Audiovisual Equipment Installers and Repairers
49-2097.00Median wage $52,600/yr19,780 employed (US)Rank #438 of 923 scored · top 47% by substitution
Install, repair, or adjust audio or television receivers, stereo systems, camcorders, video systems, or other electronic entertainment equipment in homes or other venues. May perform routine maintenance.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution29
Exposure21
Augmentation53
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
11 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
9%
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%21
panel mean rating 1.9/5 → substitution pressure 21/100
Technical feasibility todayw 20%21
panel mean rating 1.9/5 → substitution pressure 21/100
Cost vs. human wagew 15%25
panel mean rating 2.0/5 → substitution pressure 25/100
Adoption barriersw 20%inverted — strong barriers lower the score58
panel mean rating 2.7/5 (barrier strength) → substitution pressure 58/100
Sector adoption velocityw 10%17
panel mean rating 1.7/5 → substitution pressure 17/100
Task breakdown (11 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.
Keep records of work orders and test and maintenance reports.
71CI 65–77 · exposure 70 · augmentation 88 · importance 3.7/5 · click for rater detail
Keep records of work orders and test and maintenance reports.
71| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Field service industries show moderate AI adoption for administrative tasks, with many small-to-mid-size AV installation firms still using manual or semi-manual record systems. Larger integrators have adopted automation, but adoption is not yet universal or deeply penetrated across the sector. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Audiovisual installation and repair is a trade-based, moderately digitized sector where software adoption for documentation is growing but still lags behind information/professional services sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly assists technicians by auto-populating forms, suggesting relevant maintenance history, and organizing data while the human validates accuracy and adds context. This substantially reduces clerical burden without removing the technician's role in quality control. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI tools like voice transcription, auto-filled templates, and smart forms substantially speed up record-keeping while the technician remains responsible for accuracy and final review. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Recording work orders and maintenance reports involves primarily data entry and structuring of information, which current AI can handle effectively. However, some technical details and contextual judgment may still require human oversight, preventing a perfect 5. |
| Task automatability | claude-sonnet-5 | 4/5 | Recording work orders and maintenance reports is largely structured data entry and documentation, which current AI (voice-to-text, form-filling, structured logging tools) can handle with significant time savings, especially when integrated with mobile field-service apps. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While some organizations may require technician sign-off or audit trails, there are no regulatory requirements mandating human data entry for audiovisual maintenance records. Integration with existing systems and worker preference for familiar workflows present minor friction but not hard barriers. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There is no licensing or legal requirement that a human personally compile these records; it's an administrative task with minimal regulatory or liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-based document processing and record-keeping is substantially cheaper than manual data entry by technicians. A single AI system can handle thousands of records at near-zero marginal cost, making it an order of magnitude cheaper than paying labor rates for clerical work. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated dictation and structured record-keeping tools are inexpensive relative to technician time spent manually typing reports, offering substantial per-task cost savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Document management systems and form-filling automation are mature and widely deployed in field service organizations. Current OCR and data extraction tools reliably capture and organize work order information in production environments. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Field service management software with AI-assisted note-taking, voice dictation, and auto-population of maintenance logs exists and is deployed, but full end-to-end automation without technician review is not yet universal across this trade. |
Compute cost estimates for labor and materials.
66CI 52–79 · exposure 62 · augmentation 88 · importance 4.1/5 · click for rater detail
Compute cost estimates for labor and materials.
66| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Field service and construction trades are increasingly digitizing operations and adopting AI-assisted estimating tools; adoption is visible in platforms like ServiceTitan, Housecall Pro, and specialist electrical/HVAC software integrating AI cost modules. Adoption is faster in larger firms and organized sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | placeholder |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically augments this task by instantly pulling material costs, applying labor rates, and drafting estimates that technicians refine and contextualize. The human remains in control and decision-making improves with AI-generated baseline data, making this a classic high-augmentation scenario. |
| Augmentation potential | claude-sonnet-5 | 4/5 | placeholder |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Cost estimation for labor and materials involves structured data collection, calculation, and template-based report generation—tasks where current AI excels. An AI system can access inventory databases, apply labor rates and material pricing, and generate estimates with >50% time savings, though final review by a technician familiar with site-specific constraints may still be prudent. |
| Task automatability | claude-sonnet-5 | 3/5 | Cost estimation from itemized labor and materials lists is a structured, calculation-heavy task that AI can largely automate given inputs like parts lists and labor rates, though it requires accurate scoping data typically gathered on-site."},"feasibility":{"rating":3,"rationale":"Estimating software with some AI-assisted features exists and is used in trades, but fully autonomous, reliable AI-generated estimates without human review are not standard practice."},"cost_ratio":{"rating":3,"rationale":"AI-assisted estimating tools reduce time spent on calculations but still require human input for site-specific variables, so cost savings are moderate rather than order-of-magnitude."},"barriers":{"rating":2,"rationale":"No licensing requirement specifically for estimating, but businesses often want a trusted technician's judgment to avoid under/over-bidding, creating some organizational friction."},"adoption_velocity":{"rating":2,"rationale":"Audiovisual installation is a physically-oriented trade with modest digitization; adoption of AI-driven estimating tools is emerging but not widespread."},"augmentation":{"rating":4,"rationale":"AI and estimating software can significantly speed up calculations, pull historical pricing data, and generate draft estimates for technician review, meaningfully boosting productivity. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are no licensing or legal barriers to AI generating cost estimates; technicians may prefer human review for customer-facing quotes, but this is preference rather than regulation. Most organizations would adopt AI assistance with minimal friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | placeholder |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI inference cost for generating an estimate is negligible (pennies), while a human technician would spend 30–60 minutes gathering data and calculating—representing $20–$60 in loaded wages. The cost ratio is at least an order of magnitude in AI's favor. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | placeholder |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple deployed products (project management software, estimating platforms, AI-assisted spreadsheet tools) reliably perform cost estimation and generate quotes at scale. These systems are used in production environments across trades, though they typically still require human input of scope details and final approval. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | placeholder |
Read and interpret electronic circuit diagrams, function block diagrams, specifications, engineering drawings, and service manuals.
39CI 35–44 · exposure 30 · augmentation 75 · importance 3.9/5 · click for rater detail
Read and interpret electronic circuit diagrams, function block diagrams, specifications, engineering drawings, and service manuals.
39| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Audiovisual installation and repair is fragmented across small and mid-sized service firms with low digitization; while some larger organizations pilot AI-assisted documentation, production adoption of autonomous diagram interpretation remains limited. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Audiovisual repair is a low-digitization, hands-on trade with limited enterprise-scale AI tool adoption compared to information/professional services sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-powered diagram digitization, component recognition, cross-referencing with manuals, and automated highlighting of relevant sections can meaningfully accelerate technician workflow and reduce search time, allowing the human expert to focus on diagnosis and repair strategy. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Multimodal AI tools can meaningfully assist technicians by quickly explaining symbols, cross-referencing manuals, and summarizing specifications, speeding up comprehension while the technician remains responsible for physical work. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can extract and describe elements from circuit diagrams and technical drawings, interpreting them for troubleshooting, repair decisions, or compliance requires contextual judgment, spatial reasoning about physical systems, and integration with on-site observations that current AI systems cannot reliably perform end-to-end with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help interpret and explain diagrams and manuals, but the task is embedded in a physical repair workflow requiring on-site verification, making full end-to-end automation infeasible today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No legal licensing requirement mandates human interpretation of circuit diagrams, though some installations may require licensed technicians for final sign-off; organizational and safety practices typically still require human responsibility for correctness, creating moderate adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for reading diagrams, though technicians typically need broader certification for the repair job as a whole, creating some indirect friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Document digitization and AI-assisted diagram reading have modest per-unit cost, but the need for expert human review of interpretations, correction of errors, and integration with on-site context makes the total cost per repair task comparable to or only moderately below the technician's loaded wage. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-assisted diagram reading tools are cheap to run, but a technician still must review and apply the interpretation on-site, so overall task cost savings are moderate rather than dramatic. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Vision models and document parsing tools can identify circuit components and extract text from schematics, and some specialized tools assist with diagram interpretation, but deployed products show material error rates on complex or hand-drawn diagrams and require significant human verification before actionable repair decisions. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Vision-language models can parse simple schematics and technical text, but reliable interpretation of complex circuit diagrams and specs in production repair contexts is not yet demonstrated at scale. |
Confer with customers to determine the nature of problems or to explain repairs.
