Electrical and Electronic Equipment Assemblers
51-2022.00Rank #219 of 923 scored · top 24% by substitution
Assemble or modify electrical or electronic equipment, such as computers, test equipment telemetering systems, electric motors, and batteries.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution38
Exposure31
Augmentation46
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
16 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
0%
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%32
panel mean rating 2.3/5 → substitution pressure 32/100
Technical feasibility todayw 20%29
panel mean rating 2.2/5 → substitution pressure 29/100
Cost vs. human wagew 15%30
panel mean rating 2.2/5 → substitution pressure 30/100
Adoption barriersw 20%inverted — strong barriers lower the score63
panel mean rating 2.5/5 (barrier strength) → substitution pressure 63/100
Sector adoption velocityw 10%34
panel mean rating 2.4/5 → substitution pressure 34/100
Task breakdown (16 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, review, or maintain production, time, or component waste reports.
70CI 67–72 · exposure 70 · augmentation 75 · importance 3.7/5 · click for rater detail
Complete, review, or maintain production, time, or component waste reports.
70| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Manufacturing is moderately digitized and increasingly adopting shop-floor automation and analytics platforms, but many assemblers still work in laggard or mid-tier facilities with legacy systems. Broad adoption is underway in advanced plants but far from universal across the sector. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Manufacturing has moderate digitization with growing adoption of MES/IoT sensors and automated reporting, but adoption is slower and more uneven than in pure information-sector industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can assist assemblers and supervisors by auto-drafting reports, highlighting deviations, and cross-checking data in real time, freeing them to focus on root-cause investigation and corrective action rather than manual data entry and compilation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted dashboards, anomaly detection, and auto-generated summaries can significantly speed up review and highlight waste trends, meaningfully augmenting a human's ability to interpret and act on this data. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | This task is primarily documentation and record-keeping of structured data (production metrics, time logs, waste counts). Current AI systems can auto-populate reports from sensor data or existing databases, verify completeness, flag anomalies, and generate summaries—delivering well over 50% time savings. Manual review for accuracy and sign-off remain, but the bulk of the work is automatable. |
| Task automatability | claude-sonnet-5 | 4/5 | This is largely a structured data-entry, aggregation, and reporting task that current AI/software systems (ERP/MES integrations plus LLM-based report generation) can handle with substantial time savings, though some physical data collection from the shop floor still requires human input. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No legal requirement for human sign-off on internal production reports; data governance and quality assurance are organizational concerns rather than regulatory mandates. Some facilities may prefer human review for accountability, but this is soft friction, not a hard barrier. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or safety-critical sign-off is typically required for these internal administrative reports, though some quality/compliance review may still require a responsible human to verify accuracy. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Inference costs for report generation, data validation, and anomaly detection are negligible per instance. Integration and oversight overhead are modest relative to the loaded wage of an assembler spending time on manual reporting and review, yielding roughly 5–10× cost advantage for the AI path. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated data logging and report generation software is cheap to run at scale once integrated, and is far less costly than paying a worker to manually compile and review these reports repeatedly. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed RPA platforms, document-processing tools (OCR + LLMs), and manufacturing analytics systems already perform report generation, data extraction, and anomaly detection in production environments. Error rates on structured data entry and aggregation are low; the main limitation is integration with legacy systems, which is common but solvable. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Manufacturing execution systems (MES) and business intelligence tools already automate much of production/waste reporting in many factories, but many smaller assemblers still rely on manual logging and spreadsheet review, so deployment is uneven. |
Mark and tag components so that stock inventory can be tracked and identified.
69CI 55–84 · exposure 70 · augmentation 38 · importance 4.0/5 · click for rater detail
Mark and tag components so that stock inventory can be tracked and identified.
69| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Electronics manufacturing and logistics are high-digitization sectors with strong automation incentives. Barcode/RFID tagging automation is widely adopted in warehouses, contract manufacturers, and large OEM assembly lines, with measurable displacement of manual tagging roles. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Electronics manufacturing has moderate automation adoption for inventory tracking (barcodes, RFID) but full replacement of manual tagging varies widely by plant size and industry segment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While AI vision could assist humans in identifying which components to tag, the task itself (applying the mark/tag) is primarily mechanical and offers limited opportunity for human-AI collaboration that meaningfully boosts productivity beyond direct automation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Handheld scanners, label printers, and inventory software assist workers in marking and tracking components faster and more accurately, though the physical marking step often still needs human action. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | This task involves applying identifiers to physical components in a repetitive, rule-based manner. Computer vision systems can identify components, and robotic arms with marking/tagging actuators can execute the labeling end-to-end, achieving >50% time savings at equal quality compared to manual marking. |
| Task automatability | claude-sonnet-5 | 3/5 | Marking and tagging for inventory tracking can be automated via barcode/RFID labeling systems and vision-based identification, though physical placement and integration with varied components still requires setup and sometimes manual handling.'}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are minimal legal, licensing, or liability barriers to automating this task. No professional certification is required, and error costs (incorrect tag) are manageable. The main friction is organizational inertia and equipment integration at legacy facilities. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates human tagging; the main barriers are integration cost and physical handling constraints rather than regulatory or liability issues. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Robotic marking and RFID/barcode application systems have high upfront capital cost but very low per-unit operating cost once amortized. For high-volume assembly lines, the all-in cost per component marked is typically 1/3 to 1/10 the cost of human labor, placing it solidly in the favorable range. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Automated tagging equipment has meaningful upfront capital and integration costs, so for lower-volume operations the cost may be comparable to manual labor, though at scale automation becomes cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Vision-guided robotic systems for marking and barcode/RFID tagging are deployed in electronics manufacturing and warehouse environments at scale. While some edge cases (unusual component shapes, tight spaces) require human intervention, mature products perform this reliably in production settings. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Automated labeling and RFID tagging systems exist and are deployed in some electronics manufacturing lines, but many smaller assembly operations still rely on manual marking due to component variability and cost of automation. |
Read and interpret schematic drawings, diagrams, blueprints, specifications, work orders, or reports to determine materials requirements or assembly instructions.
