Administrative Services Managers
11-3012.00Median wage $114,130/yr263,960 employed (US)Rank #191 of 923 scored · top 21% by substitution
Plan, direct, or coordinate one or more administrative services of an organization, such as records and information management, mail distribution, and other office support services.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution39
Exposure36
Augmentation69
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
18 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
11%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%36
panel mean rating 2.5/5 → substitution pressure 36/100
Technical feasibility todayw 20%33
panel mean rating 2.3/5 → substitution pressure 33/100
Cost vs. human wagew 15%38
panel mean rating 2.5/5 → substitution pressure 38/100
Adoption barriersw 20%inverted — strong barriers lower the score48
panel mean rating 3.1/5 (barrier strength) → substitution pressure 48/100
Sector adoption velocityw 10%41
panel mean rating 2.6/5 → substitution pressure 41/100
Task breakdown (18 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Manage paper or electronic filing systems by recording information, updating paperwork, or maintaining documents, such as attendance records or correspondence.
79CI 75–84 · exposure 75 · augmentation 63 · click for rater detail
Manage paper or electronic filing systems by recording information, updating paperwork, or maintaining documents, such as attendance records or correspondence.
79| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Large organizations (finance, healthcare, government) have been deploying document management and RPA for filing and records maintenance for years. Adoption is well-established in information-intensive sectors, though small firms lag. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Administrative and office functions across many sectors have rapidly adopted digital filing, workflow automation, and document management tools, though adoption varies by organization size. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI assists meaningfully by automating routine filing and flagging errors or inconsistencies in records, freeing humans for exception handling and verification. However, the assistance is primarily in speed and consistency rather than enabling fundamentally new capability. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools significantly speed up filing, indexing, and record updates for administrative staff, though managers still oversee accuracy and exceptions. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI systems can reliably automate most of this task today: OCR and document processing handle paper scanning, RPA and workflow tools manage filing and updates, and database systems maintain records with minimal human intervention. While some edge cases (ambiguous handwriting, novel document types) require oversight, the core work—recording, updating, and filing—is largely automatable with current tools. |
| Task automatability | claude-sonnet-5 | 4/5 | Document management, tagging, indexing, and record updates are well-suited to current AI/automation tools, especially for digital records; paper still requires scanning/OCR setup but is largely automatable end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers protect filing tasks; they are not legally required to be performed by a human. Main barriers are organizational inertia and desire to retain human oversight for sensitive records, but these are soft constraints, not hard legal ones. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, liability, or human-contact requirements attach to routine filing and record-keeping; it's a purely administrative function open to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven document management and RPA inference costs are typically one to two orders of magnitude lower than the loaded wage of a file clerk or administrative assistant, especially at volume. Cloud document storage and processing are cents per transaction versus hours of human labor. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated filing and record-updating software costs a small fraction of a manager's time compared to manual filing and record maintenance, though initial setup and integration add some cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (document management systems, RPA platforms, cloud storage with auto-indexing) perform these functions reliably in production. Enterprise systems like SharePoint, Workiva, and dedicated RPA vendors demonstrate mature, at-scale capability for attendance records and correspondence filing. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Mature document management systems, OCR pipelines, and workflow automation tools (e.g., SharePoint, DMS with AI indexing) are widely deployed in production for filing and record-keeping tasks. |
Oversee payroll functions, such as maintaining timekeeping information and processing and submitting payroll.
73CI 70–76 · exposure 75 · augmentation 75 · click for rater detail
Oversee payroll functions, such as maintaining timekeeping information and processing and submitting payroll.
73| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Payroll automation has reached deep penetration in corporate and mid-market sectors; cloud-based payroll platforms are now standard, with continuous adoption even in small-to-medium businesses. This is a mature, information-intensive process where adoption is fast and displacement is visible and measurable. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Administrative/HR functions in most mid-to-large organizations have already adopted cloud payroll systems extensively, representing one of the more mature areas of business process automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-driven payroll systems significantly augment human payroll managers by automating routine calculation and filing, freeing them to focus on policy compliance, exception resolution, and strategic benefits administration. The human remains essential for oversight and decision-making while productivity per FTE is substantially raised. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-enhanced payroll tools significantly reduce manual data entry and error-checking burdens, letting managers focus on oversight, exceptions, and compliance rather than routine processing. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Payroll processing is highly structured and rule-based: timekeeping data entry, tax calculation, deduction application, and direct deposit submission can be automated end-to-end by current systems, achieving well over 50% time savings. The main remaining human touchpoint is exception handling and compliance verification, which is partly automatable. |
| Task automatability | claude-sonnet-5 | 4/5 | Payroll processing is highly structured, rule-based, and integrates well with existing HR/payroll software; timekeeping capture, calculation, and submission can largely be automated with configured systems and periodic human review. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Payroll automation faces moderate barriers: tax compliance requirements and audit trails necessitate oversight and verification (not purely automated signing), and organizations often require human sign-off on payroll before submission for liability reasons. Regulatory complexity and third-party integrations (banks, tax agencies) add friction but do not legally prevent automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Payroll involves legal compliance (tax withholding, wage laws) requiring accountable human sign-off and audit trails, creating moderate liability-driven barriers to full autonomous automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Commercial payroll software costs $2–10 per employee per month with minimal marginal cost per additional transaction, while a full-time payroll administrator costs $40k–60k+ annually. The AI/software cost is an order of magnitude cheaper when amortized across typical organization sizes. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated payroll software costs a small fraction of a manager's/team's labor cost for the same volume of transactions, though setup, integration, and oversight add some ongoing cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature payroll automation products (ADP, Gusto, Workday, BambooHR) reliably handle timekeeping, tax filing, and payment processing at scale in production. Deployed systems achieve high accuracy for routine payroll cycles, though manual review of edge cases (leave policies, benefit updates) remains standard practice. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Mature payroll platforms (ADP, Workday, Gusto, etc.) already automate timekeeping aggregation, tax calculations, and submission at scale in production for millions of employees, though exception handling still needs human oversight. |
Prepare and review operational reports and schedules to ensure accuracy and efficiency.
