Computer and Information Systems Managers
11-3021.00Median wage $175,140/yr670,570 employed (US)Rank #196 of 923 scored · top 21% by substitution
Plan, direct, or coordinate activities in such fields as electronic data processing, information systems, systems analysis, and computer programming.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution39
Exposure35
Augmentation75
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
17 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
6%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%34
panel mean rating 2.3/5 → substitution pressure 34/100
Technical feasibility todayw 20%37
panel mean rating 2.5/5 → substitution pressure 37/100
Cost vs. human wagew 15%37
panel mean rating 2.5/5 → substitution pressure 37/100
Adoption barriersw 20%inverted — strong barriers lower the score45
panel mean rating 3.2/5 (barrier strength) → substitution pressure 45/100
Sector adoption velocityw 10%50
panel mean rating 3.0/5 → substitution pressure 50/100
Task breakdown (17 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Prepare and review operational reports or project progress reports.
74CI 70–79 · exposure 70 · augmentation 88 · importance 3.6/5 · click for rater detail
Prepare and review operational reports or project progress reports.
74| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Tech and financial services sectors are adopting AI-powered reporting and business intelligence tools rapidly at scale; even traditional enterprises are deploying dashboards and automated report generation. Adoption is measurably deep in information-intensive industries. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | IT and tech management sectors are fast adopters of AI tools for reporting and dashboards, with widespread integration into enterprise software already occurring. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly assists managers by automating data collection, drafting summaries, and highlighting anomalies, which raises their ability to focus on analysis and decision-making. Managers routinely stay in the loop for final review, interpretation, and strategic context. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up drafting, summarizing, and formatting reports while the manager retains responsibility for accuracy, interpretation, and strategic framing. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can automatically extract, summarize, and compile data from logs, project management systems, and databases into structured reports with minimal human intervention. The task involves processing, synthesizing, and formatting information—all well-suited to current LLMs and data-processing tools—though final review and strategic judgment typically remain with humans. |
| Task automatability | claude-sonnet-5 | 4/5 | Drafting operational and progress reports from structured data (tickets, project trackers, metrics) is well within current LLM capability, especially when integrated with project management tools, though final review and contextual judgment still require human input. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few hard barriers exist; organizations can adopt AI report generation with minimal licensing friction or regulatory constraint. Some preference for human review and sign-off introduces minor friction, but nothing prevents substitution or augmentation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or regulatory requirement mandates human authorship of internal reports, though organizational preference for manager accountability and sign-off creates mild friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI automation of report preparation costs a fraction of a manager's loaded hourly wage; inference, integration, and light oversight are negligible compared to the 1–4 hours a human manager might spend compiling and formatting reports manually. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated report generation and summarization tools cost a small fraction of a manager's time compared to manually compiling and writing reports, though integration and oversight add some cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature products (business intelligence platforms, AI-powered report generators, and LLM-based tools) can reliably generate operational and project progress reports in production environments. However, nuance in interpretation and stakeholder-specific customization occasionally require human adjustment, preventing a full 5. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Copilot, Jira/Asana AI summarizers, and BI report generators exist and are used in production, but they still require human review for accuracy and organizational nuance, so scope remains narrow. |
Review project plans to plan and coordinate project activity.
60CI 32–87 · exposure 58 · augmentation 88 · importance 4.1/5 · click for rater detail
Review project plans to plan and coordinate project activity.
60| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | IT and professional services firms are already piloting and deploying AI-driven project intelligence and coordination tools in production environments; adoption is accelerating in digitized, information-sector organizations. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | IT/tech management sectors are moderately fast adopters of AI tools for planning and reporting, though full coordination automation remains at the pilot stage. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI excels at augmenting project managers by continuously analyzing plans, surfacing risks, recommending rebalancing, and freeing humans to focus on negotiation, escalation, and strategic trade-offs—a transformative productivity gain while humans remain in final-decision roles. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully assist by parsing project plans, tracking milestones, generating status updates, and highlighting bottlenecks, boosting a manager's efficiency while they retain control. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Modern AI agents can parse project plans, extract dependencies, timelines, and resource allocations, then identify scheduling conflicts, bottlenecks, and coordination issues across teams—achieving well over 50% time savings on the routine review and cross-checking portions of this task with current tools. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can summarize project plans and flag risks or dependencies, but coordinating activity involves judgment calls, stakeholder negotiation, and organizational context that current systems cannot fully replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While managers may prefer human oversight of plan quality and stakeholder communication, there are no legal licensing, regulatory, or liability barriers preventing AI from assisting or automating the review-and-flag portion; organizational friction remains mild. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but organizational accountability, cross-team politics, and the need for a responsible human decision-maker create meaningful friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | The API cost to run LLM-based plan analysis, cross-referencing, and anomaly flagging is orders of magnitude lower than the fully-loaded hourly rate of a manager or senior analyst reviewing the same plans. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply assist with plan review and summarization, but the coordination component still requires a paid manager's oversight, so overall cost savings are moderate rather than dramatic. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (e.g., project management platforms with AI assistants, workflow automation tools, and language models integrated into enterprise systems) are performing plan review, anomaly detection, and coordination-flag generation in real organizations; minor gaps remain in nuanced stakeholder negotiation, but core feasibility is demonstrated. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Project management tools with AI features (e.g., risk flagging, scheduling suggestions) exist but are narrow aids rather than autonomous coordinators used reliably at scale in production. |
Purchase necessary equipment.