54CI 35–72 · exposure 50 · augmentation 75 · importance 4.5/5 · click for rater detail
Read and interpret schematic drawings, diagrams, blueprints, specifications, work orders, or reports to determine materials requirements or assembly instructions.
54| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Manufacturing and electronics assembly are adopting AI-driven document analysis moderately; pilots exist in automotive and aerospace, but production rollout remains uneven due to legacy supply-chain documentation and customization variability. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and physical assembly sectors show slower AI adoption compared to information/professional services, with pilots for AI-assisted document reading emerging but not widespread in production assembly workflows. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI systems can assist assemblers by rapidly flagging key specifications, highlighting connections, or translating ambiguous symbols, substantially reducing time spent on document interpretation while the human assembler validates and executes. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully assist workers by summarizing specifications, flagging inconsistencies, or converting blueprints into checklists, substantially speeding comprehension while the human retains responsibility for accurate interpretation. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can reliably extract, interpret, and act on information from schematics and technical documents through vision and language models, delivering substantial time savings in identifying components and instructions without human review for routine assemblies. |
| Task automatability | claude-sonnet-5 | 2/5 | AI vision-language models can interpret simple schematics and extract text/specs, but complex blueprint interpretation feeding into physical assembly decisions still requires human verification and physical-world grounding that current systems don't reliably provide end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or liability barriers exist for using AI to interpret technical drawings; however, safety-critical contexts and custom or legacy schematics may require human verification, creating modest organizational friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for interpreting diagrams, though quality/safety consequences of misreading specs in electronics manufacturing create some organizational caution before full automation of this step. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-based document parsing and interpretation via cloud APIs costs pennies per drawing, while a skilled assembler reading and interpreting the same document costs $15–30+ per hour; the cost ratio heavily favors automation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI-assisted document parsing tools exist but require significant integration and human oversight for accuracy in a manufacturing context, making all-in cost savings modest compared to an assembler's wage for this sub-task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed computer vision and OCR products reliably extract structured data from technical drawings and schematics in production settings; systems can identify component labels, connections, and specifications with high accuracy on standard document formats. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some multimodal AI tools can parse technical drawings and extract bill-of-materials data, but deployed production systems for full schematic interpretation in electronics assembly contexts remain narrow and error-prone, not broadly reliable. |
Inspect or test wiring installations, assemblies, or circuits for resistance factors or for operation, and record results.
51CI 46–55 · exposure 42 · augmentation 75 · importance 4.3/5 · click for rater detail
Inspect or test wiring installations, assemblies, or circuits for resistance factors or for operation, and record results.
51| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Electronics manufacturing has begun adopting automated test and inspection (AOI, AXI), especially in high-volume sectors, but adoption remains patchy—many contract manufacturers and small runs still rely on manual inspection; overall velocity is middling-to-pilot stage. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Electronics manufacturing has moderate automation adoption via ATE and AOI (automated optical inspection) systems, but many assembly environments still rely on manual testing, especially in smaller-scale or prototype production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assists technicians substantially by automating tedious image scanning and resistance measurement logging, flagging anomalies for human review, and reducing transcription error; the technician remains in the loop for judgment, making this a strong augmentation case. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-enhanced test equipment and diagnostic software can significantly speed up fault detection, flag anomalies, and auto-log results, greatly boosting the productivity of a human tester who remains in the loop for judgment calls. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automate parts of this task—image-based visual inspection of wiring and automated circuit testing via instrumentation interfaces are viable—but human judgment on complex assemblies and interpretation of ambiguous results still dominates workflow, limiting full end-to-end automation to roughly half the task. |
| Task automatability | claude-sonnet-5 | 2/5 | Automated test equipment can perform electrical measurements, but the task as a physical inspection/testing workflow with manual probing and recording still requires significant human or robotic physical setup that current general AI cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Quality assurance and traceability regulations (IPC standards, aerospace/automotive certs) require documented inspection, but they do not mandate human performance of testing itself; AI-logged results can satisfy compliance if validated, creating only moderate friction rather than hard legal barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for this specific inspection task, though quality/safety certification standards (e.g., UL, IPC) may require documented human-verified processes in some industries. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI-based inspection systems (AOI hardware + software + integration) have capital and operational costs roughly comparable to or slightly cheaper than dedicated human inspectors when amortized over medium-volume production runs, but not an order of magnitude cheaper given labor costs in assembly settings. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Dedicated ATE systems can be cheaper per unit at high volume, but capital investment, fixture design, and maintenance make the ratio comparable rather than dramatically cheaper for many low-to-mid volume assembly operations. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed products exist for automated optical inspection (AOI) and electrical test equipment with digital logging, but they operate within narrow scope (standardized layouts, pre-defined defect classes) and still require technician oversight; no fully autonomous, production-scale end-to-end system replaces the full inspection-and-record loop. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Automated test equipment (ATE) and vision-based inspection systems are deployed in electronics manufacturing today, but they are narrow-purpose hardware/software systems rather than general AI, and coverage varies by product complexity and volume. |
Distribute materials, supplies, or subassemblies to work areas.