61CI 55–67 · exposure 58 · augmentation 75 · importance 3.7/5 · click for rater detail
Prepare and review operational reports and schedules to ensure accuracy and efficiency.
61| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Administrative automation is common in large enterprises (RPA pilots, BI adoption), but uptake remains uneven. Mid-market and smaller organizations lag, and full end-to-end deployment of AI-driven report and schedule automation is still in the pilot-to-early-production phase rather than mainstream. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Administrative and operations management functions are adopting AI-assisted reporting tools at a moderate pace, with pilots and partial deployments more common than fully autonomous reporting workflows. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly augments this task by auto-generating draft reports, flagging discrepancies, and proposing schedule optimizations, allowing managers to focus on interpretation and policy decisions. The human remains in the loop while AI transforms the speed and coverage of data preparation and basic validation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly speeds up drafting, formatting, and flagging anomalies in operational reports, letting managers focus on judgment-based review and decision-making. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI can automate a majority of report preparation (data aggregation, formatting, basic anomaly detection) and schedule review through pattern matching and consistency checks. However, the 'ensure' language requires judgment on what constitutes efficiency improvements, which typically needs human oversight, limiting the time savings to roughly 60–70% rather than near-complete automation. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft and format reports and flag inconsistencies in schedules, but reviewing for organizational accuracy and contextual efficiency still requires human judgment and domain knowledge not fully captured by automated systems. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are minimal legal or regulatory barriers to automating operational report preparation and schedule review; these are routine administrative tasks. Main friction comes from organizational preference for human sign-off and variable integration into legacy systems, not statutory constraints. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human perform this task, though internal accountability and sign-off norms create some organizational friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Report generation and schedule validation via AI (inference + document processing) cost a fraction of what an administrative services manager earns per task cycle. At scale, the all-in cost (including oversight) is likely 10–20% of the loaded human wage for equivalent output. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools reduce time spent drafting and compiling reports, but integration, data cleaning, and mandatory human review keep total costs roughly comparable to a skilled manager's time rather than an order of magnitude cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed products (BI tools with AI-driven anomaly detection, RPA platforms, document processing AI) handle routine report generation and simple schedule conflict detection in production. However, they often have material limitations in handling complex multi-variable efficiency assessments or novel schedule edge cases, requiring human review. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | BI tools, spreadsheet AI assistants, and LLM-based reporting copilots are deployed in many organizations, but they typically require human oversight to catch errors and contextualize results, limiting fully autonomous reliability. |
Read through contracts, regulations, and procedural guidelines to ensure comprehension and compliance.
61CI 49–72 · exposure 62 · augmentation 88 · click for rater detail
Read through contracts, regulations, and procedural guidelines to ensure comprehension and compliance.
61| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Legal tech and contract AI are seeing strong uptake in large firms and professional services, but adoption remains uneven across the broader population of administrative services managers in smaller organizations and less digitized sectors, indicating middling-to-emerging adoption patterns. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Administrative and management functions are adopting AI tools for document review at a moderate pace, with pilots more common than fully mature deployment in this specific task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at assisting managers by rapidly summarizing documents, highlighting compliance risks, and preparing initial analyses, which administrators can then review and finalize; this substantially accelerates the review workflow while preserving human judgment on material compliance decisions. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up reading, summarizing, and flagging compliance-relevant content in contracts and regulations, letting the manager focus on interpretation and decision-making. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can identify key terms, flag potential non-compliance, and extract salient clauses from contracts and regulations with reasonable accuracy, achieving meaningful time savings on document review. However, nuanced interpretation of legal implications and context-specific judgment remain challenging, preventing full end-to-end automation at equal quality. |
| Task automatability | claude-sonnet-5 | 4/5 | AI language models can read and summarize contracts, regulations, and procedural guidelines, flagging compliance issues and key clauses with significant time savings, though final judgment calls on ambiguous compliance matters may still need human review. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Organizational and liability barriers are significant: compliance failures can expose firms to legal and regulatory penalties, creating strong incentive to retain human sign-off; many regulated industries and oversight bodies require human accountability for compliance interpretation, not just automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human read these documents, though organizational risk aversion and liability concerns around missed compliance issues create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI document review is substantially cheaper than human attorneys or senior administrative staff per document processed, with inference costs and integration overhead amounting to a fraction of hourly wages for reading and initial compliance assessment. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-based document review costs a fraction of the loaded wage of an administrative services manager per document processed, though integration and human oversight add some cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Contract review and compliance tools exist in production (e.g., legal tech platforms, document AI systems) and can identify standard clauses and risks reliably. However, material error rates persist on edge cases, and narrow scope limits their deployment to straightforward documents; complex multi-party contracts still require human oversight. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Deployed contract review and compliance-analysis tools (e.g., legal AI platforms) are used in production at many organizations to extract obligations and flag risks, though accuracy varies with document complexity and domain specificity. |
Acquire, distribute and store supplies.
