First-Line Supervisors of Non-Retail Sales Workers
41-1012.00Median wage $87,520/yr214,390 employed (US)Rank #173 of 923 scored · top 19% by substitution
Directly supervise and coordinate activities of sales workers other than retail sales workers. May perform duties such as budgeting, accounting, and personnel work, in addition to supervisory duties.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution40
Exposure35
Augmentation66
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
18 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
11%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%34
panel mean rating 2.4/5 → substitution pressure 34/100
Technical feasibility todayw 20%38
panel mean rating 2.5/5 → substitution pressure 38/100
Cost vs. human wagew 15%42
panel mean rating 2.7/5 → substitution pressure 42/100
Adoption barriersw 20%inverted — strong barriers lower the score51
panel mean rating 2.9/5 (barrier strength) → substitution pressure 51/100
Sector adoption velocityw 10%40
panel mean rating 2.6/5 → substitution pressure 40/100
Task breakdown (18 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Keep records pertaining to purchases, sales, and requisitions.
84CI 75–92 · exposure 87 · augmentation 75 · importance 3.9/5 · click for rater detail
Keep records pertaining to purchases, sales, and requisitions.
84| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Sales organizations, financial services, and corporate administrative functions have rapidly adopted ERP and automated accounting systems. Public reporting and patent filings show deep, fast deployment in white-collar back-office roles. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Sales and inventory management functions have seen fast, deep adoption of automated transaction and record systems as part of standard business software stacks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assists supervisors by automating data entry and flagging anomalies, freeing them to focus on compliance review, exception handling, and strategic oversight. The human remains in a supervisory loop while productivity gains are substantial. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-enabled dashboards, anomaly detection, and auto-categorization significantly boost a supervisor's ability to track and reconcile records, though judgment calls on discrepancies still benefit from human oversight. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Record-keeping for purchases, sales, and requisitions is highly structured data entry and filing—tasks where current AI systems (RPA, document processing, ERP integrations) routinely achieve >50% time savings with equal or better accuracy. Extraction, categorization, and database updates are core automated workflows in production today. |
| Task automatability | claude-sonnet-5 | 4/5 | Recordkeeping of purchases, sales, and requisitions is largely structured data entry and reconciliation that AI-enabled ERP/CRM tools and automation scripts can handle with high time savings, though some human review of exceptions remains. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Light regulatory friction exists (audit trails, compliance documentation), but no legal requirement mandates human sign-off on routine record-keeping itself. Organizational inertia and integration overhead are the primary frictions, not hard barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates human recordkeeping for these business transactions, though some internal audit/compliance controls and error-cost concerns create mild friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven record-keeping (inference + integration overhead) costs a small fraction of a human administrator's loaded wage, often by one to two orders of magnitude once systems are deployed. One automation instance handles thousands of transactions annually. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated recordkeeping systems cost a fraction of a supervisor's time per transaction once integrated, though initial setup and occasional oversight add some cost relative to a purely human-free solution. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Mature deployed products (ERP systems, accounting software, automated document capture) reliably perform these record-keeping tasks at scale in real organizations. Optical character recognition, invoice processing, and automated ledger entry are industry-standard, production-grade capabilities. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Mature ERP, POS, and accounting software (e.g., SAP, QuickBooks, Salesforce) already automate transaction logging, invoicing, and requisition tracking in production at scale across industries. |
Prepare sales and inventory reports for management and budget departments.
74CI 72–75 · exposure 75 · augmentation 88 · importance 4.0/5 · click for rater detail
Prepare sales and inventory reports for management and budget departments.
74| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Sales and inventory reporting automation is already deeply embedded in retail and B2B sectors; most large and mid-market firms have adopted BI tools and automated reporting. Adoption continues to accelerate in smaller organizations. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Sales/inventory management sectors show moderate BI tool adoption with common piloting of automated dashboards, but many smaller non-retail sales operations still rely on manual reporting. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-generated reports substantially assist supervisors by handling data compilation, freeing them to focus on interpretation, anomaly detection, and strategic insights. The supervisor can review and annotate AI-produced reports far faster than creating them from scratch. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI-powered analytics tools significantly speed up data compilation, trend identification, and report drafting, letting supervisors focus on interpretation and decision-making. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can extract data from sales and inventory systems, generate summaries, and produce formatted reports with minimal human input. While some data validation and contextual judgment may be needed, the core task of aggregating numbers and producing structured reports achieves well over 50% time savings. |
| Task automatability | claude-sonnet-5 | 4/5 | Report generation from sales and inventory data is a structured, data-driven task well-suited to AI/BI tools that can automatically aggregate, summarize, and format outputs with minimal human editing needed for equal quality. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or legal barriers exist; reporting automation is widespread and well-accepted. Light oversight and occasional manual validation are typical, but no licensing requirement or human sign-off mandate prevents automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates human preparation of these reports; the main friction is organizational habit and data integration effort, not regulation or liability. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven reporting infrastructure costs a fraction of a full-time supervisor's loaded wage and scales to hundreds of reports. Once integrated, per-report inference and maintenance costs are negligible compared to manual labor. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated reporting software with cloud data connections costs a small fraction of a supervisor's time-equivalent labor once integrated, though initial setup and data pipeline costs are nontrivial. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature BI and reporting tools, including AI-augmented dashboards and automated report generation platforms, reliably produce sales and inventory reports in production at scale. Most mid-to-large organizations already deploy systems for this, though some customization and oversight remain standard. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Mature BI and reporting platforms (e.g., Power BI, Tableau, ERP dashboards, AI-enhanced analytics tools) are widely deployed in production to generate sales and inventory reports automatically. |
Inventory stock and reorder when inventories drop to specified levels.
