Survey Researchers
19-3022.00Median wage $69,460/yr8,290 employed (US)Rank #93 of 923 scored · top 10% by substitution
Plan, develop, or conduct surveys. May analyze and interpret the meaning of survey data, determine survey objectives, or suggest or test question wording. Includes social scientists who primarily design questionnaires or supervise survey teams.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution48
Exposure42
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
16 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
25%
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%43
panel mean rating 2.7/5 → substitution pressure 43/100
Technical feasibility todayw 20%40
panel mean rating 2.6/5 → substitution pressure 40/100
Cost vs. human wagew 15%51
panel mean rating 3.0/5 → substitution pressure 51/100
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 (16 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Review, classify, and record survey data in preparation for computer analysis.
82CI 72–92 · exposure 87 · augmentation 88 · importance 3.9/5 · click for rater detail
Review, classify, and record survey data in preparation for computer analysis.
82| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Adoption is already occurring in professional survey and market research sectors; AI-powered survey platforms and data processing tools are increasingly standard in digitized survey operations, indicating strong velocity in information-intensive industries. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Research and survey firms are adopting AI-assisted coding and cleaning tools steadily, but many still rely on manual review for complex or sensitive classification, reflecting middling adoption maturity. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI substantially augments survey data work by automating tedious classification and entry, freeing researchers to focus on quality assurance, anomaly detection, and interpretation—allowing humans to review AI outputs efficiently rather than performing rote tasks themselves. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI tools substantially speed up data classification, flagging inconsistencies, and pre-coding open-ended responses, letting researchers focus on judgment calls and quality control. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | This task is highly automatable. Data classification, structuring, and recording for computational analysis can be performed end-to-end by current AI systems (OCR for scanned responses, NLP for text classification, and automated data entry) with significant time savings and equal or better accuracy than manual work. |
| Task automatability | claude-sonnet-5 | 4/5 | Data cleaning, classification, and coding of survey responses (especially structured or even open-ended text via NLP/LLM classification) can largely be automated with modern tools, though edge cases and ambiguous responses still need human review. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal barriers exist; however, organizational inertia and data privacy/confidentiality oversight may slow adoption. Quality assurance and human review of sensitive classifications add some friction, but do not prevent automation of the bulk work. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for this task, though research integrity standards and organizational QA practices create some review friction before analysis-ready data is finalized. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-based survey data processing is orders of magnitude cheaper than manual data entry and coding, with marginal inference costs and minimal overhead compared to the loaded wage of survey coders and data entry personnel. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated data classification and validation scripts or AI coding tools cost a small fraction of analyst hours for the same volume of records, though initial setup and quality assurance add some cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Mature products exist today that reliably perform survey data entry, classification, and cleaning at scale—including optical character recognition, form parsing, and automated data validation systems deployed in production by survey firms and market research organizations. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Survey platforms (Qualtrics, SPSS, R pipelines) and LLM-based coding tools already automate much of data cleaning, validation, and categorization in production, though full reliability on messy open-ended responses still requires spot-checking. |
Analyze data from surveys, old records, or case studies, using statistical software.
77CI 67–86 · exposure 75 · augmentation 100 · importance 3.9/5 · click for rater detail
Analyze data from surveys, old records, or case studies, using statistical software.
77| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Survey research organizations, academic institutions, and market research firms have rapidly adopted statistical software and automated pipelines. This is a digitized, information-sector task with deep and widespread adoption of automation tools already in production. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Market research and social science analytics are moderately digitized with growing pilot adoption of AI-assisted analysis tools, but full production-scale automation of research analytics remains uneven across academic and applied research settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI and statistical software significantly augment researcher productivity by automating computation, generating preliminary summaries, flagging anomalies, and suggesting appropriate statistical methods, allowing researchers to focus on interpretation and insights rather than manual calculation. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI tools substantially speed up code generation, data cleaning scripts, visualization, and preliminary statistical interpretation, letting researchers focus on study design and interpretation while staying in the loop. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Statistical analysis of survey data is highly automatable; modern AI systems and statistical software can perform descriptive statistics, hypothesis testing, regression modeling, and visualization with minimal human input. The main remaining human role is interpretation and validation of results, but the computational work itself easily achieves >50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 4/5 | Statistical analysis of structured survey data (descriptive stats, regressions, cross-tabs) can largely be performed by AI-assisted tools and code-generation agents that write and execute analysis scripts, saving substantial time, though interpretation and judgment calls on messy or ambiguous data still require human input. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While some organizations require human review and sign-off on statistical findings for governance reasons, there are no legal licensing barriers preventing automated analysis itself. Adoption is primarily slowed by organizational preference for human oversight and interpretation rather than hard regulatory requirements. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human perform statistical analysis, though organizations often require methodological sign-off and quality checks before publishing findings, especially in regulated survey research (e.g., government or market research). |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | The cost of running automated statistical analysis via software is orders of magnitude cheaper than the loaded cost of a survey researcher's time spent on manual calculations and coding—computational resources cost pennies per analysis versus hours of professional labor. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Once a pipeline is set up, AI-assisted statistical analysis costs a small fraction of analyst hours per unit of output, though initial setup, data cleaning, and validation still require paid human oversight. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Deployed statistical software (R, Python libraries, SPSS, SAS) already automates data analysis at scale across academic and commercial organizations. Multiple mature products perform this reliably in production, from specialized survey platforms to general statistical packages with AI-augmented features. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like AI coding assistants integrated with R/Python/SPSS workflows, and analytics copilots (e.g., in Excel, Stata plugins, ChatGPT Code Interpreter) are deployed and used in practice, but reliability drops with complex survey weighting, missing data handling, or nuanced case study synthesis. |
Prepare and present summaries and analyses of survey data, including tables, graphs, and fact sheets that describe survey techniques and results.
