First-Line Supervisors of Office and Administrative Support Workers
43-1011.00Median wage $69,500/yr1,436,680 employed (US)Rank #200 of 923 scored · top 22% by substitution
Directly supervise and coordinate the activities of clerical and administrative support workers.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution39
Exposure35
Augmentation68
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
28 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%35
panel mean rating 2.4/5 → substitution pressure 35/100
Technical feasibility todayw 20%35
panel mean rating 2.4/5 → substitution pressure 35/100
Cost vs. human wagew 15%38
panel mean rating 2.5/5 → substitution pressure 38/100
Adoption barriersw 20%inverted — strong barriers lower the score49
panel mean rating 3.1/5 (barrier strength) → substitution pressure 49/100
Sector adoption velocityw 10%39
panel mean rating 2.6/5 → substitution pressure 39/100
Task breakdown (28 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.
Compute figures such as balances, totals, or commissions.
97CI 95–100 · exposure 100 · augmentation 75 · importance 4.0/5 · click for rater detail
Compute figures such as balances, totals, or commissions.
97| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Computation of figures via automation has been standard in office and administrative settings for decades; penetration of spreadsheets and accounting software in these sectors is near-universal, representing deep, mature adoption with minimal laggard segments. |
| Sector adoption velocity | claude-sonnet-5 | 5/5 | Office and administrative environments have near-universal adoption of spreadsheet and automated calculation tools already embedded in daily workflows. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI and automation assist supervisors by eliminating the time burden of manual calculation, allowing them to focus on review, interpretation, and decision-making; modern tools provide real-time dashboards and alerts that enhance human oversight and productivity. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools further assist by auto-generating formulas, flagging anomalies, and integrating data sources, though the underlying calculation itself is already largely automated rather than augmented. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Computing figures like balances, totals, and commissions is a straightforward arithmetic task that can be fully automated with spreadsheet formulas, accounting software, or simple scripts; modern AI and automation tools can perform this end-to-end with negligible error and 100% time savings compared to manual calculation. |
| Task automatability | claude-sonnet-5 | 5/5 | Numerical computation of balances, totals, and commissions is a well-defined, rule-based task that spreadsheets and AI tools handle fully with equal or better accuracy in seconds. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While some organizations maintain manual oversight or require human sign-off on commission calculations for audit purposes, there are no legal licensing requirements or inherent barriers preventing automated computation; adoption is primarily organizational preference and control culture rather than regulatory restriction. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, liability, or human-contact requirement blocks automated computation; this has already been standard practice for a long time. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | The cost of automated computation via spreadsheet tools or cloud-based systems is negligible (pennies per task or less) compared to the loaded wage cost of a human supervisor spending time on manual calculations, creating an order-of-magnitude or greater cost advantage. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Automated calculation costs fractions of a cent per computation versus paying a supervisor's hourly wage for manual arithmetic. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Deployed products (Excel, accounting platforms like QuickBooks, enterprise ERP systems) already perform this task reliably at scale in production environments across finance, retail, and administrative departments worldwide. |
| Technical feasibility today | claude-sonnet-5 | 5/5 | Spreadsheet software, ERP systems, and calculators have reliably automated this exact function in production for decades, with AI enhancing formula generation and error-checking. |
Maintain records pertaining to inventory, personnel, orders, supplies, or machine maintenance.
76CI 72–79 · exposure 75 · augmentation 88 · importance 3.7/5 · click for rater detail
Maintain records pertaining to inventory, personnel, orders, supplies, or machine maintenance.
76| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Administrative and office-support sectors are digitizing rapidly; many organizations already use automated inventory systems, HRIS, and document management platforms. Adoption of AI-assisted record maintenance is well underway in professional services, finance, and large enterprises. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Office/administrative sectors show moderate AI adoption with many pilots and partial deployments of automated record systems, though full displacement of supervisory record-keeping roles is still uneven across organizations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI augments supervisors by automating data entry, flagging anomalies, and surfacing trends from records, allowing supervisors to focus on judgment and corrective action. The assistant nature of these tools—flagging missing inventory, highlighting personnel updates for review—substantially raises supervisory productivity. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI tools significantly augment this task by automating data capture, flagging discrepancies, and generating reports, greatly increasing the productivity of supervisors who retain oversight responsibility. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can handle a significant portion of record maintenance tasks, including data entry, organization, categorization, and basic updates with minimal human intervention. However, some judgment calls regarding what constitutes a 'record' or how to handle exceptions may still require human oversight, preventing a full 5-rating. |
| Task automatability | claude-sonnet-5 | 4/5 | Record-keeping for inventory, orders, personnel, and maintenance is largely structured data entry and tracking, which AI-integrated systems (ERP/inventory software with AI features, automated data extraction) can handle with significant time savings, though some human oversight and judgment calls remain. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few hard legal barriers exist for automating record-keeping itself, though some regulated industries (healthcare, finance) may require audit trails and human sign-off, creating modest friction. Most organizations face only soft barriers around change management and data governance. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Some personnel records may have privacy/compliance considerations requiring human handling, but generally no licensing or legal requirement mandates a human perform routine record maintenance. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven record maintenance (automated data entry, RPA, cloud-based systems) costs a fraction of human time for the same output, especially at volume. Ongoing inference and maintenance are orders of magnitude cheaper than paying administrative staff to manually update records. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated record-keeping software substantially reduces the labor cost compared to manual entry and tracking by a supervisor, though integration and periodic human review add some cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple deployed products (enterprise document management, inventory tracking software, and RPA tools) reliably perform record maintenance at scale in production environments. Mature systems exist across inventory, HR, and supply chain domains, though integration into legacy systems sometimes remains imperfect. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Deployed inventory management systems, HRIS platforms, and CMMS (maintenance management) software with AI-assisted data entry, OCR, and automated tracking are widely used in production today across many industries. |
Monitor inventory levels and requisition or purchase supplies as needed.
74CI 72–75 · exposure 75 · augmentation 75 · importance 3.1/5 · click for rater detail
Monitor inventory levels and requisition or purchase supplies as needed.
74| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Office and administrative support operations, particularly in mid-to-large organizations and e-commerce/logistics sectors, have rapidly adopted inventory management systems with automated reordering; early-stage AI agent integration is accelerating. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Office/administrative settings show moderate but uneven adoption of automated inventory and procurement tools, with many smaller organizations still relying on manual processes. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assists supervisors by continuously monitoring inventory, flagging anomalies, and suggesting reorders, allowing the human to focus on exception handling, vendor relationships, and strategic supply decisions rather than routine tracking. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-driven inventory dashboards and predictive reorder alerts substantially boost a supervisor's efficiency in tracking stock and initiating purchases while they retain final decision authority. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI systems can track inventory levels via integration with existing databases, set reorder thresholds, and generate purchase orders automatically, saving 60–80% of manual monitoring and requisition time. However, some judgment around urgency, vendor selection, and exception handling may require human oversight. |
| Task automatability | claude-sonnet-5 | 4/5 | Inventory monitoring and reordering are highly structured, data-driven tasks that inventory management software and automated reorder systems already handle with minimal human input beyond exception review.etting up rules and thresholds allows most of the routine work to be automated.rationale trimmed.','fine'}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers exist for automated inventory tracking and basic purchasing; most friction comes from internal authorization workflows and vendor integration requirements, which are organizational rather than legal obstacles. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks automation, though organizational approval workflows and budget authority for purchasing create some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Cloud-based inventory automation and purchase-order systems cost a small fraction of a supervisor's loaded wage, especially when amortized across multiple sites or users. The cost per task completion is typically an order of magnitude lower than manual oversight. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated inventory systems cost far less per transaction than a supervisor's time spent manually checking stock and placing orders, though some human oversight remains needed. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Inventory management software with automated reordering exists and is deployed at scale in many organizations; systems can integrate with supply chain platforms and issue purchase orders. Minor friction remains in handling edge cases and vendor negotiation, but core functionality is production-ready. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Mature inventory management and procurement software (e.g., ERP systems, automated reorder point tools) are widely deployed in production and reliably track stock and trigger purchase orders. |
Prepare and issue work schedules, deadlines, and duty assignments for office or administrative staff.
67CI 55–79 · exposure 62 · augmentation 75 · importance 4.1/5 · click for rater detail
Prepare and issue work schedules, deadlines, and duty assignments for office or administrative staff.
67| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Administrative and office sectors have high digitization and are rapidly adopting workforce management automation. Scheduling software adoption is widespread in mid-to-large firms, financial services, healthcare operations, and contact centers, with clear displacement of manual scheduling in production environments. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Office/administrative sectors show moderate AI tool adoption for scheduling and workflow management, with pilots and partial deployment more common than full-scale automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI scheduling systems augment supervisor productivity by handling routine allocation, flagging conflicts, and proposing optimized schedules that supervisors can review and adjust. This allows supervisors to focus on exceptions, staff morale, and strategic workforce planning rather than manual assignment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI scheduling assistants substantially speed up drafting schedules and duty assignments, letting supervisors focus on exceptions and personnel considerations while remaining in the loop. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Scheduling, deadline setting, and basic duty assignment can be largely automated using constraint-satisfaction algorithms and workforce management software. Current systems can handle staff availability, skill matching, and workload balancing with minimal human intervention, achieving well over 50% time savings at equivalent quality for routine assignments. |
| Task automatability | claude-sonnet-5 | 3/5 | Scheduling and assignment generation can be largely automated with scheduling software or AI tools given constraints, but adapting to interpersonal dynamics, exceptions, and real-time changes still requires human judgment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or legal barriers exist; scheduling is largely a business practice matter. The main friction is organizational: supervisors may resist automation due to perceived loss of control, union agreements may constrain flexibility, and some staff prefer human-negotiated schedules, but nothing legally mandates human issuance. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement dictates human-only scheduling, but organizational trust, HR policy, and employee preference for a human point of contact create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven scheduling tools cost a fraction of a supervisor's time to run and require minimal ongoing oversight. Given typical loaded administrative supervisor costs ($50–70k annually) versus software costs of hundreds to low thousands per year, the cost advantage is an order of magnitude or more. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Scheduling software licenses and AI tools are relatively cheap, but integration, data setup, and supervisory oversight time reduce net savings, making the ratio roughly comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature scheduling software (Workday, ADP, Kronos, specialized workforce management platforms) reliably performs this at scale in production across thousands of organizations. These systems integrate with HR systems and have well-established deployment patterns, though complex multi-constraint scenarios may still require human review. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Workforce scheduling software (with optimization/AI features) is widely deployed and used in production, but supervisors typically still review and adjust outputs manually, especially for administrative/office contexts with less rigid shift patterns. |
Develop work schedules according to budgets and workloads.
67CI 55–79 · exposure 62 · augmentation 75 · importance 4.0/5 · click for rater detail
Develop work schedules according to budgets and workloads.
67| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Scheduling automation is widespread in information, finance, retail, and logistics sectors, with major software vendors (UKG, Workday, SAP) embedding AI scheduling as standard features; adoption is measurably deep and ongoing in digitized organizations. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Retail, hospitality, and administrative sectors have adopted scheduling software at moderate pace, with pilots and partial deployment common but full automation without human final approval still rare. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI scheduling tools significantly assist supervisors by automating constraint-checking and draft generation, allowing humans to focus on exception handling and fairness considerations, thereby raising supervisor productivity while maintaining human judgment over final approval. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted scheduling tools substantially speed up draft creation and constraint-checking, letting supervisors focus on exceptions and approvals rather than building schedules from scratch. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Scheduling based on budgets and workloads is predominantly a rule-based optimization problem that current AI systems handle well. LLMs and scheduling algorithms can ingest constraints, analyze workload data, and generate compliant schedules meeting 50%+ time savings, though human review of edge cases may still be needed. |
| Task automatability | claude-sonnet-5 | 3/5 | Scheduling optimization against budget and workload constraints is well-suited to algorithmic/AI tools, but integrating idiosyncratic organizational rules, employee preferences, and exceptions still requires human input, so it's roughly half-automatable with setup. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers exist for automated scheduling; organizational adoption faces modest friction around employee acceptance and integration with existing HR systems, but no hard legal requirement mandates human sign-off. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates human scheduling, but managers often retain authority to override algorithmic schedules due to labor agreements, morale concerns, or legal considerations (e.g., predictive scheduling laws). |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Scheduling software and AI tools cost a small fraction of supervisor labor (typically $0.01–$0.10 per schedule generated and optimized vs. $30–$50/hour human cost), yielding an order-of-magnitude cost advantage even accounting for oversight. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Scheduling software licenses cost less than a supervisor's time spent purely on schedule-building, but implementation, configuration, and ongoing oversight keep costs from being an order of magnitude lower. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed scheduling software, workforce management tools, and AI-powered solutions (e.g., Alteryx, UKG, Kronos with ML modules) demonstrably perform this task in production across many organizations. Performance is generally reliable for routine scheduling, though optimization quality varies by system maturity. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Workforce management software (e.g., Kronos, Deputy, When I Work) already generates schedules automatically in many industries, but these are rule-based/optimization products more than generalized AI, and human review/adjustment remains standard practice. |
Review records or reports pertaining to activities such as production, payroll, or shipping to verify details, monitor work activities, or evaluate performance.
65CI 55–75 · exposure 62 · augmentation 75 · importance 4.2/5 · click for rater detail
Review records or reports pertaining to activities such as production, payroll, or shipping to verify details, monitor work activities, or evaluate performance.
65| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Logistics, finance, and manufacturing sectors are actively deploying AI-driven record verification and monitoring in production; RPA and intelligent document processing show strong adoption rates in administrative workflows. Early adopters have moved beyond pilots to operational deployment. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Office/administrative supervisory functions are moderately digitized, with common adoption of dashboard and reporting tools, but full replacement of human review of records is still exploratory in most organizations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at flagging anomalies, summarizing trends, and presenting verified data summaries to supervisors, significantly amplifying their capacity to monitor and evaluate performance. Supervisors remain in the loop for judgment calls, but their productivity per review cycle is substantially enhanced. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools already significantly speed up review of production, payroll, and shipping data by automatically flagging discrepancies and generating summaries, greatly aiding the supervisor while they retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can reliably extract, compare, and flag discrepancies in structured records (production logs, payroll data, shipping manifests) with high accuracy, achieving significant time savings. However, nuanced performance evaluation and contextual judgment about anomalies may still require human review, preventing a full 5. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can extract, summarize, and flag anomalies in structured records like payroll or shipping logs, but verifying context, judging performance, and deciding on corrective action still require human oversight, so only part of this composite task is automatable at high quality. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While some industries (payroll, shipping) have regulatory oversight of records, the supervisory review task itself is not strictly licensed or legally mandated to be performed by a human. Organizational friction and preference for human judgment provide modest barriers, but nothing prevents substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks automation, though internal accountability for performance evaluations and payroll accuracy creates moderate organizational and liability-related caution around full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven document processing and automated monitoring cost a fraction of supervisory labor per task instance, with inference and integration overhead easily amortized across high-volume record reviews. The cost ratio strongly favors automation for routine verification work. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Automated reporting/analytics tools reduce time spent on data review substantially, but integration, data-cleaning, and human sign-off on evaluative judgments keep total cost roughly comparable to a supervisor doing this efficiently with existing software. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature products (document processing, RPA, analytics platforms) demonstrate reliable performance on record verification and data validation at scale in production environments. Payroll and logistics platforms with embedded AI verification are widely deployed, though some edge cases and subjective performance assessments remain human-dependent. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | BI dashboards, anomaly-detection tools, and LLM-based report summarizers are deployed in production for monitoring operational data, but full autonomous verification and performance evaluation across varied record types remains narrow and error-prone. |
Research, compile, and prepare reports, manuals, correspondence, or other information required by management or governmental agencies.
59CI 50–67 · exposure 58 · augmentation 88 · importance 4.0/5 · click for rater detail
Research, compile, and prepare reports, manuals, correspondence, or other information required by management or governmental agencies.
59| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Office and administrative support sectors show moderate AI adoption—document automation pilots are common, but production deployment of end-to-end report systems remains inconsistent. Organizations are experimenting but not yet exhibiting the rapid, deep displacement seen in tech or finance sectors. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Office administrative functions are adopting AI writing/reporting tools steadily but unevenly, with pilots common and full production workflows still developing in many organizations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assistants demonstrably boost supervisor productivity on research gathering, outline generation, and draft compilation, allowing supervisors to focus on judgment, verification, and customization. Generative tools and search-augmented systems are widely deployed and materially accelerate the work. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI dramatically speeds up research, drafting, and formatting of reports and correspondence while the supervisor retains responsibility for accuracy and final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automate significant portions of information gathering, data compilation, and report structure generation, but supervisory judgment on what management needs, document prioritization, and ensuring accuracy typically require human oversight. The task involves research and compilation (highly automatable) but also contextual judgment on relevance and completeness that reduces end-to-end automation gains. |
| Task automatability | claude-sonnet-5 | 4/5 | Drafting reports, manuals, and correspondence from source data is well within current LLM capabilities, especially with retrieval-augmented tools, though final compilation and validation still require human oversight. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Governmental agencies often require signed, verified documentation and supervisory attestation of information accuracy; organizational liability and risk management processes mandate human review of compliance-related reports. These requirements create meaningful friction but do not constitute hard legal barriers that prevent AI assistance. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Some reports for governmental agencies may require certified accuracy or signatures, but most internal management reporting has no licensing or legal sign-off requirement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI inference costs for research and report generation are modest, but integration with organizational data sources, quality review, and potential rework for accuracy/compliance add overhead. Overall cost approaches human wage for routine reports, though efficiency gains on high-volume, standardized reporting could shift this toward 4. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating drafts and compiling structured reports via AI tools costs a small fraction of an administrative supervisor's loaded hourly wage, even with human review layered on. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Mature products exist for information retrieval, document generation, and template-based report creation, but real-world performance is inconsistent on domain-specific accuracy and meeting exact governmental or organizational compliance requirements. Production systems handle portions reliably but rarely the full pipeline without human verification. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | AI writing assistants and document generation tools are widely deployed for drafting and summarization, but reliable end-to-end compliance-grade report compilation for management/government still involves human review in most production settings. |
Resolve customer complaints or answer customers' questions regarding policies and procedures.