60CI 32–87 · exposure 58 · augmentation 88 · importance 3.4/5 · click for rater detail
Purchase necessary equipment.
60| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | IT and information-sector organizations have rapidly adopted e-procurement and automation tools, with AI-assisted purchasing increasingly deployed in enterprise environments; adoption is faster in large and digitally mature firms. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | IT and procurement functions in many mid-to-large organizations are adopting e-procurement and AI-assisted purchasing tools, though full automation of purchasing decisions remains uncommon. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI substantially augments procurement managers by automating vendor research, price comparison, and compliance checks, allowing managers to focus on strategic sourcing and relationship management while the system handles routine transactions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can effectively assist with market research, price comparison, spec matching, and drafting purchase orders, significantly speeding up the research and documentation portions of this task. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | AI systems can today fully automate equipment purchasing—from identifying needs via inventory systems, comparing vendor specifications and pricing, generating RFQs, evaluating bids, and placing orders—all with substantial time savings and equal or better quality through data-driven decision-making. |
| Task automatability | claude-sonnet-5 | 2/5 | Purchasing equipment involves vendor negotiation, budget approval, specification matching to organizational needs, and procurement policy compliance that require contextual judgment beyond simple automation.for many organizations this remains partly manual. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While most organizations require sign-off on large purchases, the purchasing process itself has minimal legal or licensing barriers; most friction is organizational policy rather than regulatory, and systems can handle approval workflows. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Organizational procurement policies, budget authorization limits, and vendor contract requirements create moderate friction, though no licensing requirement mandates a human purchaser specifically. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated procurement systems cost a small fraction of a manager's loaded wage and can process multiple purchase cycles; the cost per transaction is orders of magnitude lower than human labor. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can assist with comparison shopping or generating purchase requisitions cheaply, but human oversight for approvals, negotiation, and vendor relationships still adds significant cost, keeping overall savings modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature procurement automation and e-procurement platforms are deployed in production across enterprises; AI-driven vendor comparison and purchase order generation are standard, though some complex negotiations or custom requirements may still require human review. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Procurement software and e-catalogs exist to streamline ordering, but AI does not reliably handle vendor selection, negotiation, and approval workflows end-to-end in production today. |
Direct daily operations of department, analyzing workflow, establishing priorities, developing standards and setting deadlines.
58CI 32–84 · exposure 58 · augmentation 88 · importance 4.1/5 · click for rater detail
Direct daily operations of department, analyzing workflow, establishing priorities, developing standards and setting deadlines.
58| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Tech and professional-services sectors show growing adoption of AI-assisted workflow management and automation, but wholesale replacement of managerial direction remains uncommon; most organizations use these tools to augment rather than replace managers. Adoption is accelerating but still in pilot and hybrid stages across most verticals. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | IT and professional services sectors are fast adopters of AI-based workflow, analytics, and project management tools, even though full managerial direction remains human-led. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI systems dramatically enhance a manager's productivity by automating routine analysis, surfacing data-driven priorities, and auto-generating schedules, freeing the manager to focus on strategic decisions, team development, and exception handling. This is a canonical augmentation scenario where AI handles structured optimization while the human retains control and judgment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by analyzing workflow data, flagging bottlenecks, suggesting priorities, and drafting standards/documentation, significantly boosting a manager's efficiency while they retain decision authority. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Current AI systems can analyze workflow patterns, identify bottlenecks, suggest priority frameworks, and propose deadline schedules based on project data and historical performance—automating substantial portions of daily operational direction with significant time savings. Tools for workflow analysis, priority optimization, and deadline-setting are mature and can operate autonomously with routine supervision. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a management task requiring judgment, interpersonal authority, and contextual decision-making across teams; AI can support analysis but cannot direct people or own accountability for outcomes.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few licensing or regulatory barriers prevent automation of workflow analysis and deadline-setting; however, organizational culture and the expectation that human managers provide accountability and contextual judgment create moderate friction. Adoption is slowed mainly by preference for human oversight rather than legal or compliance blockers. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement, but organizational hierarchy, accountability structures, and the need for human authority over staff create real friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven workflow analysis and scheduling tools cost orders of magnitude less than a manager's loaded salary while handling the data-processing and routine optimization components; however, some management oversight is typically retained. The cost advantage is substantial but not complete one-to-one replacement. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can cheaply support scheduling and reporting, but the managerial judgment and accountability component still requires a well-paid human, keeping overall cost comparable to or only slightly less than a manager's time. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed project management and workflow analysis platforms (Jira, Monday.com, AI-enhanced Asana) already perform parts of this task reliably in production; however, the full scope of 'directing daily operations' requires contextual judgment that current systems handle with occasional errors or gaps. Mature products exist but may require human review for nuanced priority trade-offs. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products exist for workflow analytics, project tracking, and scheduling assistance, but no deployed system autonomously directs daily department operations or sets binding priorities for staff. |
Stay abreast of advances in technology.
