Management Analysts
13-1111.00Median wage $101,860/yr898,280 employed (US)Rank #129 of 923 scored · top 14% by substitution
Conduct organizational studies and evaluations, design systems and procedures, conduct work simplification and measurement studies, and prepare operations and procedures manuals to assist management in operating more efficiently and effectively. Includes program analysts and management consultants.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution43
Exposure37
Augmentation76
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
11 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
9%
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%39
panel mean rating 2.6/5 → substitution pressure 39/100
Technical feasibility todayw 20%33
panel mean rating 2.3/5 → substitution pressure 33/100
Cost vs. human wagew 15%40
panel mean rating 2.6/5 → substitution pressure 40/100
Adoption barriersw 20%inverted — strong barriers lower the score60
panel mean rating 2.6/5 (barrier strength) → substitution pressure 60/100
Sector adoption velocityw 10%47
panel mean rating 2.9/5 → substitution pressure 47/100
Task breakdown (11 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.
Gather and organize information on problems or procedures.
75CI 75–75 · exposure 75 · augmentation 100 · importance 4.5/5 · click for rater detail
Gather and organize information on problems or procedures.
75| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Management consulting, financial services, and enterprise sectors are actively adopting AI-driven information gathering and document intelligence tools in production. Pilot and deployment rates are high in digitized industries, reflecting strong vendor investment and demonstrated ROI. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Management consulting and business services are professional/information-sector fields with fast, deep AI adoption for research and document analysis tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI excels at augmenting analyst productivity in this task—tools can rapidly pre-organize findings, surface patterns, and flag anomalies, allowing analysts to focus on interpretation and synthesis rather than manual data collection and filing. This creates strong human–AI collaboration potential. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up and improves the gathering, categorizing, and synthesizing of information while the analyst retains judgment over interpretation and use. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can effectively gather, classify, and organize information from documents, emails, databases, and web sources with minimal human intervention. The task involves largely mechanical information collection and structuring—natural language processing and document automation tools can handle this at scale, meeting the ≥50% time-saving threshold for most real-world applications. |
| Task automatability | claude-sonnet-5 | 4/5 | AI can rapidly search, summarize, and structure information from documents, interviews notes, and data sources, saving substantial time compared to manual gathering and organizing, though some original data collection (interviews, site visits) still requires human effort. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or regulatory barriers exist to automating information gathering and organization itself; no licensing requirement mandates human involvement. Adoption friction mainly arises from organizational preference for human review of sensitive data and internal resistance to process change. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for this sub-task, though confidentiality of internal organizational data and need for accurate context may create some organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-based information gathering (via APIs, automation services, or integrated platforms) costs significantly less per instance than human analyst time, often by an order of magnitude when processing large document volumes or routine procedural inventories. Overhead is primarily integration and setup rather than per-task inference. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-based research and summarization tools cost a small fraction of an analyst's hourly rate for comparable information-gathering and organizing output. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature products exist for information extraction, document management, and knowledge organization (enterprise search, document intelligence APIs, workflow automation platforms). These are deployed in production across many organizations, though some contexts require domain-specific fine-tuning or oversight to ensure accuracy and relevance. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Deployed tools (e.g., AI research assistants, document summarization and knowledge management platforms) reliably gather and organize textual information today, though accuracy on specialized organizational problems still needs human verification. |
Plan study of work problems and procedures, such as organizational change, communications, information flow, integrated production methods, inventory control, or cost analysis.
62CI 38–87 · exposure 58 · augmentation 88 · importance 4.1/5 · click for rater detail
Plan study of work problems and procedures, such as organizational change, communications, information flow, integrated production methods, inventory control, or cost analysis.
62| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Professional services, management consulting, and corporate sectors are actively deploying AI for analysis and strategic planning; major consulting firms and enterprises already use AI-assisted tools for workflow analysis and organizational assessment in production. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Management consulting and business services are moderately fast adopters of AI tools for research and drafting, though full study-planning automation remains at pilot stage. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically augments analyst productivity by rapidly synthesizing organizational data, generating multiple study scenarios, and proposing preliminary frameworks while the analyst focuses on judgment, stakeholder alignment, and final recommendation design. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can significantly speed up literature review, framework selection, drafting interview protocols, and organizing initial problem statements, meaningfully boosting analyst productivity while they retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | AI can autonomously analyze organizational data, review workflow documentation, identify inefficiencies in communications and production methods, and generate structured study plans with prioritized recommendations—meeting the 50% time-saving threshold at equal quality using current tools like LLMs augmented with data analysis capabilities. |
| Task automatability | claude-sonnet-5 | 2/5 | Planning a study of complex organizational work problems requires stakeholder scoping, judgment about scope, and organizational context that current AI cannot independently generate to a professional standard, though it can assist with drafting frameworks. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers exist for planning studies; organizations typically retain authority and final approval over study scope, but there is minimal legal requirement that a human analyst must personally perform the planning work, enabling rapid substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for management analysts to plan studies, but organizational trust, stakeholder buy-in, and client relationship needs create moderate friction against pure AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | An AI system executing planning tasks costs a fraction of a loaded management analyst salary (typically $80k–$120k+ annually); even accounting for oversight and integration, AI inference and API costs are orders of magnitude cheaper per completed study plan. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply generate boilerplate plan structures, but the human cost of scoping, client interviews, and validating problem definition remains dominant, so overall cost savings are modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed AI systems (LLMs, analytics platforms, business intelligence tools) already perform organizational analysis, problem identification, and study planning in production environments; however, they often require significant human validation and refinement of proposed study designs and scope rather than fully autonomous end-to-end execution. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Deployed products can help draft study plans or checklists, but no production system independently plans management-consulting studies of organizational problems reliably; this remains largely human-led with AI as a drafting aid. |
Review forms and reports and confer with management and users about format, distribution, and purpose, identifying problems and improvements.
52CI 38–67 · exposure 45 · augmentation 88 · importance 3.7/5 · click for rater detail
Review forms and reports and confer with management and users about format, distribution, and purpose, identifying problems and improvements.
52| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Many organizations are piloting AI-driven form and report analysis within management and business process improvement functions, but widespread production deployment remains uneven; adoption is faster in larger, digitally mature firms. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Management consulting and business services are moderately fast adopters of AI for document review and analytics, though the consultative human interaction remains largely untouched. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI strongly augments management analysts by automating tedious form parsing and flagging anomalies, allowing analysts to focus on stakeholder engagement, strategic recommendations, and understanding organizational context—a clear human-in-the-loop productivity multiplier. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully speed up review of forms/reports, flag inconsistencies, and draft improvement recommendations, substantially aiding analysts while they retain the consultative and judgment role. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can automate substantial portions of this task—parsing forms and reports for structural issues, checking format consistency, flagging distribution problems, and identifying common improvement patterns—achieving significant time savings. However, the conferencing and relationship-building aspects with stakeholders require human judgment and context, preventing full end-to-end automation. |
| Task automatability | claude-sonnet-5 | 2/5 | The 'confer with management and users' component requires live interpersonal negotiation and organizational judgment that current AI cannot fully replace, though document review portions are automatable. Overall end-to-end time savings likely fall well short of 50%. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few hard legal or licensing barriers exist; the main friction is organizational preference for human judgment in stakeholder discussions and decision-making, plus need for human sign-off on significant changes—but these do not prevent automation of the review phase itself. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational trust, stakeholder relationships, and context-specific judgment create moderate friction against full substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI document review and analysis is now substantially cheaper than analyst labor for the review and flagging components; integration and oversight costs are moderate, placing total AI cost well below a loaded analyst wage for equivalent output on the routine parts. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply assist with document analysis, but the human conferring, relationship management, and judgment components still require analyst time, keeping overall cost comparable to human-only delivery. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed products (document analysis, workflow automation, business intelligence tools) can reliably handle form/report review and basic improvement suggestions, but meaningful conferencing with management and users to understand nuanced organizational context remains inconsistent at scale. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools can summarize and analyze forms/reports, but no deployed product reliably conducts the full consultative process of identifying organizational problems and improvements through stakeholder dialogue. |
Design, evaluate, recommend, and approve changes of forms and reports.