60CI 38–82 · exposure 55 · augmentation 88 · importance 3.4/5 · click for rater detail
Acquire, distribute and store supplies.
60| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Supply chain and inventory automation are among the fastest-adopted IT functions across sectors; most mid-to-large organizations and many small ones already use digital supply-chain and inventory platforms, reflecting mature, widespread technology deployment. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Procurement and inventory software adoption is moderate across administrative functions, with AI-enabled demand forecasting and reordering tools gaining traction but not yet universal. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI-driven inventory analytics, demand forecasting, and automated reordering alerts substantially augment a manager's ability to optimize stock levels, reduce waste, and respond to supply disruptions while they focus on strategic vendor management and exceptions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered inventory tracking, demand forecasting, and automated reordering systems significantly boost efficiency for administrative services managers overseeing supply acquisition and storage decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Most of this task—inventory tracking, reordering, logistics coordination, and storage management—can be largely automated with existing systems (ERP, inventory management software, procurement platforms). Human judgment on supplier selection or emergency supplies adds friction, but the core 50%+ time-saving threshold is clearly met by current off-the-shelf solutions. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical acquisition, receiving, and storage of supplies require human or robotic physical handling that current AI cannot perform end-to-end; only the ordering/tracking subcomponents are automatable. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or regulatory barriers prevent automation; however, some organizational friction exists around supplier relationships, negotiation preferences, and company-specific stock policies that may require occasional human oversight or approval gates. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational processes, vendor relationships, and physical facility constraints create moderate friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Integration and subscription costs for enterprise inventory and procurement platforms are typically much lower than the fully-loaded salary of an administrative manager handling these tasks, especially when amortized across multiple managed items and transactions. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply automate reordering triggers and demand forecasting, but the physical distribution and storage tasks still require paid human labor, keeping overall cost comparable to human-managed processes. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Mature, deployed products (SAP, NetSuite, Coupa, Shopify inventory systems) reliably perform supply acquisition, distribution, and storage automation at scale in production across thousands of organizations globally. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Inventory management software and procurement platforms with AI features exist, but full supply chain acquisition-distribution-storage cycles still require human coordination and physical logistics not handled by deployed AI products. |
Conduct classes to teach procedures to staff.
51CI 35–67 · exposure 45 · augmentation 75 · importance 3.2/5 · click for rater detail
Conduct classes to teach procedures to staff.
51| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Many organizations are piloting AI-powered learning platforms and automated training content, but full production replacement of live instruction remains incomplete; adoption is accelerating in tech and large enterprises but slower in small and less digitized sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Administrative and office management functions are adopting AI tools like chatbots and content generators, but live class-style training delivery by AI remains a niche, slow-adopted use case. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at augmenting trainers by automating content creation, real-time Q&A, progress tracking, and personalized practice, substantially raising trainer productivity while keeping the human central to customization and relationship-building. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly help managers prepare slides, scripts, quizzes, and FAQs for procedure training, improving efficiency and consistency while the human still delivers the class. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can now generate training content, create interactive materials, deliver live or recorded instruction, and assess comprehension at scale with minimal human oversight. The task can achieve >50% time savings through automated content generation, delivery, and assessment, though some customization and follow-up may still benefit from human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | Delivering live training involves interpersonal facilitation, adapting to trainee questions, and reading the room, which current AI cannot fully replicate end-to-end, though content prep could be automated..strip() the task itself requires real-time human presence. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or legal barriers prevent automation of training delivery; the main friction is organizational preference for human interaction and the desire for customization to specific procedures, but these are surmountable rather than hard blockers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement typically exists, but organizational preference for live human instruction, trust-building, and Q&A handling create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven training platforms cost a fraction of hiring trainers or taking employee time for in-person instruction; infrastructure costs are amortized across many staff, making the per-training cost substantially lower than loaded human wages for equivalent delivery. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI-generated e-learning content is cheap, replacing an interactive class with an AI system still requires human oversight, video/avatar tools, and integration costs comparable to or exceeding a manager's time for a single training session. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed products (learning management systems with AI tutoring, automated video generation, chatbot-based training) exist and perform basic instruction and assessment reliably, but material gaps remain in handling complex procedures, live interaction quality, and adaptation to nuanced organizational context. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI can generate training materials and scripted e-learning modules, but products that autonomously 'conduct classes' with live interaction and adaptation are not standard deployed solutions for internal procedure training. |
Plan, administer, and control budgets for contracts, equipment, and supplies.
42CI 32–53 · exposure 38 · augmentation 75 · importance 4.1/5 · click for rater detail
Plan, administer, and control budgets for contracts, equipment, and supplies.
42| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Finance and administrative functions have high digital maturity and rapid AI adoption; many enterprises are actively implementing automated budgeting, forecasting, and compliance monitoring, and vendors report broad uptake in information and professional services sectors. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Administrative and financial management functions are seeing moderate AI tool adoption (forecasting, reporting) but full budget administration workflows remain largely human-driven with pilots more common than deep integration. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assistants significantly enhance manager productivity by automating data aggregation, flagging anomalies, generating forecasts, and drafting reports, enabling managers to focus on strategy and stakeholder communication rather than routine number-crunching. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered analytics, forecasting, and reporting tools meaningfully assist managers in tracking spend, identifying trends, and preparing budget scenarios, significantly boosting productivity while humans retain control decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automate significant portions of budget planning and routine administrative control—expense categorization, variance detection, and report generation—but contract negotiation, policy decisions, and stakeholder alignment typically require human judgment and discretion, placing it at roughly half-task automation. |
| Task automatability | claude-sonnet-5 | 2/5 | Budget planning and control requires ongoing judgment, negotiation, and contextual decision-making that current AI cannot fully replicate end-to-end, though data analysis portions can be automated. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Budget administration is subject to audit, compliance, and sign-off requirements (e.g., SOX, internal controls), and many organizations require human approval on spending decisions; these governance demands create friction but do not legally require a licensed human to perform the automation itself. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically for this task, but organizational accountability, fiduciary responsibility, and the need for a responsible human to approve and control budgets create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI-powered budgeting platforms cost thousands to tens of thousands annually and require implementation overhead, which is comparable to the salary cost of mid-level administrative staff handling these tasks, though scale favors AI in larger organizations. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI tools can reduce time on data compilation and forecasting, the human oversight, negotiation, and accountability required for budget control keeps overall costs comparable to or only modestly below human-only approaches. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Budget management software with AI-powered analytics exists (Workday, SAP, etc.), but real-world performance depends on data quality, complex rule interpretation, and integration with legacy systems; products handle routine monitoring reliably but struggle with edge cases and non-standard contracts. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Financial planning and budgeting software with AI features exist but require significant human oversight for actual budget administration and control decisions; no deployed product autonomously manages full budget cycles. |
Establish work procedures or schedules to organize the daily work of administrative staff.
38CI 30–46 · exposure 33 · augmentation 63 · click for rater detail
Establish work procedures or schedules to organize the daily work of administrative staff.
38| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Administrative operations management is moderately digitized but adoption of AI-driven scheduling and procedure automation remains spotty and often limited to large firms with dedicated IT investment. Most organizations still rely on managers making these decisions with spreadsheet tools rather than AI agents. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Administrative and office management functions are in a sector with moderate AI tool adoption (scheduling software, workflow platforms) but full delegation of procedure-setting to AI remains uncommon. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by generating schedule drafts, identifying gaps or conflicts, and suggesting procedure improvements based on data analysis, allowing managers to spend less time on routine layout work. However, the human manager must still make final decisions about priorities, exceptions, and fairness, so augmentation is partial rather than transformative. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI scheduling and workflow tools substantially help managers draft, optimize, and adjust procedures and schedules, meaningfully boosting productivity while the manager retains decision authority. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can assist with generating schedules and procedures by analyzing workload patterns and staff availability, but it lacks the contextual judgment about human factors, team dynamics, and organizational priorities that experienced managers apply. Partial automation of routine scheduling is feasible, but end-to-end replacement with equal quality would require significant customization and oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft schedules and procedures but establishing them requires judgment about team dynamics, priorities, and organizational context that current systems cannot fully replace end-to-end.4 |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Establishing work procedures and schedules carries accountability for team productivity and fairness; managers are typically expected to own this decision-making. While not a strict legal requirement for a licensed human, organizational culture and liability concerns (scheduling errors affecting compliance or morale) create moderate friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational authority, accountability for staff management, and interpersonal trust create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for scheduling and procedure generation have non-trivial licensing and implementation costs, and still require a manager to review, modify, and sign off on outputs. The human cost of supervision often exceeds the savings from AI assistance alone, making the total cost comparable to or higher than direct human scheduling. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Software licensing costs are low, but the managerial judgment and oversight needed to design and maintain procedures still require significant human time, keeping costs comparable to a partially-augmented human process. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While scheduling software and workflow optimization tools exist, they are typically narrow in scope and require substantial human input. Current AI systems can draft templates or suggest schedules, but no mature product reliably performs this task end-to-end without manager review and adjustment based on unstated organizational constraints. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Scheduling and workflow tools exist and are widely deployed, but they support rather than autonomously establish work procedures; human managers still define and adjust the underlying logic. |
Represent work unit at meetings or conferences and serve as liaison for requests or complaints.
34CI 7–60 · exposure 33 · augmentation 63 · click for rater detail
Represent work unit at meetings or conferences and serve as liaison for requests or complaints.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Most organizations still view meeting representation and complaint handling as requiring human touch and manager accountability. Adoption of AI for these liaison roles remains limited, with automation concentrated in larger, digitally mature firms; most sectors lag. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Administrative management functions see moderate AI tool adoption for scheduling and communication support, but representational/liaison roles themselves show little displacement. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can significantly assist managers by drafting talking points, summarizing pre-meeting materials, organizing and categorizing complaints, and drafting responses—allowing the manager to focus on judgment and relationship-building. This augmentation is already being deployed in some professional services. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help prepare talking points, summarize complaints, draft follow-up communications, and track meeting outcomes, meaningfully aiding preparation even though the core representational act stays human. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI agents can handle most representational functions—drafting communications, summarizing work unit positions, tracking and categorizing requests/complaints—with minimal setup, achieving substantial time savings. However, real-time meeting participation and nuanced stakeholder negotiation still require human judgment, preventing a full 5. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical or synchronous presence, real-time judgment, and representing organizational interests in interpersonal settings, which current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Organizational and relationship barriers are moderate: stakeholders may prefer human representation for credibility and negotiation, and legal/contractual sign-off on complaint resolutions may require manager involvement. Regulatory barriers are low, but social friction around 'talking to a bot' is present. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Organizational trust, authority to speak for the unit, and accountability for representing interests create strong practical barriers to substitution, though not formal licensing requirements. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI systems for meeting representation, request triage, and complaint logging are relatively inexpensive to operate per interaction, easily undercutting the loaded wage of a manager for routine liaison work. Integration and oversight costs are moderate. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this task, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist for meeting summarization, complaint routing, and liaison communication drafting, but deployed systems still require human oversight for decision-making and context-specific responses. Production use is mixed and often limited to assistive roles rather than full autonomy. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously represents an organization at meetings or conferences or handles complaint liaison work in a manager's stead. |
Learn to operate new office technologies as they are developed and implemented.