48CI 41–55 · exposure 42 · augmentation 75 · importance 4.4/5 · click for rater detail
Resolve customer complaints or answer customers' questions regarding policies and procedures.
48| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Chatbots and IVR systems are widespread, but deployment of AI for substantive complaint resolution remains in pilot and limited-scope stages. Many firms test these tools but hesitate to fully automate supervisor-level judgment and escalation decisions. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Customer service and administrative support functions have moderate AI adoption with many pilots and partial deployments, but full supervisory complaint resolution remains largely human-led. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI augmentation is strong here: suggestion engines, policy search, ticket routing, and response drafting measurably assist supervisors in handling volume and speed. A supervisor can leverage AI to stay in control while handling more interactions faster. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can draft responses, pull up policy information, summarize complaint history, and suggest resolutions, significantly speeding up the supervisor's handling of these tasks while they retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can handle routine policy inquiries and generate template responses, resolving actual complaints typically requires judgment, empathy, and authority to make exceptions or escalate—elements that demand human discretion and accountability. Current AI systems struggle with nuanced customer frustration and contextual problem-solving at 50% time savings. |
| Task automatability | claude-sonnet-5 | 3/5 | AI chatbots and agents can handle routine policy questions and standard complaint resolution, but escalated or emotionally charged complaints requiring judgment, empathy, and discretion still need human supervisors. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Customer-facing roles carry reputational and liability risk; many organizations prefer human touch for complaint handling, and regulatory scrutiny varies by industry. No strict licensing requirement, but organizational preference for human contact and fear of automation errors create moderate friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational policy often mandates human sign-off for exceptions, refunds, or escalations, and customers often expect a human decision-maker for complaints. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | Automated customer service systems have dropped in deployment cost, but full first-line supervisor capability—including complaint resolution and escalation authority—requires oversight and human fallback, making total cost roughly comparable to a junior employee rather than an order of magnitude cheaper. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-driven support tools are cheap for high-volume simple queries, but supervisor-level complaint resolution requiring context, authority, and relationship management still requires human involvement, keeping blended cost comparable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed chatbots and virtual agents handle basic FAQ-style questions and policy lookups reliably, but production systems still show material limitations with complex complaints, emotional dynamics, and non-standard cases. Most organizations still require human handoff for resolution. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Deployed customer service AI (chatbots, virtual agents) handles FAQ-type policy questions reliably, but resolving genuine complaints as a supervisory function with authority to make exceptions is far less mature in production. |
Analyze financial activities of establishments or departments and provide input into budget planning and preparation processes.
40CI 25–55 · exposure 38 · augmentation 75 · importance 3.9/5 · click for rater detail
Analyze financial activities of establishments or departments and provide input into budget planning and preparation processes.
40| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While finance and administrative functions are digitizing, budget planning remains a human-led process in most organizations. Supervisors use data tools but do not delegate the planning decision itself; adoption of autonomous budget analysis agents remains in pilot stages, not production deployment at scale. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Office/administrative and finance-adjacent supervisory roles are seeing moderate AI tool adoption for reporting and analytics, but full workflow integration into budget planning remains at the pilot stage in many organizations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at augmenting this task: automated financial dashboards, trend detection, variance analysis, and scenario modeling can significantly boost a supervisor's productivity in analyzing activities and preparing budget inputs while the human retains decision authority and contextual judgment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly augments this task by automating data compilation, trend identification, and draft budget narratives, letting supervisors focus on judgment calls and departmental prioritization. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can extract and summarize financial data and identify basic trends, the task requires judgment about departmental needs, strategic priorities, and contextual business decisions that supervisors integrate into budget planning. Current AI lacks the organizational context and stakeholder input integration needed for end-to-end autonomous performance. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can analyze financial data, generate reports, and draft budget projections quickly, but the supervisory judgment, contextual departmental knowledge, and stakeholder negotiation involved in budget planning still require human input, limiting full automation.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Budget planning and approval typically require sign-off by authorized managers and supervisors; financial controls, audit requirements, and organizational governance frameworks create regulatory and procedural barriers to full automation without human accountability and approval. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement dictates that only a human can analyze financial activity, though organizational approval processes and accountability for budget decisions create moderate friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Financial analysis tools and AI services are available, but the need for supervisory oversight, validation, and integration with organizational strategy means total cost (tool subscription + human review time) remains comparable to or higher than the human wage for this knowledge work. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools reduce time spent on data aggregation and trend analysis, but licensing, integration with existing financial systems, and required human review keep costs roughly comparable to a supervisor's own time investment for this partial task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Tools exist for financial data extraction and basic reporting, but deployed products do not reliably perform the interpretive and advisory components of budget analysis and planning that require understanding organizational strategy and constraints. Most systems operate as analytical aids rather than autonomous performers. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Financial analytics and BI tools (e.g., Excel Copilot, ERP AI modules) reliably generate spend analysis and forecasts today, but integrating these into a supervisor's actual budget input process still requires manual customization and validation. |
Coordinate or perform activities associated with shipping, receiving, distribution, or transportation.
35CI 32–38 · exposure 25 · augmentation 63 · importance 3.2/5 · click for rater detail
Coordinate or perform activities associated with shipping, receiving, distribution, or transportation.
35| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Logistics and warehouse operations have adopted management software and some automation (WMS, sorting robots), but first-line supervisory coordination—especially exception handling and vendor management—remains largely human-driven, with pilots common but deep displacement rare. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Logistics and warehousing sectors have adopted automation and tracking software at a moderate pace, with pilots of AI-driven routing and inventory systems becoming more common but full replacement of supervisory coordination is rare. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist supervisors via route optimization, real-time inventory visibility, automated exception alerts, and documentation, meaningfully raising oversight capacity without removing the human from decision authority and accountability. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered scheduling, inventory forecasting, and route optimization tools meaningfully boost supervisor productivity in planning and tracking shipments, even though the supervisor remains essential for on-site coordination. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Shipping and receiving involve physical coordination of goods, route optimization, and real-time decision-making that remain largely dependent on human logistics expertise. While AI can optimize routes and manage documentation, the core task of coordinating physical distribution with multiple stakeholders and exceptions requires substantial human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a supervisory/coordination task involving physical logistics, exception handling, and personnel management that AI cannot execute end-to-end; software can support scheduling and tracking but not the full coordination role. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Coordination roles involve legal accountability for shipments, liability for loss or damage, and customer contact requirements that create moderate friction. Regulatory compliance (DOT, hazmat, insurance) applies to decisions but does not require a licensed human to execute all tasks. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational reliance on physical presence for coordinating receiving docks, staff schedules, and problem resolution creates moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI solutions for logistics (planning software, automation hardware) involve significant integration and maintenance costs; the loaded wage for a first-line supervisor remains competitive, especially when accounting for the judgment and accountability required. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI logistics tools reduce some administrative costs but a human supervisor is still needed for on-site coordination, staff management, and exception handling, keeping overall cost savings modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Existing AI systems can handle narrow components (label reading, route planning, inventory tracking via software), but no integrated product reliably manages the full coordination task including dynamic adjustments, vendor communication, and exception handling in real operations. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Warehouse management and logistics software (WMS/TMS) exist and are widely deployed for tracking and routing, but supervising staff and physically coordinating shipping/receiving remains human-led with only partial digital support. |
Arrange for necessary maintenance or repair work.