73CI 67–79 · exposure 70 · augmentation 100 · importance 4.5/5 · click for rater detail
Prepare and present summaries and analyses of survey data, including tables, graphs, and fact sheets that describe survey techniques and results.
73| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Research, government, and marketing organizations rapidly adopt BI platforms and AI-assisted analytics tools for report generation. Adoption is well-established in information-intensive sectors with strong digitization. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Market research and survey research firms are adopting AI-assisted reporting tools, but many still rely on manual analyst review, especially for methodological write-ups, placing adoption at a middling pace. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI substantially boosts human productivity by automating tedious visualization and summary generation, allowing researchers to focus on interpretation and insight. Humans remain central to methodology review and narrative framing, making this strong augmentation. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up drafting of tables, visualizations, and narrative summaries, letting researchers focus on interpretation and quality control while AI handles the formatting and first-draft synthesis. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can readily generate tables, graphs, and summary statistics from structured survey data with minimal human input. However, interpreting results, selecting appropriate visualizations, and ensuring contextual accuracy still benefits from human oversight, preventing a full 5 rating. |
| Task automatability | claude-sonnet-5 | 4/5 | AI can generate tables, charts, and written summaries from structured survey data quickly, and current LLM-based tools with code execution can produce most of a fact sheet or report draft with substantial time savings. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or regulatory barriers prevent automation; survey presentation is not a licensed profession, and error costs for incorrect tables or graphs are typically low enough for organizational tolerance. Minimal human-contact requirement. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for producing survey summaries, though organizations often want a credentialed researcher to validate methodology descriptions and sign off on findings for credibility/rigor reasons. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI tools for generating visualizations and summaries cost a fraction of human analyst time; a single API call or BI tool run can replace hours of manual chart creation and basic writeup, yielding >10× cost advantage. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating standard tables, graphs, and narrative summaries via AI tools costs a small fraction of analyst time compared to manual report preparation, though oversight adds some cost back. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (Python libraries, BI tools with AI features, and LLM-based analytics platforms) reliably produce charts, summaries, and basic analyses from survey data in production settings. Minor limitations exist around complex methodological nuance, but core functionality is mature. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like ChatGPT with data analysis, BI tools with AI summarization, and survey platforms (e.g., Qualtrics AI) generate charts and narrative summaries today, but human review is typically needed to catch misinterpretations of methodology or statistical nuance. |
Write proposals to win new projects.
73CI 59–87 · exposure 70 · augmentation 88 · importance 4.0/5 · click for rater detail
Write proposals to win new projects.
73| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Research organizations and consulting firms—highly digitized, information-sector heavy—are adopting proposal-writing AI tools rapidly, with pilots and production deployment common. Early-stage adoption is visible in major research institutions and professional services. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Professional services and research firms are adopting AI writing assistants at a moderate pace, with proposal drafting a common but not yet universal pilot use case. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically assists researchers by generating initial drafts, filling boilerplate, structuring arguments, and iterating on messaging while the researcher refines strategy, claims, and customization. Productivity gains are substantial and measurable when deployed as an assistant. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially speeds up drafting, editing, and formatting of proposals, letting researchers focus on strategy and customization while the tool handles boilerplate content generation. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | AI can generate full proposals at >50% time savings with current off-the-shelf tools (LLMs, templates, data integration). The task requires chaining information retrieval, structured writing, and formatting—all well-established AI capabilities that can produce competitive output without human rework. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft substantial portions of survey research proposals (boilerplate methodology, background, budget templates) but requires human input on client-specific strategy, pricing, and win-theme judgment, so only partial time savings are achievable end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or regulatory barriers exist to proposal automation. Organizational friction is mild (preference for human authorship/sign-off, internal quality processes) but does not prevent adoption; proposals are internally managed outputs, not customer-facing regulated deliverables. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human write proposals, but organizational trust, client relationships, and accountability for commitments made in the proposal create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI inference and integration cost is negligible (dollars) compared to a senior researcher's loaded wage (>$100/hour) for proposal writing; even with oversight overhead, the cost ratio favors AI by an order of magnitude or more. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI drafting tools cost a small fraction of a researcher's hourly rate for producing first-draft proposal text, though human review and strategic input remain necessary, keeping it below a full order-of-magnitude savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple deployed products (Claude, GPT-4, specialized proposal software) can draft proposals reliably, though final human review and customization remain standard practice. Production use is common in consulting and research firms, though error rates on technical claims and budget accuracy require oversight. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like ChatGPT, Copilot, and specialized proposal-writing tools are used in production for grant/proposal drafting, but outputs still require heavy editing for accuracy, client fit, and competitive positioning in survey research. |
Monitor and evaluate survey progress and performance, using sample disposition reports and response rate calculations.
