General and Operations Managers
11-1021.00Median wage $105,770/yr3,503,020 employed (US)Rank #268 of 923 scored · top 29% by substitution
Plan, direct, or coordinate the operations of public or private sector organizations, overseeing multiple departments or locations. Duties and responsibilities include formulating policies, managing daily operations, and planning the use of materials and human resources, but are too diverse and general in nature to be classified in any one functional area of management or administration, such as personnel, purchasing, or administrative services. Usually manage through subordinate supervisors. Excludes First-Line Supervisors.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution35
Exposure31
Augmentation72
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
17 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
6%
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%30
panel mean rating 2.2/5 → substitution pressure 30/100
Technical feasibility todayw 20%32
panel mean rating 2.3/5 → substitution pressure 32/100
Cost vs. human wagew 15%35
panel mean rating 2.4/5 → substitution pressure 35/100
Adoption barriersw 20%inverted — strong barriers lower the score43
panel mean rating 3.3/5 (barrier strength) → substitution pressure 43/100
Sector adoption velocityw 10%42
panel mean rating 2.7/5 → substitution pressure 42/100
Task breakdown (17 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.
Review financial statements, sales or activity reports, or other performance data to measure productivity or goal achievement or to identify areas needing cost reduction or program improvement.
71CI 61–81 · exposure 62 · augmentation 100 · importance 4.2/5 · click for rater detail
Review financial statements, sales or activity reports, or other performance data to measure productivity or goal achievement or to identify areas needing cost reduction or program improvement.
71| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | BI and analytics adoption in corporate environments is rapid and deep; nearly all mid-to-large enterprises deploy dashboards and automated reporting, and smaller firms increasingly adopt cloud BI tools. This is a leading edge of enterprise automation. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Finance and management functions are among the fastest adopters of AI analytics and reporting tools, with widespread deployment of BI/AI dashboards in professional services and corporate settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI-driven dashboards, forecasting, and anomaly detection directly enhance manager productivity by surfacing insights, automating routine calculations, and freeing time for strategic decisions. The human manager remains central to judgment but works with AI-generated insights as a powerful lever. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially enhances managers' ability to review and interpret large volumes of performance data quickly, surfacing key trends and reducing time spent on manual data compilation while the human retains decision authority. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can extract, summarize, and compare financial data, identify trends, and flag performance gaps with high accuracy. However, strategic interpretation of root causes and judgment about which improvements matter most still typically requires human oversight, preventing a full 5-rating. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can rapidly analyze financial statements and generate insights on trends and anomalies, but the judgment-laden interpretation and contextualized decision-making about goals and cost reduction still requires human oversight, so only partial automation meets the 50% threshold. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing requirement mandates human review of performance data; internal governance and audit practices may request sign-off, but the analysis itself is not legally protected. Organizational friction around tool adoption exists but is not a hard barrier. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI-assisted financial review, though internal governance, data security, and accountability for strategic decisions create some organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Modern cloud-based analytics and BI tools cost a fraction of a manager's loaded wage per report cycle, and inference-time cost for dashboards and alerts is negligible. Automation delivers continuous monitoring that would require many hours of manual review. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated data aggregation and report generation via AI tools is substantially cheaper than manual analyst time for routine performance monitoring, though initial integration and data cleaning costs offset some savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature BI and analytics platforms (Tableau, Power BI, modern ERP systems) now routinely automate data ingestion, performance dashboarding, and anomaly detection at scale in production. Some judgment-heavy interpretation still requires human review, but the core analytic workflow is reliably deployed. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | BI and analytics tools with AI-driven insights (e.g., dashboards, anomaly detection, LLM-based report summarization) are deployed in many organizations, but reliability varies and most still require human validation of conclusions. |
Prepare staff work schedules and assign specific duties.
67CI 59–75 · exposure 62 · augmentation 88 · importance 4.0/5 · click for rater detail
Prepare staff work schedules and assign specific duties.
67| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Workforce scheduling software is widely adopted in retail, hospitality, healthcare, and logistics. Digital scheduling tools are now standard in larger and mid-market firms; adoption is established and accelerating. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Adoption is substantial in retail/hospitality/healthcare but many smaller organizations still schedule manually or with basic spreadsheets, giving middling overall penetration. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | Scheduling tools significantly enhance manager productivity by auto-generating compliant schedules, flagging coverage gaps, and accommodating constraints. The manager remains in control while AI removes tedious manual assignment work. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI scheduling tools significantly boost manager productivity by auto-generating draft schedules, flagging conflicts, and optimizing coverage while the manager retains final approval. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | Scheduling can be partially automated (constraint-solving, shift assignments) but requires human judgment on workload, staff preferences, compliance, and operational changes. Current AI tools handle routine shift generation but typically need human review and adjustment. |
| Task automatability | claude-sonnet-5 | 4/5 | Scheduling software and AI-based workforce management tools can generate optimized staff schedules and duty assignments from constraints, historical data, and demand forecasts, meeting the time-saving bar for most of the mechanical work.deras. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or legal barriers exist; scheduling is discretionary management. Union contracts or labor regulations add minor friction, but no licensing or hard legal requirement mandates human review of schedules. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks automated scheduling, though labor law compliance (union rules, predictive scheduling laws) and manager sign-off create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Scheduling software costs $100–500/month for small teams and reduces manager time substantially. The all-in cost (software + oversight) is typically well below the loaded wage of a manager spending 4–8 hours weekly on scheduling. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Subscription-based scheduling software costs a small fraction of a manager's hourly wage-equivalent time spent manually building schedules, though some human review/adjustment remains. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Several scheduling software products exist (deputy, workforce.com, etc.) with algorithmic scheduling, but they require manual input, handle edge cases poorly, and still depend on manager sign-off. Deployment is common but reliability issues persist. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Deployed products (Deputy, When I Work, Kronos, ADP scheduling with AI optimization) reliably generate schedules in production across retail, hospitality, and healthcare today. |
Set prices or credit terms for goods or services, based on forecasts of customer demand.