44CI 30–59 · exposure 38 · augmentation 75 · importance 3.2/5 · click for rater detail
Design, evaluate, recommend, and approve changes of forms and reports.
44| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Management consulting and business process sectors adopt automation selectively, and approval workflows tend to be conservative. Deployment of autonomous form-design tools in production remains uncommon; pilots exist but displacement is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Management analysis and business operations functions are adopting AI tools steadily but unevenly, with many firms still piloting rather than fully deploying automated design/approval workflows. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist analysts by generating initial form drafts, flagging missing fields, and comparing form versions, meaningfully reducing iteration time while the analyst retains judgment over design and approval decisions. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up drafting, redesigning, and evaluating forms/reports, letting analysts focus on approval and strategic judgment while AI handles most of the legwork. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate form layouts and evaluate existing forms for completeness, the task requires business judgment about stakeholder needs, regulatory compliance, and organizational context that AI cannot reliably assess end-to-end. Approval authority, a critical component, remains human-dependent. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft and redesign forms/reports and evaluate them against stated criteria, but final approval and judgment calls on organizational fit require human oversight, limiting full end-to-end automation.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Approval of form changes often requires sign-off from specific roles or compliance departments; some organizations have regulatory or internal audit requirements tied to human responsibility. These create moderate friction, though not absolute legal barriers in most cases. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI-assisted design work, though organizational approval processes and accountability for changes create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI services for form design and evaluation carry integration and oversight costs that approach or exceed the cost of having a management analyst perform the task, particularly when approval and quality assurance are factored in. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Drafting and evaluating form/report designs via AI is very cheap relative to analyst hours, though some human review cost remains for final sign-off. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI tools can assist with form generation and basic evaluation, but no deployed product reliably handles the full cycle of designing, evaluating, recommending, and approving forms with sufficient judgment for production use. Most applications remain limited to drafting support rather than autonomous decision-making. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Document generation and analysis tools (e.g., AI drafting assistants, form-builder AI features) exist and are used in production, but approval workflows and nuanced evaluation of business impact are not reliably automated by deployed products. |
Prepare manuals and train workers in use of new forms, reports, procedures or equipment, according to organizational policy.
44CI 32–55 · exposure 38 · augmentation 88 · importance 3.8/5 · click for rater detail
Prepare manuals and train workers in use of new forms, reports, procedures or equipment, according to organizational policy.
44| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Some organizations use AI to accelerate training material creation, but adoption remains pilot-heavy rather than production-standard. Most firms still rely on analysts and trainers to develop and deliver training, with AI assist rather than replacement being the norm. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Professional/business services sectors are moderate-to-fast adopters of AI for documentation and training content, though actual delivery of training remains more traditional in many firms. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI strongly augments this task by generating initial drafts of manuals, suggesting procedural documentation, and automating formatting—allowing analysts to focus on customization, policy alignment, and effective training delivery. These tools noticeably boost analyst productivity while keeping human judgment in control. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI is highly effective at helping analysts draft, structure, and revise manuals and training materials quickly, significantly boosting productivity while humans finalize and deliver training. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate manual drafts and training content automatically, the task requires customization to organizational policy, worker skill assessment, and adaptive instruction—elements that still demand human oversight and iteration. Partial automation (content generation, initial drafting) exists, but end-to-end deployment with ≥50% time savings at equal quality remains infeasible. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft manuals and training materials from source documentation quite effectively, but delivering live training, answering worker questions, and tailoring to organizational culture still requires human involvement, so only part of the task meets the 50% threshold. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Organizational policy compliance and the need for human trainers to assess worker comprehension create moderate friction, though no hard legal requirement mandates human involvement. Regulatory coverage varies by industry, and organizations often prefer human-delivered training for accountability. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement typically applies, though organizational policy compliance and quality control create some review friction before materials or training are finalized. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI can lower the cost of initial content generation, but the total cost (AI drafting + human expert review + training delivery oversight + quality assurance) remains comparable to or higher than a management analyst doing it directly, especially for compliance-critical training. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drastically cuts drafting time and cost, but human trainers/facilitators are often still needed for interactive training, keeping blended costs roughly comparable to fully human-delivered processes in many orgs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI products can produce training materials and instructional documents, but deploying them reliably in production requires human review for accuracy, policy compliance, and worker comprehension outcomes. No mature product independently handles the full cycle of manual preparation, training delivery, and worker competency validation. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like generative AI writing tools and LMS platforms with AI-generated content are used in production for manual drafting, but full training delivery integration is narrower and less mature. |
Document findings of study and prepare recommendations for implementation of new systems, procedures, or organizational changes.
41CI 25–57 · exposure 38 · augmentation 88 · importance 4.2/5 · click for rater detail
Document findings of study and prepare recommendations for implementation of new systems, procedures, or organizational changes.
41| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While professional services firms are piloting AI-assisted analysis, production deployment of fully autonomous recommendation systems remains limited; most adoption is augmentative drafting rather than replacement, and significant organizational hesitation persists around relying on AI for strategic decisions. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Consulting and professional services are among the faster-adopting sectors for generative AI tools in report writing and analysis support. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially assist by drafting initial documentation, organizing findings, and generating recommendation templates, which management analysts can then refine and contextualize, materially raising productivity while the human retains final authority over recommendations. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI significantly speeds up drafting, summarizing findings, and structuring recommendations, serving as a strong productivity multiplier while the analyst retains final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with drafting portions of findings and recommendations, but synthesizing study results into coherent, context-sensitive implementation strategies requires human judgment about organizational dynamics, stakeholder priorities, and contingency planning that current systems cannot reliably execute end-to-end. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft reports, synthesize findings, and structure recommendations from provided data, but truly novel analysis, stakeholder-specific judgment, and organizational nuance still require human input and validation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Management recommendations often require sign-off by senior leadership and carry liability implications if faulty; organizational change requires human accountability and buy-in that cannot be delegated to automated systems, creating strong adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human analyst, though client trust, accountability for recommendations, and reputational risk create some friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI inference costs for generating documentation are low, but the overhead of human verification, fact-checking against study data, and refining recommendations to ensure organizational fit makes the all-in cost per high-quality output comparable to or exceeding a skilled analyst's time. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI can cut drafting time substantially, but consultants still need to review, verify, and tailor content to client context, so total cost savings are moderate rather than order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While large language models can generate documentation and outline recommendations, no deployed product reliably performs the full task of accurately documenting complex study findings and producing actionable implementation strategies without substantial human review and correction. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Generative AI tools are widely used today to draft consulting deliverables and reports, but they require significant human editing and fact-checking, limiting reliability for final client-facing recommendations. |
Analyze data gathered and develop solutions or alternative methods of proceeding.
37CI 32–41 · exposure 25 · augmentation 75 · importance 4.5/5 · click for rater detail
Analyze data gathered and develop solutions or alternative methods of proceeding.
37| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Many organizations have adopted analytics tools and early AI dashboards, but management consulting and strategy-level analysis remains cautiously exploratory; pilots are common but rarely displace senior analyst roles at scale. Enterprise adoption is growing but unevenly across sectors. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Consulting and analyst-heavy sectors are adopting AI tools for research and drafting at a moderate pace, though full analytical judgment remains largely human-driven. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at accelerating data discovery, pattern recognition, and generating candidate solutions, allowing analysts to focus on validation and implementation strategy. Current systems demonstrably boost analyst productivity through automated data prep, anomaly detection, and scenario modeling. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially accelerates data analysis, pattern identification, and drafting of alternative solutions, letting analysts focus on judgment and stakeholder negotiation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Data analysis itself is increasingly automatable with BI tools and AI, but developing solutions and alternative methods requires domain expertise, business context, and strategic judgment that current systems struggle with reliably. AI can accelerate parts of analysis but cannot consistently produce actionable, contextually appropriate solutions without human direction. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires synthesizing organizational context, stakeholder priorities, and judgment to craft viable recommendations, which current AI can support but not reliably originate end-to-end at equal quality. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Organizations expect human accountability for strategic recommendations and solutions, creating moderate friction against full automation. However, no strict licensing requirement or legal barrier prevents AI participation in analysis itself, only in final sign-off and implementation decisions. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational trust, accountability for recommendations, and client preference for human judgment create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI reduces data processing costs significantly, but developing robust solutions still requires skilled analysts to validate, contextualize, and implement findings. The all-in cost of AI-augmented analysis is competitive with junior analysts but typically more expensive than commodity data processing alone. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI can cheaply produce data summaries and draft options, but human oversight, validation, and stakeholder-specific tailoring remain costly, making overall cost roughly comparable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Tools exist for data analysis and visualization, but end-to-end solution development remains a human-driven process in production. Current AI systems can assist with data patterns and suggestions but lack the organizational knowledge and accountability judgment needed for deployed decision systems. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools can summarize data and suggest options, but deployed products don't reliably generate business-viable alternative solutions without heavy human framing and validation. |
Recommend purchase of storage equipment and design area layout to locate equipment in space available.
33CI 30–35 · exposure 25 · augmentation 63 · importance 3.2/5 · click for rater detail
Recommend purchase of storage equipment and design area layout to locate equipment in space available.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI-driven layout and equipment-selection tools is still in the pilot phase in most organizations; most management analysts continue to use traditional CAD, spreadsheets, and manual processes rather than automated AI solutions. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Management analysis and facilities planning are moderately digitized but layout/space planning tasks still rely on physical inspection and traditional consulting workflows, showing slow AI adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist with equipment cost comparison, space calculations, and draft layouts that analysts then review and refine, moderately raising productivity without replacing human judgment on final design and stakeholder alignment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted CAD, 3D modeling, and layout optimization tools can meaningfully speed up the design and equipment comparison process while the analyst still makes final judgment calls. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate storage equipment recommendations based on specifications and cost data, designing area layouts requires spatial reasoning, constraint satisfaction, and iterative refinement that current systems struggle with end-to-end. The task involves both data analysis and practical design judgment that typically needs human review and adjustment. |
| Task automatability | claude-sonnet-5 | 2/5 | This task combines physical space assessment, equipment recommendations, and layout design requiring site-specific knowledge that AI cannot fully gather or verify independently.assessed only partial slices (like generating a draft layout) can be automated, not the full end-to-end decision.rating |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | No hard legal barriers exist, but organizational friction is moderate: stakeholder review and buy-in on spatial designs is typical, and many organizations prefer human judgment on layout to avoid costly errors in physical space allocation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement typically applies, but organizational preference for physical inspection and vendor negotiation creates moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI tools (design software, planning assistants) still require significant human oversight and iteration, keeping total cost per completed task comparable to or higher than a management analyst's time; full automation cost advantage is not yet achieved. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can cheaply generate layout drafts, but the human cost of site assessment, equipment vetting, and stakeholder consultation remains significant, keeping overall cost comparable to human-led work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some CAD and space-planning tools exist with AI assistance, but deployable production systems that reliably handle the full pipeline of equipment selection and layout design are limited. Most real organizations still rely heavily on manual design or specialized consultants rather than AI-driven automation. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some CAD/space-planning tools with AI features exist, but no mature product autonomously recommends equipment purchases and designs layouts reliably without heavy human input and site visits. |
Develop and implement records management program for filing, protection, and retrieval of records, and assure compliance with program.