69CI 59–79 · exposure 62 · augmentation 75 · importance 4.4/5 · click for rater detail
Monitor and evaluate survey progress and performance, using sample disposition reports and response rate calculations.
69| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Survey research firms and market research organizations have widely adopted automated dashboards and BI tools for real-time performance tracking. This is a standard feature in commercial survey platforms and has achieved high penetration in the information services sector. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Survey research and market research firms have moderate digitization and are adopting automated reporting tools, though full AI-driven evaluation is still emerging. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI dashboards and alerts substantially augment researcher productivity by surfacing trends, anomalies, and metrics in real time, allowing researchers to focus on interpretation and corrective action rather than manual data compilation. The human remains in the loop for decision-making while AI accelerates insight generation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-generated dashboards, anomaly detection, and automated rate calculations substantially speed up a researcher's ability to monitor and evaluate survey progress. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can automatically generate sample disposition reports, calculate response rates, and flag performance anomalies with high accuracy. The task involves mostly data aggregation and routine calculations that modern BI and data-processing systems handle reliably, achieving well over 50% time savings compared to manual monitoring. |
| Task automatability | claude-sonnet-5 | 3/5 | Calculating response rates and generating disposition summaries from structured data is straightforward for AI/automation tools, but interpreting performance trends and deciding corrective actions requires human judgment.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few hard legal or regulatory barriers exist; survey monitoring is internal quality assurance with no mandatory human sign-off requirement. The main friction is organizational inertia and preference for human oversight, but no licensed authority is legally required to perform this task. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational trust in automated survey quality judgments and need for methodological oversight create some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated monitoring systems cost a fraction of a researcher's hourly wage once configured. The per-task inference and integration cost is trivial compared to the loaded salary of a survey researcher who would otherwise manually compile reports. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated dashboards and scripts can compute and monitor metrics continuously at a fraction of the cost of a human analyst reviewing reports manually. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Production survey management platforms and BI tools already provide automated dashboards for sample disposition and response rate monitoring. While some edge cases in complex survey designs may require human interpretation, the core monitoring and calculation tasks are reliably performed by deployed systems. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Survey platforms (Qualtrics, SPSS, R packages) already automate disposition reporting and rate calculations, but full evaluative monitoring with contextual decision-making is not fully productized. |
Write training manuals to be used by survey interviewers.
66CI 59–72 · exposure 58 · augmentation 88 · importance 3.4/5 · click for rater detail
Write training manuals to be used by survey interviewers.
66| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Survey research firms and market research organizations are beginning to pilot AI for content generation, but adoption remains in early-to-middling phases; many organizations still rely on traditional manual writing, and training-material stakes encourage cautious, human-led processes. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Survey research and market research firms are moderately adopting AI for documentation and content generation, though not yet universally embedded in production workflows. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI substantially aids human survey researchers by rapidly generating initial drafts, suggested section outlines, and baseline language that expert authors then refine—transforming time-to-first-draft and enabling focus on methodological rigor rather than prose generation. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up drafting, formatting, and revising training manuals while researchers retain oversight for accuracy and domain-specific content. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can generate draft training manual sections efficiently (e.g., question scripting, compliance guidelines, interview protocols), but typically requires significant human review, customization for organizational context, and validation of accuracy—achieving roughly 50% time savings with setup, not fully autonomous end-to-end production. |
| Task automatability | claude-sonnet-5 | 4/5 | Drafting training manuals is largely text generation based on established survey protocols, which current LLMs handle well with human review for accuracy and organizational specifics. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Training manuals are internal organizational documents without strict licensing or liability barriers; quality and consistency expectations create some friction, but no legal requirement for human authorship prevents or constrains AI-assisted drafting. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or regulatory requirement mandates a human author training manuals; organizations can freely adopt AI drafting tools. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI inference cost (per draft) is negligible compared to human labor; even accounting for integration, oversight by a senior survey methodologist, and iteration, AI-assisted generation is substantially cheaper than hiring a technical writer to compose from scratch. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating draft text via LLMs is far cheaper than hours of a researcher's time, though review and customization still add some human cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Large language models can produce usable manual drafts in production (e.g., via ChatGPT, Claude), but material limitations persist: risk of outdated best practices, insufficient domain specificity for complex survey designs, and need for expert human verification before deployment to interviewers. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | AI writing tools are widely used for manual and documentation drafting, but survey-specific procedural accuracy and instructional design still require human editing and validation. |
Conduct research to gather information about survey topics.