58CI 50–66 · exposure 42 · augmentation 88 · importance 3.9/5 · click for rater detail
Stay abreast of advances in technology.
58| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Many IT organizations use AI-assisted tools for technology intelligence (Gartner, Forrester digests, AI-powered RSS aggregators), but adoption remains mixed and many managers still rely on manual scanning and peer networks. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | IT and management professionals in fast-adopting sectors (tech, professional services) already widely use AI-driven research and summarization tools as part of routine work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI systems substantially augment this task by filtering signal from noise, summarizing key developments, and highlighting emerging trends—allowing managers to make faster, more informed decisions while remaining in control of strategic interpretation. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI significantly enhances a manager's ability to track and digest technology developments through summarization, alerts, and trend analysis, while the human retains judgment on strategic relevance. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI systems can aggregate and summarize technology news, staying abreast requires judgment about relevance, strategic importance, and organizational context—decisions that need human evaluation and cannot consistently achieve 50% time savings at equal quality end-to-end. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can aggregate, summarize, and surface relevant technology news and research via search and summarization tools, saving significant time, but synthesizing strategic relevance still requires human judgment.dealt |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | This task involves no licensing requirement or legal mandate for human involvement; the primary barrier is organizational preference for human judgment on strategic technology priorities rather than hard legal constraints. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There are no licensing, regulatory, or liability barriers to using AI tools for staying informed about technology trends. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven news aggregation and summarization services are relatively inexpensive compared to the human cost of manually reading journals, blogs, and forums; integration with existing systems is straightforward. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-powered summarization and alerting tools are inexpensive relative to a manager's time spent manually scanning publications, making this comparatively cheap to augment. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Newsletter aggregators, AI-powered news summarizers, and research tools exist and can filter technology announcements, but they produce false positives and require significant human curation to distinguish genuinely relevant advances from hype. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like AI news aggregators, research assistants, and summarization tools are deployed and used by professionals today, though they require curation and verification rather than being fully autonomous.dealt |
Provide users with technical support for computer problems.
47CI 37–57 · exposure 42 · augmentation 75 · importance 4.0/5 · click for rater detail
Provide users with technical support for computer problems.
47| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Many IT organizations have piloted AI helpdesk bots and ticket routing systems, but production deployment remains uneven—mostly for high-volume simple requests rather than full technical problem resolution. Tech-forward sectors show faster adoption, but overall displacement is modest relative to headcount. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | IT and tech support functions are among the fastest-adopting areas for AI, with widespread deployment of AI-assisted helpdesks and chatbots across many industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at augmenting human technicians by surfacing knowledge base articles, auto-populating diagnostics, suggesting solutions, and triaging tickets—significantly boosting technician productivity. This assistant role is widely deployed and demonstrably improves throughput and resolution time while the expert human remains central. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly boosts efficiency for technical support staff and managers by pre-diagnosing issues, drafting responses, and surfacing knowledge base articles, while humans handle complex or sensitive cases. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI chatbots can handle common password resets and basic troubleshooting, end-to-end technical support for computer problems requires diagnosis of varied hardware/software issues, customer relationship management, and escalation judgment that remains largely manual. Few scenarios achieve 50% time savings at equal quality without human expert involvement. |
| Task automatability | claude-sonnet-5 | 3/5 | AI chatbots and diagnostic tools can resolve many common technical support issues (password resets, basic troubleshooting), but complex or novel problems still require human judgment and escalation, especially at the managerial oversight level implied here. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Organizations typically require human oversight and sign-off on critical system fixes; liability for incorrect remote support creates friction. However, no licensing requirement legally mandates a human perform tier-1 technical support, allowing partial automation adoption in practice. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI from providing tech support, though organizational trust, security concerns, and complex troubleshooting create moderate friction against full replacement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI support agents (cloud-hosted chatbots, tier-1 automation) cost less per interaction than junior technicians, but complex tickets still require expensive senior engineer time. For the full task including escalation and resolution, total cost often approaches human labor costs once integration and oversight are factored in. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI support tools reduce ticket volume and staffing costs substantially, but licensing, integration, and human escalation still keep costs roughly comparable to a lean human support team rather than an order of magnitude cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | AI-powered helpdesk products exist (ticketing automation, FAQ bots, basic diagnostics) but rely heavily on human technicians for complex problems, device-specific issues, and cases requiring hands-on debugging. Production deployments show material gaps in coverage and error rates, especially for novel or enterprise-specific problems. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | IT helpdesk copilots and AI ticketing/triage systems are deployed widely (e.g., ServiceNow AI, Microsoft Copilot for IT), but they handle tier-1 issues with moderate reliability and still route complex cases to humans. |
Evaluate data processing proposals to assess project feasibility and requirements.
37CI 25–50 · exposure 38 · augmentation 75 · importance 3.6/5 · click for rater detail
Evaluate data processing proposals to assess project feasibility and requirements.
37| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While IT and finance sectors are digitizing, the actual automation of managerial feasibility decisions remains limited; most organizations use AI for proposal summarization and preliminary analysis rather than autonomous decision-making, limiting deep adoption of end-to-end automation. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | IT management functions in tech-forward sectors are piloting AI-assisted proposal review and analytics tools, but production-scale autonomous evaluation is still uncommon. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at augmenting this task by rapidly extracting requirements, identifying risk patterns, summarizing proposals, and flagging unusual aspects, materially accelerating the manager's evaluation cycle while the human retains judgment authority. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully speed up reviewing technical specs, cost estimates, and risk factors, letting managers focus on strategic judgment and stakeholder alignment. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can assist in analyzing data processing proposals, identifying feasibility risks, and extracting requirements, but the holistic judgment of project viability—balancing technical, organizational, and business factors—typically requires human expertise and context that current systems struggle to replicate fully. A manager would likely still need to validate conclusions and make final decisions. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help summarize and analyze proposals but the core judgment—weighing organizational fit, risk, budget, and strategic priorities—requires human contextual expertise not replicable end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Management authority and accountability for feasibility assessment typically reside with the manager role; organizations view this as a judgment task requiring human sign-off due to liability for project failures. Organizational inertia to delegate this decision-making to AI is substantial. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but organizational accountability, budget authority, and managerial sign-off create real friction against full delegation to AI systems. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | The cost of AI analysis (including inference, prompt engineering, and mandatory human review/validation) is comparable to paying a manager or senior analyst to perform the task, especially when factoring in the risk of missed nuances that require rework. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply process document text, but the human oversight, stakeholder negotiation, and accountability needed for feasibility decisions keep overall cost comparable to or above a manager's time for this narrow subtask. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Tools exist (LLMs, business intelligence platforms, document analysis systems) that can extract proposal details, flag risks, and summarize requirements, but they operate with material error rates in understanding nuanced constraints, hidden dependencies, and organizational context. Production use typically requires significant human oversight. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some enterprise tools use AI for document analysis and requirements extraction, but no deployed product autonomously evaluates feasibility of IT proposals with reliable business judgment. |
Manage backup, security and user help systems.
36CI 32–40 · exposure 34 · augmentation 75 · importance 4.2/5 · click for rater detail
Manage backup, security and user help systems.
36| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Enterprise IT has adopted monitoring and ticketing automation at scale, but strategic oversight and security incident response remain human-led. Adoption is mature for tooling but not for replacing managers' judgment on backup and security strategy. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | IT and information management functions are among the fastest-adopting sectors for AI tooling, with widespread use of automated monitoring, alerting, and support-ticket triage systems already embedded in enterprise IT operations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-driven dashboards, anomaly detection, automated ticket triage, and alert summarization significantly assist managers in monitoring and responding faster. These tools meaningfully boost productivity while keeping the manager in the loop for decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools significantly augment this task by automating routine monitoring, anomaly detection, and first-line help desk responses, freeing managers to focus on strategic decisions and complex incident handling. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with routine backup monitoring, security alerting, and help desk categorization, the task requires ongoing policy decisions, incident response judgment, and system architecture choices that need human oversight. Current AI systems cannot autonomously manage these systems end-to-end at the required 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can automate parts of monitoring backups, flagging security alerts, and triaging help desk tickets, but the overall managerial responsibility—deciding policies, escalations, resource allocation, and accountability—requires human judgment and oversight, so end-to-end automation at equal quality is not yet achievable. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulatory compliance (HIPAA, SOC 2, etc.), liability for security breaches and data loss, and the legal requirement for authorized personnel to certify backups and security controls create strong adoption barriers. Many organizations require human sign-off on security and backup policies. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement blocks automation, but data security, compliance liability, and organizational risk tolerance create meaningful friction against fully delegating security/backup management to AI. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI monitoring and automation tools have meaningful costs (licensing, infrastructure, maintenance), and managers still must oversee, interpret alerts, and make decisions. The all-in cost remains comparable to or higher than the manager's salary contribution for this subset of their work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While automated monitoring tools are cheap per-alert, the managerial oversight, incident response coordination, and accountability still require a well-paid IT manager, so overall cost savings versus the human role are moderate at best. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist for automated backup scheduling, security monitoring dashboards with ML anomaly detection, and AI-powered help desk ticketing, but they all require human management for complex incidents, policy exceptions, and strategic decisions. Reliability remains material with false positives and gaps in edge cases. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like automated backup monitoring tools, SIEM/security alerting systems, and AI-driven helpdesk chatbots (e.g., ServiceNow AI, Microsoft Copilot for IT) are deployed in production, but they handle narrow sub-tasks rather than the full managerial oversight function reliably. |
Review and approve all systems charts and programs prior to their implementation.
36CI 28–44 · exposure 30 · augmentation 75 · importance 3.7/5 · click for rater detail
Review and approve all systems charts and programs prior to their implementation.
36| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Tech-forward organizations are rapidly adopting AI-assisted code review (scanning, pattern matching), but the final approval step remains with managers. Pilots are common but full automation of approval authority is rare and organizationally resisted. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | IT and software sectors are fast adopters of AI-assisted code review, static analysis, and CI/CD tooling, reflecting the broader fast-adoption pattern in professional/technical services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI provides substantial assistance by automatically scanning for common issues, flagging security anti-patterns, and summarizing code changes, which accelerates a manager's ability to review and approve. The manager's judgment remains essential, but AI meaningfully raises their throughput and coverage. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI code review assistants, static analyzers, and documentation tools significantly speed up the manager's ability to evaluate systems charts and programs before approval, even though final sign-off remains human. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Reviewing and approving systems charts and programs requires nuanced judgment about architectural soundness, security implications, and organizational fit. While AI can perform partial checks (code style, syntax validation, security pattern matching), the approval decision itself demands expert human judgment that AI cannot reliably substitute at scale. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a judgment-and-accountability task requiring managerial sign-off on system design correctness, security, and business alignment; AI can assist review but cannot independently perform the approval function with equal quality end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulatory and organizational frameworks often require a named manager to sign off on system implementations, and liability for failed systems typically rests with that human approver. Most enterprises treat approval as a legally and professionally accountable human decision. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement typically exists, but organizational accountability, liability for system failures, and internal governance policies create meaningful friction against fully delegating approval to AI. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI-powered code review tools cost substantially less than manager time per review, but integration and oversight still require significant human involvement. The all-in cost approaches parity with a manager's hourly rate when accounting for false negatives and the need for final human sign-off. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can cheaply flag issues in code/charts, but the accountable review-and-approve step still requires a paid manager's time and judgment, so overall cost savings are limited relative to the full task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist for code review assistance (e.g., GitHub Copilot, static analysis tools) and can flag issues, but they operate with material error rates in catching subtle architectural or security problems. No mature product reliably performs end-to-end approval independent of human review in production environments. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Code review and static analysis tools exist and are deployed, but no product performs the managerial 'approval' role reliably; AI-assisted code review is common but final sign-off remains a human function in production settings. |
Control operational budget and expenditures.
34CI 28–41 · exposure 30 · augmentation 75 · importance 3.5/5 · click for rater detail
Control operational budget and expenditures.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Financial and IT organizations are adopting AI-assisted budgeting tools at moderate pace for forecasting and alerts, but autonomous control over actual spending remains rare due to governance requirements and organizational inertia. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | IT and finance functions are adopting AI-assisted budgeting and forecasting tools at a moderate pace, but full budget control remains human-led in most organizations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI dashboards, predictive analytics, and automated alerts significantly enhance a manager's ability to monitor spending patterns, forecast variances, and flag anomalies faster than manual analysis, substantially raising productivity while the human retains decision authority. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can significantly aid forecasting, anomaly detection, and expenditure tracking, improving a manager's efficiency in budget oversight while decisions stay with the human. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with budget tracking, reporting, and anomaly detection, but actual control over expenditures requires human judgment on business priorities, trade-offs, and strategic decisions that current systems cannot reliably make end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | Budget control involves judgment calls, negotiation, and accountability for tradeoffs that current AI cannot autonomously own, though AI can assist with tracking and forecasting spend., limiting full end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Budget control carries fiduciary responsibility and regulatory compliance obligations; in most organizations, a human manager must legally approve expenditures and maintain accountability, creating strong organizational and legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but organizational governance, fiduciary responsibility, and accountability for spending decisions create real friction against full delegation to AI. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI-powered budget tools reduce administrative overhead compared to manual tracking, but a manager's salary remains the dominant cost component; automation handles only portions of the task, making overall cost savings modest. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Software for expense tracking is cheap, but the managerial oversight, approval authority, and accountability still require a human manager, keeping overall cost comparable to human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Commercial budgeting and financial management software with AI features exists and performs routine monitoring and alerts, but human oversight remains essential for authorization and policy decisions, limiting fully autonomous operation. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Financial dashboards and BI tools provide monitoring and alerts, but no deployed product independently 'controls' budgets and expenditures with authority over decisions. |
Evaluate the organization's technology use and needs and recommend improvements, such as hardware and software upgrades.
32CI 32–32 · exposure 25 · augmentation 75 · importance 3.7/5 · click for rater detail
Evaluate the organization's technology use and needs and recommend improvements, such as hardware and software upgrades.
32| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | IT and finance sectors have adopted AI-assisted planning tools at a middling pace; pilot programs are common but automation of the full assessment-and-recommendation workflow remains rare in production. Most organizations still rely primarily on human managers for this strategic function. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | IT management sectors are moderately fast adopters of AI tools for analysis and reporting, though strategic recommendation-making remains largely human-led in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI currently provides strong augmentation by rapidly analyzing large datasets of hardware/software performance, costs, and compatibility, then surfacing organized recommendations that managers refine. This substantially accelerates the information-gathering and option-generation phases while leaving strategic decisions to humans. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly speed up research, benchmarking, and drafting of technology assessments and recommendation reports, meaningfully boosting manager productivity while they retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can analyze current technology stacks and generate basic upgrade recommendations using existing inventory data, but the evaluation requires understanding organizational context, strategic goals, and business constraints that demand human judgment. The task involves stakeholder consultation and prioritization that current systems cannot fully automate. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires understanding organizational context, politics, budget constraints, and strategic priorities that AI cannot independently assess; AI can assist with analysis but not autonomously evaluate and recommend at the level required. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Moderate barriers exist: organizational IT governance often requires sign-off by licensed IT professionals, and liability concerns around failed technology recommendations create incentives for human accountability. However, no legal requirement strictly prohibits AI assistance in this domain. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but organizational trust, accountability for costly infrastructure decisions, and vendor negotiation dynamics create real friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-powered analysis tools exist but require integration with existing systems, ongoing oversight, and human validation of recommendations. The loaded cost of these systems plus necessary human review is comparable to or higher than having experienced IT managers conduct the evaluation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply generate draft comparisons or reports, but the human oversight, stakeholder interviews, and validation needed keep overall cost comparable to a manager's time rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI tools can scan IT environments and suggest upgrades based on technical specs, no deployed product reliably performs end-to-end organizational technology assessment and recommendation at production scale. Existing solutions are narrowly scoped (e.g., asset management or compliance checking) and require significant human validation. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product autonomously performs full technology needs assessments and recommendations for organizations; existing tools provide research/analysis support but require heavy human synthesis and judgment. |
Assign and review the work of systems analysts, programmers, and other computer-related workers.