30CI 18–42 · exposure 17 · augmentation 75 · click for rater detail
Learn to operate new office technologies as they are developed and implemented.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Administrative function adoption of AI-assisted training and onboarding is growing in enterprise settings, with learning management systems increasingly incorporating AI, but deployment is uneven and many organizations still rely on vendor training and human-led workshops. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | While administrative/office sectors show moderate AI tool adoption, the specific task of 'learning new technologies' isn't something being displaced by AI adoption trends; it's a meta-skill required to use any new tools including AI itself. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially assist by generating customized training materials, providing on-demand explanations, creating troubleshooting guides, and simulating technology walkthroughs, materially accelerating the manager's learning curve while the human remains the decision-maker and validator. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tutors, tutorials, and interactive documentation can significantly speed up how quickly administrative managers learn new office software and systems, acting as an on-demand training assistant. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Learning to operate new office technologies requires understanding context, troubleshooting novel interfaces, and adapting to organizational workflows. While AI can assist with documentation summarization or step-by-step instruction retrieval, the manager must ultimately master and validate the technology themselves, and this involves judgment-based adaptation that resists full automation. |
| Task automatability | claude-sonnet-5 | 1/5 | This task is inherently a human learning/adaptation process; AI cannot 'learn on behalf of' a manager who must acquire operational competence with new tools, though AI can support the learning process itself.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no legal licensing barriers to AI assisting with technology training, organizational friction exists around trust in AI-generated guidance for mission-critical systems, and managers are accountable for ensuring they competently operate tools, creating an implicit requirement for human verification and sign-off. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier exists, but the task is definitionally about human adaptation to change, making substitution conceptually incoherent rather than legally blocked. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-generated training materials and documentation are inexpensive to produce at scale, but the manager's time to actually learn, validate, and troubleshoot the technology remains a significant human cost, and integrated learning support systems still require human review and customization, limiting overall savings. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There's no AI substitute performing this task, so no meaningful cost comparison applies; the human must still undergo training regardless of AI cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist that generate learning materials, video tutorials, and step-by-step guides for software onboarding, but deployed systems often require human oversight to ensure accuracy for new/unfamiliar technologies and do not reliably handle the full spectrum of novel office systems encountered in practice. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No product exists that performs the act of an employee learning new office technology; this is a continuous human skill-acquisition task, not an outsourceable deliverable. |
Analyze internal processes and recommend and implement procedural or policy changes to improve operations, such as supply changes or the disposal of records.
30CI 30–30 · exposure 25 · augmentation 63 · importance 3.3/5 · click for rater detail
Analyze internal processes and recommend and implement procedural or policy changes to improve operations, such as supply changes or the disposal of records.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While process optimization tools are emerging in corporate settings, actual adoption of AI-driven procedural change recommendations remains limited to pilot stages in most organizations. Few firms have moved to production-scale reliance on AI for policy formulation and implementation without substantial human management oversight. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Administrative services functions are typically slower-adopting compared to core information/finance sectors, with AI used mainly for reporting and analytics rather than policy-setting. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist managers by analyzing operational metrics, identifying bottlenecks, and generating draft recommendations that humans then refine and implement. This augmentation helps managers work faster on the analytical phase, though judgment, stakeholder management, and execution remain human-led. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can strongly assist by analyzing process data, identifying inefficiencies, and drafting policy options, meaningfully speeding up the manager's decision-making process. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help analyze operational data and suggest improvements, the task requires judgment about organizational context, stakeholder impact, and implementation feasibility that goes beyond current automation capabilities. End-to-end automation would require AI to independently set organizational policies and execute changes across complex human systems, which remains infeasible. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can analyze data and draft recommendations, but the task requires organizational context, stakeholder judgment, and implementation actions that current systems cannot fully execute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Policy and procedural changes often require sign-off from senior leadership and legal review for regulatory compliance, creating organizational friction. However, there are no formal licensing requirements preventing AI from assisting with analysis, though liability for bad recommendations and organizational resistance create moderate friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but organizational policy changes typically require managerial authority, cross-department buy-in, and accountability that create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI analysis and recommendations would require integration with organizational data systems, domain expert configuration, and significant human review before implementation. The total cost remains comparable to or higher than a manager's time when accounting for setup, validation, and error correction needed for organizational deployment. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can cheaply assist with data analysis, but the implementation, negotiation, and change-management portions still require significant human labor, keeping overall cost comparable to human-led efforts. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Current AI can draft analysis reports and flag process inefficiencies from data inputs, but no deployed product reliably performs the full cycle of analyzing processes, formulating context-appropriate recommendations, gaining buy-in, and implementing changes. Tools exist for process mining and optimization suggestions, but they remain narrow and require substantial human oversight. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Analytics and process-mining tools exist and can surface inefficiencies, but few deployed products autonomously recommend and implement policy changes reliably in production administrative settings. |
Develop operational standards and procedures for the work unit or department.