49CI 37–61 · exposure 38 · augmentation 88 · importance 4.2/5 · click for rater detail
Conduct research to gather information about survey topics.
49| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Research organizations are experimenting with AI-assisted literature review and data gathering, but adoption of autonomous AI for conducting research remains limited. Most usage is supplementary rather than displacing the core research function. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Survey research sits within social science/market research and professional services sectors where AI search and summarization tools are already widely adopted for background research tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI substantially augments researchers by automating literature searches, organizing information, identifying patterns in preliminary data, and generating research summaries, allowing researchers to focus on interpretation, methodology design, and validation decisions. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up gathering background information, generating literature summaries, and surfacing relevant sources, letting researchers focus on designing methodology and interpreting findings. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with literature reviews, data compilation, and research design, but cannot independently conduct primary research requiring human interaction, stakeholder engagement, or judgment about research quality and relevance. The core work of gathering original information typically requires human direction and evaluation. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can rapidly gather background information, summarize literature, and identify relevant prior surveys, but synthesizing this into a research strategy still requires human judgment about topic framing and question design. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Survey research often requires IRB approval, human oversight of methodology, and client trust in the researcher's judgment. Organizations face moderate friction adopting AI for primary research tasks due to quality assurance needs and professional accountability expectations. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for background research; some organizational preference for expert-vetted sources but no hard regulatory barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-powered research assistance (literature search, data aggregation, preliminary analysis) costs far less than hiring researchers for these components, making the cost ratio highly favorable for the automatable portions of the task. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-assisted research (search, summarization, literature review) is dramatically cheaper than hours of manual research per topic, though final vetting adds some human cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI tools can search literature and compile existing information at scale, no deployed product reliably conducts independent research gathering with the quality assurance survey researchers require. Systems exist for information retrieval but not for the full research synthesis and validation needed. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Deployed tools like AI search assistants and research copilots reliably retrieve and summarize information, but they still miss nuance, produce errors, and require human verification for research-grade rigor. |
Produce documentation of the questionnaire development process, data collection methods, sampling designs, and decisions related to sample statistical weighting.
37CI 25–50 · exposure 33 · augmentation 63 · importance 4.2/5 · click for rater detail
Produce documentation of the questionnaire development process, data collection methods, sampling designs, and decisions related to sample statistical weighting.
37| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Survey research remains a specialized field with slower digitization than general information work. Organizations currently use AI primarily for minor documentation tasks; production adoption of AI-generated methodology documentation is rare and cautious due to quality and liability concerns. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Survey research and market research firms are moderately adopting AI writing tools for reports and documentation, though it's not yet standard practice industry-wide. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by drafting initial methodology descriptions, organizing documentation structures, and suggesting standard language, raising researcher productivity in the documentation phase. However, the human expert must remain central to validating all technical claims and decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI is well-suited to drafting boilerplate methodology text, organizing technical details, and improving clarity, substantially speeding up the writing portion while the researcher supplies and verifies substantive content. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft sections describing methodology and generate structured documentation templates, the task requires domain expertise to justify statistical choices, articulate sampling design rationale, and ensure accuracy of technical decisions—elements that demand human judgment beyond template-filling. Significant human review and correction would be needed. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft methodology documentation from structured inputs (survey design specs, sampling parameters, weighting formulas), but requires human-provided accurate details and review to ensure fidelity, so only partial time savings are realistic without setup. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Professional survey standards, peer review expectations, and institutional review boards typically require that methodological documentation be authored or signed off by qualified statisticians. Regulatory and ethical accountability for sampling design decisions create strong legal and reputational barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but documentation must accurately reflect real methodological decisions and often faces institutional/journal or client review, creating moderate quality-control friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI reduces drafting time but requires substantial expert review to ensure technical accuracy and methodological soundness, limiting cost savings. The loaded cost of skilled survey researchers reviewing and correcting AI output remains comparable to or higher than direct human authorship for complex statistical decisions. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drafting is cheap per word, but the need for accurate technical detail, verification against actual survey parameters, and researcher review narrows the cost advantage over a trained analyst who already has full context. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably produces complete, production-ready documentation of survey methodology end-to-end. AI tools can assist in drafting text and organizing information, but they lack the specialized knowledge to independently validate or author the technical statistical content without expert oversight. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | General LLM writing assistants can produce draft methodology sections, but no specialized product reliably compiles complete, audit-ready survey documentation including weighting decisions without significant human input and verification. |
Collaborate with other researchers in the planning, implementation, and evaluation of surveys.