29CI 25–32 · exposure 25 · augmentation 63 · importance 4.0/5 · click for rater detail
Assign and review the work of systems analysts, programmers, and other computer-related workers.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI in core managerial functions like work assignment and review has been slow; most organizations still rely on human managers for these tasks, and pilots in tech companies have been limited. Trust in AI judgment for personnel-affecting decisions remains low across sectors. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Tech sector management is a fast-adopting environment for AI tools generally, but the specific managerial task of assigning/reviewing work remains largely human-led with AI as a support tool rather than a replacement. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist managers by providing performance dashboards, workload analysis, and skill-matching recommendations, helping humans make faster, more informed assignment and review decisions. The human manager remains essential for accountability and contextual judgment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI code review tools, sprint planning assistants, and analytics dashboards can meaningfully help managers track work quality and workload distribution, improving decision speed and consistency. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with performance tracking and work distribution recommendations, the task inherently requires understanding context, individual team member capabilities, and real-time problem-solving that current systems cannot reliably replicate at scale. Review and assignment involve judgment calls that exceed the 50% time-savings threshold when quality and coverage are equivalent. |
| Task automatability | claude-sonnet-5 | 2/5 | Task assignment and review require managerial judgment, team context, and interpersonal negotiation that current AI cannot fully replicate end-to-end, though AI can assist with parts like code review or task tracking. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant organizational and accountability barriers exist: managers bear legal and performance responsibility for team output, there is strong organizational preference for human judgment in personnel decisions, and many firms require human sign-off on work allocation for compliance and cultural reasons. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement, but organizational hierarchy, accountability for personnel decisions, and need for human judgment in performance evaluation create meaningful friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Implementing a system to replace managerial work assignment and review would require substantial integration, ongoing training, and human oversight to avoid errors. The total cost (inference, setup, liability, human review) remains higher than or comparable to a mid-level manager's loaded wage for equivalent output quality. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Replacing a manager's judgment-based assignment and review function would still require significant human oversight and correction, keeping costs comparable to or only modestly cheaper than human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No mature deployed product reliably performs full end-to-end work assignment and review for technical teams in production. Existing tools (project management software, skill-matching systems) cover narrow aspects but lack the judgment and contextual understanding needed for comprehensive task oversight. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some project management and code-review tools use AI to flag issues or suggest task allocation, but no deployed product autonomously assigns and reviews human worker output at a management level reliably. |
Develop computer information resources, providing for data security and control, strategic computing, and disaster recovery.
29CI 25–32 · exposure 25 · augmentation 75 · importance 3.9/5 · click for rater detail
Develop computer information resources, providing for data security and control, strategic computing, and disaster recovery.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While IT security tools are widely adopted, the strategic management task of developing integrated information resources remains primarily human-driven in most organizations. Adoption of full automation for this role is nascent; most firms use AI as assistive analytics within human-led governance structures. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | IT and information management sectors show above-average AI adoption for security tooling and cloud infrastructure management, but the strategic managerial layer of this task sees more pilot-stage than fully deployed AI-driven decision-making. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly assists managers through automated vulnerability assessments, compliance reporting, disaster recovery simulations, and security posture dashboards. These tools meaningfully raise manager productivity in monitoring and planning, though the manager remains responsible for strategy and decision-making. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly assists this task through automated security monitoring, threat intelligence synthesis, risk assessment reporting, and disaster recovery simulation, meaningfully increasing manager productivity while strategic decisions remain human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with specific elements like security monitoring, vulnerability scanning, and disaster recovery planning documentation, the strategic decisions about resource allocation, control frameworks, and organizational risk trade-offs require human judgment. End-to-end automation would sacrifice the contextual business understanding and accountability needed for effective governance. |
| Task automatability | claude-sonnet-5 | 2/5 | This task involves high-level strategic planning, risk assessment, and organizational judgment about security posture and disaster recovery priorities, which requires contextual understanding of business operations that current AI cannot fully replace.dur AI can assist with analysis but not own the end-to-end strategic development. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: regulatory compliance (HIPAA, SOC 2, financial regulations) typically requires authorized personnel sign-off; liability for data breaches and recovery failures falls on responsible managers; and most organizations require a qualified human executive to own these strategic decisions and their consequences. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no strict licensing requirement mandates a human perform this specific task, security and compliance frameworks (e.g., SOX, HIPAA, industry regulations) often require accountable human sign-off on security and disaster recovery policies, creating moderate organizational and liability friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for security and infrastructure monitoring are available but require significant human oversight, training, and integration into existing systems. The all-in cost (tools, integration, validation, liability) remains comparable to or higher than the managerial expertise required, especially given error consequences. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | The strategic and judgment-intensive nature of this task means AI tools require significant human oversight and integration, so cost savings are modest rather than order-of-magnitude, though AI can reduce time spent on data gathering and analysis components. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Current AI tools can generate security policies and scan for vulnerabilities, but no deployed product reliably performs the full task of developing integrated information resources with security, strategic computing, and disaster recovery coordination. Most products handle isolated components rather than the orchestrated, organization-wide governance this task demands. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | While AI tools exist for security monitoring, threat detection, and some DR automation, no deployed product independently develops comprehensive information resource strategy, security architecture, and disaster recovery planning at the managerial decision-making level. |
Consult with users, management, vendors, and technicians to assess computing needs and system requirements.