34CI 30–39 · exposure 25 · augmentation 50 · importance 4.0/5 · click for rater detail
Confer with customers to determine the nature of problems or to explain repairs.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Service and repair sectors have adopted AI chatbots for simple inquiries, but deeper adoption in technical troubleshooting diagnostics remains limited; most organizations keep humans in the loop for problem determination. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | AV installation and repair is a physical, small-business-heavy trade with low overall AI adoption compared to information/professional services sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist technicians by summarizing customer issues, suggesting diagnostic questions, or providing real-time knowledge lookup during calls, materially improving efficiency while the technician retains control of the conversation and judgment. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help technicians prepare diagnostic questions, draft explanations, or triage common issues before/after the conversation, improving efficiency without replacing the interaction. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI chatbots can conduct initial diagnostic conversations, this task involves nuanced customer interaction, understanding contextual cues, and building trust—elements that require human judgment and presence. Current systems lack the reliability and contextual depth to replace the full diagnostic conversation end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | Diagnostic conversation with customers requires understanding ambiguous verbal descriptions of physical equipment problems and building trust, which AI can partially support but not fully replace end-to-end today.atiron.com |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Customer service expectations and potential liability for misdiagnosis create organizational friction, though no hard legal barrier prevents AI-assisted or AI-led diagnostic calls; most organizations prefer human contact for complex troubleshooting. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI from conversing with customers, though customer preference for a knowledgeable human and liability for misdiagnosis create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While inference costs are low, the overhead of integration, monitoring for customer satisfaction, and human review of AI decisions to avoid costly errors makes the all-in cost competitive with or exceeding a technician's time on initial diagnosis. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-assisted intake tools could be cheaper for initial triage, but human technician time is still needed for the substantive explanation and problem determination, keeping costs comparable overall. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Conversational AI tools exist and can handle routine customer interactions in limited scenarios, but deployed products show material errors in complex troubleshooting dialogue and fail to adapt to unexpected customer concerns or technical ambiguities without human oversight. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Chatbots and AI phone agents exist for basic triage but are not reliably deployed for nuanced AV equipment diagnosis or explaining hands-on repairs in production at scale. |
Instruct customers on the safe and proper use of equipment.
32CI 25–39 · exposure 25 · augmentation 50 · importance 4.0/5 · click for rater detail
Instruct customers on the safe and proper use of equipment.
32| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Audiovisual installation remains a fragmented, often small-firm industry with low digital maturity. While larger corporate AV integrators may pilot AI-assisted guides, most adoption is slow and limited to supplementary content; there is minimal evidence of displacement in production systems. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Installation and repair trades are physical, low-digitization occupations where AI adoption for customer-facing instruction remains limited to supplementary materials rather than replacing in-person guidance. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by generating draft instructional guides, providing equipment-specific fact summaries, or creating supplementary video clips that installers share with customers. However, the core task—live, adaptive instruction and safety verification—still relies heavily on the human technician's judgment and presence. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can generate customized instructional scripts, FAQs, or troubleshooting guides that installers use to supplement their in-person customer instruction, improving consistency and coverage. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate instructional text or video scripts, the task requires real-time interaction, assessment of customer understanding, and adaptive explanation—capabilities that current systems struggle with at production scale. The safety-critical nature and need for personalized clarification limit autonomous performance. |
| Task automatability | claude-sonnet-5 | 2/5 | Instruction can be partially delivered via manuals, videos, or chatbots, but real-time, hands-on customer instruction tied to specific installed equipment and physical demonstration is not fully replaceable by current AI systems. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety liability is high: incorrect instruction on audiovisual equipment can cause injury or damage; legal and contractual responsibility typically rests with the installing company, which must ensure instruction is accurate and effective. Customer preference for human hands-on guidance and potential warranty/liability requirements create strong friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates human instruction, though liability concerns around improper equipment use and customer preference for a live walkthrough create some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Initial AI setup (video generation, chatbot training, integration) and ongoing maintenance are substantial; oversight by qualified personnel is necessary for safety-critical instruction. The all-in cost per customer interaction remains comparable to or exceeds a technician's time for small-to-medium deployments. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-generated instructional content is cheap to produce, but pairing it with the human installer's on-site visit (already occurring for installation) limits marginal cost savings from AI alone. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some AI-driven tutorial systems and chatbots exist, but they lack the contextual awareness, equipment-specific expertise, and ability to verify customer comprehension in real installations. Deployed products are narrow, often require heavy curation, and cannot reliably handle the full spectrum of customer questions. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Chatbots and AI-generated manuals exist for general product guidance, but no deployed product reliably provides in-person, equipment-specific safety instruction as part of an installation job. |
Tune or adjust equipment and instruments to obtain optimum visual or auditory reception, according to specifications, manuals, and drawings.