35CI 32–38 · exposure 25 · augmentation 75 · importance 4.3/5 · click for rater detail
Collaborate with other researchers in the planning, implementation, and evaluation of surveys.
35| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Tech-forward research organizations and market research firms have begun adopting AI for survey analysis and preliminary drafting, but adoption remains in the pilot-to-early-production stage. Most survey research workflows still center on human researchers, with AI as an emerging supplement rather than established practice. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Survey research spans academia, market research, and government sectors with moderate AI tool adoption for data analysis and drafting, but collaborative planning workflows remain largely human-driven. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist survey researchers by auto-generating draft questionnaires, performing rapid statistical analysis, identifying patterns in responses, and summarizing findings—all while researchers retain design decisions, validation, and strategic oversight. This is a strong augmentation case where AI boosts productivity without removing human judgment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully assist by drafting survey instruments, summarizing literature, analyzing pilot data, and generating meeting notes, enhancing researcher productivity throughout collaborative planning. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with survey design and statistical analysis, but the collaborative planning and implementation phases require human judgment, stakeholder negotiation, and domain expertise. The task inherently involves human coordination and creative problem-solving that current AI cannot fully replace. |
| Task automatability | claude-sonnet-5 | 2/5 | Collaboration is inherently interpersonal and requires negotiation, judgment, and relationship management that current AI cannot fully replace, though AI can support subtasks like drafting plans or analyzing pilot data.4 |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Survey research in many contexts (government, regulated industries) may require credentialed researchers for sign-off and liability reasons, creating some barriers. However, many academic and commercial surveys face fewer licensing requirements, leaving adoption to organizational choice rather than legal mandate. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates human collaboration, but organizational norms and the inherently social nature of team-based research create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI assistance for survey tasks (design, analysis) is relatively inexpensive, but when accounting for the overhead of integration, human oversight, and validation across a collaborative team, the all-in cost approaches or exceeds the cost of experienced researchers doing the work directly. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human collaboration costs remain necessary since AI cannot substitute for team decision-making, though AI can reduce time spent on some prep work, yielding only modest cost savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI tools exist for survey drafting and basic analysis, no deployed product reliably manages the full collaborative workflow of survey planning, implementation, and evaluation with human teams. Current systems lack the contextual understanding needed for effective real-time research collaboration. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product manages cross-team collaborative survey planning end-to-end; existing tools assist with document sharing, scheduling, or draft generation but not the collaborative reasoning itself. |
Conduct surveys and collect data, using methods such as interviews, questionnaires, focus groups, market analysis surveys, public opinion polls, literature reviews, and file reviews.
34CI 30–38 · exposure 25 · augmentation 75 · importance 4.8/5 · click for rater detail
Conduct surveys and collect data, using methods such as interviews, questionnaires, focus groups, market analysis surveys, public opinion polls, literature reviews, and file reviews.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Survey research is concentrated in academia, market research firms, and government—sectors with moderate digital adoption. Most organizations still rely on traditional human-led survey methods; AI augmentation of analysis is growing, but wholesale displacement remains limited and slow. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Market research and social science fields are increasingly adopting AI for survey design, transcription, and analysis, though full-cycle survey administration adoption is still uneven. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly assists survey researchers by automating questionnaire drafting, coding open-ended responses, identifying themes in focus group transcripts, and synthesizing literature reviews. These augmentations raise researcher productivity meaningfully while the human remains the decision-maker on design, interpretation, and participant engagement. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially assists with survey design, sampling frame construction, transcription of interviews, and literature synthesis, meaningfully boosting researcher productivity even though humans remain central to fieldwork. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate questionnaires, analyze responses, and assist with literature reviews, conducting interviews and focus groups—core components requiring human rapport and adaptive questioning—remains largely manual. The task combines structured (automatable) and unstructured (human-dependent) elements, falling short of the 50% time-saving threshold end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help design questionnaires, analyze text/literature, and even conduct chatbot-based interviews, but administering diverse survey modalities (in-person interviews, focus groups, phone polls) still requires substantial human execution and rapport-building. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Organizational and ethical norms favor human researchers for data collection and participant consent; IRB oversight and client expectations create friction. However, no legal license is required, and automation of subtasks (coding, analysis) faces only moderate adoption resistance rather than hard legal barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No strict licensing requirement for conducting surveys, but methodological rigor, IRB/ethics compliance, and client trust in the human researcher's own data collection create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for questionnaire generation and analysis are inexpensive, but human researchers remain essential for interview conduct, relationship-building, and quality assurance. The all-in cost of human researchers plus AI assistance is comparable to or exceeds replacing them entirely, since sampling, recruitment, and interpretation still require domain expertise. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can cut costs for survey design and literature review, but human-run focus groups, phone interviews, and fieldwork still require paid staff, keeping overall costs closer to human-driven processes. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI can reliably perform narrow subtasks (questionnaire design, response coding, basic literature review) but no deployed product reliably executes the full survey cycle—interviews, focus groups, data collection quality control, and synthesis—without substantial human oversight and intervention. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some products exist for online survey distribution, chatbot interviews, and automated literature review, but no deployed system reliably handles the full range of methods (focus groups, public opinion polls, file reviews) end-to-end. |
Support, plan, and coordinate operations for single or multiple surveys.