49CI 41–56 · exposure 42 · augmentation 75 · importance 4.0/5 · click for rater detail
Set prices or credit terms for goods or services, based on forecasts of customer demand.
49| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Large retailers, hospitality chains, and financial services have actively adopted algorithmic pricing and credit decisioning in production. Smaller firms lag, but the sectors with high task frequency are digitized and moving fast. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Pricing algorithms are well-adopted in e-commerce and travel but adoption is uneven across the broader population of general managers in slower-moving sectors like manufacturing and services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at synthesizing demand forecasts, competitive pricing data, and inventory signals to present optimized recommendations to managers. This substantially accelerates decision-making and broadens the analysis space, with humans retaining final approval and strategic adjustment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-driven demand forecasting and pricing recommendation tools meaningfully boost managers' ability to set data-informed prices and credit terms while the human retains final decision authority. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automate significant parts of demand forecasting and price-optimization calculations, but human judgment on competitive positioning, margin strategy, and credit risk assessment typically remains essential. The task involves both technical components (demand modeling) that AI handles well and strategic/contextual decisions that still require oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | Pricing decisions require synthesizing market context, strategic goals, and demand forecasts with judgment calls that go beyond data analysis; AI can support but not fully replace this end-to-end.rationale ends here. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Legal liability for discriminatory pricing and credit decisions creates material regulatory friction (Fair Lending Act, antitrust scrutiny). Customer expectations and competitive norms also create adoption friction, though no explicit licensing barrier prevents the automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement mandates a human set prices, but liability for credit decisions, contractual authority, and organizational approval chains create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven pricing engines have low marginal inference cost and can process vastly more data than human analysts, achieving significant per-decision savings when deployed at scale. However, integration, validation, and oversight overhead mean the ratio is favorable but not extreme. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Pricing analytics tools reduce analyst hours but require licensing, integration, and ongoing calibration, so total cost is often comparable to a skilled analyst rather than drastically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Dynamic pricing and demand-forecasting systems exist in production (retail, hospitality, airlines), but most real deployments require human review of recommendations, especially for credit terms and high-stakes pricing decisions. Fully autonomous systems are rare outside narrow domains like e-commerce. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Dynamic pricing and demand-forecasting software are deployed in retail, travel, and e-commerce, but final credit terms and price-setting for many businesses still require human sign-off and contextual judgment. |
Develop or implement product-marketing strategies, including advertising campaigns or sales promotions.
41CI 25–57 · exposure 38 · augmentation 88 · importance 3.6/5 · click for rater detail
Develop or implement product-marketing strategies, including advertising campaigns or sales promotions.
41| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While marketing teams are piloting AI for content generation and analytics, actual strategy development remains human-driven in most organizations. Adoption is concentrated in larger tech and consumer goods firms; most sectors still rely on human strategists for campaign strategy formulation. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Marketing and advertising functions have seen fast, deep AI tool adoption (generative ad copy, programmatic ad buying, analytics), reflecting the broader trend in professional/information-sector adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly augments marketing managers by automating competitive analysis, generating multiple creative directions, running multivariate testing scenarios, and drafting copy—allowing strategists to focus on higher-level positioning and decision-making. This is one of the areas where AI is already transforming productivity within human-led processes. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI significantly boosts productivity in ideation, copywriting, audience segmentation, and campaign performance analysis, while managers retain final strategic control and oversight. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with generating marketing copy, analyzing competitor campaigns, and drafting promotional concepts, but developing holistic product-marketing strategies requires understanding market positioning, brand equity, customer psychology, and business objectives—judgments that demand human strategic insight and accountability. Current systems cannot reliably replace the full end-to-end strategy development loop. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft campaign concepts, ad copy, and promotional plans quickly, but selecting and implementing a coherent strategy requires business judgment, budget tradeoffs, and stakeholder alignment that current AI cannot fully own end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant adoption barriers exist: marketing strategies directly affect brand positioning and revenue, creating high error-cost asymmetry and requiring executive sign-off; fiduciary duty and liability concerns make organizations reluctant to delegate strategy to automation; and customer-facing brand decisions typically remain under human authority. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for marketing strategy work, though organizational risk tolerance and brand reputation concerns create some friction against fully automated decision-making. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools reduce the cost of some strategic components (copywriting, data analysis) but the full loaded cost of strategy development—including setup, prompt engineering, human validation, and legal/brand review—remains comparable to or higher than paying a marketing manager for strategic direction. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools reduce content creation and analysis costs substantially, but a manager's time integrating strategy, negotiating budgets, and coordinating implementation remains costly, keeping overall cost comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed products (generative AI, marketing analytics tools) can produce draft campaign materials and analyze campaign performance, but no mature production system reliably executes full strategy development and implementation independent of human oversight. Marketing automation platforms handle execution but not strategy formulation. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Marketing AI tools (copywriting, ad optimization, campaign analytics platforms) are widely deployed in production, but full strategy development and cross-channel implementation still require human oversight and decision-making. |
Monitor suppliers to ensure that they efficiently and effectively provide needed goods or services within budgetary limits.