41CI 30–51 · exposure 38 · augmentation 25 · importance 3.7/5 · click for rater detail
Distribute materials, supplies, or subassemblies to work areas.
41| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Large contract manufacturers and electronics giants (Apple, Samsung suppliers) are piloting and deploying AGVs and bin-picking robots, but small-to-medium assembly shops still rely primarily on manual distribution. Adoption is faster in high-volume, highly structured facilities but remains inconsistent across the sector. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing, especially electronics assembly, has moderate but uneven automation adoption; robotics adoption is growing but slower than in software-centric sectors and concentrated in larger firms. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Material distribution is largely a standalone logistics task; AI/robotics augmentation (e.g., optimized pick lists or route suggestions) offers modest productivity gains but does not fundamentally transform the human's ability to perform the core task when it remains manual. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-based logistics/routing software can optimize material flow and inventory management, offering some assistance, but the physical task of distribution itself sees limited direct augmentation for the human doing it. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Current AI systems lack the physical manipulation and real-time spatial reasoning needed to autonomously distribute materials across a shop floor at scale. While logistics software can optimize routes, the actual physical distribution task requires robotic hardware integration that remains immature for variable, unstructured factory environments. |
| Task automatability | claude-sonnet-5 | 3/5 | Physical distribution of materials to work areas can be automated with AGVs/AMRs and conveyor systems, but requires facility-specific integration rather than an off-the-shelf software solution. current AI (software) alone cannot perform this without robotic hardware. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | No licensing requirement exists for material distribution, but organizational friction is moderate: warehouses must physically integrate AGVs, train operators, and ensure safety compliance. Adoption requires upfront capital commitment and facility redesign, which creates adoption friction even where technically feasible. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barriers, but organizational friction (capital cost, facility layout changes, safety certification for robots near workers) creates moderate adoption friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Warehouse automation and AGV systems are capital-intensive (six figures minimum) with substantial integration costs; ROI requires high-volume, repetitive operations. For typical small- to mid-sized electronics assembly facilities, the loaded wage of a material handler remains cheaper than full system deployment and maintenance. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AGV/AMR systems require significant capital investment, facility redesign, and maintenance, so while cheaper at scale over time, near-term cost parity or even higher upfront cost versus a low-wage assembler is common. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some warehouses use automated guided vehicles (AGVs) for bulk material movement, but these operate in controlled, mapped environments and require significant setup. General-purpose distribution in typical assembly facilities relies heavily on human judgment about placement, fragility, and just-in-time scheduling—deployed systems handle only narrow, pre-defined scenarios. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Mobile robots and automated material handling systems are deployed in many electronics manufacturing plants today, but adoption is uneven and many facilities still rely on manual distribution, especially smaller ones. |
Instruct customers in the installation, repair, or maintenance of products.
37CI 34–41 · exposure 25 · augmentation 75 · importance 3.2/5 · click for rater detail
Instruct customers in the installation, repair, or maintenance of products.
37| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Many electronics manufacturers have deployed AI-assisted FAQs and video tutorials, but human technician support lines remain the norm for complex issues; adoption is uneven across segments (consumer electronics faster than industrial), and production-scale autonomous instruction remains limited. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and equipment assembly sectors are generally slower adopters of AI-driven customer service compared to pure information/service sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at drafting clear instructions, generating step-by-step visuals, and organizing knowledge bases that technicians reference in real time. Current systems significantly boost a human instructor's speed and consistency in delivering customized guidance while they handle customer judgment and novel problem-solving. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly assist by providing instant access to manuals, troubleshooting guides, and chat-based support, improving efficiency and consistency of human-delivered instruction. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate instructional content and documentation, fully automating customer interaction requires real-time technical diagnosis, adaptive explanation based on customer feedback, and handling unforeseen installation issues—capabilities that current systems struggle with reliably. The task involves substantial human judgment and problem-solving that AI cannot yet replace end-to-end at the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 2/5 | Instruction can be partially handled via chatbots or video guides, but real-time, hands-on customer instruction with troubleshooting and physical demonstration is not fully replaceable by current AI end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there is no legal requirement for a licensed human to deliver installation instructions, customer liability concerns, product warranty implications, and organizational preference to avoid costly support failures create moderate friction against full automation. Companies often retain human verification or escalation for complex or high-liability products. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement typically applies, but customer preference for human interaction and liability concerns around improper installation create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI-generated video guides and chatbot support are cheaper per interaction than live technician time, but require significant overhead in content creation, quality assurance, and human escalation paths, making per-unit costs roughly comparable to blended human labor models in many deployment scenarios. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-driven support (chatbots, video tutorials) is cheaper than dedicated human instructors for common cases, but complex or nonstandard instruction still requires costly human intervention, balancing the ratio. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Chatbots and video tutorial systems exist but typically handle only scripted, straightforward scenarios; they fail on complex troubleshooting, spatial reasoning about customer-specific installations, and maintaining customer confidence through technical difficulty. No production system reliably instructs customers independently across the full range of installation and repair scenarios. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Deployed products like chatbots and AI-generated manuals exist for basic Q&A, but reliable, adaptive instruction for varied customer issues in production settings is limited and narrow in scope. |
Explain assembly procedures or techniques to other workers.