29CI 25–32 · exposure 25 · augmentation 63 · importance 3.9/5 · click for rater detail
Consult with users, management, vendors, and technicians to assess computing needs and system requirements.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI for direct stakeholder consultation in this role is slow; most organizations still rely on managers and specialists for needs assessment. While some AI-assisted documentation and information gathering is emerging in IT shops, autonomous or near-autonomous consultation with executives and end-users remains rare in production. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | IT and professional services sectors are moderately fast AI adopters, with meeting summarization and requirements-gathering copilots in pilot/production use, but full consultation automation remains rare. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist managers by pre-analyzing technical data, drafting requirement summaries, scheduling and organizing stakeholder input, and suggesting potential system architectures—helping the manager prepare and structure consultations more efficiently while the human retains decision authority. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can transcribe and summarize meetings, draft requirement documents, generate clarifying questions, and synthesize vendor comparisons, meaningfully boosting manager productivity while they remain the decision-maker. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with gathering technical information and drafting requirement documents, the core task requires real-time stakeholder dialogue, judgment about conflicting priorities, and interpersonal trust-building that current systems cannot fully replicate. Some preparation and documentation steps could be automated, but the critical consultation and assessment phases remain fundamentally human-led. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires live stakeholder consultation, negotiation, reading organizational politics, and synthesizing ambiguous needs into requirements—core relational and judgment work that AI cannot yet fully replace end-to-end.notorious |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant organizational and implicit barriers exist: stakeholders typically expect direct dialogue with an accountable human manager; vendors and executives require relationship continuity; and system requirements assessment carries liability risk if mishandled, making human sign-off legally and operationally necessary. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier exists, but organizational trust, accountability for decisions, and the need for a human to own vendor/management relationships create real friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of AI systems for consultation (including training, integration, and human oversight to ensure accuracy and relationship continuity) remains comparable to or exceeds the marginal cost of having a manager conduct these consultations directly, given the reputational risk of failures. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | A human manager's time consulting stakeholders is not easily replaced by AI at scale; while AI can cut prep/documentation time, the core consultative interactions still require paid human time, keeping cost savings modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs end-to-end needs assessment and stakeholder consultation today. While AI chatbots and documentation tools exist, they lack the contextual understanding, relationship management, and accountability that stakeholders expect in this role, and production systems are not handling this at scale. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI meeting assistants and requirements-drafting tools exist and are used in production, but no deployed product independently conducts stakeholder consultations and requirement assessment reliably without human management. |
Recruit, hire, train and supervise staff, or participate in staffing decisions.
18CI 7–28 · exposure 17 · augmentation 63 · importance 3.9/5 · click for rater detail
Recruit, hire, train and supervise staff, or participate in staffing decisions.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While large tech and finance firms pilot AI-assisted recruiting tools, adoption remains narrow in scope and slow across broader sectors; most organizations still rely on human managers for final hiring and supervision decisions, with AI playing only a marginal support role. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | HR tech adoption in tech-adjacent management roles is moderate, with AI-assisted recruiting tools increasingly common but full automation of hiring/supervision still rare in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI tools assist with candidate sourcing, scheduling, and initial screening, moderately raising manager productivity in the recruitment pipeline. However, augmentation is limited to discrete early-stage tasks; the core supervisory and relationship-building work remains unmultiplied. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI meaningfully assists with resume screening, drafting job postings, scheduling, and even generating interview questions, improving manager efficiency while humans retain decision authority. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with screening resumes, scheduling, and initial candidate assessment, the full end-to-end task requires nuanced human judgment on cultural fit, team dynamics, and strategic hiring decisions that AI cannot reliably execute at the 50% time-saving threshold. Supervision and performance management remain fundamentally interpersonal. |
| Task automatability | claude-sonnet-5 | 1/5 | Recruiting, hiring, and supervising staff involves relational judgment, negotiation, and accountability that current AI cannot execute end-to-end; no off-the-shelf system performs this full task autonomously. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Hiring decisions carry legal liability (discrimination, wrongful termination), employment law compliance, and union/HR requirements in many jurisdictions. Managers must personally sign off on hiring and discipline; regulatory and organizational barriers are substantial. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Hiring and personnel decisions carry significant legal liability (discrimination, labor law) and typically require human accountability and sign-off, creating strong organizational and regulatory barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The loaded cost of a manager performing hiring and supervision far exceeds what AI recruitment tools can justify economically, since the manager's expertise and accountability remain essential. AI augments specific steps but cannot replace the salary burden. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can cut costs on sourcing and screening, but the overall task still requires substantial manager time for interviews, decisions, and supervision, keeping costs comparable to human-driven processes. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Recruiting platforms with AI-assisted screening exist in production, but they handle only narrow segments (resume parsing, initial filtering) with documented bias issues and high false-negative rates. End-to-end hiring, training, and supervision decisions still require human managers; no deployed system performs the full task reliably. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products exist for resume screening, interview scheduling, and candidate scoring, but no deployed system reliably conducts full hiring decisions or ongoing supervision without heavy human involvement. |
Develop and interpret organizational goals, policies, and procedures.