39CI 32–46 · exposure 30 · augmentation 75 · importance 3.4/5 · click for rater detail
Monitor suppliers to ensure that they efficiently and effectively provide needed goods or services within budgetary limits.
39| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Manufacturing, procurement, and logistics sectors are actively piloting AI-driven supplier monitoring, but deployment is still dominated by traditional ERP and vendor management systems with manual oversight. Adoption is accelerating but not yet at scale in most organizations. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Supply chain and procurement analytics tools are moderately adopted in larger organizations, with pilots for AI-driven vendor risk monitoring becoming more common but not yet universal. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at synthesizing supplier data, identifying anomalies, and highlighting risk areas—substantially raising a manager's ability to detect problems early and allocate attention. The manager retains responsibility for strategic decisions, but AI dashboards and alerts measurably enhance visibility and decision velocity. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly enhance this task by aggregating supplier performance data, flagging budget deviations, and predicting risks, letting managers focus on decisions and relationships. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can monitor supplier metrics (delivery times, costs, quality scores) from structured data and flag outliers, the task requires contextual judgment about supplier relationships, negotiation readiness, and trade-offs between cost, quality, and reliability that demand human oversight. Current systems cannot autonomously manage the full feedback loop at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Supplier monitoring involves relationship judgment, negotiation, and contextual decision-making about quality tradeoffs that AI cannot fully replace, though data tracking portions can be automated. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Supply chain decisions often involve contractual obligations, vendor relationships, and strategic considerations that create organizational friction. Regulatory compliance (especially in regulated industries) and the need for human judgment in supplier disputes or escalations provide moderate adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational accountability for procurement decisions and vendor relationships creates moderate friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI-powered monitoring tools reduce manual data gathering and reporting, but require domain expertise to tune, validate, and act on insights. The combined cost of inference, integration, and necessary human oversight is roughly comparable to employing personnel for routine supplier monitoring. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI dashboards can cheaply flag performance metrics, but the human oversight, judgment calls, and vendor relationship management still required keep overall costs comparable to a human manager's effort. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed supply chain analytics and vendor management platforms can track KPIs and generate alerts, but they operate within narrow scopes (data quality, integration maturity vary widely) and require significant human interpretation of supplier performance and corrective actions. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Procurement analytics and supplier scorecarding tools exist and are deployed, but comprehensive supplier oversight combining data review, escalation, and relationship management is not fully handled by any single product. |
Manage the movement of goods into and out of production facilities to ensure efficiency, effectiveness, or sustainability of operations.
35CI 28–42 · exposure 30 · augmentation 75 · importance 4.0/5 · click for rater detail
Manage the movement of goods into and out of production facilities to ensure efficiency, effectiveness, or sustainability of operations.
35| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Manufacturing and logistics sectors have adopted AI-powered planning tools at pilot and early-implementation stage, but autonomous, end-to-end goods movement management without human directors remains uncommon. Adoption is faster in large enterprises than small-to-mid operations. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Manufacturing and logistics sectors show moderate AI adoption with pilots in predictive analytics and inventory optimization, but full production-scale autonomous management remains uncommon. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI systems significantly augment manager productivity through real-time visibility dashboards, demand forecasting, exception alerts, and optimization recommendations. Managers using these tools can handle larger volumes and make faster, data-informed decisions while retaining control and accountability. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered demand forecasting, inventory optimization, and logistics analytics tools substantially improve a manager's ability to monitor and adjust goods flow, though the manager remains essential for decision-making and coordination. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with inventory tracking, demand forecasting, and route optimization, the task requires real-time judgment about facility constraints, supplier relationships, and operational trade-offs that demand human oversight. Full end-to-end automation with 50% time savings at equal quality is not achievable with current systems. |
| Task automatability | claude-sonnet-5 | 2/5 | While AI/optimization software can support scheduling and routing decisions, the overall management of goods flow requires real-time coordination, exception handling, and cross-functional judgment that current systems cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Managers legally sign off on production decisions and supply commitments; liability for disruptions, regulatory compliance (environmental, safety), and customer-relationship accountability typically require a licensed or accountable human. Many sectors impose explicit requirements that a responsible human manager oversee operations. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human specifically, but organizational complexity, liability for supply chain failures, and need for cross-departmental authority create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI solutions for supply chain optimization require significant integration, customization, and ongoing human oversight costs. The all-in cost (software, implementation, monitoring) remains comparable to or higher than the salary of a manager performing routine aspects of this task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Enterprise-grade logistics optimization software carries significant licensing, integration, and maintenance costs that are not dramatically cheaper than a manager's salary, especially given the need for human oversight. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed supply chain and inventory management products exist, but they function primarily as decision-support tools rather than autonomous executors. Most production facilities still rely on human managers to interpret recommendations and handle exceptions, contingencies, and relationship management. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Deployed supply chain management and WMS/ERP systems with AI-driven forecasting and optimization exist and are used in production, but they augment rather than fully perform the managerial oversight role, and error rates require human intervention. |
Plan store layouts or design displays.
