires physical presence and mobility that current AI systems lack.dictionary |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Workplace culture and organizational friction create significant barriers: many organizations prefer human interaction for document delivery, accountability chains often require human sign-off, and liability for lost or damaged work typically rests with the responsible human worker rather than an automated system. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier exists, but the physical nature of delivery creates a practical barrier since software-based AI cannot perform this without physical embodiment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Physical delivery automation (robots, drones, logistics automation) remains expensive compared to human couriers in most office environments. Integration costs and the need for infrastructure redesign make AI-based delivery more expensive than employing office staff or messengers for this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI has no direct mechanism to perform physical delivery, so any automation would require robotics or human couriers, making AI more expensive or infeasible compared to a human simply walking the work over. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs end-to-end physical delivery of office machine output in production environments. Delivery coordination software exists, but autonomous physical fulfillment of this specific task at scale is not demonstrated in standard office settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical delivery of completed office work; this remains a manual, physical logistics task. |
Move heat units and clamping frames over screen beds to form Braille impressions on pages, raising frames to release individual copies.
10CI 5–15 · exposure 0 · augmentation 13 · importance 2.6/5 · click for rater detail
Move heat units and clamping frames over screen beds to form Braille impressions on pages, raising frames to release individual copies.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This occupation operates in a specialized, low-volume sector (accessibility services) with minimal digitization pressure and limited capital investment in automation, indicating laggard technology adoption patterns. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | This niche manual/physical operation within a small occupational category shows minimal AI adoption; the field remains dependent on specialized mechanical equipment and manual operation rather than AI systems. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance in controlling the mechanical timing and pressure adjustments required for this physical equipment operation; the task is fundamentally about dexterous hardware control rather than information processing. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could potentially assist with quality control checks or workflow scheduling around this task, but offers little direct assistance to the physical act of moving heat units and clamping frames. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves precise physical manipulation of multiple mechanical components (heat units, clamping frames, screen beds) requiring coordination and timing. Current AI systems lack the embodied dexterity and real-time sensorimotor control needed to reliably operate this specialized equipment end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task involving specialized Braille embossing equipment, requiring manual dexterity and physical operation of heat units and clamping frames that current AI cannot perform without robotic embodiment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Braille production may be subject to accessibility regulations and quality standards that require human oversight or certification. The specialized equipment and small-batch nature of Braille work creates organizational friction against automation investment. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this specific task, but the physical nature of equipment operation and quality control needs create some organizational friction against pure automation without specialized machinery investment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital equipment, integration costs, and safety considerations for automating this specialized Braille printing operation would substantially exceed the wages of a skilled operator, particularly given the low-volume nature of many Braille production runs. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based substitute for this physical task, so any comparison would require robotics with actuators and sensors, which would be far more costly to develop and deploy than employing a human operator. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product performs this specific Braille embossing operation autonomously. The task requires integration with legacy specialized equipment and real-time mechanical feedback that current AI systems are not designed to handle. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs this specific physical embossing operation; this requires either a human operator or specialized dedicated mechanical/robotic embossing machinery, not general AI. |
Related occupations — Office & Administrative Support
How to read this
A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.
What would change this score
New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.