33CI 30–35 · exposure 25 · augmentation 50 · importance 4.0/5 · click for rater detail
Explain assembly procedures or techniques to other workers.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing and assembly are slower to digitize and adopt AI agents; most shops still rely on experienced workers and printed manuals. While documentation tools are adopted, autonomous explanation systems are not in production at scale in typical assembly plants. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing/assembly sectors show slower AI adoption for floor-level training tasks compared to information-based industries, with pilots for AR/AI training tools still uncommon in most facilities. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist workers by generating procedure summaries, creating visual aids, and drafting training materials that a supervisor then refines and delivers. The human trainer remains central, but AI augmentation can improve preparation and consistency of explanations. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can support this task via generated instructional content, translation, or knowledge-base lookup that a worker uses while explaining procedures, offering moderate productivity gains without replacing the human trainer. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate assembly procedure documentation and explanations, the real-time, interpersonal teaching of techniques—responding to worker confusion, demonstrating physical procedures, and verifying understanding—requires human presence and interaction. AI could draft materials but cannot replace the dynamic explanation in person. |
| Task automatability | claude-sonnet-5 | 2/5 | Explaining assembly procedures to coworkers is a communication/training task rooted in physical demonstration and hands-on context that current AI cannot fully replicate on the shop floor, though some explanatory content could be pre-scripted or supplemented by AI-generated documentation.5rating remains low because the interactive, situational teaching aspect resists full automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Although there is no strict licensing requirement, manufacturing workplaces often prefer experienced workers for mentoring due to liability concerns, worker safety culture, and the tacit knowledge embedded in explanation. Organizational friction is moderate but meaningful. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational reliance on experienced human workers for real-time troubleshooting and peer instruction creates moderate friction against substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The loaded cost of a skilled assembler explaining procedures is relatively low (wages at $20–25/hr), and AI still requires significant setup, knowledge engineering, and human oversight to match quality. All-in AI cost per explanation is not yet cheaper. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Creating AI-generated training materials is cheap, but the actual in-person explaining/mentoring by a skilled worker isn't easily replaced without added integration and oversight costs, keeping AI cost savings limited for this specific interpersonal task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI systems can produce written guides and procedural documents, but deployed products do not reliably conduct real-time skill training or adaptive explanation to workers at the bench. Chatbots exist but lack the embodied demonstration and contextual feedback that this task demands. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products exist for generating training manuals, videos, or chatbots that answer procedural questions, but no deployed system reliably substitutes for a human explaining live assembly techniques on a production line. |
Pack finished assemblies for shipment, and transport them to storage areas, using hoists or handtrucks.
33CI 30–35 · exposure 20 · augmentation 38 · importance 3.4/5 · click for rater detail
Pack finished assemblies for shipment, and transport them to storage areas, using hoists or handtrucks.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing and warehousing sectors show modest automation adoption in picking and transport, but fine-grained packing of diverse finished goods remains largely manual. Current real-world deployment is sparse relative to pilot interest. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and warehousing are moderate-to-slow adopters of full automation for physical material handling compared to information-sector AI adoption, though large-scale logistics operations are investing steadily. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered vision systems can assist workers by identifying correct packing orientation or flagging damage, and lightweight collaborative robots can augment lifting of heavy assemblies; these assistive tools improve workflow without requiring full autonomy. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-enabled tools like smart handtrucks, RFID-guided routing, or automated packing-list generation can somewhat assist workers, but the physical core of the task sees limited direct AI augmentation today. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Packing and transporting physical assemblies requires dexterous manipulation in unstructured environments. While vision-guided robotic systems exist, they typically handle only standardized, rigid parts in controlled settings; finished electronics assemblies vary in size, fragility, and material composition, making reliable end-to-end automation without significant engineering infeasible at current capability. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical packing and transport of assemblies requires manipulation, dexterity, and mobility that current general-purpose AI/robotics cannot yet do end-to-end reliably outside narrow, structured warehouse settings.dd Some sub-steps (e.g., using an AGV in a fixed-route warehouse) can be automated but not the whole task as described.dd |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Physical safety and liability around fragile electronics and coworker proximity create some friction, but no legal licensing or mandatory human sign-off exists. Organizational and capital-investment barriers are moderate but not hard regulatory or liability walls. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human for packing/transport, but safety regulations around powered equipment (hoists, forklifts) and facility layout create moderate operational friction for automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic systems capable of this work (arm + vision + gripper + integration) carry capital and operational costs comparable to or exceeding entry-level warehouse labor; ROI requires high-volume, repetitive, standardized scenarios that do not characterize diverse assembly packing. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Robotic material-handling systems require significant capital investment (robots, sensors, facility retrofits) that often exceeds the cost of a warehouse worker performing this task, especially for low-volume or variable assemblies. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs this full task (packing + transport with hoists/handtrucks) at production scale. Specialized warehouse robots handle simplified picking or transport separately, but integrated packing-for-shipment remains primarily research and narrow-case automation. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Deployed AGVs and robotic palletizers exist in large distribution centers, but generalized packing plus hoist/handtruck transport of varied assemblies is still narrow-scope and not standard in most electronics assembly plants. |
Measure and adjust voltages to specified values to determine operational accuracy of instruments.