33CI 30–35 · exposure 25 · augmentation 63 · importance 3.0/5 · click for rater detail
Arrange for necessary maintenance or repair work.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Office and administrative environments are digitizing, but maintenance management remains fragmented across small vendors, property teams, and legacy systems. Adoption of AI-driven arrangement tools is still in pilot phases at most organizations; production-scale displacement is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Office administrative sectors are moderately digitizing, but facilities/maintenance coordination remains a laggard area with mostly manual or semi-automated ticketing systems rather than AI agents. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist supervisors by maintaining vendor lists, drafting maintenance requests, and tracking repair history or status. These augmentations reduce administrative friction, but the supervisor must still evaluate need, authorize work, and verify completion, keeping human judgment central. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can draft maintenance requests, track vendor communications, prioritize issues, and auto-schedule appointments, meaningfully speeding up a supervisor's arranging work while they retain final control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Arranging maintenance or repair work requires identifying problems, obtaining quotes, authorizing work, and coordinating schedules. Current AI can draft requests or find vendor contact information, but assessing the actual need for repairs, evaluating repair quality, and making authorization decisions typically require human judgment and accountability. AI cannot reliably perform the full end-to-end task. |
| Task automatability | claude-sonnet-5 | 2/5 | Coordinating repairs involves phone calls, scheduling, vendor selection, and judgment about urgency that current AI can partially assist but not fully execute end-to-end without human oversight and physical-world coordination.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Authorization and liability for repair decisions typically rest with the supervisor or management, creating some friction against automation. However, there are no formal legal requirements preventing an AI-assisted or delegated arrangement system, only organizational and risk-management preferences. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI from arranging repairs, but organizational trust, vendor relationships, and accountability for facility issues create some friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI solutions (scheduling, communication tools, document processing) still require substantial human review and decision-making per arrangement. The overhead of oversight and error correction often approaches or exceeds the cost of a supervisor directly managing the task, making the cost ratio unfavorable for full automation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While ticketing automation is cheap, the human coordination, vendor negotiation, and follow-up still require labor, so all-in AI cost savings are modest compared to a supervisor's time. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with scheduling tools and vendor directories exist, deployed products do not reliably arrange complex maintenance work autonomously. Most real deployments require significant human oversight in vendor selection, cost approval, and work verification, limiting mature end-to-end automation. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some facilities-management software and AI chatbots can log tickets and route requests, but reliable autonomous arranging of vendors and scheduling repairs is not yet a mature, widely deployed product capability. |
Interpret and communicate work procedures and company policies to staff.
31CI 25–36 · exposure 25 · augmentation 75 · importance 4.0/5 · click for rater detail
Interpret and communicate work procedures and company policies to staff.
31| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Most organizations view policy communication and interpretation as a core supervisory responsibility that strengthens culture and accountability; adoption of AI-led interpretation remains limited and confined to drafting aids rather than autonomous replacement of the communicative role. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Office/administrative settings are adopting AI assistants at a moderate pace for internal communications and HR-adjacent tasks, though production-grade autonomous policy communication is uncommon. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can significantly augment supervisors by drafting policy guides, generating FAQs, translating complex procedures into plain language, and flagging inconsistencies—allowing supervisors to focus on dialogue and contextual adaptation with staff while staying firmly in control. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly help supervisors draft clear policy explanations, summarize procedures, and answer routine questions, improving efficiency while the supervisor retains final interpretive authority. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft procedural documents and generate policy summaries, interpreting nuanced procedures and communicating them contextually to staff requires understanding organizational culture, employee backgrounds, and real-time clarification—tasks that demand human judgment and adaptive dialogue. Current AI cannot reliably handle the full supervisory communication loop end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can draft and explain policy content, but genuine interpretation for specific staff situations and authoritative communication as a supervisor requires human judgment, context, and accountability that current systems cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Supervisory authority and accountability for policy interpretation and staff alignment carry implicit fiduciary and management responsibilities; many organizations maintain compliance and liability requirements that a licensed manager must personally oversee, creating structural barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but organizational trust, accountability for correct policy interpretation, and employee preference for human supervisors create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-generated policy documents and communication drafts reduce some administrative burden, but the core task—genuine supervisory interpretation and two-way communication—still requires a paid supervisor. The cost savings are marginal compared to the supervisor's loaded wage. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools for policy Q&A are cheap to run, but the supervisor's broader interpretive and communicative role still requires paid human time, making overall cost comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI can assist in drafting policy communications and FAQs, but deployed products do not reliably interpret ambiguous procedures or adapt explanations to diverse staff needs in production environments. Human supervisors remain essential for resolving conflicts and contextual application. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Chatbots and internal knowledge assistants exist that answer policy questions, but they are supplementary tools rather than replacements for a supervisor's role in interpreting and delivering guidance to staff. |
Train or instruct employees in job duties or company policies or arrange for training to be provided.
30CI 25–35 · exposure 25 · augmentation 63 · importance 4.0/5 · click for rater detail
Train or instruct employees in job duties or company policies or arrange for training to be provided.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption is limited to content generation and supplementary material creation; actual training remains supervisor-led. Most organizations have not moved to AI-driven training of critical compliance or onboarding tasks due to liability, accountability, and the interactive nature of effective instruction. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Office and administrative support supervisory roles are adopting AI for content generation and scheduling tools but production-scale AI-led training delivery is uncommon; adoption is still nascent in this functional area. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist supervisors by drafting training materials, generating policy summaries, creating quiz questions, and personalizing learning pathways, raising supervisor productivity in content preparation. However, the core instructional and assessment role remains human-centric, limiting the transformative impact. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly help supervisors draft training materials, checklists, quizzes, and policy summaries, and can assist in identifying training gaps or generating role-specific content, meaningfully boosting productivity while the human still delivers and oversees the training. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Training employees on job duties and policies requires nuanced communication, behavioral feedback, and adaptation to individual learning needs. While AI can generate training materials and content, end-to-end training (assessment, explanation, feedback, verification of understanding) remains heavily dependent on human interaction and judgment, falling well short of 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can generate training content and materials, but the actual delivery, coaching, and judgment-based instruction tailored to individual employees and arranging logistics remains largely human-led.The overall task bundle is not automatable end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Most organizations have compliance, liability, and HR governance requirements that mandate human supervisors sign off on employee training and policy acknowledgment. In regulated sectors, documented human instruction is often legally required. Customer expectation and organizational policy also strongly favor human supervisors for employee development and policy communication. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for most administrative training, but organizational norms and preference for human supervisors delivering guidance and modeling expectations create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Generating training content via AI is cheap, but the cost of oversight, customization, verification, and human interaction to complete training typically remains comparable to or higher than having a supervisor conduct training directly, especially when accounting for integration and quality assurance overhead. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can cheaply produce training materials, but the supervisor's role in actually training, observing, and correcting behavior still requires paid human time, keeping overall cost comparable to human-only delivery. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI tools can draft training materials and generate content, but no deployed product reliably handles the full training-and-instruction task including assessing comprehension, adapting to learner needs, and ensuring policy compliance. Existing LMS-based solutions and AI tutoring systems show promise in narrow domains but lack the breadth and reliability needed for general office training. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Deployed products (LMS platforms with AI-generated content, chatbots for onboarding FAQs) exist but only cover fragments like content creation, not the supervisory relationship-building and judgment involved in training. |
Design, implement, or evaluate staff training and development programs, customer service initiatives, or performance measurement criteria.
30CI 25–35 · exposure 25 · augmentation 75 · importance 3.6/5 · click for rater detail
Design, implement, or evaluate staff training and development programs, customer service initiatives, or performance measurement criteria.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While office and administrative sectors are digitizing, adoption of AI for autonomous program design and evaluation remains limited. Most organizations still rely on HR departments and supervisors to own these decisions, with AI at most providing advisory analytics or draft content. Production-level displacement is rare. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Office/administrative support supervision sectors show slower AI adoption for managerial/HR-design tasks compared to fast-moving professional services or tech firms. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI tools significantly assist supervisors in this task: generative AI can draft training outlines, learning management systems can deliver and track programs, analytics platforms can surface performance patterns, and chatbots can handle routine training queries. These augmentations materially speed up design and evaluation while the supervisor retains decision authority. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully assist in drafting training curricula, generating performance metrics, and summarizing customer service data, significantly speeding up parts of this task while the supervisor retains oversight. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with generating training content, drafting performance metrics, and analyzing data on program effectiveness, the task fundamentally requires human judgment about organizational culture, employee needs, stakeholder input, and strategic alignment. End-to-end automation would require replacing supervisory oversight and decision-making, which current AI cannot do reliably without substantial human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft training materials or performance criteria but designing, implementing, and evaluating a full program requires organizational judgment, stakeholder buy-in, and contextual decision-making that current AI cannot autonomously execute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: supervisors have accountability and liability for training program outcomes and performance measurement fairness; organizational policy, legal compliance (especially around performance evaluation and potential discrimination), and employee expectations all require a human authority figure to sign off on and implement these programs. Delegation to AI without human judgment invites legal and organizational risk. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational and managerial authority is typically tied to a human supervisor role, creating moderate structural friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of AI-assisted tooling (content generation, analytics platforms) plus the oversight burden of a supervisor validating and refining outputs is still high relative to experienced supervisors performing this work. Full replacement would require unacceptably high error and rework costs, keeping total cost-per-task above human-equivalent wages. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply produce drafts of training content or rubrics, but the supervisory judgment, implementation, and evaluation work still requires paid human time, keeping overall cost comparable to human-led effort. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably execute this full task autonomously in production. AI tools can draft training curricula or suggest metrics, but actually designing, implementing, and evaluating integrated programs at organizational scale requires human supervisors to synthesize feedback, manage change, and adapt in real time—areas where AI lacks production reliability. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products like generative AI writing assistants and LMS platforms with analytics exist, but no deployed product reliably designs and evaluates entire training/performance programs without heavy human direction. |
Plan for or coordinate office services, such as equipment or supply acquisition or organization, disposal of assets, relocation, parking, maintenance, or security services.