33CI 30–35 · exposure 25 · augmentation 63 · importance 2.9/5 · click for rater detail
Plan store layouts or design displays.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Retail and quick-service restaurant sectors have shown slow, limited adoption of AI layout tools. Most stores rely on corporate templates, consultant redesigns, or manager experience; AI-driven layout optimization remains in pilot phase rather than standard practice. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Retail management is a moderately digitized sector with growing interest in AI merchandising tools, but adoption for actual layout/display design remains in early pilot stages rather than widespread production use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist managers by generating multiple layout options, simulating traffic patterns, and rendering visualizations quickly, materially speeding the exploration phase. However, final judgment on aesthetic fit and business goals remains firmly human, limiting transformative augmentation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered planogram and visualization tools can meaningfully speed up ideation, spatial planning, and visual mockups, giving managers a strong productivity boost while they retain final design control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate layout suggestions and render visualizations, the task requires spatial reasoning, aesthetic judgment, and business logic (traffic flow, product placement strategy) that current systems struggle with end-to-end. Layout design typically needs human refinement and approval, preventing the 50% time-savings-at-equal-quality threshold. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can generate layout ideas or mockups (e.g., via generative design tools) but physical execution, spatial judgment, and integration with real store constraints still require substantial human involvement, limiting time savings below the 50% threshold for full end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Store layout decisions often require sign-off from store managers, regional leadership, and marketing teams. There is no legal licensing requirement, but organizational approval workflows and reliance on tacit judgment about customer behavior create moderate friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human perform this task, though customer experience considerations and organizational preferences create some friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI visualization and layout-suggestion tools require subscription costs plus human design oversight and iteration, making the all-in cost comparable to or higher than hiring a retail designer or operations manager to plan layouts themselves. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI design tools can cut some ideation time cheaply, but the overall task still requires significant human oversight, site visits, and iteration, keeping costs closer to comparable rather than order-of-magnitude cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI tools exist for rendering 3D store layouts and suggesting arrangement patterns, but they operate within narrow templates and rarely replace human designers in production. Real-world store layouts demand integration with inventory systems, customer behavior data, and brand strategy—most deployed tools are visualization aids rather than autonomous planners. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some retail planogram software and AI-assisted design tools exist, but they are narrow-scope aids rather than reliable end-to-end systems performing full store layout planning in production at scale. |
Perform personnel functions, such as selection, training, or evaluation.
30CI 28–32 · exposure 30 · augmentation 75 · importance 3.8/5 · click for rater detail
Perform personnel functions, such as selection, training, or evaluation.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Mid-sized and larger organizations are adopting AI-assisted HR tools (screening, analytics) at a moderate pace, but AI-only personnel decisions remain rare and cautious; smaller firms lag significantly in adoption. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | HR tech adoption (ATS, AI screening, performance dashboards) is moderate and growing in white-collar sectors, but full personnel management remains largely human-led with AI as pilot-stage support. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI substantially augments HR professionals by automating resume filtering, suggesting training interventions, and aggregating performance metrics, enabling managers to focus on relationship-based judgment and strategic workforce planning. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI meaningfully assists managers by drafting job postings, screening candidates, generating training materials, and summarizing performance data, improving efficiency while humans retain decision authority. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with resume screening, training content generation, and evaluation scoring, the full end-to-end task of personnel selection, training, and evaluation requires human judgment on cultural fit, interpersonal dynamics, and nuanced performance assessment. Current AI falls short of the 50% time-saving threshold for the complete task. |
| Task automatability | claude-sonnet-5 | 2/5 | Personnel functions require judgment, interpersonal assessment, and legal accountability that AI can support but not fully execute end-to-end at equal quality with major time savings across the whole task bundle. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Personnel decisions carry high legal and reputational risk (employment law, discrimination liability); many organizations require human sign-off and documented decision rationale, and employment law in many jurisdictions mandates human accountability for hiring and termination decisions. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Hiring, evaluation, and termination decisions carry significant legal/liability exposure (discrimination law, labor regulations) and typically require human sign-off, creating strong organizational and regulatory barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Deployed HR technology tools reduce some administrative burden, but the core personnel functions still require substantial human expertise and judgment; total all-in cost of AI solutions plus oversight approximates or exceeds the cost of experienced HR staff. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can cut costs on sub-tasks like screening resumes, but the overall personnel function still requires manager time for interviews, coaching, and judgment calls, keeping blended cost comparable to human-only execution. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products like applicant tracking systems with AI screening, learning management platforms with AI tutoring, and performance analytics tools exist in production, but they handle narrow slices of the broader personnel function and typically require significant human oversight and decision-making. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products exist for resume screening, interview scheduling, and performance analytics, but no deployed system reliably handles full-cycle selection, training design, and evaluation without heavy human involvement. |
Plan or direct activities, such as sales promotions, that require coordination with other department managers.