31CI 30–32 · exposure 25 · augmentation 50 · importance 3.8/5 · click for rater detail
Measure and adjust voltages to specified values to determine operational accuracy of instruments.
31| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Large-scale electronics manufacturing has adopted automated test equipment, but adoption is uneven: small contract manufacturers and non-standardized instrument assembly still rely on manual measurement by technicians, reflecting middling overall adoption velocity. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Electronics manufacturing has some automated testing but overall assembly and calibration adoption of AI-driven adjustment remains slow, concentrated in high-volume fixed-function ATE rather than flexible AI agents. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted test analysis tools can help technicians interpret multi-parameter voltage readings and flag anomalies, but the core tasks of physical measurement and calibration adjustment still require human presence and decision-making, providing useful but partial productivity gains. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted diagnostic software and data logging can help technicians interpret readings and flag anomalies faster, but the core physical measurement and adjustment still requires direct human action. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI-controlled automated systems can perform voltage measurements via sensors and adjust some circuits, the task requires physical manipulation of test equipment, real-time interpretation of variable instrument responses, and judgment about acceptable tolerance ranges—capabilities that are not reliably automated end-to-end by current general systems. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical manipulation of hardware, probing test points, and adjusting components, which current AI systems cannot perform without robotic embodiment; only the data-analysis portion is automatable today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Quality control and safety regulations often require human verification of critical voltage measurements and sign-off on instrument accuracy before release; however, automated test systems are increasingly used for initial screening, creating moderate adoption barriers rather than absolute prohibitions. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement typically, but quality/safety-critical calibration work often requires certified technician sign-off and traceable calibration procedures, creating moderate organizational and compliance friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Automated voltage testing equipment requires expensive capital investment, integration, and calibration overhead; for low-volume or varied instrument work, the total cost per task often exceeds or rivals the loaded wage of a skilled technician performing the measurement. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Specialized ATE can be cost-effective at high volume, but general AI systems add no cost advantage here since the task is physical and requires calibrated instrumentation and robotics, which remain expensive relative to a technician for lower-volume work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Specialized test automation equipment exists in manufacturing, but it is domain-specific hardware, not a deployed general AI product that can reliably perform this task across instrument types and conditions without significant customization and setup. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Automated test equipment (ATE) exists for voltage measurement in production lines, but adjustment of instruments typically still requires human technicians or specialized fixed-function hardware, not general AI systems. |
Fabricate or form parts, coils, or structures according to specifications, using drills, calipers, cutters, or saws.
30CI 25–35 · exposure 25 · augmentation 38 · importance 3.7/5 · click for rater detail
Fabricate or form parts, coils, or structures according to specifications, using drills, calipers, cutters, or saws.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Assembly and fabrication sectors show slower AI adoption than information industries; most deployment remains confined to high-volume, repetitive scenarios rather than flexible part-fabrication tasks, and small-to-medium equipment assembly shops lag significantly. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Electronics manufacturing has adopted robotics and automation for high-volume production, but many assembly tasks involving custom fabrication with hand tools remain manual, especially in lower-volume or job-shop settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted design tools, computer vision for inspection, and automated drill/saw guidance can improve assembler productivity and reduce errors, but the human operator typically remains central to task execution and quality assurance. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with specification interpretation, quality inspection via computer vision, or work instructions, but offers limited direct augmentation of the physical forming/cutting process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While some aspects like CNC-controlled cutting or drilling can be automated, the full task requires physical manipulation, precision measurement with calipers, and adaptation to material variations that current general-purpose AI systems cannot reliably execute end-to-end in unstructured manufacturing environments. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a physical fabrication task requiring manual dexterity and tool operation; current general-purpose AI cannot perform this end-to-end, though robotic automation (non-AI or AI-assisted) can handle narrow, repetitive sub-tasks in controlled settings. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Manufacturing environments often have quality control, safety, and liability requirements that mandate human inspection and sign-off; worker displacement concerns and established union contracts in some sectors create organizational and regulatory friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but quality control, safety certification for machinery, and the need for physical precision create moderate organizational friction to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Dedicated industrial automation for this task requires significant capital investment in robots, fixtures, and setup; for flexible, low-volume part fabrication, human labor remains more cost-effective than the total integration cost of AI-driven systems. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Industrial robotic automation requires significant capital investment, engineering, and maintenance; for small-batch or varied assembly work typical of this occupation, human labor often remains cost-competitive versus custom robotic cells. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Specialized industrial robots and CNC machines exist for specific fabrication steps, but no deployed AI system reliably performs the integrated task of fabricating coils, structures, and diverse parts with varied tools while interpreting specifications and adjusting for quality without human oversight. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Robotic arms and CNC-style fixed automation exist in electronics manufacturing for specific repetitive forming/cutting operations, but flexible AI-driven systems that handle varied specifications with calipers, drills, and saws reliably are still limited to high-volume, narrowly defined production lines. |
Assemble electrical or electronic systems or support structures and install components, units, subassemblies, wiring, or assembly casings, using rivets, bolts, soldering or micro-welding equipment.