67CI 59–75 · exposure 67 · augmentation 88 · importance 3.8/5 · click for rater detail
Inventory stock and reorder when inventories drop to specified levels.
67| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Inventory automation is widespread in logistics, distribution, and sales operations; most mid-to-large organizations use ERP or specialized inventory software, indicating rapid and deep adoption in digitized sectors. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Wholesale and non-retail sales sectors show moderate digitization with inventory software common, but many smaller supervisory roles still rely on manual or semi-automated processes, placing this in the middle range of adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-powered demand forecasting and inventory analytics significantly augment supervisors' ability to optimize stock levels, predict shortages, and reduce waste while keeping humans in charge of policy and exceptions. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI-driven inventory dashboards and predictive reorder alerts substantially boost a supervisor's ability to monitor stock levels and anticipate shortages while they retain oversight and decision authority. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | Checking inventory levels against thresholds can be fully automated with ERP or inventory management systems, but the decision to reorder often requires judgment about demand forecasts, supplier lead times, and budget constraints that typically need human oversight or intervention. |
| Task automatability | claude-sonnet-5 | 4/5 | Inventory tracking and reorder-point triggering is a well-structured, rules-based task that inventory management software and AI-driven demand forecasting systems already handle largely autonomously, though physical stock verification still requires human or sensor involvement. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Inventory management is often subject to internal approval workflows and financial controls; some organizations require human authorization before large reorders, and supplier relationships may require personal contact, creating moderate friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates human execution of reordering; the main friction is organizational trust in automated triggers and handling exceptions like supplier issues or damaged goods. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated inventory systems are inexpensive to operate once deployed (minimal marginal cost per check), making them substantially cheaper than repeated manual stock-counts and order placement by a supervisor. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated inventory and reorder systems run at low marginal cost per SKU compared to a supervisor's time spent manually checking stock, though there is upfront integration cost and ongoing data maintenance. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Inventory tracking and automated reorder systems are mature and widely deployed in production across retail and distribution; however, pure autonomy without human approval is less common, so most implementations require some human sign-off. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Mature inventory management systems (e.g., ERP modules, automated replenishment software with AI forecasting like those from SAP, Oracle, or specialized retail inventory tools) are deployed at scale in production today for exactly this function. |
Coordinate sales promotion activities, such as preparing merchandise displays and advertising copy.
58CI 55–61 · exposure 50 · augmentation 75 · importance 3.8/5 · click for rater detail
Coordinate sales promotion activities, such as preparing merchandise displays and advertising copy.
58| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Retail and sales organizations are actively experimenting with AI copywriting and visual content generation tools; adoption is growing rapidly in e-commerce and larger firms. Production deployment is expanding faster than in many other sectors, though smaller retailers lag behind. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Retail and sales sectors are adopting AI tools for marketing content and copywriting at a moderate pace, with pilots and partial integration common but full workflow automation still rare. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI substantially assists supervisors by rapidly generating multiple copy variants, suggesting display layouts, and automating routine coordination tasks, allowing them to focus on strategy and brand oversight. The human supervisor remains central to final decisions and execution coordination. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially speeds up drafting advertising copy and can suggest display concepts or layouts, meaningfully boosting the productivity of supervisors managing these promotional tasks. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | Current AI can assist significantly with advertising copy generation and basic merchandise display suggestions, but requires human oversight for brand alignment, creative judgment, and final approval. While roughly half of the work could be automated (text drafting, display layout suggestions), the integration and coordination of multiple promotional elements still depends on human decision-making. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can generate advertising copy and design suggestions for merchandise displays quickly, but coordinating physical display setup and aligning with store-specific promotional strategy still requires human oversight and execution.the copy-generation half is highly automatable while the physical coordination half is not. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers exist for promotional coordination; liability is low relative to other roles. The main friction is organizational preference to maintain brand control and supervisor responsibility for promotional strategy, but these are surmountable through AI-assisted workflows rather than strict blockers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human for this task, though organizational preferences and the physical/logistical nature of display work create some friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-generated copy and display layouts are now significantly cheaper per iteration than hiring copywriters or designers, especially for routine promotional materials. The cost advantage is substantial, though not quite an order of magnitude when factoring in oversight and refinement labor. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI copy generation is cheap, but the full task includes physical coordination and management oversight that still requires paid human labor, keeping overall costs comparable rather than dramatically lower. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | AI products exist for copywriting (e.g., GPT-4, specialized marketing tools) and can generate display concepts, but deployment in real sales operations remains patchy. Error rates in brand consistency and product accuracy, plus the need for human oversight before implementation, prevent full production reliability. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Generative AI tools (e.g., copywriting assistants, design tools like Canva AI) are deployed in production for ad copy and basic visual layouts, but no product autonomously coordinates end-to-end promotional activities including physical merchandising. |
Plan and prepare work schedules, and assign employees to specific duties.
52CI 50–55 · exposure 50 · augmentation 75 · importance 3.8/5 · click for rater detail
Plan and prepare work schedules, and assign employees to specific duties.
52| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Workforce management software is widely deployed across retail, hospitality, and healthcare, but typically in assisted, human-review mode rather than fully autonomous. Adoption is steady but not rapid for end-to-end automation; many small and mid-sized firms still use manual or semi-manual processes. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Scheduling automation is common in adjacent sectors (retail, hospitality) but sales supervisor roles in non-retail contexts show more moderate, pilot-level adoption of AI-driven scheduling tools. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | Modern scheduling tools effectively assist supervisors by proposing schedules, flagging conflicts, and enforcing constraints, substantially reducing planning time and error. Supervisors retain authority over final assignments, and the technology measurably raises their scheduling throughput and consistency. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI scheduling assistants meaningfully speed up draft creation, conflict detection, and workload balancing, letting supervisors focus on judgment calls and exceptions. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | Scheduling and employee assignment can be partially automated using constraint-solving algorithms and workforce management software, but context-dependent decisions (skill matching, employee preferences, complex constraints) still require human oversight. An AI could generate candidate schedules with 30–50% time savings, but production use typically involves human review and adjustment. |
| Task automatability | claude-sonnet-5 | 3/5 | AI scheduling tools can generate draft schedules and shift assignments based on constraints and demand data, but final adjustments for team dynamics, exceptions, and personal circumstances still typically require human judgment.time saving is meaningful but full end-to-end automation is uncommon. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | No legal licensing barrier exists, but scheduling involves employee relations, labor law compliance (overtime rules, fairness), and organizational resistance to automated decisions affecting work-life balance. Union contracts and labor regulations also add friction to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI scheduling, though labor agreements, union rules, or company policy may require managerial sign-off on schedules and duty assignments. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | Software subscriptions (typically $10–50 per employee per month) plus integration and oversight time roughly match the cost of a supervisor spending 2–5 hours weekly on manual scheduling. The all-in cost is comparable, not dramatically cheaper. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Scheduling software licenses are cheap relative to manager time spent, but integration, data upkeep, and exception handling still require human oversight, keeping the ratio moderate rather than order-of-magnitude cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Workforce scheduling products exist (e.g., Deputy, When I Work, Kronos) and perform routine assignment and scheduling, but they operate in assisted mode: humans set constraints, review outputs, and override recommendations. No fully autonomous end-to-end solution handles all edge cases and exceptions reliably without human sign-off. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Workforce management software with scheduling algorithms is widely deployed (e.g., retail/logistics scheduling tools), but non-retail sales team scheduling with duty assignment still often needs manager review and override. |
Prepare rental or lease agreements, specifying charges and payment procedures for use of machinery, tools, or other items.
51CI 34–67 · exposure 50 · augmentation 88 · importance 3.3/5 · click for rater detail
Prepare rental or lease agreements, specifying charges and payment procedures for use of machinery, tools, or other items.
51| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption remains moderate and concentrated in large, high-volume rental operations (equipment leasing, vehicle rental chains). Small to mid-size rental businesses and supervisory roles still rely heavily on manual templates and human drafting, and the pace of AI-driven displacement has been slow because of liability concerns and regulatory friction. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Sales and equipment rental industries show moderate digitization with growing use of CLM/contract automation tools, but broad displacement of supervisory contract-prep tasks is still uneven across firms of varying size. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-powered lease drafting and term suggestions can substantially accelerate supervisor workflows—auto-populating boilerplate, flagging missing standard clauses, and generating first-draft language—enabling supervisors to focus on customization, negotiation, and approval rather than writing from scratch. This is a high-augmentation scenario where AI productivity gains are substantial while the human remains gatekeeping. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI-based contract templates and drafting assistants substantially speed up preparation of lease agreements, letting supervisors review and finalize rather than draft from scratch. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft template-based lease language and populate standard fields with extracted data, preparing agreements requires domain-specific legal judgment about charges, liability terms, and payment conditions that vary materially by jurisdiction and rental asset. Current systems cannot reliably handle the full complexity, exception handling, and legal liability exposure without substantial human review and modification. |
| Task automatability | claude-sonnet-5 | 4/5 | Drafting standardized rental/lease agreements with specified charges and payment terms is highly templatable text generation that current LLMs handle well, especially with contract-drafting tools populating fields from structured data. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Rental and lease agreements carry legal and financial liability; errors can expose companies to contract disputes, regulatory non-compliance, and revenue loss. Many jurisdictions impose requirements that agreements meet specific statutory language or be reviewed by designated personnel, and lender/insurance requirements often mandate human oversight of material terms. These create meaningful friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for drafting typical equipment rental agreements, though liability concerns around contract errors create moderate incentive for human review before finalization. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI-assisted lease generation (template + auto-fill + oversight) likely costs less than a fully manual legal-grade drafting process, but supervisors still invest significant time reviewing and customizing terms. The loaded cost approaches parity when compliance review and human oversight are factored in, especially for complex or non-standard rentals. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating a lease agreement via AI-assisted templates costs a fraction of a supervisor's time-equivalent, though some oversight cost remains for accuracy and legal compliance. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Document generation products and contract templates exist (e.g., DocuSign, LawGeex, lease generators), and some can auto-populate basic fields, but deployed systems typically require significant human review, customization, and lawyer sign-off. Production use is common in high-volume rental operations but with material error rates when terms deviate from standard templates. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Contract-generation and CLM software (e.g., DocuSign, PandaDoc, various legal AI tools) exist and are used in production, but most deployments still require human review/customization for specific equipment terms and liability clauses, limiting fully autonomous reliability. |
Analyze details of sales territories to assess their growth potential and to set quotas.
50CI 41–59 · exposure 42 · augmentation 88 · importance 3.8/5 · click for rater detail
Analyze details of sales territories to assess their growth potential and to set quotas.
50| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Mid-market and enterprise sales organizations have adopted BI tools for territory analysis, but quota-setting remains largely manual oversight. Pilots are common in professional services and finance; widespread autonomous quota-setting is not yet standard. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Sales and business services sectors show moderate-to-fast adoption of AI-driven analytics tools, with pilots common but full replacement of managerial judgment still developing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | Current analytics platforms significantly assist supervisors by auto-generating territory reports, highlighting growth signals, and suggesting quota bands. This augmentation allows supervisors to focus on strategic adjustments rather than data collection, substantially raising their analytical productivity. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI-powered analytics dashboards significantly enhance a supervisor's ability to assess territory potential and calibrate quotas, combining data at a scale humans could not manually replicate while keeping the manager in control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can extract and analyze sales data, market metrics, and territory demographics in bulk, the task requires contextual judgment about growth potential and competitive dynamics that current systems handle only partially. Supervisors must weigh qualitative factors (team capability, local competition, seasonal patterns) that exceed typical analytics automation. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can process sales data, market trends, and historical performance to generate territory analysis and suggested quotas, but final judgment integrating strategic/political/relationship factors typically remains human.5tion is well-supported by analytics tools but not fully hands-off. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | No legal licensing requirement, but organizational friction is moderate: supervisors and sales leadership often prefer human judgment on quota-setting due to team morale and accountability concerns. Some CRM implementations face adoption resistance. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI use, though organizational trust, need for managerial accountability, and negotiation with sales reps over quotas create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | BI and analytics tools cost thousands to tens of thousands annually, far less than the loaded cost of a full-time supervisor running this analysis manually. Once implemented, marginal cost per analysis is very low. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Analytics tools reduce time spent on data crunching substantially, but licensing costs plus required managerial oversight keep the net cost comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Business intelligence and CRM platforms (Salesforce, Tableau) can now perform data aggregation and basic territory analysis, but deployed solutions typically flag anomalies and suggest quotas rather than autonomously setting them. Production systems exist but still require human review and adjustment. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | CRM and sales analytics platforms (e.g., Salesforce Einstein, Tableau, Clari) already provide territory analysis and quota-setting recommendations in production, though accuracy varies and human review is standard. |
Monitor sales staff performance to ensure that goals are met.
46CI 41–50 · exposure 34 · augmentation 75 · importance 4.3/5 · click for rater detail
Monitor sales staff performance to ensure that goals are met.
46| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Sales organizations widely adopt analytics and performance dashboards, but full automation of supervisory monitoring remains a pilot-phase initiative; most deployments augment rather than replace supervisors, indicating moderate adoption velocity. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Sales operations broadly use analytics and CRM tools with growing AI features, representing middling-to-good adoption, though full automation of supervisory monitoring remains uncommon. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI dashboards and real-time performance alerts substantially boost supervisor productivity by surfacing trends, anomalies, and at-risk metrics automatically, allowing supervisors to focus coaching and intervention where needed rather than manual data gathering. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered dashboards, automated alerts, and predictive analytics substantially improve a supervisor's ability to track performance and identify issues in real time. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can automate data collection and basic performance tracking against quantitative metrics (sales numbers, activity logs), but monitoring requires judgment about individual circumstances, coaching decisions, and qualitative assessment of effort and obstacles—tasks requiring human reasoning and interpersonal understanding. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can compile and flag performance metrics against targets, but the supervisory judgment of interpreting results, coaching, and deciding responses requires human involvement, so only partial time savings are achievable. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While no legal requirement mandates a human supervisor, organizational culture, labor law nuances around performance discipline, and the expectation that supervisors provide coaching and development create meaningful friction against full automation; oversight by a human remains the norm. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI-assisted monitoring, though organizational preference for human oversight of personnel and performance management creates some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI monitoring (CRM analytics, activity tracking) costs significantly less per month than a supervisor's loaded salary, and these systems are increasingly embedded in standard software, making the marginal cost very low compared to full human oversight. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Dashboard and analytics tools are relatively cheap to run compared to a supervisor's time spent manually pulling reports, but the human judgment component still requires paid supervisory time, keeping costs roughly comparable overall. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Existing systems can track and report sales metrics automatically, and some CRM platforms include performance dashboards; however, reliable end-to-end monitoring that meaningfully replaces supervisor judgment in real organizations remains limited, with material gaps in contextual understanding and exception handling. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | CRM and sales analytics dashboards (Salesforce, HubSpot) already automate performance tracking and alerting in production, though they only cover the monitoring/reporting portion, not the full supervisory task. |
Formulate pricing policies on merchandise according to profitability requirements.
44CI 39–50 · exposure 30 · augmentation 75 · importance 4.1/5 · click for rater detail
Formulate pricing policies on merchandise according to profitability requirements.
44| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | E-commerce and retail sectors show steady but incremental adoption of AI-assisted pricing tools; however, deployment is typically limited to dynamic optimization rather than full policy formulation. Many organizations still rely on manual oversight, reflecting middling adoption patterns. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Non-retail sales supervision sectors show slower AI adoption compared to finance or tech; pricing tools are used more in large retail/e-commerce than in general non-retail sales management. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at surfacing profitability data, market comparisons, and price sensitivity scenarios, substantially augmenting a supervisor's ability to formulate informed policies. The human remains in the loop for strategic judgment, but AI-assisted analysis significantly accelerates and improves policy design. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI pricing analytics and forecasting tools can meaningfully assist supervisors by surfacing profitability trends, elasticity data, and scenario modeling to inform policy decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Pricing policies require complex judgment about market conditions, competitive positioning, and profitability trade-offs that extend beyond routine calculation. While AI can analyze historical data and suggest price points, formulating coherent policies involves strategic decision-making that typically requires human oversight and business context. |
| Task automatability | claude-sonnet-5 | 2/5 | Pricing strategy formulation requires synthesizing market context, competitive positioning, and business judgment that AI can support but not autonomously decide with equal quality at scale today.4o rrationale reflects partial task automation potential. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Pricing decisions are ultimately the responsibility of management and the organization, not a regulated professional function requiring licensing. Legal and compliance constraints are minimal, though organizational risk tolerance and accountability structures create moderate friction to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational accountability for profitability decisions and stakeholder trust create moderate friction against full delegation to AI. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Current AI pricing analysis tools are relatively inexpensive to operate (cloud-based analytics, SaaS pricing software) compared to the loaded cost of a supervisor's time spent manually reviewing market data and drafting policies. However, integration and oversight overhead prevent a full 5-point rating. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-driven pricing analytics tools can lower analysis costs substantially, but the judgment and accountability component of policy-setting still requires paid managerial time, keeping costs roughly comparable when factoring in oversight. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | AI pricing tools exist in production (e-commerce platforms, dynamic pricing engines) but are typically narrow in scope—optimizing around narrow metrics rather than formulating holistic policies. Real-world deployment often requires human review and strategic input, indicating material limitations in fully autonomous policy generation. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Pricing analytics and dynamic pricing tools exist and are deployed in some retail/enterprise contexts, but formulating overarching policy tied to profitability goals still requires human strategic oversight and is not a mature turnkey product for this supervisory role. |
Listen to and resolve customer complaints regarding services, products, or personnel.
33CI 25–41 · exposure 30 · augmentation 75 · importance 3.9/5 · click for rater detail
Listen to and resolve customer complaints regarding services, products, or personnel.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While complaint tracking software is widespread, actual AI-driven autonomous resolution remains nascent in most sectors. Adoption is primarily in triage and escalation support; most organizations maintain human supervisors for final complaint resolution, reflecting cautious, slow displacement. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Retail/service and sales sectors have moderate AI adoption for customer service functions, with chatbots and CRM-integrated tools common but supervisor-level complaint resolution still largely manual. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can significantly augment supervisors by auto-summarizing complaints, suggesting solutions based on historical cases, flagging high-priority or similar issues, and extracting key details. This allows supervisors to respond faster and more consistently while retaining authority over resolution. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can draft responses, summarize complaint history, suggest resolutions, and flag sentiment/urgency, meaningfully speeding up a supervisor's complaint-handling workflow while the human retains final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can handle routine complaint categorization and triage, but resolving complaints typically requires understanding nuanced customer sentiment, empathy, negotiation, and authority to make binding decisions about compensation or service recovery. Current systems lack reliable judgment for non-standard cases and cannot fully replace the supervisor's discretionary problem-solving. |
| Task automatability | claude-sonnet-5 | 2/5 | AI chatbots can triage and resolve simple, scripted complaints, but supervisor-level complaints often involve escalated, emotionally charged, or personnel-related issues requiring judgment, authority, and relationship management that current AI cannot fully replicate. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Customer-facing complaint resolution often carries liability implications, regulatory oversight in consumer protection, and customer expectations for human accountability. Many organizations require supervisors to own complaint resolution and sign off on remedies, creating legal and contractual barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement, but organizational norms, customer expectations of human empathy, and personnel-related sensitivity (e.g., complaints about staff) create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Deploying AI for complaint handling incurs infrastructure, integration, and oversight costs. Given that supervisors must still verify resolutions, review sensitive cases, and handle escalations, the all-in cost approaches or exceeds the supervisor's marginal cost for hands-on complaint resolution. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI can cheaply handle routine complaint logging and initial responses, but complex escalations still require human supervisor time, keeping overall cost comparable once oversight and exception-handling are included. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Chatbots and complaint-management systems can log and route complaints, but no deployed product reliably handles resolution end-to-end without human oversight. Production systems require significant human review, especially for complaint legitimacy assessment and authority to commit resources. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Customer service AI products reliably handle first-tier complaint intake and simple resolutions in production, but escalated complaints requiring supervisory judgment and interpersonal repair are still mostly handled by humans. |
Examine products purchased for resale or received for storage to determine product condition.
33CI 30–35 · exposure 25 · augmentation 50 · importance 3.9/5 · click for rater detail
Examine products purchased for resale or received for storage to determine product condition.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Retail and wholesale sectors show slower AI adoption overall; product inspection automation is in pilot phase rather than mature deployment. Most organizations still rely on human first-line supervisors for this gatekeeping task, with AI tools rarely in production at scale. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Wholesale trade and non-retail supervisory sectors show slower AI adoption compared to office/professional services; physical inspection tasks are not a priority for automation compared to digital documentation tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI vision can assist supervisors by flagging suspicious items or providing photographic records, reducing time spent on routine inspections and improving consistency. However, the human supervisor remains necessary for final judgment on borderline cases and accountability. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-powered imaging tools and mobile apps can help flag anomalies or defects, and inventory management systems can pre-screen incoming shipments, providing moderate assistance to a human doing the final quality check. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can detect obvious damage or defects, this task requires nuanced judgment about acceptability thresholds, storage conditions, and context-specific quality standards that vary by product type and customer expectations. Current systems cannot reliably replace human inspection end-to-end without substantial human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical inspection of goods for damage, defects, or quality issues requires sensory/manual handling that current general-purpose AI cannot perform end-to-end; some visual inspection could be camera-assisted but full task automation is limited. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | No legal licensing requirement exists for product inspection, but organizational friction is moderate: supervisors have accountability for quality decisions, customer complaints create liability risk, and many organizations prefer human judgment for high-value inventory. Switching to AI-only inspection faces organizational resistance. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but liability for missed defects, need for physical presence and handling, and integration with existing warehouse workflows create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI vision hardware, software licensing, and integration costs are substantial, while first-line supervisors perform this task as part of broader duties. For low-margin retail operations, the cost per inspection remains comparable to or higher than direct human labor. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Deploying vision systems or IoT sensors for incoming goods inspection requires significant capital and integration, often exceeding the cost of a supervisor doing visual/tactile checks as part of broader duties. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Computer vision products exist for quality control in manufacturing, but deployment in general retail/storage contexts with diverse product types remains limited and error-prone. Most deployed systems are narrow (specific product lines) rather than generalizable, and human inspection remains the production standard. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Computer vision quality-inspection products exist in narrow domains (e.g., manufacturing defect detection) but are not broadly deployed for general merchandise receiving/storage condition checks by non-retail sales supervisors. |
Examine merchandise to ensure correct pricing and display, and that it functions as advertised.