29CI 25–32 · exposure 25 · augmentation 63 · importance 3.8/5 · click for rater detail
Plan or direct activities, such as sales promotions, that require coordination with other department managers.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Even in information-sector organizations, actual automation of managerial coordination is rare; most adoption remains in advisory or analytical domains. Executives retain decision-making control over activities that shape resource allocation and inter-team relationships, slowing substitution velocity. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Management and operations functions show moderate AI adoption for planning support and analytics, but actual cross-departmental coordination and directive tasks remain largely human-led in most organizations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by preparing data summaries, suggesting scheduling options, and flagging interdependencies, improving the efficiency of human coordination. However, the core task of negotiation and direction remains primarily human-driven, limiting augmentation impact to workflow optimization rather than transformation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by drafting promotion plans, analyzing data, summarizing inputs from various departments, and tracking action items, boosting the manager's efficiency in orchestrating these activities. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task requires complex stakeholder coordination, judgment about cross-departmental dependencies, and real-time negotiation—activities that remain difficult for current AI systems. While AI can draft promotional calendars or identify scheduling conflicts, actual direction and negotiation with human managers requires executive judgment and relationship management that falls short of the 50% time-saving bar. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires cross-functional judgment, negotiation, and real-time adaptation to organizational politics and priorities that current AI cannot fully replicate end-to-end, though AI can assist with scheduling and drafting plans. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | General managers occupy positions of organizational authority and fiduciary responsibility. Stakeholders expect direct human communication and accountability; replacing human judgment in cross-functional planning carries liability and organizational legitimacy risks that create friction against automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement, but organizational authority structures, accountability for outcomes, and interpersonal trust needed to direct peer managers create meaningful friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While AI tools are inexpensive, the task requires high-value human oversight to ensure alignment with business strategy and relationship preservation. The overhead of reviewing, correcting, and validating AI-generated coordination plans approaches or exceeds the cost of direct human execution. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can cheaply support planning documents or communications, but the actual coordination, persuasion, and directive authority still require a human manager, so all-in cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs end-to-end coordination of interdepartmental activities at scale. AI systems can assist with scheduling or data aggregation, but negotiation, conflict resolution, and final decision-making in real organizational contexts remain human-dependent in production settings. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product autonomously plans and directs cross-departmental initiatives; existing tools support scheduling, communication, or data analysis but leave coordination and decision-making to humans. |
Implement or oversee environmental management or sustainability programs addressing issues such as recycling, conservation, or waste management.
28CI 25–30 · exposure 25 · augmentation 63 · importance 3.1/5 · click for rater detail
Implement or oversee environmental management or sustainability programs addressing issues such as recycling, conservation, or waste management.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI for sustainability monitoring is emerging in large enterprises but remains fragmented; most organizations still rely on traditional management oversight, and cultural and regulatory inertia slow transition to automated systems in this domain. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Sustainability functions are adopting AI-assisted reporting and analytics tools gradually, but broad managerial oversight tasks remain low-tech and adoption is uneven across industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist managers by automating emissions tracking, generating compliance reports, identifying recycling optimization opportunities, and alerting to waste management anomalies, though final decisions and stakeholder communication remain manager responsibilities. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully assist with tracking waste/energy metrics, drafting sustainability reports, benchmarking against regulations, and identifying efficiency opportunities, boosting manager productivity while they retain oversight. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with data analysis, reporting, and compliance tracking for sustainability programs, end-to-end automation is infeasible because the task requires strategic decision-making, stakeholder engagement, policy development, and on-site oversight that demand human judgment and accountability. |
| Task automatability | claude-sonnet-5 | 2/5 | Designing, implementing, and overseeing sustainability programs requires cross-functional coordination, stakeholder buy-in, and physical operational changes that AI cannot execute end-to-end; AI can support data analysis and reporting but not the oversight and implementation itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulatory requirements, organizational liability for environmental compliance, and fiduciary duties mean that senior managers must legally sign off on sustainability programs; many jurisdictions require certified professionals for certain environmental certifications, creating hard barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement typically applies, but regulatory reporting obligations, liability for compliance failures, and organizational change management create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While AI can automate parts of data collection and report generation (reducing costs modestly), the core oversight and strategic functions still require substantial human management, making the all-in cost comparable to or higher than human-only approaches for small-to-medium programs. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can cheaply generate reports or track metrics, but the managerial oversight, vendor coordination, and physical program implementation still require significant human labor, keeping overall cost comparable to or only modestly below human-only execution. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some tools exist for environmental monitoring, waste tracking, and sustainability reporting, but no deployed product reliably performs the full oversight and implementation responsibilities autonomously; human managers remain essential for program design and cross-organizational coordination. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products exist for sustainability reporting, carbon tracking, and waste analytics, but no deployed system autonomously implements or oversees a full sustainability program in an organization. |
Recommend locations for new facilities, or oversee the remodeling or renovating of current facilities.