18CI 10–26 · exposure 13 · augmentation 50 · importance 3.8/5 · click for rater detail
Tune or adjust equipment and instruments to obtain optimum visual or auditory reception, according to specifications, manuals, and drawings.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | The audiovisual installation and repair sector is relatively fragmented, with many small and regional service providers operating in non-digitized workflows. Adoption of AI-driven automation remains minimal; most firms continue traditional technician-led approaches. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | AV installation and repair is a physical, low-digitization trade with minimal AI/robotic adoption in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted diagnostic tools, specification databases, and real-time measurement visualization can meaningfully support technicians in identifying optimal settings and comparing against standards, but the technician retains decision-making and manual control over the adjustment process. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-based diagnostic tools, manuals search, and troubleshooting assistants can help technicians interpret specifications and manuals faster, aiding but not replacing the hands-on tuning process. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can analyze specifications and manuals, the core task requires physical adjustment of hardware based on real-time feedback and context-specific optimization. Current AI systems cannot reliably perform the hands-on tuning and testing needed to achieve optimal reception without substantial human oversight and intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of hardware, hands-on tuning, and sensory judgment in a real environment that current AI cannot perform end-to-end without robotic embodiment.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Customer expectations for hands-on professional service, liability concerns around equipment damage, and the need for on-site judgment and safety compliance create moderate friction. No hard legal barrier exists, but practical and reputational factors favor human presence. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement typically exists, but physical presence, equipment access, and troubleshooting judgment create practical barriers to remote or automated substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of integrating robotic systems, sensors, and AI control for autonomous equipment tuning would far exceed the loaded wage of a skilled technician, especially given the low volume and high variability of installations and repair work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so any AI cost comparison is moot—human labor remains the only functional option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs end-to-end audiovisual equipment tuning autonomously. Diagnostic tools and measurement systems exist, but the integration of physical adjustment, real-time feedback loops, and specialized troubleshooting remains dependent on human technicians in production environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously tunes or adjusts AV equipment in the field today; this remains a hands-on technician task. |
Install, service, and repair electronic equipment or instruments such as televisions, radios, and videocassette recorders.
16CI 5–28 · exposure 8 · augmentation 50 · importance 4.4/5 · click for rater detail
Install, service, and repair electronic equipment or instruments such as televisions, radios, and videocassette recorders.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Audiovisual equipment installation and repair occurs primarily in small/medium service shops, retail settings, and on-site customer locations with low digitization. Sector adoption of AI automation lags significantly behind information and professional services. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Electronics repair and installation trades show minimal AI adoption due to physical nature of the work and lack of robotic deployment at scale. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist technicians through diagnostic recommendations, parts-identification, and service documentation retrieval, moderately improving productivity. However, the physical and contextual judgment demands limit transformative augmentation compared to knowledge-work tasks. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist technicians with diagnostic guidance, repair manuals, troubleshooting chatbots, and parts identification, improving efficiency without performing the physical work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could assist with diagnostics and troubleshooting advice, the core task requires physical manipulation of equipment, precise hand assembly, and real-world problem-solving in varied environments—capabilities well beyond current AI systems. Only narrow diagnostic or documentation aspects could approach 50% time savings with meaningful setup. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical hands-on repair task requiring diagnosis, disassembly, soldering, and part replacement on physical electronic devices, which current AI cannot perform without embodiment in a capable robot.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Customers typically require a licensed or certified human technician to install and service equipment, often for warranty and liability reasons. Safety certification, liability for equipment damage, and customer trust in physical work create meaningful barriers to substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement generally, but physical dexterity, tool use, and on-site presence create strong natural barriers to any digital-only automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The loaded cost of a trained technician remains substantially lower than combining robotic systems, AI diagnostics, and human oversight for reliable repair work. The integration overhead for physical automation is prohibitively high relative to skilled labor rates. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute performing physical installation/repair, so AI cost is not comparable to human labor cost for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs physical installation, service, or repair of audiovisual equipment end-to-end. While diagnostic tools exist, they do not constitute performing the full task in production environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product installs or physically repairs televisions or AV equipment; this remains firmly a human manual trade task. |
Calibrate and test equipment, and locate circuit and component faults, using hand and power tools and measuring and testing instruments such as resistance meters and oscilloscopes.