31CI 30–32 · exposure 25 · augmentation 63 · importance 3.6/5 · click for rater detail
Develop and implement records management program for filing, protection, and retrieval of records, and assure compliance with program.
31| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While information-rich sectors (finance, legal, healthcare) digitize records systems, the design and implementation of records management programs itself remains largely manual and slower to adopt AI—pilots exist but production displacement of management analysts' program design work is still uncommon. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Management analysts work across many sectors including some fast-adopting ones like finance and professional services, but records management specifically has seen only moderate AI tool adoption, mostly in pilot or narrow use cases. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist management analysts by automatically classifying documents, flagging compliance gaps, recommending filing structures, and generating audit reports, thereby raising analyst productivity in designing and monitoring the program without removing human judgment on organizational fit and policy choices. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist in classifying documents, drafting retention schedules, flagging compliance gaps, and searching records, substantially boosting the human analyst's efficiency even though it does not replace them. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with classification, filing rules, and compliance checking, implementing a full records management program requires ongoing organizational change management, stakeholder engagement, and contextual judgment about institutional needs that current AI systems cannot fully automate. The task has enough human-centric organizational elements that even with setup, AI cannot achieve the 50% time-saving threshold end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can assist in drafting policies and organizing digital taxonomies, but designing a full program plus assuring ongoing compliance requires organizational judgment, stakeholder negotiation, and physical/legal record considerations that current systems cannot autonomously execute end-to-end.4 |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Records management often touches regulated domains (legal, financial, healthcare) with compliance requirements and organizational policies that create some friction, but these are not hard legal barriers requiring a licensed human to sign off. Most barriers are organizational inertia and the need for human judgment rather than regulatory prohibition. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically for this task, but compliance assurance often intersects with legal/regulatory retention requirements and organizational accountability, creating moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI tools for records management (classification, metadata extraction, compliance scanning) are relatively inexpensive per document, but the full integration cost, human oversight, and the salary of a management analyst designing and implementing the program means the all-in cost remains comparable to or higher than human-only execution for smaller-to-medium implementations. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI can cut some drafting and classification time, the human oversight, legal review, and compliance monitoring components remain costly, so all-in AI cost is not dramatically cheaper than an analyst's loaded wage for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some deployed products exist for document classification and compliance auditing, but end-to-end program implementation—which requires designing policies, training staff, managing resistance, and adapting to an organization's unique structure—remains largely a human-led function with AI as a supporting tool, not a reliable autonomous performer. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Document management and records-retention software exist and are widely deployed, but AI-driven autonomous design and enforcement of a records management program is not a mature deployed product category; most tools still require heavy human configuration and oversight. |
Interview personnel and conduct on-site observation to ascertain unit functions, work performed, and methods, equipment, and personnel used.