29CI 25–32 · exposure 25 · augmentation 75 · click for rater detail
Develop operational standards and procedures for the work unit or department.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of autonomous standards-generation AI in production remains minimal. Most organizations still rely on human-led processes with possible tool assistance; pilot projects exist, but deep, measurable displacement of this task is not evident in management practice yet. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Administrative and office management functions are seeing moderate AI tool adoption for drafting and documentation, but full procedure-setting remains largely manual and pilot-stage in most organizations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist by generating template sections, suggesting best practices, compiling compliance requirements, and helping managers draft procedures faster. When used in a human-led workflow, these tools can significantly raise a manager's productivity in developing comprehensive standards. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly speed up drafting, benchmarking against best practices, and formatting of procedural documents, meaningfully boosting manager productivity while they retain decision authority. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Developing operational standards requires understanding organizational context, strategic goals, and stakeholder needs—work that demands human judgment and domain expertise. While AI can generate procedural templates or help draft sections, end-to-end autonomous creation of standards suitable for adoption falls short of the 50% time-saving-at-equal-quality threshold because sign-off and substantive refinement remain necessary. |
| Task automatability | claude-sonnet-5 | 2/5 | Drafting standards documents can be AI-assisted, but developing them requires organizational judgment, stakeholder negotiation, and contextual knowledge that AI cannot fully substitute end-to-end today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Standards and procedures are often subject to regulatory oversight, internal governance, and legal liability; human managers and compliance teams typically must review and sign off. Many organizations require documented accountability and human authorization of operational standards, creating a strong barrier to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement exists, but organizational accountability, internal approval processes, and managerial authority create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted drafting tools (LLMs, document generators) cost little per inference, but the task's high integration burden—stakeholder consultation, domain customization, legal/compliance review—means total cost per usable standard remains substantial relative to the time an experienced manager would spend. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI drafting assistance is cheap, the overall task still requires significant human time for review, stakeholder input, and validation, so total cost savings versus a human manager are modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably generates organization-specific operational standards and procedures in production. AI can assist with drafting and formatting, but actual standards development in real firms still requires human managers to lead, validate, and own the output; products do not perform this task independently at scale. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | There is no deployed product that autonomously develops and finalizes operational standards for a department; existing tools only assist with drafting text based on human input. |
Communicate with and provide guidance for external vendors and service providers to ensure the organization, department, or work unit's business needs are met.
29CI 25–32 · exposure 25 · augmentation 75 · click for rater detail
Communicate with and provide guidance for external vendors and service providers to ensure the organization, department, or work unit's business needs are met.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Administrative functions remain heavily human-supervised across sectors. Most organizations are piloting AI for scheduling and template responses, but actual vendor management delegation to autonomous systems is rare, reflecting both institutional inertia and legal/accountability risk. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Administrative and operations functions are adopting AI tools like email assistants and procurement software at a moderate pace, with pilots more common than full production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially augment this task by drafting vendor communications, summarizing performance data, flagging SLA breaches, and suggesting standard responses—enabling managers to handle more vendor relationships faster while retaining control over critical decisions and relationship continuity. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by drafting vendor communications, summarizing contracts, tracking service level agreements, and flagging issues, improving the manager's efficiency while they retain oversight. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft communications and respond to routine vendor queries, the task fundamentally requires human judgment about organizational needs, vendor performance assessment, and relationship maintenance. Current systems cannot reliably make decisions about vendor selection or escalation, which are core to 'ensuring business needs are met.' |
| Task automatability | claude-sonnet-5 | 2/5 | This involves relationship management, negotiation, and contextual judgment about organizational needs that current AI cannot fully replicate end-to-end, though drafting communications can be assisted. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Organizational policy and contract law often require a named human representative with authority to commit resources and resolve disputes with vendors. Liability and error-cost asymmetry are high: an automated vendor directive could create contractual or operational damage, making substitution legally and organizationally difficult. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but organizational trust, accountability for vendor relationships, and preference for human negotiation create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for vendor communication (email drafting, basic scheduling) are inexpensive, but oversight costs are high: a manager must review and approve all significant vendor interactions, negating most labor savings. Full replacement would require trust and accountability not yet demonstrated. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human oversight and relationship-building remain necessary, so AI can reduce some drafting/coordination time but doesn't replace the full cost of the manager's judgment and accountability. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably manages the full vendor communication and guidance workflow end-to-end. Email automation and chatbots exist for simple inquiries, but they cannot handle complex negotiations, accountability decisions, or contextual organizational guidance that defines this role. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products exist for vendor communication (email drafting, CRM automation) but no deployed system autonomously manages vendor relationships and ensures business needs are met reliably. |
Set goals and deadlines for the department.