34CI 30–38 · exposure 25 · augmentation 63 · importance 4.5/5 · click for rater detail
Support, plan, and coordinate operations for single or multiple surveys.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Survey research organizations tend to be traditional, research-focused institutions with lower digital-maturity adoption patterns; while some may pilot AI-assisted scheduling, production adoption of autonomous survey coordination remains minimal. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Survey research organizations (market research, academia, government) are moderately adopting AI for data collection and analysis, but operational coordination remains a laggard area with pilots more common than full deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist with scheduling optimization, resource tracking, timeline management, and status reporting, allowing human coordinators to focus on complex decision-making, but productivity gains are partial rather than transformative. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI project management and scheduling tools, along with generative AI for drafting timelines, communications, and status reports, meaningfully boost productivity for the human coordinator. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with scheduling, resource allocation, and basic coordination logistics, the task requires significant human judgment on survey design priorities, stakeholder management, and operational problem-solving that resist full automation. Current AI falls well short of the 50% time-saving threshold for end-to-end survey operation orchestration. |
| Task automatability | claude-sonnet-5 | 2/5 | Coordinating operations for surveys involves multi-stakeholder scheduling, budget management, vendor coordination, and adaptive decision-making that current AI cannot fully execute end-to-end without heavy human oversight.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Survey operations involve data governance, methodological decisions, and regulatory compliance (especially with sensitive populations) that typically require sign-off from qualified researchers, creating moderate friction against full automation without human oversight. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this coordination role, but organizational reliance on human judgment for logistics, stakeholder relationships, and contingency planning creates moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integrating AI for coordination oversight, validation, and ongoing management still requires substantial human supervision and error-correction, making all-in costs comparable to or exceeding direct human coordination labor for complex surveys. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can reduce some administrative overhead, but human oversight, vendor negotiation, and adaptive coordination still require significant paid labor, keeping costs comparable to or only modestly below human-only operations. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No production-grade system reliably handles multi-survey operational planning and coordination end-to-end. Project management tools exist but require heavy human input for survey-specific constraints; AI agents capable of autonomous survey operations coordination are not mature in deployed settings. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some project-management and scheduling AI tools exist to assist with logistics, but no deployed product autonomously plans and coordinates full survey operations reliably in production. |
Determine and specify details of survey projects, including sources of information, procedures to be used, and the design of survey instruments and materials.
33CI 25–41 · exposure 25 · augmentation 75 · importance 4.5/5 · click for rater detail
Determine and specify details of survey projects, including sources of information, procedures to be used, and the design of survey instruments and materials.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Survey research occurs in academia, government, and market research—mixed-digitization sectors with strong professional norms and regulatory requirements. Adoption of AI tools is slow and incremental (drafting aids only), not production-level displacement. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Market research and social science sectors are adopting AI-assisted survey tools at a middling pace, with pilots and partial integration common but full end-to-end automation still rare. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can significantly assist survey researchers by generating question drafts, identifying methodological options, and providing formatting suggestions, thereby accelerating design iteration and freeing the researcher to focus on validation and strategic choices. This augmentation is already demonstrable in practice. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially speeds up drafting survey instruments, suggesting question wording, and outlining procedures, meaningfully boosting researcher productivity while they retain design authority. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft survey questions and suggest methodologies based on templates, determining survey scope, selecting appropriate information sources, and designing instruments require domain expertise, stakeholder input, and iterative refinement that current AI cannot fully handle end-to-end at scale. The task involves substantial human judgment about research validity that AI cannot reliably replace. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can draft survey instruments and suggest methodologies given a prompt, but determining information sources, sampling design, and project scope requires domain judgment, stakeholder input, and contextual decisions that current AI cannot fully own end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Survey research often requires institutional review board (IRB) approval, human researcher authorization, and professional expertise in social science methods; the final design and sign-off typically must come from a qualified human researcher, creating a hard organizational and regulatory barrier to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human for survey design, but methodological rigor, client trust, and accountability for research validity create moderate organizational friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI assistance (LLMs, survey design tools) has meaningful costs in infrastructure and integration, while the time savings over a human survey researcher's loaded wage are modest due to the need for expert oversight and rework. The cost advantage is limited. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drafting tools are cheap per use, but the human researcher must still review, validate, and finalize designs, so overall cost savings are moderate rather than order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI tools can assist with question generation and format suggestions, but no deployed product reliably performs the full task of specifying survey projects from scratch, including source validation, procedure design, and instrument construction at production quality. Existing products require heavy human oversight and iteration. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Deployed products (chatbots, survey-building tools like Qualtrics AI features) can generate draft questions, but no production system reliably designs full survey methodology and instrument specifications without heavy human oversight. |
Consult with clients to identify survey needs and specific requirements, such as special samples.