30CI 25–35 · exposure 25 · augmentation 50 · importance 3.9/5 · click for rater detail
Examine merchandise to ensure correct pricing and display, and that it functions as advertised.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Retail and sales sectors are moderately digitizing, but in-store supervisory auditing remains labor-intensive and human-driven; adoption of automation for this specific task is nascent and largely confined to pilot projects in larger enterprises. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Non-retail sales supervision is not a heavily digitized sector; AI adoption for physical merchandise inspection tasks is still nascent outside large retail chains. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted tools (real-time pricing verification, image flagging of display anomalies, automated inventory checks) can help supervisors work faster and catch some issues, though human judgment and physical presence remain essential for reliable auditing. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Mobile apps and computer vision tools can help flag pricing discrepancies or display issues for human follow-up, improving efficiency of the human-led inspection process. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Visual inspection of merchandise for pricing, display, and basic functionality can be partially automated using computer vision, but requires significant domain expertise and physical manipulation in real retail environments. Current AI systems struggle with the contextual judgment needed to verify that items 'function as advertised' across diverse product types and conditions. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical inspection of merchandise for correct pricing, display, and function requires in-person handling and visual/manual verification that current AI cannot fully replace end-to-end.rating |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Supervisory roles carry legal and quality accountability; liability for incorrect pricing or undetected defects rests with human management oversight. Organizational practice and customer expectations also reinforce that a human supervisor must validate merchandise standards and sign off on compliance. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but physical presence and hands-on functional testing of products create practical friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Deploying vision systems with integration, oversight, and exception handling for in-store auditing remains comparatively expensive relative to a supervisor's hourly wage, particularly when accounting for the physical inspection and manual correction steps that remain necessary. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Deploying vision systems or robotics for physical merchandise checks requires camera infrastructure and integration costs that often exceed the low marginal cost of a supervisor's visual walk-through. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While computer vision can detect some pricing and display issues in controlled settings, no mature production system reliably performs full end-to-end audits of merchandise condition, pricing accuracy, and advertised functionality across varied retail environments at scale. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Computer vision systems can check shelf pricing and planogram compliance in some retail deployments, but broad functional testing of merchandise and display verification remains largely manual in non-retail sales settings. |
Confer with company officials to develop methods and procedures to increase sales, expand markets, and promote business.
29CI 25–32 · exposure 25 · augmentation 63 · importance 3.8/5 · click for rater detail
Confer with company officials to develop methods and procedures to increase sales, expand markets, and promote business.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While professional services and information sectors digitize, this task involves senior-level strategy and cross-functional conferencing, which remain predominantly human-led even in tech-forward firms. Adoption of AI for this specific task is minimal and moves slowly. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Sales and business strategy functions are adopting AI tools (CRM analytics, forecasting) at a moderate pace, but the collaborative strategic conferring itself sees slower, more cautious adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by preparing market analyses, drafting procedure documents, summarizing competitor data, and organizing proposals—genuinely raising supervisor productivity on background work. However, the core conferencing and consensus-building remain human-driven. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can strongly support this task by generating market analyses, sales data insights, and draft proposals that supervisors bring into discussions with officials, meaningfully boosting productivity while humans retain the interactive decision-making role. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task requires strategic judgment, stakeholder negotiation, and organizational understanding. While AI can draft reports or summarize market data, the core work—conferring with officials, synthesizing input, and committing to specific procedural changes—demands human decision-making and accountability that current systems cannot reliably perform end-to-end at 50% time savings with equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires strategic judgment, negotiation, organizational context, and interpersonal deliberation among officials that current AI cannot conduct end-to-end, though it can support analysis and idea generation.4 Real-time cross-functional conferring with executives remains fundamentally human-led. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: organizational hierarchy requires human officials to sign off on strategic changes; liability and decision authority rest with humans; internal corporate governance typically mandates human-led strategy conferencing; and error costs (poor business decisions) are borne by the organization. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but organizational trust, accountability for strategic decisions, and the need for interpersonal rapport with company officials create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The loaded cost of a first-line supervisor includes salary, benefits, and organizational authority. AI inference is cheap but cannot replace the seniority, judgment, and liability acceptance inherent in this role. Integration and human oversight would approach or exceed the supervisor's cost. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | The core deliverable is a human relational/strategic activity; AI can cheaply produce supporting reports, but the actual conferring and decision-making still requires paid managerial time, keeping overall costs comparable to human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs strategic business conferencing and procedure development. AI can assist with data analysis and documentation, but the negotiation, consensus-building, and final authorization required to 'develop methods and procedures' remain human-led in production environments. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product autonomously confers with executives to set sales strategy; AI is used as a research/analytics aid, not as a substitute for the meeting and negotiation itself. |
Hire, train, and evaluate personnel.