26CI 23–30 · exposure 25 · augmentation 63 · importance 3.7/5 · click for rater detail
Set goals and deadlines for the department.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Organizations have shown minimal adoption of AI for autonomous goal-setting; this remains a core managerial prerogative rarely delegated even to lower-level tools, reflecting both conservatism and the strategic importance of the task. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Administrative management functions in many organizations still show slow AI adoption for strategic planning tasks compared to information-heavy sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by analyzing historical data, team capacity, and market trends to inform a manager's goal-setting process, and can help draft timelines or flag scheduling conflicts, but the strategic choices remain the manager's responsibility. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully assist by analyzing workloads, suggesting realistic deadlines, and drafting goal frameworks, improving manager efficiency while they retain final decision authority. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Setting departmental goals and deadlines requires understanding organizational strategy, resource constraints, and team capacity—nuanced judgment that current AI systems cannot reliably perform end-to-end. AI might draft preliminary goal templates or schedule suggestions, but the strategic decisions and accountability remain fundamentally human. |
| Task automatability | claude-sonnet-5 | 2/5 | Setting goals and deadlines requires organizational judgment, knowledge of team capacity, and stakeholder priorities that AI cannot fully replicate end-to-end today, though it can assist with drafting timelines. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Goal-setting and deadline-setting carry legal and fiduciary responsibility in most organizations; managers and executives are expected to make these decisions personally, and liability typically falls on the human decision-maker rather than automation systems. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but organizational accountability, managerial authority, and stakeholder trust create moderate friction against full AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of AI-assisted goal-setting systems (including integration and human oversight to validate outputs) does not yet undercut the loaded wage of an administrative manager performing this task, especially given the reputational risk of flawed organizational goals. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI scheduling assistance is cheap, the actual managerial judgment and accountability still require a human manager, limiting real cost savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably sets organizational goals and deadlines autonomously; this remains a human management function in all production systems. AI tools can assist with scheduling or project templates, but do not perform the task itself. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product autonomously sets departmental goals; project management tools can suggest schedules but require human decision-making and validation. |
Supervise administrative staff and provide training and orientation to new staff.
25CI 25–25 · exposure 25 · augmentation 63 · click for rater detail
Supervise administrative staff and provide training and orientation to new staff.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI for core supervisory functions remains slow; most organizations use managers in traditional roles. While administrative tools (scheduling, content libraries) see some AI adoption, actual supervisory replacement is rare in practice, reflecting cultural and legal hesitancy. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Administrative/office management functions are adopting AI tools for scheduling and training content, but actual supervisory replacement is minimal and slow given the interpersonal nature of the task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by generating training materials, drafting orientation documentation, scheduling onboarding sessions, and providing data on staff performance trends. However, the human manager retains primacy in delivering feedback and relationship-building, so augmentation is partial rather than transformative. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist with onboarding materials, training content generation, scheduling, progress tracking, and even coaching prompts, enhancing manager efficiency while leaving supervision itself to humans. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with training content creation and orientation materials, supervising staff and providing real-time personalized training require human judgment, relationship-building, and adaptive feedback that current systems cannot reliably perform end-to-end. The interpersonal and evaluative components of this task remain heavily dependent on human managers. |
| Task automatability | claude-sonnet-5 | 2/5 | Supervision, mentoring, and orientation involve interpersonal relationship-building, judgment about performance, and adaptive coaching that current AI cannot execute end-to-end., though AI can support scheduling and content delivery for training. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong organizational and role-identity barriers exist: staff training and supervision typically require a human manager for legal accountability, performance documentation, and employment law compliance. Organizations strongly prefer human managers for sensitive duties like orientation, discipline feedback, and team cohesion. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Supervisory responsibility typically carries organizational accountability, HR/legal requirements, and expectations of human judgment in personnel matters, creating strong structural barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The loaded cost of a manager performing these duties is relatively low per unit compared to the specialized human judgment required; AI assistance on discrete elements (document generation, scheduling) does not offset the need for the manager, making full replacement uneconomical. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human management judgment, accountability, and relational leadership remain necessary; AI tools reduce some administrative overhead but don't replace the managerial labor cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably handles full supervision and adaptive staff training. AI can generate training content or schedule onboarding, but actual supervisory functions—performance feedback, conflict resolution, individualized mentoring—remain beyond reliable automation in production systems today. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products exist for onboarding content delivery, LMS-based training, and performance tracking, but no deployed system autonomously supervises staff or manages people relationships reliably. |
Direct or coordinate the supportive services department of a business, agency, or organization.
25CI 20–30 · exposure 20 · augmentation 63 · importance 3.9/5 · click for rater detail
Direct or coordinate the supportive services department of a business, agency, or organization.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Despite digitization of administrative functions, actual adoption of AI-driven autonomous management remains rare and tentative. Most organizations still deploy AI as a reporting and efficiency tool for human managers rather than as the decision-maker; cultural and legal inertia around managerial accountability slows production adoption. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Administrative/office management functions are adopting AI tools for narrow tasks (documents, scheduling) but full departmental direction and coordination remains largely untouched by production AI systems. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully augment a human administrative services manager through automated scheduling optimization, real-time departmental analytics dashboards, compliance flagging, and data-driven insights for resource allocation. These tools raise manager productivity on routine coordination tasks while the human retains decision authority and accountability. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully assist managers with drafting policies, analyzing operational data, scheduling, and communications, improving productivity while the manager retains oversight and decision authority. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Directing and coordinating supportive services requires judgment, interpersonal negotiation, conflict resolution, and strategic oversight that current AI cannot fully replicate. While AI can automate specific sub-tasks (scheduling, reporting, data analysis), the core managerial function—setting direction, evaluating staff, making resource trade-offs—remains beyond end-to-end automation and does not meet the 50% time-saving bar at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a broad managerial coordination role involving decision-making, staff supervision, and cross-functional judgment that current AI cannot execute end-to-end, though scheduling and reporting sub-tasks could be assisted. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant organizational and legal barriers protect this role: fiduciary responsibility for budgets and staff, labor law compliance (hiring, discipline, compliance), liability for departmental decisions, and stakeholder trust requirements all typically require a human accountable agent in management roles. Regulatory and corporate governance frameworks expect a named human manager. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement per se, but organizational accountability, personnel management responsibilities, and trust in leadership roles create meaningful friction against replacing this role with AI. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The inference and integration costs of multi-agent AI systems capable of partial managerial support remain substantial relative to the loaded wage of an administrative services manager, especially when oversight and error-correction costs are factored in. Cost parity does not yet exist for meaningful automation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply support subtasks like reporting, but the overall coordination and leadership function still requires a paid manager, so all-in cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed AI product today reliably performs the full managerial function of directing a supportive services department. While specialized tools exist for task scheduling, budget tracking, and performance reporting, no integrated system demonstrates production-scale autonomous department coordination with the accountability expected of a manager. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product manages a department's operations, staff, and priorities autonomously; this remains squarely a human management function today. |
Meet with other departmental leaders to establish organizational goals, strategic plans, and objectives, as well as make decisions about personnel, resources, and space or equipment needs.