29CI 25–32 · exposure 25 · augmentation 63 · importance 4.5/5 · click for rater detail
Consult with clients to identify survey needs and specific requirements, such as special samples.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Survey research occurs in professional services and academia, which show middling AI adoption overall; while survey software vendors are integrating AI helpers, autonomous AI replacing consultative roles remains rare and pilots are early-stage rather than production displacement. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Market research and survey firms are professional services adopting AI moderately for drafting and analysis, but client-facing consultation remains a human-led, slower-adopting activity. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by suggesting survey designs from historical templates, flagging sampling issues, and drafting requirement summaries, thereby reducing rework and speeding the initial needs-assessment phase while the survey researcher maintains control and client relationship. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can help prepare briefing materials, summarize past client requirements, draft questionnaires, and suggest sampling strategies, meaningfully boosting researcher productivity during consultations. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist in drafting survey questionnaires and suggesting sample strategies from existing templates, the consultative aspect requires understanding nuanced client business contexts, negotiating tradeoffs, and building trust—tasks where current AI systems lack the contextual judgment and real-time interactivity needed for reliable end-to-end automation at 50% time savings. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a consultative, relationship-driven task requiring clarifying client needs interactively, which current AI can support but not autonomously perform end-to-end with equal quality time savings.atur.The negotiation and judgment-heavy discovery process resists full automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Client trust, legal accountability for survey design specifications, professional liability for flawed sampling recommendations, and the expectation of a qualified human expert signature create strong adoption barriers; many organizations and regulated industries require a licensed or certified survey professional to own the consultation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement mandates a human specifically, but client trust, relationship management, and accountability for methodological decisions create meaningful organizational friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI assistance (chatbots, document drafting) reduces overhead but does not yet reach cost parity with a survey researcher's loaded wage for the full consultation task, since client interaction, requirement gathering, and bespoke design still demand skilled human labor. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Because a human researcher must still lead the client interaction and interpret nuanced requirements, AI mainly adds a supplementary cost (transcription, summarization tools) rather than replacing the billable consulting time. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably perform independent client consultations for survey design end-to-end; existing tools are narrow (survey builders, sample calculators) and require human interpretation and relationship management. AI can support with information retrieval and drafting suggestions, but the consultative handoff remains human-driven. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product autonomously conducts client consultations to define survey scope and sampling requirements; AI is used at most as a note-taker or draft-generator alongside a human researcher. |
Direct and review the work of staff members, including survey support staff and interviewers who gather survey data.
28CI 25–30 · exposure 25 · augmentation 63 · importance 4.2/5 · click for rater detail
Direct and review the work of staff members, including survey support staff and interviewers who gather survey data.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Survey research organizations are laggards in AI adoption relative to tech/finance sectors. While survey firms experiment with data quality flagging, actual supervisory automation remains rare. Most survey research remains conducted by regional, mid-sized firms with limited AI infrastructure investment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Survey research and market research firms are adopting AI for data quality checks and analytics, but adoption of AI for direct staff supervision and personnel management remains nascent. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist with performance monitoring (automated reports on interviewer metrics, data quality dashboards, anomaly detection in responses), allowing human supervisors to focus coaching and personnel decisions on flagged issues. This augmentation improves manager productivity without removing them from the loop. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI dashboards, quality control flags, and automated interviewer performance analytics can meaningfully help a supervisor track and manage survey staff more efficiently. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI could assist with some review processes (analyzing interview quality metrics, flagging anomalies in data collection) but directing and reviewing staff work requires judgment, motivation, and accountability that currently relies on human managers. The full end-to-end management function cannot achieve the 50% time-saving threshold with existing systems. |
| Task automatability | claude-sonnet-5 | 2/5 | Directing and reviewing human staff involves judgment, motivation, performance evaluation, and interpersonal management that current AI cannot fully replicate end-to-end.of course some monitoring/reporting sub-tasks can be automated but core supervisory work cannot. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: employment law requires human accountability for personnel decisions; survey organizations have institutional liability for data quality and staff conduct; professional standards expect human judgment in managing data collection teams. Customers and regulators expect human supervisors to sign off on survey methodology and staff performance. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically bars AI from this, but organizational and legal norms around personnel management, accountability, and HR practices create real friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of implementing AI review systems (infrastructure, training, oversight by human managers) plus the legal and HR liability costs of algorithmic performance management currently exceed the savings from reduced human supervisory time, especially in smaller survey research teams. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI monitoring tools are cheap to run but still require a human manager to interpret results and actually direct staff, so total cost savings versus a human supervisor are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably perform full supervisory functions including staff direction and performance review. AI can support quality checks on survey data and flag data-quality issues, but actual management responsibilities (personnel decisions, coaching, conflict resolution) remain in human hands in all production survey research environments. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | There are workforce analytics and quality-monitoring tools that flag interviewer errors or performance metrics, but no deployed product autonomously directs and manages survey staff. |
Direct updates and changes in survey implementation and methods.