28CI 25–30 · exposure 25 · augmentation 63 · importance 2.8/5 · click for rater detail
Recommend locations for new facilities, or oversee the remodeling or renovating of current facilities.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While location analytics tools are available, adoption for autonomous facility recommendations remains limited. Most organizations still rely on human managers and consultants for these strategic decisions; AI adoption is primarily in supporting analysis rather than decision displacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Facilities and real estate decision-making in general management remains a low-digitization, judgment-heavy process with limited AI agent deployment compared to faster-adopting sectors like finance or software. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by providing demographic analysis, cost comparisons, renovation design renderings, and logistics modeling, enabling managers to evaluate options faster. However, the human manager retains central decision-making authority and final judgment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully assist with demographic/market analysis, cost forecasting, and design visualization for renovations, boosting manager productivity even though the final decision remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with data analysis for location recommendations (demographic, cost, logistics analysis) and generate renovation design options, but the task inherently requires human judgment on strategic fit, stakeholder alignment, and final site selection. End-to-end automation with ≥50% time savings at equal quality is not achievable today. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can support site-selection analysis and cost modeling for renovations, but the final recommendation requires integrating local knowledge, negotiation, business strategy, and stakeholder judgment that current AI cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: facility decisions involve capital expenditure requiring executive and board approval, legal/financial liability for poor siting or renovation choices, and organizational governance requiring human sign-off. These decisions typically cannot be delegated away from human leadership. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement mandates a human specifically, but real estate transactions, zoning compliance, and capital investment decisions typically require executive sign-off and legal/financial accountability, creating moderate organizational and liability friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for location analytics and design visualization have non-trivial costs (software licenses, integration, human review of outputs), and the task requires significant management oversight. Total cost is comparable to or higher than a human manager's time on this task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI-assisted data analysis is cheap, but the overall task still requires expensive human site visits, negotiations, and project oversight, so total cost savings are modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Tools exist for location analytics and renovation visualization, but no deployed product reliably performs the full recommendation task independently. Systems provide inputs to human decision-making rather than autonomous facility selection and oversight. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Location-analytics and real-estate decision-support tools exist but they inform rather than autonomously perform this task; no deployed product independently recommends and manages facility siting/renovation decisions. |
Direct administrative activities directly related to making products or providing services.
26CI 25–28 · exposure 25 · augmentation 63 · importance 4.0/5 · click for rater detail
Direct administrative activities directly related to making products or providing services.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Although manufacturing and services are digitizing, the substitution of AI for the general manager directing operations remains minimal and primarily limited to narrow, delegated tasks rather than the full directing role. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Many organizations are adopting AI-based dashboards and workflow tools to support managers, but full direction of administrative activity remains human-led with moderate uptake of assistive tools. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist with dashboards, predictive analytics on production metrics, and workflow recommendations, meaningfully improving a manager's visibility and efficiency on parts of the task, though the core directing responsibility remains with the human. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly aids managers via data synthesis, scheduling, report generation, and communication drafting, boosting productivity even though the human retains directive control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Only narrow aspects of directing administrative activities (e.g., scheduling, simple report generation) can be automated; the core task requires real-time judgment, stakeholder coordination, and adaptive decision-making that current AI systems cannot reliably execute end-to-end without significant human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | This task involves directing people, coordinating operations, and making contextual judgment calls that require situational awareness and authority AI cannot exercise end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Organizational accountability structures, legal liability for product/service quality, and the irreplaceable need for human judgment in directing operations create strong adoption barriers; customers and regulators typically expect human decision-making at the management level. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Directing administrative activities implies managerial accountability, decision authority, and often legal/organizational responsibility that typically must rest with a designated human manager. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools that might assist (workflow orchestration, analytics platforms) cost money and still require the manager's loaded wage; there is no scenario where AI replaces the manager's oversight function at lower total cost today. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Because human managerial judgment, accountability, and interpersonal direction remain necessary, AI only offsets a small support portion of the task, limiting cost savings relative to a full-time manager. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs this task autonomously in production; while AI assists with specific subcomponents (scheduling, analytics), the integrated direction of administrative activities to serve production/service goals remains beyond current system capabilities. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools can support scheduling, reporting, and data aggregation for administrative activities, but no deployed product autonomously directs administrative operations tied to production or service delivery. |
Direct or coordinate financial or budget activities to fund operations, maximize investments, or increase efficiency.
26CI 25–28 · exposure 25 · augmentation 75 · importance 3.9/5 · click for rater detail
Direct or coordinate financial or budget activities to fund operations, maximize investments, or increase efficiency.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI in finance is growing but primarily in narrow domains (transaction processing, fraud detection). Strategic financial direction and budget coordination remain tightly held by human management; pilot and proof-of-concept projects are common but production autonomy is rare. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Finance-adjacent functions in professional/managerial settings are adopting AI tools for forecasting and analytics at a moderate pace, but full coordination of budget activities remains human-led. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI substantially assists managers by automating budget variance analysis, forecasting scenarios, identifying optimization opportunities, and consolidating data—significantly raising manager productivity. The manager remains in the loop for strategy and decisions, but AI handles time-intensive analytical work. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools significantly enhance budget analysis, forecasting, scenario modeling, and reporting, materially boosting a manager's efficiency while they retain decision authority. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can analyze financial data, generate budget forecasts, and identify cost-saving opportunities, the task of directing and coordinating financial activities requires strategic judgment, stakeholder negotiation, and accountability that current systems cannot provide end-to-end. AI handles components (reporting, variance analysis) but not the full decision-making and coordination loop. |
| Task automatability | claude-sonnet-5 | 2/5 | This task involves cross-functional coordination, judgment calls on tradeoffs, and organizational authority that current AI cannot exercise end-to-end; AI can support analysis but not direct or coordinate the activity itself.rating |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: fiduciary responsibility, regulatory compliance (SOX, GAAP, internal audit requirements), and organizational governance typically mandate that a licensed/qualified human manager have decision authority over budget direction. Legal and liability frameworks require human accountability. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Financial decision-making carries fiduciary responsibility, regulatory reporting obligations, and liability that typically require accountable human managers or officers to authorize and coordinate. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI financial analytics tools cost thousands annually plus integration, but the human manager's loaded cost (salary, benefits, oversight) typically exceeds AI savings for this broad strategic function. Cost parity is not yet reached at scale. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While analytics tools are cheap, the managerial coordination, negotiation, and accountability functions still require human oversight, keeping all-in cost comparable to or higher than AI-only alternatives for full task substitution. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Financial planning tools and analytics platforms exist, but deployed AI systems do not reliably direct or coordinate actual budget activities autonomously. Existing solutions provide decision support rather than autonomous direction; human managers remain required for final authorization and coordination. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Financial planning and BI tools assist with budget analysis and forecasting, but no deployed product autonomously directs or coordinates budget activities across an organization. |
Establish or implement departmental policies, goals, objectives, or procedures in conjunction with board members, organization officials, or staff members.