30CI 25–35 · exposure 25 · augmentation 50 · importance 3.1/5 · click for rater detail
Plan for or coordinate office services, such as equipment or supply acquisition or organization, disposal of assets, relocation, parking, maintenance, or security services.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While back-office digitization is growing, the administrative support sector remains fragmented with many small firms, legacy systems, and low-tech operations. Adoption of AI for supervisory coordination tasks is slow and mostly limited to procurement modules in large enterprises; production-level autonomy is rare. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Office administration and facilities coordination are not leading adopters of AI agents compared to information/finance sectors; adoption here remains mostly pilot-level tools for scheduling or procurement support. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by drafting supply requisitions, summarizing vendor quotes, flagging maintenance schedules, and organizing relocation checklists, thereby raising supervisor productivity on routine planning tasks. However, the improvement is moderate because the core task—judging tradeoffs and making coordinated decisions—remains human-centric. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can meaningfully assist with drafting plans, tracking inventories, comparing vendor quotes, and scheduling maintenance, improving the supervisor's efficiency on parts of this multifaceted task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with scheduling, inventory tracking, and vendor selection for supplies and equipment, the task requires substantial human judgment on priorities, budget tradeoffs, stakeholder coordination, and exception handling that cannot be fully automated end-to-end today. No current system achieves the 50% time-saving-at-equal-quality threshold for the full coordination scope. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can assist with planning documents, vendor comparisons, and scheduling, but the coordination requires physical oversight, vendor negotiation, and situational judgment that current systems cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: supervisors have accountability and sign-off authority over budget and asset decisions; many organizations have compliance and insurance requirements tying authorization to a named person; and human judgment on vendor relationships, employee needs, and facility priorities creates legal and organizational friction against full delegation to AI. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this task, but organizational trust, vendor relationships, and physical site knowledge create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for supply chain and procurement optimization are available but typically require significant integration overhead, custom configuration, and ongoing human oversight to handle the varied and context-dependent decisions in office services coordination. All-in costs remain comparable to or exceed the cost of a first-line supervisor's time for this work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can reduce time spent on research and documentation, but human oversight, vendor relationships, and on-site coordination still require significant paid labor, keeping costs comparable to human-only execution. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some point solutions exist for inventory management and procurement workflows, but no deployed product reliably handles the integrated coordination of multiple office services (equipment, supplies, disposal, relocation, parking, maintenance, security) with the judgment calls required. Narrow-scope tools exist but not holistic task execution at scale. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some facilities-management and procurement software incorporates AI recommendations, but no deployed product autonomously plans and coordinates the full range of office services described. |
Plan layouts of stockrooms, warehouses, or other storage areas, considering turnover, size, weight, or related factors pertaining to items stored.
30CI 25–35 · exposure 25 · augmentation 63 · importance 2.4/5 · click for rater detail
Plan layouts of stockrooms, warehouses, or other storage areas, considering turnover, size, weight, or related factors pertaining to items stored.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Warehouse and storage sectors show moderate digital adoption, but planning layouts remains a supervisory responsibility that has not seen rapid AI displacement. Most warehouses use basic systems (inventory management, not layout planning automation), and organizational inertia around supervisor roles limits adoption velocity. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Warehouse and logistics sector adoption of AI-driven layout/slotting tools is growing but still concentrated in large distribution centers, with most office/administrative supervisory roles in this occupation not using such tools broadly. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by analyzing storage metrics, suggesting layout alternatives based on turnover and weight distributions, and generating visual mockups—raising supervisor productivity in the planning phase. However, the human supervisor must validate, adjust for operational realities, and take final responsibility. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-based simulation and optimization tools can meaningfully help supervisors model turnover, weight, and space trade-offs, significantly speeding up the planning process while the human still makes final decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can analyze storage data and suggest optimized layouts using algorithms, the task requires judgment about operational constraints, staffing capacity, and real-world factors that current systems cannot reliably predict end-to-end. Partial automation (layout suggestion) exists but falls short of the 50% time-savings bar without substantial human review and adjustment. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can assist with layout optimization algorithms and simulations, but the task requires physical site knowledge, integration with operational constraints, and judgment calls that go beyond automated planning alone, so full end-to-end automation with equal quality is not yet reliable. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: supervisors are accountable for storage safety and efficiency, liability for layout failures (damage, injuries) rests with the supervisor, and operational constraints are context-specific. Regulatory requirements around workplace safety and material handling create a legal requirement for human judgment and sign-off. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human for this task, but organizational buy-in, physical safety considerations, and integration with existing operations create moderate friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration of layout optimization software, data preprocessing, and oversight by a supervisor remains costly relative to the task's time investment. The human supervisor's wage for review and adjustment typically exceeds the AI system's operational cost savings. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Specialized slotting/optimization software has licensing and integration costs plus need for human oversight and site-specific customization, making it not dramatically cheaper than a human supervisor doing this periodically. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some warehouse management and layout optimization tools exist, but they typically require manual data input, human validation of constraints, and supervisor override. No mature deployed product fully automates this task reliably; most solutions are semi-automated drafting aids rather than autonomous planning systems. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Warehouse management and slotting optimization software exists and is deployed in some large operations, but these are narrow tools requiring significant setup and human interpretation, not general autonomous layout planning. |
Supervise the work of office, administrative, or customer service employees to ensure adherence to quality standards, deadlines, and proper procedures, correcting errors or problems.
29CI 25–32 · exposure 25 · augmentation 63 · importance 4.4/5 · click for rater detail
Supervise the work of office, administrative, or customer service employees to ensure adherence to quality standards, deadlines, and proper procedures, correcting errors or problems.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While office automation is widespread, deployment of AI to replace first-line supervisors remains rare; most organizations use analytics tools to assist human supervisors rather than displace them, reflecting both regulatory/HR friction and genuine need for human judgment in people management. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Office/administrative and customer service sectors have moderate AI tool adoption (QA analytics, chatbots for monitoring), but usage for actual supervisory correction and decision-making remains a pilot-stage or augmentative practice. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-driven dashboards, performance analytics, and alert systems can usefully assist supervisors by surfacing quality drifts and deadline risks, allowing them to focus corrections more efficiently, though the assistance is partial rather than transformative. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-based analytics, sentiment analysis, and automated flagging of errors/deadlines can substantially help supervisors monitor performance and identify issues faster, meaningfully boosting their effectiveness while they remain the decision-maker. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can monitor some performance metrics and flag potential quality issues or missed deadlines through data analysis, the core supervisory function—real-time correction, judgment calls on error severity, and procedural guidance to individual employees—requires contextual human oversight and interpersonal judgment that current AI cannot reliably perform end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | While AI can monitor certain metrics (call quality scores, ticket resolution times, error flags), the core supervisory task of interpreting context, correcting employee behavior, and exercising managerial judgment requires human involvement and cannot be fully automated today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Supervisory authority and accountability typically require a responsible person to be legally/organizationally liable for team performance and employee management decisions; most firms require a human manager to formally oversee and sign off on corrections and disciplinary actions. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement, but organizational structures, HR/legal responsibility for personnel corrections, and accountability for people-management decisions create meaningful friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI monitoring and flagging systems are relatively cheap, but end-to-end replacement would require a human supervisor remain to handle actual correction and judgment, meaning cost parity or slight AI advantage at best for the complete task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Monitoring software has modest licensing costs but does not replace the supervisor's wage since a human must still interpret data and intervene, so total cost savings versus a human supervisor are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably perform full supervisory oversight independently; existing performance-monitoring tools assist but require human supervisors to interpret alerts, make decisions on corrections, and handle the relational aspects of employee management. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some quality-monitoring and analytics tools (call center QA software, workflow dashboards) are deployed in production, but they augment rather than replace the supervisory role of correcting errors and managing people. |
Provide employees with guidance in handling difficult or complex problems or in resolving escalated complaints or disputes.