24CI 16–32 · exposure 20 · augmentation 63 · importance 4.2/5 · click for rater detail
Direct updates and changes in survey implementation and methods.
24| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Survey research is a data-intensive professional field, but adoption of AI for directing methodology changes is minimal because the task fundamentally involves human expertise, responsibility, and organizational standing. Most adoption remains in assistive tools (analysis, sampling design) rather than in directing changes to implementation. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Research and market research firms are adopting AI tools for survey design and analysis assistance at a moderate pace, though methodology direction remains human-led. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist survey researchers in recommending changes (e.g., analyzing quality metrics, suggesting sampling improvements, drafting change documentation), enhancing a human director's productivity. However, the augmentation is partial—the human must evaluate, decide, and own the decision—not transformative of the entire task. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by analyzing past survey performance, suggesting methodological adjustments, and drafting documentation, boosting researcher productivity while humans retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Directing updates and changes to survey implementation requires human judgment about methodological soundness, stakeholder coordination, and strategic trade-offs. While AI can assist in identifying needed changes and drafting documentation, the decision-making authority and responsibility for steering survey methodology changes must remain with human experts. Current AI lacks the contextual authority and accountability to direct these changes end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | This task requires judgment calls about survey design, methodology tradeoffs, and organizational decision-making that current AI cannot reliably direct end-to-end, though AI can suggest options.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: survey researchers directing methodology changes typically hold professional positions of authority and accountability that organizations expect humans to occupy. Regulatory compliance (especially in market research, healthcare surveys), client trust, and professional responsibility create friction against full automation or delegation to AI agents. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement exists, but professional standards, client trust, and accountability for survey validity create moderate organizational friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A survey research director's loaded cost is substantial ($80k–120k+), and the task requires experienced human judgment that AI cannot yet replicate. AI tools that might assist (workflow automation, draft documentation) cost far less, but cannot assume the directing role, making full cost substitution impossible and total cost of hybrid approaches potentially comparable to human-only. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Because a human researcher must still evaluate and approve methodological changes, AI mainly adds a supplementary cost layer rather than replacing the labor, keeping cost savings modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs the function of directing survey methodology updates in production settings. This task requires institutional authority, accountability, and nuanced methodological expertise that current AI systems cannot independently exercise in organizations. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product autonomously directs survey methodology changes in production; existing tools assist with data collection and analysis but not high-level methodological direction. |
Hire and train recruiters and data collectors.
23CI 16–30 · exposure 17 · augmentation 50 · importance 3.2/5 · click for rater detail
Hire and train recruiters and data collectors.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Most survey research firms, particularly smaller and mid-sized organizations in this sector, have adopted AI recruitment tools only as supplements to human hiring; full automation remains rare and pilot-stage in this fragmented, compliance-sensitive domain. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Survey research firms are a relatively small, specialized sector with limited AI adoption in HR-type functions, tending toward slower uptake. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist recruiters by automating candidate sourcing, resume screening, and generating training modules, raising HR team productivity on these subtasks while humans remain responsible for final hiring decisions and training quality assurance. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with drafting job postings, screening resumes, and creating training materials, providing moderate productivity gains while humans retain the core hiring/training role. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Hiring and training is heavily dependent on human judgment, relationship-building, and organizational context that current AI cannot fully handle. While AI can assist with candidate screening or create training materials, the core tasks of interviewing, assessing cultural fit, and conducting interactive training require human-to-human interaction that AI systems cannot replicate at scale. |
| Task automatability | claude-sonnet-5 | 1/5 | Hiring and training staff requires interpersonal judgment, interviewing, and hands-on coaching that current AI cannot execute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: hiring decisions carry legal liability for discrimination, employment law varies by jurisdiction, and many organizations have compliance requirements mandating human sign-off on hiring and training decisions. Training also often requires human accountability and certification. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but hiring decisions carry legal/liability risk (discrimination law) and organizations prefer human judgment for people management, creating moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for recruitment and training exist but typically require substantial human oversight, custom integration, and skilled personnel to configure and manage, making them only marginally cheaper than direct human HR work when accounting for total cost of implementation and error correction. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cut some screening/document costs, but human decision-making, interviews, and in-person training still dominate the cost structure, keeping overall savings modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs end-to-end hiring and training autonomously. AI can support resume screening and generate training content, but actual hiring decisions and interactive training delivery remain dependent on human HR professionals in production environments. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools exist for screening resumes and generating training materials, but no product autonomously hires and trains recruiters/data collectors in practice. |
Related occupations — Life, Physical & Social Science
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.