29CI 21–38 · exposure 17 · augmentation 25 · importance 4.4/5 · click for rater detail
Assemble electrical or electronic systems or support structures and install components, units, subassemblies, wiring, or assembly casings, using rivets, bolts, soldering or micro-welding equipment.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Electrical equipment assembly in manufacturing has seen steady robotic adoption in large-scale production (automotive, consumer electronics), but many contract manufacturers and smaller shops still rely heavily on human assembly. Adoption is sector-dependent and unevenly distributed. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing/assembly sectors adopt automation unevenly; large-scale electronics manufacturers use robotics extensively, but this is slow, capital-intensive adoption rather than fast software-driven diffusion, and many assemblers work in smaller-scale or custom production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI provides minimal on-the-fly assistance to human assemblers; computer vision can highlight component positions or flag misalignments, but this is narrow and not transformative for manual soldering and riveting work that demands continuous human dexterity and judgment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-assisted vision systems and quality-inspection tools can support human assemblers by flagging defects or guiding placement, but they don't substantially transform the core hands-on assembly and soldering work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can identify components and positions, the physical precision work of riveting, bolting, soldering, and micro-welding requires specialized robotic hardware with sub-millimeter accuracy. Current general-purpose AI cannot autonomously perform these fine-motor assembly operations end-to-end without extensive task-specific mechanical setup, falling well short of 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual assembly task requiring dexterous manipulation, fine motor control, and use of tools like rivets, bolts, and soldering equipment; current AI (software/LLMs) cannot perform this physical work at all, and robotics for such flexible assembly remain limited to structured, high-volume lines rather than general-purpose deployment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Quality assurance and worker safety standards (UL, IEC certifications) require documented human inspection of solder joints and electrical connections, though automation can handle intermediate steps. Regulatory oversight applies to manufacturing processes rather than to the automation itself, creating moderate adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but quality control, safety certification for soldering/welding, and physical workspace integration create moderate organizational and capital barriers to swapping in robotics. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Specialized robotic systems for soldering and precision assembly carry high capital and integration costs ($100k–$1M+), and still require human oversight and rework. For typical assembly wages ($20–$30/hour), the all-in cost per unit assembled often exceeds human labor, especially for low-volume or mixed-product work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Specialized robotic assembly cells are costly to design, program, and maintain for variable products; for high-volume standardized tasks cost can favor automation, but for general flexible assembly described here, human labor often remains cheaper or comparable given capital and integration costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Robotic assembly systems exist in factories but are highly specialized, programmed for specific products, and require substantial integration per product variant. No general-purpose deployed AI product reliably handles the diverse component types, solder joint inspection, and alignment tolerances across different electrical systems without custom engineering. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Robotic arms perform some soldering/fastening in high-volume electronics manufacturing (e.g., PCB assembly lines), but general assembly of varied electrical/electronic systems with wiring and casings still relies heavily on manual labor; fully autonomous robotic assembly for varied small-batch tasks is not mature. |
Adjust, repair, or replace electrical or electronic components to correct defects and to ensure conformance to specifications.
28CI 21–35 · exposure 20 · augmentation 50 · importance 4.2/5 · click for rater detail
Adjust, repair, or replace electrical or electronic components to correct defects and to ensure conformance to specifications.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing and assembly sectors show moderate digitization, but physical repair tasks lag behind information-centric automation; most high-volume assembly uses specialized fixed robots rather than adaptive AI agents, and human repair technicians remain central to quality control. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Electronics manufacturing has some automation (pick-and-place, AOI) but flexible robotic repair of defects is still uncommon; sector adoption of general AI for physical repair remains slow. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist technicians by diagnosing faults, identifying correct replacement components, and guiding repair procedures, meaningfully improving productivity on diagnosis and parts lookup, though the technician retains hands-on control of the actual repair work. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-powered diagnostic tools, defect-detection vision systems, and troubleshooting guides can meaningfully assist workers in identifying issues faster, even though the physical repair remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While some diagnosis and component identification can be partially automated, the hands-on adjustment, repair, and replacement of physical components requires dexterity, spatial reasoning, and situational judgment that current AI systems struggle with. End-to-end automation at 50% time savings is not yet demonstrated at scale. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical manipulation, fine motor dexterity, diagnostic judgment, and hands-on repair of physical components—current AI systems cannot perform the physical manipulation portion, only assist with diagnostics or documentation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While not legally gated, there are moderate organizational and technical barriers: quality assurance requirements, warranty liability for defective repairs, need for visual inspection and human sign-off, and variability in component types and defects that make standardized automation difficult. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement typically exists, but quality/safety conformance testing and liability for defective electronics create moderate organizational caution before removing human inspection/repair steps. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current robotic systems capable of electronic assembly and repair are capital-intensive and require significant setup, making the total cost per task-equivalent comparable to or higher than a skilled human assembler's loaded wage. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic/automated repair systems capable of flexible diagnosis-and-repair are far more expensive to develop and deploy than employing an assembler, especially for variable defect types. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI-powered diagnostic tools exist and can identify some defects, but reliable end-to-end repair involving physical manipulation and context-dependent component replacement remains research-stage or narrow-scope in production. Human technicians still perform the majority of actual repair work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously adjusts, repairs, or replaces physical electronic components; robotic repair systems remain research-stage or narrowly scoped to specific high-volume manufacturing lines, not general defect correction. |
Clean parts, using cleaning solutions, air hoses, and cloths.