25CI 25–25 · exposure 25 · augmentation 75 · importance 3.7/5 · click for rater detail
Establish or implement departmental policies, goals, objectives, or procedures in conjunction with board members, organization officials, or staff members.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While some enterprises use AI for policy drafting assistance, actual policy establishment and implementation remains largely manual and human-led. Adoption of end-to-end AI automation for this task is minimal; most use remains at the assistance level in forward-looking organizations. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | General management functions in most organizations remain low on AI-driven transformation for governance and policy-setting, with adoption concentrated on drafting support rather than replacing the collaborative decision process. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at augmenting this task by generating policy drafts, summarizing stakeholder input, checking for inconsistencies with existing procedures, and organizing documentation. A manager using AI assistants can iterate faster and more comprehensively than without, while retaining full decision authority and accountability. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully help managers draft policy documents, summarize meeting inputs, benchmark best practices, and prepare objectives, significantly speeding up the preparatory work even though final decisions remain human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft policy language and suggest procedural frameworks, establishing policies requires negotiation, alignment with organizational values, legal review, and stakeholder buy-in that demand human judgment and authority. Current systems cannot autonomously navigate the political and organizational dimensions required to implement policies across departments. |
| Task automatability | claude-sonnet-5 | 2/5 | This task requires organizational judgment, stakeholder negotiation, and contextual authority that current AI cannot autonomously exercise; AI can draft policy language but cannot conduct the interpersonal consensus-building or make final decisions. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: organizational policies typically require authorization from board members or senior management, legal/compliance review, and human accountability for implementation outcomes. The authority to establish binding policies is delegated to specific human roles and cannot be fully delegated to AI systems. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Governance responsibilities typically require accountable human officers and board approval; policy-setting authority is often tied to fiduciary duty, corporate bylaws, or legal accountability that cannot be delegated to a non-human system. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for policy drafting are relatively inexpensive, but the cost of AI-assisted policy work (including human review, revision, and stakeholder engagement) remains high compared to the task's overall value when factoring in mandatory human oversight and decision-making throughout the process. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While drafting assistance is cheap, the actual task requires a manager's time in meetings, negotiation, and decision-making that AI cannot substitute, so overall cost savings are limited to peripheral documentation work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some tools exist to assist with policy drafting and process documentation, but no deployed product reliably performs the full task of establishing and implementing departmental policies in real organizations. The task requires organizational authority and consensus-building that AI cannot execute independently. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI writing assistants can help draft policy documents or meeting agendas, but no deployed product actually establishes or implements organizational policy by coordinating with board members and staff. |
Perform sales floor work, such as greeting or assisting customers, stocking shelves, or taking inventory.
21CI 7–35 · exposure 13 · augmentation 38 · importance 3.9/5 · click for rater detail
Perform sales floor work, such as greeting or assisting customers, stocking shelves, or taking inventory.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Retail has begun piloting inventory robots and self-checkout, but widespread adoption of AI handling customer interactions and floor work remains slow; most general retailers still rely on human associates, with only niche high-tech retailers and a few logistics companies running scaled robotic pilots. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Retail is a moderately digitizing sector with some self-checkout and inventory-robot pilots, but physical floor tasks like stocking and greeting remain overwhelmingly human-performed with slow robotic adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist with inventory lookup, real-time stock visibility, and customer-facing chatbots for simple questions, raising productivity on routine tasks; however, the personal judgment needed for problem-solving customer service and the physical coordination of floor work limit the scope of meaningful augmentation. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with inventory tracking software, demand forecasting, or handheld scanning tools that support a manager's oversight of these tasks, but it doesn't materially transform the hands-on floor work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While inventory systems and shelf-stocking robots exist, greeting and assisting customers with context-specific needs remains difficult for current AI; the task mixes routine work (stocking) with high-touch interpersonal elements, and end-to-end automation at 50% time-saving would require integrated physical robots, computer vision, and conversational AI working seamlessly—still not standard in most retail settings. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical presence on a sales floor to greet customers, physically stock shelves, and handle merchandise—none of which current AI systems can perform as they lack physical embodiment for general retail environments. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: direct customer-contact requirement (customers often expect human interaction), liability for service failures, workplace safety regulations around moving equipment, and organizational inertia in labor-heavy retail; most retailers retain humans despite cost pressure due to customer experience and brand risk. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human specifically, but customer preference for human interaction and the physical/organizational complexity of retail floors create real friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integrating computer vision, mobile robots, conversational AI, and inventory systems for end-to-end floor work remains capital-intensive; the all-in cost per task (hardware, software, integration, oversight, failures) still exceeds the loaded wage of a retail associate for this work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Physical robotic systems capable of flexible shelf-stocking and customer interaction are far more expensive to deploy and maintain than paying a human worker for these tasks. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Inventory management systems and some robotic shelf-stocking pilots exist, but customer greeting and assistance at acceptable quality remain rare in production; most deployed systems handle only narrow subsets (barcode scanning, static product lookup) rather than the full task of customer interaction and dynamic floor support. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs general physical retail floor work like stocking shelves or in-person customer greeting at scale; robotics for this remain research/pilot stage with very narrow scope (e.g., limited inventory-scanning robots). |
Direct non-merchandising departments of businesses, such as advertising or purchasing.