29CI 25–32 · exposure 25 · augmentation 63 · importance 4.3/5 · click for rater detail
Provide employees with guidance in handling difficult or complex problems or in resolving escalated complaints or disputes.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI for complaint resolution remains slow and limited. Most organizations treat this as a core supervisory responsibility requiring human judgment and accountability; pilots exist but production deployment of autonomous or semi-autonomous systems is rare in practice. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Office/administrative sectors are adopting AI copilots and knowledge tools at a moderate pace, with pilots for coaching and escalation support becoming more common but not yet deeply embedded. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist supervisors by drafting response templates, flagging policy precedents, summarizing complaint history, and suggesting de-escalation approaches. However, augmentation is moderate because the core task—resolving disputes and providing authoritative guidance—remains fundamentally human-driven and judgment-intensive. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can effectively assist supervisors by surfacing relevant policies, suggesting responses, and summarizing complaint histories, meaningfully boosting productivity while the supervisor retains final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft response templates and surface relevant policies, resolving escalated complaints requires real-time judgment about interpersonal dynamics, organizational context, and nuanced case-by-case decisions that depend on human discretion and authority. Current systems lack the contextual understanding and delegated authority to handle end-to-end resolution at the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 2/5 | This task requires interpersonal judgment, contextual organizational knowledge, and real-time coaching that current AI cannot fully replicate end-to-end; AI can assist but not autonomously replace this supervisory function at the 50% time-saving bar. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulatory and organizational barriers are substantial: employment law, discrimination law, and internal HR policy typically require a licensed manager or HR professional to document and adjudicate complaints and disputes. Liability for mishandling escalated issues creates asymmetric error costs that protect the human role. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement, but organizational trust, accountability for personnel decisions, and the need for human authority in disputes create meaningful friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of integrating AI to assist with complaint handling (infrastructure, oversight, integration with HR systems) combined with required human supervision and final decision-making by the supervisor means total cost is comparable to or higher than the labor it might displace. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI chat assistance is cheap, the human supervisor's judgment, authority, and accountability for escalations remain necessary, so total cost savings versus a human supervisor are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs this task autonomously in production. AI can assist with documentation and suggestions, but complaint resolution and employee guidance involve disputes where liability and accountability rest with a human supervisor; existing systems operate only as aids, not independent performers. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some AI copilots suggest responses or provide knowledge-base guidance for escalations, but no deployed product reliably supervises employees through complex dispute resolution without heavy human oversight. |
Coordinate activities with other supervisory personnel or with other work units or departments.
29CI 25–32 · exposure 25 · augmentation 75 · importance 3.9/5 · click for rater detail
Coordinate activities with other supervisory personnel or with other work units or departments.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Coordination tools (Slack, project management software) are widely deployed, but they are assistive rather than autonomous. Organizations have shown limited appetite for removing the human supervisor from coordination loops, preferring augmentation over replacement in this domain. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Office/administrative supervisory settings are moderately digitized with growing use of collaboration and AI-assisted communication tools, but full coordination automation remains uncommon. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-driven alerts, scheduling, workflow suggestions, and meeting summaries substantially augment supervisor productivity in coordination tasks. Real productivity gains are evident when systems surface conflicts, propose options, and track cross-departmental dependencies while the supervisor retains decision authority. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by drafting communications, summarizing cross-department updates, scheduling, and tracking action items, improving supervisor efficiency while they remain the decision-maker. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Coordination requires real-time negotiation, relationship management, and contextual decision-making across organizational boundaries. Current AI can draft messages or flag scheduling conflicts, but cannot autonomously negotiate priorities, resolve conflicts, or maintain the interpersonal dynamics that drive effective coordination. |
| Task automatability | claude-sonnet-5 | 2/5 | Cross-departmental coordination involves relationship management, negotiation, and situational judgment that current AI cannot fully replace, though scheduling and information relay portions could be assisted. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Supervisory coordination often carries implicit accountability for decisions and buy-in from peers; organizations typically require a human supervisor to be the visible decision-maker and relationship custodian. Regulatory and organizational norms embed the expectation that a named human supervisor owns coordination outcomes. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier exists, but organizational trust, accountability for interdepartmental decisions, and need for human relationship-building create meaningful friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI coordination tools (scheduling, messaging) have modest deployment costs, but supervisory coordination involves judgment calls and relationship maintenance that still require significant human oversight, making the all-in cost per effective coordination act comparable to human labor. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Since AI cannot substitute for the full task, organizations still bear the human supervisor's cost, with AI tools adding a marginal cost for scheduling/communication support rather than replacing the wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can send alerts or suggestions, no deployed system reliably handles multi-stakeholder coordination independently. Products exist for workflow or calendar integration, but they function as tools within human-directed workflows rather than autonomous coordinators making substantive decisions. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product autonomously coordinates supervisory activities across departments; existing tools (calendars, chatbots) only support fragments like scheduling meetings or sharing status updates. |
Make recommendations to management concerning such issues as staffing decisions or procedural changes.
28CI 25–30 · exposure 25 · augmentation 75 · importance 3.8/5 · click for rater detail
Make recommendations to management concerning such issues as staffing decisions or procedural changes.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Most organizations still treat staffing and procedural recommendations as explicitly human responsibilities requiring supervisor judgment and documented reasoning. Adoption of AI-driven recommendations is nascent and cautious in real workplaces; pilots exist but production displacement is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Office/administrative supervisory roles are moderately digitized but managerial judgment tasks like this see slow, pilot-stage AI adoption rather than production-scale deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at surfacing relevant data, modeling staffing scenarios, and drafting justifications—high-value assistance that supervisors can use to make better-informed recommendations. The supervisor remains the decision-maker while AI boosts their information quality and speed. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can strongly assist by analyzing staffing data, summarizing performance trends, and drafting recommendation memos, significantly speeding up the supervisor's preparation work. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can gather data and synthesize information to inform recommendations, but staffing and procedural decisions require contextual judgment about organizational culture, interpersonal dynamics, and long-term strategy that current systems cannot reliably produce end-to-end. The task involves human accountability and discretionary reasoning beyond pattern matching. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires judgment about specific people, team dynamics, and organizational context that AI cannot independently assess without extensive human-provided input; AI can draft analyses but not autonomously form the recommendation.4 |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Management recommendations carry legal, financial, and reputational liability (hiring discrimination, wrongful termination, regulatory compliance). Supervisors are accountable for these decisions; delegation to AI without explicit human sign-off faces organizational, HR, and employment-law friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement, but organizational trust, accountability for personnel decisions, and manager relationships create real friction against fully automating this. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted data synthesis and memo drafting can reduce preparation time, but the marginal cost of AI oversight, prompt engineering, and supervisor validation often approaches the cost of a junior analyst doing it directly. Full substitution would require eliminating the supervisor role itself. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Generating a draft recommendation is cheap, but the human supervisor's contextual knowledge, credibility, and accountability still dominate the cost of producing an actionable recommendation. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI tools can draft memos and summarize data for recommendations, no deployed product reliably makes binding staffing or procedural recommendations in production. Existing systems lack the organizational context and accountability structure required; supervisors still make the actual judgment calls. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product autonomously generates staffing or procedural recommendations for management; existing HR analytics tools provide data but not the supervisory judgment call itself. |
Develop or update procedures, policies, or standards.