25CI 25–25 · exposure 25 · augmentation 50 · importance 3.9/5 · click for rater detail
Hire, train, and evaluate personnel.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While some large tech and finance firms use AI-assisted recruiting tools, most sales organizations and small-to-mid-sized firms rely on human supervisors for hiring and evaluation; adoption remains in the pilot and supplementary-tool phase rather than production replacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Non-retail sales sectors show moderate AI adoption in recruiting tools and LMS platforms, but supervisory hire/train/evaluate functions themselves see slow, cautious adoption due to compliance concerns. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI augments hiring by screening resumes, scheduling interviews, and generating performance feedback templates, improving supervisor efficiency on administrative tasks, but the core interpersonal, judgment-driven elements (final selection, one-on-one training, contextual evaluation) remain human-centric. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can meaningfully assist with drafting job postings, screening candidates, generating training materials, and structuring performance review documentation, improving supervisor efficiency without replacing their judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with resume screening, scheduling, and generating evaluation templates, but hiring, training, and evaluating personnel require significant human judgment, legal compliance, interpersonal nuance, and accountability that current systems cannot handle end-to-end with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Hiring, training, and evaluating employees involve judgment, interpersonal assessment, and legal/organizational context that current AI cannot fully replicate end-to-end, though some sub-components (resume screening, training content) can be aided.4 |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Legal exposure is substantial: hiring and employment decisions face discrimination liability, regulatory oversight (EEO, FCRA), and organizational risk if delegated to AI; many firms require human sign-off and maintain legal review of hiring decisions, creating hard barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Hiring and performance evaluation carry significant legal liability (discrimination, labor law compliance) and typically require human sign-off, making this a strongly protected supervisory function. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for recruitment and evaluation platforms are useful but require significant human oversight and legal review, making the all-in cost (tooling, integration, human review, liability management) comparable to or exceeding direct supervisory effort. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can reduce costs for screening and training material creation, but the overall task still requires substantial human oversight, interviews, and judgment calls, keeping all-in costs comparable to human-led processes. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI tools exist for resume parsing and basic assessment, no mature production system reliably performs the full hiring, training, and evaluation workflow independently; HR professionals currently use AI as a narrow filter, not as a deployed end-to-end solution. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products exist for resume screening, interview scheduling, and e-learning content generation, but no deployed system reliably performs the full hire-train-evaluate cycle autonomously in production. |
Provide staff with assistance in performing difficult or complicated duties.
19CI 7–30 · exposure 13 · augmentation 50 · importance 4.1/5 · click for rater detail
Provide staff with assistance in performing difficult or complicated duties.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Sales organizations are moderately digitized but supervisory coaching remains deeply interpersonal; adoption of AI assistance in this domain is still pilot-stage, with most firms relying on CRM integration and training platforms rather than AI-driven real-time coaching or decision support. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Sales supervision involves interpersonal, on-the-floor guidance in sectors with mixed digitization; AI adoption for this specific interpersonal coaching function remains nascent. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully augment supervisors by surfacing relevant customer data, suggesting talking points from past cases, or flagging escalation patterns, but the core act of coaching requires human judgment, empathy, and authority that AI cannot replace, leaving augmentation moderate but not transformative. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can provide supervisors with information, best practices, or quick reference guidance to relay to staff, aiding but not replacing the assistance process. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can provide information retrieval and documentation of procedures, the core task requires real-time judgment about what 'difficult or complicated' means in context, adaptive coaching, and nuanced interpersonal problem-solving that current systems cannot reliably deliver end-to-end at scale. AI falls well short of the 50% time-saving threshold for this supervisory role. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires in-person mentorship, hands-on problem-solving, and situational judgment tailored to specific staff and contexts that current AI cannot perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong organizational and human-contact barriers exist: supervisors are expected to maintain direct authority, coaching relationships, and accountability for team performance; customers and staff expect human judgment and mentoring, not algorithmic routing or canned advice. Regulatory and contractual frameworks often mandate human supervisory sign-off. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement exists, but organizational reliance on human judgment, trust, and interpersonal relationship-building creates moderate friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Supervisory labor is highly specific to organizational context and relationship capital; deploying AI assistance tools plus infrastructure still costs significantly relative to the loaded wage when the task requires live, adaptive intervention rather than batch processing. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system that replaces this supervisory function, so cost comparison favors the human who can adaptively assist and be present. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably assists supervisors in real-time coaching and complex troubleshooting in field sales contexts; AI systems can generate templates or search knowledge bases but cannot substitute for or reliably augment a supervisor's judgment on the fly in production environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for a supervisor physically or interactively guiding staff through complex, situational job duties in non-retail sales settings. |
Direct and supervise employees engaged in sales, inventory-taking, reconciling cash receipts, or performing specific services.