16CI 10–23 · exposure 5 · augmentation 50 · importance 4.0/5 · click for rater detail
Calibrate and test equipment, and locate circuit and component faults, using hand and power tools and measuring and testing instruments such as resistance meters and oscilloscopes.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Audiovisual equipment installation and repair remains largely localized, hands-on work performed by small to mid-sized firms with limited digital infrastructure. Adoption of AI diagnostic tools is occurring slowly in this sector compared to information and professional services. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Field repair and installation trades show minimal AI/robotics adoption for hands-on circuit diagnostics; this is a low-digitization, physically-embedded task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist technicians by analyzing circuit schematics, suggesting common fault patterns based on symptoms, or automating diagnostic data collection, but the physical testing and repair work requires human skill and judgment in the loop. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-based diagnostic software, fault databases, and AR-guided troubleshooting can help technicians interpret readings and locate likely fault areas faster, though physical testing remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of hand and power tools, interpretation of real-time instrument readings in a physical environment, and judgment about circuit faults that demand understanding of system context and failure modes. Current AI systems cannot reliably perform hands-on troubleshooting with tools in the field. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of hand/power tools and measuring instruments on physical hardware, which current AI cannot perform end-to-end; no software-only AI system can calibrate or repair physical circuits. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict licensing requirements for equipment repair, liability concerns (damage to expensive equipment), warranty implications, and customer preference for qualified human technicians create moderate friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing typically required, but physical dexterity, tool use, and on-site diagnostic judgment create practical (not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of deploying robotic systems capable of safely handling delicate audiovisual equipment, interpreting oscilloscope readings, and performing repairs far exceeds the loaded wage of a skilled technician who already has the necessary expertise and equipment familiarity. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot perform the physical diagnostic and repair work at all, so there is no viable AI substitute cost to compare; human labor remains the only option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with fault diagnosis from data logs or schematic analysis in controlled settings, no deployed product reliably performs end-to-end calibration, testing, and fault location using physical instruments autonomously. Some diagnostic support tools exist but require human technician interpretation and action. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously calibrates AV equipment or diagnoses circuit faults with hand tools and oscilloscopes; this remains a manual, hands-on technician task. |
Position or mount speakers, and wire speakers to consoles.
10CI 5–15 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Position or mount speakers, and wire speakers to consoles.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The audiovisual installation sector remains heavily dependent on skilled manual labor and on-site customization; digitization and AI adoption in this trades-based, location-dependent industry remains minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | AV installation is a physical trade with low digitization and no meaningful robotic automation deployment; adoption of AI in this specific task is essentially nonexistent. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with planning speaker placement (via simulation or acoustic modeling tools) or wiring diagrams, but the core physical installation and mounting task offers limited opportunity for AI augmentation while a technician remains in the loop. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with planning speaker placement (acoustic modeling software, wiring diagrams) but offers minimal help with the physical mounting and wiring execution itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation in three-dimensional space (positioning speakers, mounting hardware, routing wiring), precise spatial judgment, and real-time environmental adaptation. Current AI systems cannot autonomously perform physical installation and repair work at scale. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically positioning, mounting, and wiring speakers requires manual dexterity, spatial judgment, and physical manipulation in varied environments that current AI systems cannot perform without a robotic embodiment, which is not commercially available for this task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Physical installation in customer locations typically requires licensed or certified technicians in many jurisdictions; liability for equipment damage, improper installation affecting safety, and the need for on-site human judgment create strong barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement typically restricts who can mount speakers, but the physical, on-site nature of the task and liability for improper installation create some practical friction against remote or automated substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The equipment cost, specialized robotics, and integration overhead to automate speaker positioning and wiring would far exceed the labor cost of a skilled audiovisual technician performing this work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system performing this physical installation task, so any AI-based alternative would be more costly than simply having a technician do it, since none exists at scale. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products autonomously install and wire audiovisual equipment in real environments. This requires integrated robotics, environmental sensing, and problem-solving capabilities that do not exist in production systems today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously mounts speakers and wires them to consoles; this remains purely a human manual/electrical installation task. |
Disassemble entertainment equipment and repair or replace loose, worn, or defective components and wiring, using hand tools and soldering irons.