26CI 21–31 · exposure 20 · augmentation 13 · importance 4.0/5 · click for rater detail
Clean parts, using cleaning solutions, air hoses, and cloths.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing sectors show moderate automation, but labor-intensive assembly remains prevalent in smaller facilities. Adoption of cleaning automation is slower than higher-value tasks, with many shops still using manual methods due to cost and flexibility concerns. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Electronics manufacturing has adopted some automation for precision tasks, but low-value manual cleaning steps are lower priority and adoption of robotics for this specific task is slow. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers limited assistance for this task; computer vision could potentially flag contaminated parts, but the hands-on, sensory nature of cleaning—feeling for texture, adjusting pressure—resists meaningful augmentation with current tools. |
| Augmentation potential | claude-sonnet-5 | 1/5 | There is little role for AI software to meaningfully assist a human physically cleaning parts with solvents and air hoses; this is a hands-on physical task outside typical AI augmentation scope. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Cleaning parts requires manual dexterity, judgment about cleanliness levels, and handling of delicate components. While industrial robots can perform some repetitive cleaning with fixed parameters, the task involves variable part geometries, material sensitivity, and quality assessment that current AI systems cannot reliably automate end-to-end without human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a physical manual task involving handling parts, solvents, and tools; current AI (software) cannot perform this without embodied robotics, which are not general-purpose enough for varied cleaning tasks at scale today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Some regulatory requirements exist around chemical handling and worker safety, and organizations may prefer human oversight of part quality. However, no hard legal requirement prevents automation, creating moderate friction rather than hard barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but safety/OSHA handling of solvents and physical workspace integration create moderate friction against quick automation swaps. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized cleaning equipment, maintenance, chemical costs, and oversight requirements are expensive. The relatively low hourly wage of assembly workers means full automation with hardware setup and integration costs exceeds the savings on this particular task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic cleaning cells requiring custom fixtures, solvent handling, and calibration are far more capital-intensive than paying an assembler to clean parts by hand, making AI/robotics costlier per unit output today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Industrial cleaning systems exist for specific, standardized parts, but no deployed product reliably cleans arbitrary parts with the judgment and adaptability humans provide. Current solutions are narrowly scoped and require extensive customization; production deployment at scale is limited. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No widely deployed product performs manual parts-cleaning with cleaning solutions and air hoses in electronics assembly settings; this remains a manual/robotic automation niche rather than an AI product category. |
Position, align, or adjust workpieces or electrical parts to facilitate wiring or assembly.
24CI 13–35 · exposure 13 · augmentation 25 · importance 4.2/5 · click for rater detail
Position, align, or adjust workpieces or electrical parts to facilitate wiring or assembly.
24| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing adoption of positioning automation is sector-specific and limited to high-volume, standardized production lines. Most small to medium assembly operations rely on manual positioning due to product variability and setup costs, limiting broad adoption velocity. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Electronics assembly is automated in large-scale manufacturing via fixed robotics, but AI-driven adaptive positioning is not widely deployed; this sub-task lags behind digitized information work in adoption speed. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AR/visual guidance systems offer limited assistance by highlighting alignment targets, but positioning remains primarily a manual, tactile task where AI augmentation has minimal impact on worker productivity compared to tools already in use. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-assisted vision guidance or work instructions can somewhat help human assemblers verify alignment, but the physical positioning itself sees little direct AI augmentation today. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While robotic systems can position some standardized parts, the task requires spatial reasoning, fine motor control, and adaptive adjustment to varying workpiece conditions. Current general-purpose AI lacks the dexterous manipulation and real-time sensing to handle the variability and precision required end-to-end without significant human oversight and rework. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring fine motor dexterity and real-time visual-tactile feedback that current general-purpose AI systems cannot perform end-to-end; robotic manipulation of small electrical parts remains a hard, unsolved problem outside narrow fixed setups.atable at scale.iss |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Assembly work is primarily in unionized or regulated manufacturing environments where workforce displacement creates organizational friction and negotiation requirements. While no explicit licensing bars automation, labor relations and facility constraints create moderate adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates human performance, but physical workspace integration, safety requirements, and quality control introduce real organizational friction to automating this step. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robot-based positioning systems require substantial capital investment and integration costs that are only economical for high-volume repetitive tasks. For typical assembly contexts with product variation, the all-in cost (hardware, programming, maintenance, oversight) remains comparable to or exceeds human labor cost. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Custom robotic manipulation systems capable of this fine positioning work require expensive engineering, fixtures, and vision systems that typically cost more than human labor for small-to-medium batch assembly. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Specialized industrial robots perform positioning in controlled, high-volume environments, but these are narrowly scoped solutions, not general AI products. General-purpose AI systems do not reliably perform this task in production; deployed solutions remain domain-specific automation rather than broadly applicable AI. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No general deployed product reliably positions and aligns arbitrary electrical/electronic workpieces for assembly; existing robotic solutions are highly customized, task-specific fixtures rather than adaptable AI-driven systems. |
Confer with supervisors or engineers to plan or review work activities or to resolve production problems.