16CI 7–25 · exposure 13 · augmentation 50 · importance 4.0/5 · click for rater detail
Direct and supervise employees engaged in sales, inventory-taking, reconciling cash receipts, or performing specific services.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI supervision tools is slow and piecemeal; organizations rely on supervisors for compliance, culture, and accountability. Pilot projects focus on dashboards and alerts, not autonomous supervision, reflecting reluctance in sectors where this task predominates (sales, service). |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Sales/wholesale sectors are adopting AI for analytics and CRM but not for direct people-management functions, which remain human-led. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist supervisors through real-time inventory analytics, cash-reconciliation flagging, and sales performance dashboards, improving decision-making on staffing and compliance. However, the human supervisor remains central to delegation, motivation, and accountability. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with scheduling, inventory tracking, and reconciling reports, giving supervisors useful data support even though core supervisory judgment remains human. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could assist with monitoring inventory systems and reconciling cash receipts automatically, the core supervisory functions—directing employees, performance management, conflict resolution, and real-time decision-making—require human judgment and interpersonal engagement that current systems cannot reliably handle end-to-end at scale. |
| Task automatability | claude-sonnet-5 | 1/5 | Direct supervision of employees performing physical and interpersonal tasks requires in-person presence, judgment, and authority that current AI cannot replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: employment law and liability require a human accountable for hiring, discipline, and team decisions; organizational culture favors in-person leadership; staff require human mentorship and direct feedback that regulators and courts expect from a responsible supervisor. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Organizational and legal accountability for personnel management, employment decisions, and liability strongly favor a human supervisor of record. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI can automate only portions of the supervisory workload (reporting, analytics); the loaded cost of a human supervisor remains lower than deploying AI systems plus the required human oversight and exception handling across all supervisory responsibilities. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this supervisory function, so no meaningful cost comparison to a human supervisor's wage exists. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably handles full supervisory duties (staff direction, performance coaching, disciplinary decisions). Narrow capabilities exist for inventory tracking and cash reconciliation, but integrated supervisory AI systems in production remain scarce and typically require heavy human oversight. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product supervises non-retail sales employees directly; AI tools support scheduling or reporting but do not perform managerial supervision. |
Visit retailers and sales representatives to promote products and gather information.
6CI 0–13 · exposure 0 · augmentation 38 · importance 4.1/5 · click for rater detail
Visit retailers and sales representatives to promote products and gather information.
6| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Sales supervision in non-retail sectors has not adopted AI automation for field visits; this remains a human-intensive, relationship-driven function with minimal digital transformation in the core activity. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Sales and retail relationship management sectors show moderate digitization but face-to-face sales visits remain a laggard area for AI substitution despite CRM tool adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with basic preparation (competitor research, data synthesis on retailer performance) or post-visit CRM documentation, but offers limited transformation of the core interpersonal and negotiation work that defines this task. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with trip planning, market research, product information prep, CRM data logging, and follow-up communications, improving efficiency around the visit itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires in-person relationship-building, negotiation, and real-time adaptation to individual retailers' needs—capabilities far beyond current AI. No AI system can autonomously visit physical locations, build trust, or conduct dynamic sales conversations. |
| Task automatability | claude-sonnet-5 | 1/5 | Requires physical travel, in-person relationship building, and real-time human interaction with retailers and reps; current AI cannot perform physical site visits or embodied social engagement. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong barriers exist: the task inherently requires a human representative for legal accountability and relationship credibility, retailers expect human contact and trust-building, and organizational norms around sales and partnership require a licensed employee as company representative. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but strong organizational and relationship-based friction since retailers expect human sales presence and trust-building, which is a soft but real barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI cannot perform this task at all, so the cost comparison is moot—the service requires a human supervisor at a wage substantially below what would be needed to develop and deploy an autonomous system capable of this work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the physical travel and in-person component at all, so it is not cheaper—there is no AI alternative to compare cost against for the core activity. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs autonomous field visits to retailers or conducts promotional negotiations. This task sits entirely outside current AI capability boundaries; it is research-stage at best. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical retail visits or in-person promotional interactions; this remains entirely a human physical-presence task. |
Attend company meetings to exchange product information and coordinate work activities with other departments.
6CI 0–13 · exposure 0 · augmentation 38 · importance 3.6/5 · click for rater detail
Attend company meetings to exchange product information and coordinate work activities with other departments.
6| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task involves no measurable AI adoption because it fundamentally requires human presence and interpersonal engagement. Organizations have no path to automating meeting attendance and inter-departmental coordination. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Sales supervision is a mixed-digitization sector where AI tools are used for reporting and CRM support, but actual meeting attendance and interdepartmental coordination remain human-led with limited AI penetration. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide modest assistance by summarizing meeting notes, flagging action items, or preparing briefing materials beforehand, but it cannot transform the core task of live coordination and information exchange that requires the supervisor's judgment and presence. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with meeting transcription, summarization, action-item tracking, and preparing product information beforehand, improving efficiency around the task even though the core human interaction remains essential. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Attending and meaningfully participating in company meetings requires real-time coordination, interpersonal judgment, and contextual decision-making that current AI cannot perform end-to-end. While AI could summarize meeting content or draft agendas, it cannot replace the supervisor's presence and active role in cross-departmental coordination. |
| Task automatability | claude-sonnet-5 | 1/5 | Attending meetings and coordinating with other departments requires physical/virtual presence, real-time judgment, relationship management, and organizational authority that AI cannot substitute for end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong organizational and social barriers protect this task: the supervisor's presence, judgment, and authority are essential for effective cross-departmental coordination. Meeting attendance and participation are intrinsically tied to the supervisory role itself. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement, but organizational norms, accountability structures, and the need for a human representative with authority to coordinate and negotiate create real friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems have no cost advantage here because the core task—attending and coordinating in real-time meetings—cannot be automated. Any cost would be incurred on top of the human supervisor's wage, not in replacement of it. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Since AI cannot perform the actual coordination and decision-making role, there is no viable AI substitute cost to compare against the human's wage for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can autonomously attend and participate in meetings, exchange information, and coordinate activities across departments. Meeting attendance and live interaction remain fundamentally human activities; AI cannot substitute for the supervisor's presence and authority. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product attends meetings and coordinates cross-departmental work autonomously on a supervisor's behalf; AI meeting tools only support note-taking or summarization, not substantive participation. |
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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.