18CI 7–28 · exposure 13 · augmentation 75 · importance 3.7/5 · click for rater detail
Develop and interpret organizational goals, policies, and procedures.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Most organizations treat goal-setting and policy development as core leadership functions with limited appetite for AI autonomy; adoption remains at the assistance stage (copilots for drafting) rather than displacement. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | IT management sectors adopt AI tools quickly for drafting and analysis support, but adoption of AI in actual policy-setting authority remains minimal and cautious. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assistants meaningfully accelerate policy drafting, compliance interpretation, and impact analysis, allowing managers to synthesize stakeholder input and regulatory requirements more quickly while maintaining final authority over organizational direction. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by drafting policy language, summarizing precedents, analyzing data trends, and modeling scenarios, substantially boosting manager productivity while the human retains final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft policies and interpret existing goals, developing organizational goals requires strategic judgment, stakeholder alignment, and business context that remain firmly in human hands. AI can assist with data synthesis and templating but cannot autonomously set direction at the ≥50% time-saving bar. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires strategic judgment, organizational context, stakeholder alignment, and accountability that current AI cannot originate or own end-to-end; AI cannot substitute for the managerial decision-making role. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Organizational governance, fiduciary responsibility, and stakeholder accountability create strong barriers; boards and executives must legally own and authenticate strategic goals and major policies, limiting substitution potential. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Organizational governance, fiduciary responsibility, and accountability structures require a human manager to own and be answerable for goals and policies, creating strong structural barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI assistance for policy drafting is inexpensive, but human oversight and decision-making dominate the total cost, making AI only a minor cost reducer rather than a substantive economic replacement. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-only substitute performing this function, so cost comparison favors the human manager who bears accountability and judgment responsibility. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably develops organizational goals end-to-end; deployed systems can only assist with policy drafting and summarization of existing documentation. The strategic and contextual requirements exceed current production capabilities. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product independently develops or interprets organizational policy; this remains a human executive function with AI only as an input source. |
Meet with department heads, managers, supervisors, vendors, and others, to solicit cooperation and resolve problems.
6CI 0–13 · exposure 5 · augmentation 50 · importance 4.1/5 · click for rater detail
Meet with department heads, managers, supervisors, vendors, and others, to solicit cooperation and resolve problems.
6| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task is core to management authority and identity; organizations show no appetite to remove managers from stakeholder meetings. Adoption of AI for meeting assistance is nascent and limited to note-taking or scheduling, not substitution for the manager's role. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | While IT/management sectors adopt AI tools broadly, adoption of AI specifically for interpersonal negotiation and conflict resolution meetings remains minimal and exploratory. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by preparing briefing documents, identifying common complaints across meetings, drafting follow-up emails, and tracking action items, but the manager must lead and decide. This moderate augmentation improves preparation and follow-through without changing the fundamental human role. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help managers prepare talking points, summarize prior issues, draft follow-up communications, or analyze vendor performance data ahead of these meetings, improving efficiency without replacing the interaction itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time negotiation, relationship-building, and problem-solving among stakeholders with conflicting interests—activities that depend on emotional intelligence, authority, and accountability that current AI cannot provide autonomously. Even with agents, the legal and organizational requirement for a human manager to own the resolution makes end-to-end automation implausible. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a synchronous, interpersonal negotiation and relationship-management task requiring real-time judgment, authority, and trust-building that current AI cannot substitute for end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Organizational hierarchy, accountability structures, and vendor relationships legally and culturally require a human manager with decision authority to represent the department and resolve conflicts. Stakeholders expect and demand human leadership presence, and liability for poor decisions falls on the manager. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Organizational authority, accountability, and interpersonal trust required to resolve conflicts and direct cooperation create strong structural barriers to full automation, though not formal licensing requirements. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Automating this task would require deploying an agentic system to run cross-organizational meetings and resolve disputes, which is far more expensive and error-prone than the hourly cost of a manager already on payroll. The human overhead for oversight would exceed the base wage. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-only substitute performing this function, so cost comparison favors the human entirely; AI tools add marginal cost without replacing the task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI can help draft agendas, summarize meeting notes, or flag common issues, but no deployed system can reliably conduct meetings, negotiate disputes, or resolve vendor conflicts independently. Meeting tools exist, but they augment rather than replace the manager's presence and judgment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product conducts these managerial meetings autonomously; AI at best supports scheduling or note-taking, not the substantive negotiation and problem resolution. |
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.