29CI 20–38 · exposure 20 · augmentation 63 · importance 4.1/5 · click for rater detail
Interview personnel and conduct on-site observation to ascertain unit functions, work performed, and methods, equipment, and personnel used.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Management consulting and operations analysis are relatively digitized sectors, but firms remain cautious about automating the core human-facing interview and observation work due to relationship and credibility concerns. Adoption of AI-assisted tools is growing, but full automation velocity remains low. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Management consulting and analyst roles are in a sector with moderate AI adoption for research and reporting, but the fieldwork-heavy interview/observation component sees slower uptake of automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by transcribing interviews, extracting themes from notes, and proposing workflow analyses, helping analysts organize and synthesize findings faster. However, the augmentation is incremental rather than transformative, as human judgment and interpersonal skills remain central to the task. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully assist with pre-interview question preparation, real-time transcription, summarizing observations, and synthesizing findings, significantly boosting analyst productivity while the human remains central to gathering the data. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help analyze some observation data and generate summaries, conducting interviews and on-site observations requires subjective judgment, interpersonal rapport, and real-time environmental assessment that current AI cannot reliably replicate end-to-end. AI tools could potentially assist with 20–30% of the work (transcription, preliminary coding) but cannot replace the core human-centered data-gathering process. |
| Task automatability | claude-sonnet-5 | 2/5 | The core work requires in-person observation, interpersonal rapport-building, and adaptive probing during interviews that current AI cannot autonomously conduct, though AI can help with transcription and note synthesis afterward.rating reflects limited end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Client trust, stakeholder buy-in, and organizational expectations that a qualified analyst will conduct interviews and observations in person create significant friction. Regulatory and professional norms in management consulting favor direct human engagement; liability for incorrect observations and recommendations often falls on the credentialed analyst. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human specifically, but organizational trust, need for physical presence, and interpersonal judgment during interviews create moderate practical barriers to full substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI services for transcription and analysis are cheap, but they still require a human analyst to conduct the interviews and observations, meaning total cost remains dominated by labor. AI provides minor cost offset, not an order-of-magnitude reduction. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Since AI cannot perform the on-site interviewing and observation itself, a human analyst's time is still required for the bulk of the task, so cost savings are limited to auxiliary support like note-taking or summarization. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably conducts interviews or on-site observations autonomously today. AI can support transcription and note analysis post-hoc, but end-to-end execution of this task—building trust, probing responses, navigating physical sites—remains research-stage and undeployed in production. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product independently conducts on-site observational fieldwork and personnel interviews to assess organizational functions; existing tools only assist with transcription, survey collection, or summarization after humans gather the data. |
Confer with personnel concerned to ensure successful functioning of newly implemented systems or procedures.
22CI 16–28 · exposure 16 · augmentation 50 · importance 4.5/5 · click for rater detail
Confer with personnel concerned to ensure successful functioning of newly implemented systems or procedures.
22| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While information-sector firms use AI monitoring and reporting tools, the core task of conferring with personnel to ensure system success remains predominantly manual. Adoption of AI for this interpersonal validation work is nascent and limited to narrow augmentation roles. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Management consulting and professional services are moderately fast adopters of AI tools generally, though this specific interpersonal coordination task sees limited direct AI deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by pre-drafting talking points, summarizing stakeholder feedback from surveys or logs, and flagging common concerns—helping the analyst prepare and synthesize information more efficiently while the human leads engagement. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help analysts prepare talking points, summarize feedback, track implementation issues, and draft follow-up communications, improving efficiency around the human-led conferring process. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Conferencing with multiple stakeholders to validate system functionality requires understanding context, interpersonal dynamics, and nuanced feedback—areas where current AI struggles. While AI could draft summary documents or flag common issues, genuine consensus-building and relationship management with personnel remain heavily human-dependent. |
| Task automatability | claude-sonnet-5 | 2/5 | This task centers on live interpersonal conferring, relationship management, and real-time judgment about organizational dynamics, which current AI cannot substitute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Organizations typically require a human analyst or manager to sign off on system readiness and stakeholder alignment; personnel also expect to confer with a human decision-maker. Regulatory and organizational norms around change management create substantial friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement strictly mandates a human, but organizational trust, accountability for change management outcomes, and stakeholder relationships create real friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI summarization and monitoring tools are cheap, but they cannot replace the loaded cost of a management analyst conducting these conferences; oversight and human follow-up would still be required, making the total cost-to-value ratio unfavorable compared to human performance. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot perform the core human conferring function, so there is no viable cost substitution; a human consultant is still required. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably conducts stakeholder conferences to ensure system adoption and address concerns autonomously. Video conferencing copilots and summarization tools exist, but substituting an AI agent for the management analyst in these interpersonal validation conversations is not demonstrated in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously confers with personnel to troubleshoot and ensure new system adoption; this remains a human relational activity. |
Related occupations — Business & Financial Operations
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.