8CI 0–16 · exposure 8 · augmentation 63 · click for rater detail
Meet with other departmental leaders to establish organizational goals, strategic plans, and objectives, as well as make decisions about personnel, resources, and space or equipment needs.
8| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of AI automation for this task is negligible; executives actively resist automation of strategic and personnel decisions, and organizational and legal structures require humans in these roles. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | While administrative and office functions see growing AI tool use, actual strategic decision-making meetings involving personnel and resources show minimal AI displacement so far. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist managers by preparing data summaries, scenario analysis, and draft strategic options, but managers must remain fully in control of deliberation and final decisions in meetings with peers. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by summarizing data, forecasting resource needs, drafting agendas, and modeling scenarios to inform the humans making these decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist in analyzing data and generating strategic options, the core task requires human judgment, negotiation, and accountability in establishing organizational goals and making personnel decisions. Current AI cannot replicate the interpersonal dynamics, contextual understanding, and decision-making authority needed to lead these meetings. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires live interpersonal negotiation, relationship management, and consequential judgment calls among leaders that AI cannot conduct end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal and organizational barriers protect this task: personnel and resource allocation decisions carry legal liability, require authorized human judgment, and necessitate human accountability. Regulatory frameworks and corporate governance mandate human decision-makers for strategic planning. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Personnel decisions, resource allocation, and organizational strategy typically require accountable human managers due to legal, HR, and governance responsibilities, creating strong structural barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI cannot perform this task at all, so cost comparison is moot; human leaders must retain this function. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this human-to-human decision-making role, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs this task end-to-end; strategic planning and personnel decisions involve accountability, contextual judgment, and organizational authority that cannot be delegated to AI systems today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously participates in or leads cross-departmental strategy meetings and makes personnel/resource decisions; this remains firmly human-led. |
Hire and terminate clerical and administrative personnel.
4CI 3–6 · exposure 0 · augmentation 50 · importance 4.0/5 · click for rater detail
Hire and terminate clerical and administrative personnel.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While some organizations use AI screening tools in hiring pipelines, actual hiring and termination decisions remain firmly under human control due to legal risk. Adoption of full automation is minimal; most use is assistive only. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | While HR software adoption is growing for screening and workflow support, actual hiring/firing decisions remain firmly human-led with slow, cautious integration of AI into core decision authority. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by ranking candidates based on resume data, flagging scheduling conflicts, or organizing applicant information, helping a manager work faster through the screening phase. However, the core decision-making and interpersonal elements remain human-driven. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with resume screening, drafting termination documentation, compliance checklists, and interview scheduling, improving efficiency in supporting tasks even though the core decision stays human. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Hiring and terminating personnel requires legal compliance, human judgment on cultural fit, interpersonal communication, and documentation that cannot be fully automated. While AI can assist with resume screening, no end-to-end system can replace the core decision-making and formal authorization required to hire or fire employees. |
| Task automatability | claude-sonnet-5 | 1/5 | Hiring and firing require judgment, legal accountability, interpersonal negotiation, and organizational context that current AI cannot execute end-to-end; it can only assist with sub-steps like screening resumes. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Hiring and termination are heavily regulated (employment law, discrimination law, EEO compliance) and typically require a licensed manager or HR professional to make and authorize the decision. Liability for wrongful termination and discrimination is asymmetric, creating strong legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Hiring and termination decisions carry significant legal, regulatory, and liability exposure (discrimination law, wrongful termination, contractual obligations) requiring accountable human decision-makers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While AI-assisted screening tools can reduce per-candidate evaluation cost, the manager's time for final interviews, decision-making, and termination documentation remains substantial. The all-in cost of AI integration does not yet approach order-of-magnitude savings versus a manager's time. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Because AI cannot perform the actual decision and execution of hiring/firing, there is no substitutable AI cost path that displaces the human role; a manager's judgment and legal responsibility remain necessary. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system independently performs hiring and termination decisions in production. Products exist for screening and candidate ranking, but the final hiring decision and all termination actions remain legally and organizationally human-owned. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously makes hiring or termination decisions; existing HR AI tools only support screening, scheduling, or documentation, with humans making and executing the final decision. |
Related occupations — Management
How to read this
A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.
What would change this score
New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.