21CI 16–25 · exposure 20 · augmentation 63 · importance 3.5/5 · click for rater detail
Direct non-merchandising departments of businesses, such as advertising or purchasing.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While some larger organizations pilot AI dashboards and analytics for managers, actual displacement of management functions remains rare and slow. Most sectors retain traditional management hierarchies, and cultural/legal resistance to removing the human decision-maker in supervisory roles is high. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | While AI tools are being piloted in management support (analytics, scheduling), actual delegation of departmental direction to AI is rare and slow-moving across sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist department managers with reporting generation, performance analytics, trend identification, and administrative scheduling, raising their analytical capability and freeing time for strategic work, but the augmentation remains partial and concentrated on information synthesis rather than judgment or interpersonal functions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist managers with data analysis, drafting communications, budget tracking, and forecasting, enhancing decision-making even though the human retains directive control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Managing non-merchandising departments requires strategic decision-making, interpersonal negotiation, conflict resolution, and real-time adaptation to human dynamics that current AI cannot perform end-to-end. While AI can assist with data analysis and scheduling, the core supervisory and leadership functions remain fundamentally human-dependent and would not meet the 50% time-saving threshold at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Directing a department involves ongoing judgment, personnel management, strategic decisions, and interpersonal leadership that current AI cannot execute end-to-end; AI can support pieces (reports, analysis) but not the directive role itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Substantial barriers exist: organizational authority and accountability for department decisions rest legally and culturally with a human manager, fiduciary and employment law often requires a named responsible party, and stakeholder relationships demand human trust and judgment that AI cannot substitute for or sign off on independently. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Organizational structure, accountability, legal responsibility for decisions, and need for human leadership create strong barriers to full automation of directing a department. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of AI systems capable of meaningful department-management assistance (integrated analytics, forecasting, workflow automation) plus the required human oversight is still comparable to or exceeds the value delivered relative to a manager's loaded wage, particularly when factoring in setup and integration costs. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this managerial role, so no viable cost comparison favors AI; human judgment and accountability remain necessary. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably perform full department management autonomously. AI tools exist for specific subtasks (budget tracking, meeting scheduling, performance metrics), but production systems do not handle the integrated strategic and people-management dimensions of directing a department. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously manages/directs a business department; this remains squarely a human management function with AI only as a supporting tool. |
Direct and coordinate activities of businesses or departments concerned with the production, pricing, sales, or distribution of products.
18CI 11–25 · exposure 13 · augmentation 75 · importance 4.1/5 · click for rater detail
Direct and coordinate activities of businesses or departments concerned with the production, pricing, sales, or distribution of products.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While some digitization of managerial dashboards and analytics is common, actual displacement of directive and coordinative functions by autonomous AI remains negligible; most adoption is in pilot and augmentation phases, with human managers still holding primary accountability. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | While management sectors use AI analytics tools, actual delegation of coordination and directive authority to AI systems remains rare and mostly pilot-stage. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully augment managers by automating report generation, scenario modeling, anomaly detection in operations, and resource optimization suggestions—allowing managers to spend more time on strategy and people; this assistive role is increasingly realized in production systems today. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI dashboards, forecasting, and reporting tools significantly enhance a manager's ability to monitor and coordinate production, pricing, and sales activities. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with some components—data analysis, reporting, scheduling—directing and coordinating people, setting strategic priorities, and making judgment calls in real-time across interdependent business functions requires sustained human oversight and contextual decision-making that current systems cannot reliably perform end-to-end at a 50% time saving bar. |
| Task automatability | claude-sonnet-5 | 1/5 | Directing and coordinating cross-functional business activities requires real-time judgment, authority, and interpersonal leadership that current AI cannot execute end-to-end.ate that current AI cannot replicate as an integrated whole. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant legal, fiduciary, and organizational barriers exist: managers are legally responsible for business performance, employment decisions, and regulatory compliance; liability for errors falls on the organization; and stakeholders (employees, clients, boards) expect and often require human leadership presence and accountability. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Organizational accountability, legal responsibility for business decisions, and the need for human authority over employees and operations create strong structural barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The inference cost of AI systems capable of handling manager-level decision support is modest, but integration, domain customization, and required human oversight still substantially exceed the per-task cost savings, making the ratio unfavorable for full displacement. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Since AI cannot perform the managerial coordination function itself, there is no comparable AI-only cost basis, making it more expensive relative to a human manager's actual output. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product today reliably manages the full directive and coordinative role of a general manager; AI tools exist for specific sub-tasks (demand forecasting, dashboards) but cannot autonomously handle exception management, personnel motivation, cross-functional negotiation, or accountability for outcomes. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously directs and coordinates department-level business operations; existing tools only support fragments like reporting or scheduling. |
Related occupations — Management
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.