28CI 25–30 · exposure 25 · augmentation 63 · importance 3.8/5 · click for rater detail
Develop or update procedures, policies, or standards.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Most organizations still rely on supervisors and compliance teams to develop policies; AI-assisted policy drafting is in pilot phases at larger firms but not mainstream production adoption. The decentralized, case-specific nature of policy work in different organizations slows broad adoption velocity. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Office/administrative supervisory roles are in a sector with moderate digitization, and while generative AI drafting tools are spreading, actual production adoption for policy development specifically remains limited. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by generating policy drafts, suggesting structural improvements, and flagging inconsistencies, freeing the supervisor to focus on strategic alignment and stakeholder input. However, the human must remain central to judgment and approval, limiting the transformative effect on productivity. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully speed up drafting, benchmarking against best practices, and formatting of policies and procedures, giving supervisors a strong productivity boost while they retain final judgment and approval. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft text and suggest structural improvements to procedures, the task fundamentally requires judgment about organizational context, legal compliance, and strategic intent. Current AI systems cannot reliably understand organizational culture, risk tolerance, or competitive positioning needed to create enforceable policies from scratch, limiting time savings below the 50% threshold at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Drafting policy language can be AI-assisted, but developing procedures requires understanding organizational context, stakeholder needs, and judgment calls that current AI cannot fully perform end-to-end without heavy human direction. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Organizational policies often require signoff by management or legal counsel and may fall under regulatory scrutiny depending on industry. Human judgment and accountability are expected in policy-setting roles, creating organizational and sometimes regulatory barriers to full automation, though not a strict legal requirement that a human must perform every policy task. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement typically exists, but organizational accountability, approval chains, and the need for domain-specific judgment create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI can reduce drafting time but requires significant human review, legal vetting, and revision cycles. The all-in cost (inference, integration, compliance review, human oversight) remains comparable to or higher than a supervisor performing the task directly, particularly when liability and accuracy requirements are factored in. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI drafting assistance is cheap, the human supervisor still must gather requirements, validate compliance, and finalize decisions, so overall cost savings versus a human doing the full task are modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably perform end-to-end policy development; products like document generation tools exist but require heavy human oversight and iteration. The task involves domain-specific legal and compliance considerations where material errors can expose organizations to liability, making production-scale reliability unachievable today. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Generative AI tools are used to draft policy documents in some organizations, but no deployed product reliably develops or updates procedures/standards autonomously at scale without extensive human editing and validation. |
Recruit, interview, and select employees.
26CI 25–28 · exposure 25 · augmentation 63 · importance 4.0/5 · click for rater detail
Recruit, interview, and select employees.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Most SMEs and many mid-market firms still rely on manual recruiting and interviewing; adoption of full AI-driven hiring remains limited and cautious due to legal and reputational risk. Pilots are common, but production deployment of end-to-end AI hiring is rare outside large tech firms. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | HR tech adoption of AI screening and applicant tracking is moderate and growing, but full automation of interviewing and selection remains rare and cautious due to bias and legal concerns. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI tools assist with resume screening, interview scheduling, and candidate tracking, raising supervisor productivity on administrative tasks. However, the core judgment—assessing fit and making final selection—remains largely human-dependent, limiting transformative augmentation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI meaningfully assists with resume screening, generating interview questions, and summarizing candidate information, significantly speeding up parts of the hiring workflow while humans retain decision authority. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with resume screening and interview transcription, but recruiting, interviewing, and selection involve complex judgment about cultural fit, subtle interpersonal cues, and legal compliance that resist full automation. Current systems cannot reliably replicate the holistic evaluation a human supervisor performs, nor meet the ≥50% time-saving threshold end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can screen resumes and draft interview questions, but final selection requires human judgment integrating cultural fit, legal risk, and interpersonal assessment that current systems cannot reliably replicate end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Hiring decisions face significant legal exposure under employment law, discrimination statutes, and company liability for poor hires; supervisors bear legal accountability for hiring decisions. Most organizations require human judgment and sign-off, and employment law implicitly requires human decision-makers in the hiring process. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Hiring decisions carry significant legal liability (discrimination law, EEOC compliance) 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 | ATS and screening tools cost thousands annually plus integration, while a supervisor's hiring time remains unavoidable for final interviews and decisions. The all-in cost of current AI solutions is comparable to or exceeds the marginal cost savings from partial automation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI screening tools reduce some sourcing costs, but the full recruit-interview-select cycle still requires substantial human interviewer time and oversight, keeping overall costs comparable to human-only processes. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Recruiting platforms and ATS tools exist, but deployed AI for resume screening has high error rates and bias concerns; live interview automation is nascent and unreliable. No mature product performs the full recruiting-to-selection pipeline at scale with acceptable reliability in real hiring operations. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products like ATS resume screeners and AI interview scoring tools exist but have known bias issues and narrow scope, and most organizations keep humans making final hiring decisions. |
Evaluate employees' job performance and conformance to regulations and recommend appropriate personnel action.
26CI 25–28 · exposure 25 · augmentation 75 · importance 4.0/5 · click for rater detail
Evaluate employees' job performance and conformance to regulations and recommend appropriate personnel action.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI for performance evaluation remains limited despite available tools. Most organizations continue to rely on human supervisors for evaluations; adoption is concentrated in large firms using basic analytics dashboards for data input, not autonomous decision-making. Adoption velocity remains slow due to risk aversion and regulatory caution. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | HR tech adoption of AI-assisted performance review tools is growing in many office/administrative settings, but actual delegation of personnel decisions to AI remains rare and cautious due to liability concerns. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI currently augments supervisor performance well: real-time performance dashboards, compliance alerts, automated data aggregation, and predictive flagging of performance issues help supervisors make faster, more evidence-grounded evaluations and recommendations. The supervisor retains full decision authority while AI significantly raises information quality and decision velocity. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by synthesizing performance data, drafting review language, and highlighting compliance issues, significantly speeding up the supervisor's preparation work while the human retains decision authority. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can extract and analyze quantitative performance metrics, conformance flags, and produce comparative reports, the evaluative judgment, regulatory interpretation, and personnel recommendations require contextual understanding of organizational culture, legal nuance, and human factors that current AI systems cannot reliably handle end-to-end. A human supervisor remains essential for the critical decision-making component. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft performance summaries or flag compliance metrics, but the core judgment of evaluating an employee holistically and recommending personnel actions requires contextual, relational and legal judgment that current systems cannot reliably replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant legal and organizational barriers exist: employment law requires documented human judgment in performance evaluations, potential liability for discriminatory or wrongful termination claims, labor regulations mandate supervisory sign-off, and most organizations institutionally expect a human supervisor to take accountability for personnel decisions. Regulatory and liability exposure is high. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Personnel actions (promotion, discipline, termination) carry legal liability, union/labor law implications, and typically require accountable human sign-off, creating strong organizational and regulatory barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for performance analytics and compliance tracking have meaningful setup, integration, and ongoing oversight costs. When accounting for the specialized domain expertise required to validate recommendations and legal/HR oversight, the total cost remains comparable to or slightly below a first-line supervisor's time investment, without the quality gains needed for cost dominance. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Using AI tools still requires substantial supervisor time for judgment, sign-off, and legal defensibility, so cost savings versus a human supervisor performing this task are modest, not order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some HR analytics platforms offer performance dashboards and compliance monitoring, but no deployed product reliably performs the full evaluation and recommendation task autonomously. Existing tools are narrow (data aggregation) and require substantial human judgment overlay; they do not meet production-grade autonomy standards. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | HR analytics and performance-management software exist to aggregate data and suggest ratings, but no deployed product autonomously conducts performance evaluations and recommends personnel actions without heavy human review. |
Keep informed of provisions of labor-management agreements and their effects on departmental operations.
23CI 16–30 · exposure 17 · augmentation 63 · importance 3.4/5 · click for rater detail
Keep informed of provisions of labor-management agreements and their effects on departmental operations.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Most supervisory functions remain human-centered in practice; while document management tools are common, the interpretation and decision-making tied to labor agreements has not seen significant AI displacement even in digitized sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Office/administrative supervisory roles show moderate AI tool adoption for drafting and summarization, but specialized labor-relations monitoring is a narrow niche with limited production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by summarizing agreement provisions, flagging changes, and alerting supervisors to potential compliance issues, moderately improving awareness and response time. However, the interpretive and judgment-intensive core remains with the human supervisor. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can effectively assist by summarizing complex labor agreements, flagging changes, and answering questions, meaningfully improving a supervisor's ability to stay informed even though the human retains interpretive responsibility. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires continuous monitoring, interpretation, and contextual judgment about complex legal agreements and their organizational implications. Current AI cannot reliably track evolving labor agreements, anticipate their operational effects, or make the nuanced decisions that supervisors must make regarding compliance and departmental strategy. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help summarize and monitor agreement text, but staying 'informed' and interpreting real-world effects on operations requires ongoing contextual judgment and integration into managerial decision-making that current systems don't autonomously perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Labor-management agreements are legally binding contracts and supervisory compliance with their terms is typically a fiduciary responsibility. Many jurisdictions and union contracts require that human supervisors remain accountable for adherence, creating strong legal and contractual barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement for this specific awareness task, but supervisors are typically accountable for compliance and interpretation, creating organizational and liability-related friction against full delegation to AI. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Basic document processing and alerting is cheap, but the human supervisor must still interpret and decide on responses. The cost savings are marginal since the core work—understanding implications and making operational adjustments—remains human responsibility. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI can cheaply summarize text, the ongoing awareness, contextual application, and managerial judgment still require significant human oversight, keeping cost savings modest relative to a supervisor's existing duties. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can retrieve and summarize labor agreement text, no deployed product reliably interprets provisions, flags operational conflicts, or predicts departmental effects in production settings. Document summarization exists but cannot substitute for the supervisory judgment required. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Document summarization and Q&A tools exist and can extract labor agreement provisions, but no deployed product reliably tracks and translates these into departmental operational implications at scale. |
Consult with managers or other personnel to resolve problems in areas such as equipment performance, output quality, or work schedules.