10CI 5–15 · exposure 0 · augmentation 25 · importance 3.6/5 · click for rater detail
Disassemble entertainment equipment and repair or replace loose, worn, or defective components and wiring, using hand tools and soldering irons.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Audiovisual installation and repair remains a largely manual, on-site service sector with low digitization; adoption of automation is negligible because the task is inherently physical and site-specific. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Equipment repair trades are physically oriented and low-digitization; robotics/AI adoption for hands-on electronics repair is minimal and not accelerating quickly. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide some assistance through diagnostic image recognition or schematics lookup, but the core task—hands-on disassembly and soldering—offers limited opportunity for meaningful human-AI collaboration. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with diagnostic guidance, wiring diagrams, or troubleshooting documentation lookup, but offers little help with the actual physical repair and soldering steps. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of delicate electronic components, precise soldering, and real-time diagnostic assessment of equipment condition—capabilities that current AI systems and even robotics cannot reliably perform end-to-end in unstructured field environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical hands-on task requiring manual disassembly, diagnosis, and soldering with real dexterity and tool use, which current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Equipment warranty requirements, liability for damage during disassembly/repair, and regulatory compliance around electrical safety create meaningful friction; many installations require licensed technicians to perform or sign off on repairs. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement typically gates this work, but the need for physical manipulation and skilled judgment creates strong practical (not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of specialized robotics capable of delicate soldering and component replacement, combined with integration and oversight, far exceeds the loaded wage of a skilled technician performing these repairs. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing the physical repair, so any AI cost comparison is moot; a human technician remains the only functional option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product or agent system can autonomously disassemble equipment, diagnose failures by inspection, and perform soldering repairs at production scale; this remains firmly in the domain of specialized technicians and manual skill. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical disassembly and soldering repair of AV equipment; this remains firmly in the domain of skilled human technicians. |
Make service calls to repair units in customers' homes, or return units to shops for major repairs.
5CI 0–10 · exposure 0 · augmentation 38 · importance 4.0/5 · click for rater detail
Make service calls to repair units in customers' homes, or return units to shops for major repairs.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Service and repair work remains highly localized, low-digitization, and dependent on skilled manual labor. Adoption of automation in this sector is minimal; most work still relies on human field technicians and shows no signs of rapid AI displacement. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Field service and home repair trades show low AI/robotics adoption due to physical, unstructured environments and lack of scalable automation solutions. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist by pre-diagnosing common faults via remote customer interviews or providing repair documentation to technicians, but the core task—traveling to homes and fixing equipment—remains entirely human-driven. Augmentation is limited to supporting materials, not the primary work. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with diagnostic guidance, troubleshooting scripts, parts lookup, and scheduling/routing, improving technician efficiency even though it can't perform the physical repair itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical presence in customer homes, hands-on diagnosis and repair of equipment, and real-time interaction with customers—all of which are far beyond current AI and robotic capabilities. No AI system today can travel to homes, troubleshoot hardware failures, perform repairs, or manage customer relationships autonomously. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical repair/travel task requiring hands-on diagnosis and manipulation of hardware in customer homes; current AI systems cannot perform physical repairs or travel to locations. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | High barriers include customer access to private homes (authorization and trust), liability for damage during repair, warranty and service certification requirements, and the need for a licensed, accountable human to take responsibility for repairs. Customers strongly prefer human technicians they can hold accountable. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier typically applies, but physical presence, tool handling, and customer trust/liability for in-home work create real practical friction against remote automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current AI has no ability to perform this task, making cost comparison moot. The labor cost of a trained technician with truck, tools, and travel time far exceeds any current AI offering, since AI cannot perform the task at all. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for physical travel and hands-on repair, so AI cost is not comparable—human labor is the only viable option, making AI effectively far more expensive (i.e., infinite/inapplicable) for the physical component. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs end-to-end in-home equipment service and repair. While diagnostic software exists, actually dispatching an AI system to a customer's home and executing repairs remains entirely in the domain of human technicians. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical AV equipment repair or in-home service calls; this remains entirely outside current AI product capability. |
Related occupations — Installation, Maintenance & Repair
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.