22CI 14–30 · exposure 20 · augmentation 63 · importance 3.6/5 · click for rater detail
Confer with supervisors or engineers to plan or review work activities or to resolve production problems.
22| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Manufacturing remains relatively laggard in AI adoption for decision-making authority; supervisory conferencing is deeply human-centered in most plants, with limited incentive to replace face-to-face problem resolution. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and assembly sectors have historically slower AI adoption for collaborative decision-making tasks compared to information/professional services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist by summarizing production metrics, recommending solutions, or organizing data for review, meaningfully speeding the human-led conference, but the core negotiation and decision-making remain human-directed. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools (e.g., production analytics, chatbots summarizing issues, predictive maintenance alerts) can meaningfully prepare data and insights that make these conferences more productive, even though humans still conduct them. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task requires real-time problem-solving, interpersonal negotiation, and judgment about production constraints that demand human decision-making authority. While AI could draft summaries or flag issues, the collaborative planning and authority-dependent resolution are not automatable end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | This task requires real-time interpersonal negotiation, contextual judgment about physical production issues, and often on-the-spot decision-making that current AI cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: supervisory and engineering sign-off on production decisions are often implicit or explicit organizational/legal requirements, and error costs (safety, quality, delivery) create liability asymmetry that demands human accountability. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing barrier, but organizational norms, accountability for production decisions, and the need for human judgment in resolving issues create meaningful friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current AI inference costs for multi-turn dialogue and problem-solving are low, but the task requires human supervisors/engineers to remain present and accountable; any AI assistance merely adds overhead rather than substituting the human cost. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI could support documentation or data analysis cheaply, but the core conferring and problem-solving still requires human labor, so overall cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably conduct supervisory planning conferences or resolve production problems independently. Chatbots can simulate dialogue but cannot access live factory data, make binding decisions, or replace the accountability of human authority in production settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously confers with supervisors/engineers to resolve shop-floor production problems; this remains a human-to-human collaborative activity. |
Drill or tap holes in specified equipment locations to mount control units or to provide openings for elements, wiring, or instruments.
21CI 7–35 · exposure 13 · augmentation 25 · importance 3.9/5 · click for rater detail
Drill or tap holes in specified equipment locations to mount control units or to provide openings for elements, wiring, or instruments.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Assembly manufacturing, particularly in small-to-medium firms and contract shops, has slower AI adoption than information sectors. While large-scale electronics manufacturing has some automation, drilling/tapping-specific automation remains limited and concentrated in high-volume operations. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Electronics assembly is a moderately automated sector with robotics in high-volume lines, but overall adoption of full automation for varied drilling/tapping tasks remains slow outside large-scale manufacturing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal real-time assistance to a human performing manual drilling and tapping; computer vision aids (part detection, jig alignment hints) exist in research but see limited deployment. The task remains largely human-manual with little productivity amplification from current AI. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with design specifications, hole placement verification, or quality inspection, but offers little direct assistance to the physical drilling/tapping act itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While drilling and tapping are mechanically repetitive, they require precise positioning based on equipment-specific specifications, visual inspection, and real-time adjustment for material variations. Current AI systems cannot reliably perform the full assembly context (locating equipment, adapting to material properties, quality-checking results) end-to-end with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a precise physical drilling/tapping operation requiring manual dexterity, tool handling, and physical positioning on real equipment; no off-the-shelf AI system can perform this physical manipulation task today.confusion |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Physical task execution in controlled manufacturing environments, equipment safety requirements, and quality assurance sign-offs create organizational and regulatory friction. Many facilities have established equipment and worker qualification standards that slow automation adoption. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but physical workspace constraints, need for flexible handling of varied equipment, and capital costs for automation create meaningful organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic drilling/tapping solutions involve high capital investment, programming, and integration costs that typically exceed the loaded wage of skilled assemblers, especially for small-batch or custom work where setup amortization is poor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic drilling systems capable of this task require significant capital investment in specialized hardware and integration far exceeding the cost of a human assembler for low-to-medium volume work typical of this occupation. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Robotic drilling/tapping systems exist in manufacturing but require significant setup, custom tooling, and integration for each product variant. No deployed off-the-shelf AI system reliably performs this task across the diversity of equipment types and mounting requirements assemblers encounter. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | While CNC drilling exists for high-volume manufacturing, this task as described (assembler manually drilling/tapping specified locations) is not performed by deployed general AI products; it requires robotic hardware, not AI software, and remains largely manual in most assembly contexts. |
Related occupations — Production
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.