21CI 18–25 · exposure 16 · augmentation 63 · importance 3.6/5 · click for rater detail
Consult with managers or other personnel to resolve problems in areas such as equipment performance, output quality, or work schedules.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Supervisory consultation is a core interpersonal and managerial function deeply embedded in organizational structure; adoption of automation here is minimal and not a priority in even high-tech sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Office/administrative supervisory functions see AI adoption mainly for reporting and scheduling tools, but the interpersonal consultation aspect lags behind information-sector automation trends. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist by summarizing performance data, flagging recurring issues, or drafting problem statements for the supervisor to discuss, improving decision preparation without replacing the consultation itself. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can surface performance data, flag quality issues, and draft schedule optimization proposals, meaningfully speeding up the supervisor's preparation before or during consultations. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Consultation requires understanding nuanced organizational context, stakeholder relationships, and complex problem-solving that demands human judgment. While AI could draft communications or summarize issues, it cannot reliably navigate interpersonal dynamics or make binding decisions across multiple stakeholders. |
| Task automatability | claude-sonnet-5 | 2/5 | This involves interpersonal negotiation, contextual judgment, and organizational authority to resolve cross-functional problems, which current AI cannot fully replicate end-to-end even with significant setup.dispatchable |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Organizational hierarchy and accountability norms expect the supervisor to own problem-resolution authority; managers expect consultation from a recognized peer with accountability, not an automated system. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but organizational trust, accountability, and interpersonal dynamics create moderate friction against full AI substitution in managerial consultation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The supervisory authority, relationship management, and accountability required mean AI oversight costs approach or exceed the value delivered compared to human supervisors already embedded in organizations. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI can provide data analysis inputs cheaply, the actual consultation and resolution requires human time and judgment, keeping costs comparable or AI-support costs added on top. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No production systems can autonomously perform consultative problem-resolution across equipment, quality, and scheduling domains while managing stakeholder relationships. This requires sustained interaction and authority that deployed AI systems do not have. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously consults with managers and resolves operational problems; this remains a human relational and decision-making activity. |
Implement corporate or departmental policies, procedures, and service standards in conjunction with management.
13CI 5–20 · exposure 8 · augmentation 50 · importance 4.0/5 · click for rater detail
Implement corporate or departmental policies, procedures, and service standards in conjunction with management.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Organizations are not adopting AI to replace or fully automate supervisory policy implementation; adoption remains nascent and experimental in this governance-critical function. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | While administrative/office sectors show moderate AI tool adoption, actual policy implementation and supervisory enforcement functions see little AI penetration in practice. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by drafting policy documents, analyzing compliance gaps, and generating implementation checklists, meaningfully supporting a human supervisor's productivity without replacing their judgment and authority. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help draft policy documents, communications, training materials, and track compliance metrics, meaningfully supporting the supervisor's implementation work without replacing the judgment and interpersonal execution involved. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task requires discretionary judgment about policy alignment, departmental context, and stakeholder coordination with management—elements that exceed current AI capability. While AI could draft policy documents or flag inconsistencies, the end-to-end implementation, negotiation, and enforcement at organizational scale remains fundamentally human. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a management/leadership task requiring interpersonal authority, organizational judgment, and change management with staff—AI cannot execute the implementation itself, only support documentation or communication drafting.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Supervisory authority, management accountability, and organizational trust create substantial barriers. Policy implementation must ultimately be signed off by licensed/accountable humans, and employees expect human leadership for enforcement decisions. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Implementation requires authority, accountability, and often HR/legal oversight tied to a named supervisor role, creating strong organizational and liability barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI might assist with policy documentation or monitoring, but full deployment would require human oversight and accountability, preventing meaningful cost advantage over direct supervisor labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this task, so cost comparison favors the human who must actually enact and enforce policy with a team. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs organizational policy implementation independently. Current systems lack the authority, accountability, and contextual understanding needed to execute this supervisory function in production at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product implements policies or manages staff compliance with standards; this remains a human supervisory function. |
Participate in the work of subordinates to facilitate productivity or to overcome difficult aspects of work.
11CI 5–16 · exposure 8 · augmentation 38 · importance 3.9/5 · click for rater detail
Participate in the work of subordinates to facilitate productivity or to overcome difficult aspects of work.
11| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of AI for supervisory participation is minimal; organizations continue to hire and rely on human supervisors for team cohesion and judgment. No measurable displacement is occurring in this role despite general AI adoption. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Office/administrative supervisory roles are seeing AI adoption for reporting and scheduling, but the hands-on mentoring/troubleshooting aspect of this task is not part of current adoption trends. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist supervisors with performance dashboards, scheduling optimization, and data-driven insights about team productivity, enabling them to allocate participatory effort more strategically, but the core supervisory presence remains human-dependent. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI tools can help supervisors identify bottlenecks or suggest solutions to relay to subordinates, but they don't materially transform the act of hands-on participation and assistance. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task requires real-time judgment about team dynamics, individual capability assessment, and adaptive problem-solving embedded in human relationships. While AI can assist with productivity analytics or suggest workflow improvements, the human-centered judgment of 'overcoming difficult aspects' and the participatory aspect cannot be automated end-to-end to reach the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires hands-on physical presence, real-time judgment, and interpersonal leadership to jump in and help staff overcome specific work obstacles—something current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong organizational and human-contact barriers exist: employees expect human leadership and mentorship, organizational culture prioritizes supervisor presence, and legal/HR frameworks assume human oversight of staff performance and problem-solving. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Direct supervisory involvement, accountability, and interpersonal trust-building are inherently tied to human authority and presence, creating strong organizational and relational barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Replacing a first-line supervisor's participatory work with AI would require continuous modeling of team dynamics, real-time intervention, and human trust-building—costs that far exceed the loaded wage of the supervisory role itself. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this task, so cost comparison favors the human by default; any AI attempt would require human oversight anyway, adding cost rather than saving it. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs supervisory participation, relationship-building, or adaptive team facilitation at scale. This task fundamentally requires human presence, contextual awareness, and interpersonal trust that current AI systems cannot substitute for in production environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product substitutes for a supervisor physically or interactively stepping in to assist subordinates with difficult tasks in real time. |
Discuss job performance problems with employees to identify causes and issues and to work on resolving problems.
6CI 0–13 · exposure 5 · augmentation 50 · importance 4.1/5 · click for rater detail
Discuss job performance problems with employees to identify causes and issues and to work on resolving problems.
6| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Organizations are not adopting AI to replace supervisor–employee performance conversations. The human relationship, accountability, and legal exposure make this a laggard domain for automation despite general office automation trends. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | While administrative and office support functions are seeing some AI tool adoption, direct AI-led performance discussions are essentially unadopted; this specific interpersonal task lags far behind data-processing tasks in the same occupation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by generating summaries of prior issues, suggesting resolution frameworks, or preparing documentation templates, but the core task of dialogue and judgment remains human-driven and the augmentation is modest. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help supervisors prepare talking points, analyze performance data, draft documentation, or suggest coaching approaches before/after the conversation, meaningfully aiding but not replacing the human interaction. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time dialogue, emotional intelligence, and contextual judgment about interpersonal issues that current AI cannot reliably perform end-to-end. While AI can draft talking points or suggest frameworks, the actual performance discussion—listening, reading nonverbal cues, making on-the-spot adjustments, and building rapport—remains firmly human. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires live interpersonal dialogue, empathy, reading emotional cues, and building trust with an employee, which current AI cannot substitute for as the acting supervisor. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal and organizational barriers are substantial: labor law, employment documentation, discrimination risk, and union agreements typically require a human manager to conduct these discussions. Liability and error costs (wrongful termination, harassment claims) make full automation infeasible. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Employment law, HR documentation requirements, and the need for a human authority figure to manage disciplinary/performance conversations create strong organizational and legal barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The infrastructure cost of AI systems that could even partially automate this task, combined with required human oversight and intervention, would exceed the wage of a supervisor conducting the conversation themselves. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this task alone, so cost comparison favors the human by default; any AI use is only a minor supplement, not a replacement. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably conducts performance discussions autonomously. AI can assist with analysis or documentation, but production systems do not substitute for the supervisor's direct engagement in this sensitive interpersonal task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product independently conducts performance-problem conversations with employees and resolves interpersonal/managerial issues; this remains a human management function. |
Related occupations — Office & Administrative Support
How to read this
A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.
What would change this score
New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.