Market Research Analysts and Marketing Specialists
13-1161.00Median wage $78,760/yr899,580 employed (US)Rank #55 of 923 scored · top 6% by substitution
Research conditions in local, regional, national, or online markets. Gather information to determine potential sales of a product or service, or plan a marketing or advertising campaign. May gather information on competitors, prices, sales, and methods of marketing and distribution. May employ search marketing tactics, analyze web metrics, and develop recommendations to increase search engine ranking and visibility to target markets.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution56
Exposure50
Augmentation88
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
13 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
23%
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%50
panel mean rating 3.0/5 → substitution pressure 50/100
Technical feasibility todayw 20%51
panel mean rating 3.0/5 → substitution pressure 51/100
Cost vs. human wagew 15%55
panel mean rating 3.2/5 → substitution pressure 55/100
Adoption barriersw 20%inverted — strong barriers lower the score70
panel mean rating 2.2/5 (barrier strength) → substitution pressure 70/100
Sector adoption velocityw 10%65
panel mean rating 3.6/5 → substitution pressure 65/100
Task breakdown (13 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.
Monitor industry statistics and follow trends in trade literature.
77CI 75–80 · exposure 75 · augmentation 100 · importance 3.7/5 · click for rater detail
Monitor industry statistics and follow trends in trade literature.
77| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Financial services, consulting, and marketing firms—sectors with high digitization—have already adopted AI-driven market intelligence and trend-monitoring tools at scale. Public adoption is visible in widespread use of platforms like Semrush, Similarweb, and proprietary AI systems for competitive intelligence. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Marketing and research functions are within professional services/information sectors with fast documented AI tool adoption for research and trend-tracking tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI excels at assisting analysts by surfacing relevant signals, organizing trends, and highlighting anomalies in real time, significantly boosting the analyst's ability to stay informed and identify patterns without removing human judgment from interpretation or strategy decisions. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI dramatically augments analysts by surfacing, summarizing, and flagging relevant trends, letting humans focus on interpretation and strategic application. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI systems can automatically ingest, summarize, and flag relevant industry statistics and trends from multiple sources (APIs, RSS feeds, publications) with high speed and consistency. While human judgment on significance often remains valuable, the core monitoring and trend-following work is substantially automatable, meeting the ≥50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 4/5 | AI tools can continuously scan trade publications, aggregate statistics, and summarize trends far faster than manual review, meeting the time-saving threshold for most of this monitoring work.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or regulatory barriers prevent automation; no licensing is required to monitor statistics or trends. Light friction exists (preference for human interpretation, quality assurance needs), but nothing blocks deployment of monitoring automation. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, liability, or regulatory requirement mandates human performance of this monitoring task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Subscription-based monitoring tools and AI agents cost significantly less than hiring and maintaining a full-time analyst for continuous trade-literature surveillance; a single system can serve an entire team, making the per-task cost a small fraction of annual analyst salary. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Subscription-based AI monitoring tools cost a small fraction of an analyst's hourly wage for continuous literature scanning and summarization. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature products (Bloomberg terminals, Facteus, custom ML pipelines at major firms, and general LLM agents with web search) already perform continuous industry monitoring and trend detection in production. Error rates on factual extraction are low, though context-dependent importance judgments sometimes require human validation. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Deployed products like news/aggregation AI, market intelligence platforms (e.g., AlphaSense, Crayon) and LLM-based summarizers already perform this reliably in production for many analysts. |
Prepare reports of findings, illustrating data graphically and translating complex findings into written text.
75CI 75–75 · exposure 75 · augmentation 100 · importance 4.6/5 · click for rater detail
Prepare reports of findings, illustrating data graphically and translating complex findings into written text.
75| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Marketing and research teams in information, professional services, and tech sectors are rapidly adopting AI report generation and data visualization tools; pilots and production use are measurable and accelerating. Smaller or less-digitized firms lag, but mainstream adoption is evident. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Marketing and analytics functions are fast adopters of AI tools for reporting and visualization, with widespread production use of AI-assisted BI and writing tools. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically amplifies analyst productivity by instantly converting data tables into draft visualizations and narrative summaries, allowing humans to focus on strategic interpretation and messaging refinement. This is a canonical augmentation case. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up drafting, charting, and translating data into narrative, while analysts retain control over interpretation and strategic framing. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can now reliably convert raw data into visualizations and draft written summaries of complex findings using tools like GPT-4, Tableau automation, and data-to-viz systems, achieving significant time savings. However, judgment calls on framing, audience-appropriate messaging, and strategic narrative require human oversight, preventing a full 5-rating. |
| Task automatability | claude-sonnet-5 | 4/5 | LLMs can draft narrative reports, generate charts from data, and translate statistical findings into readable prose with substantial time savings, though final review and framing still typically involve human judgment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing or legal requirement mandates a human analyst; output quality expectations and organizational preference for human judgment are the main friction points. Most firms can adopt these tools without regulatory restriction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for this task, but organizational quality control and brand/reputation concerns around inaccurate reporting create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | API costs for LLM-based report generation and visualization automation are typically a small fraction of professional analyst wages per report, especially for routine or templated analyses. Integration and oversight add overhead but remain well below human labor costs. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating a data report and visualizations via AI costs a small fraction of analyst hourly wages, though some human oversight cost remains. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Production systems (ChatGPT, Claude, Jasper, data-viz platforms) demonstrably generate reports, charts, and written summaries at scale in professional settings, though quality and accuracy still require human review. Reliability is good but not yet flawless across all complexity levels. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Deployed tools (e.g., ChatGPT with code interpreter, BI platforms with AI narrative generation, Tableau/PowerBI AI summaries) reliably produce draft reports and visualizations today, though accuracy on nuanced interpretation varies. |
Gather data on competitors and analyze their prices, sales, and method of marketing and distribution.
71CI 61–81 · exposure 62 · augmentation 100 · importance 4.0/5 · click for rater detail
Gather data on competitors and analyze their prices, sales, and method of marketing and distribution.
71| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Marketing and competitive intelligence teams already widely adopt AI-driven tools for competitor monitoring; SaaS platforms and web-scraping agents are industry standard in information-intensive sectors like SaaS, e-commerce, and finance. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Marketing and research functions are within professional/information services, a sector with fast AI tool adoption, and competitive intelligence tools are increasingly integrated into workflows. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dashboards and automated reports dramatically enhance analyst productivity by surfacing competitor moves in real time, freeing humans to focus on strategic interpretation and actionable insights rather than manual data collection. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially accelerates data collection, summarization, and pattern identification for competitor analysis, letting analysts focus on strategic interpretation. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can fully automate competitor price monitoring, sales tracking (via public data), and marketing channel analysis using web scraping, APIs, and NLP on competitor marketing materials. This easily meets the 50% time-saving threshold for data gathering and preliminary analysis, though strategic interpretation may benefit from human judgment. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can gather publicly available competitor data, summarize pricing pages, and synthesize marketing patterns, but verifying accuracy, accessing paywalled/proprietary sources, and interpreting distribution strategy still require human judgment and validation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal legal or regulatory barriers exist for monitoring publicly available competitor data. Some friction arises from data privacy compliance (GDPR, CCPA) and terms-of-service restrictions on scraping, but these are technical rather than licensing barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates human performance; main friction is data access restrictions and the need for judgment on strategic interpretation, not regulatory barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Competitive intelligence software and AI agents cost a fraction of employing full-time analysts to manually track competitors across pricing sites, social media, and advertising platforms. Automation is orders of magnitude cheaper than equivalent manual labor. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-driven data gathering and summarization is substantially cheaper than manual competitive research, though some cost remains for human verification and integration of tools. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature, deployed products (e.g., Semrush, SimilarWeb, Brandwatch, custom web-scraping agents) reliably monitor competitor pricing, website traffic, and marketing campaigns at scale. These systems perform reliably in production across many organizations, though some data sources require integration. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like AI-powered web research agents and market intelligence tools (e.g., Crayon, Klue, or LLM-based research assistants) exist and are used in production, but they still have notable error rates and require human review of scraped/synthesized data. |
Collect and analyze data on customer demographics, preferences, needs, and buying habits to identify potential markets and factors affecting product demand.
68CI 61–75 · exposure 62 · augmentation 100 · importance 4.4/5 · click for rater detail
Collect and analyze data on customer demographics, preferences, needs, and buying habits to identify potential markets and factors affecting product demand.
68| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Marketing and research-heavy sectors (consumer goods, tech, e-commerce, finance) have already widely adopted AI analytics, BI dashboards, and predictive segmentation tools; production use is common and growing in mid-market and enterprise. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Marketing and market research functions have seen fast adoption of AI/analytics tools for customer segmentation and demand forecasting, consistent with professional services adoption patterns. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI powerfully augments market analysts by automating data aggregation, generating segmentation hypotheses, and surfacing anomalies, freeing analysts to focus on strategic interpretation and business storytelling. Productivity gains are substantial while human judgment remains central. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially enhances analysts' ability to process large datasets, generate insights, and visualize trends, greatly boosting productivity while humans retain interpretive and strategic roles. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Data collection from structured sources (surveys, databases, transaction logs, social media) is highly automatable; AI can classify customer segments, detect preference patterns, and flag demand drivers at scale. Human judgment on strategy and nuance remains valuable, but ≥50% time savings on the core analytical pipeline is achievable with current tools. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can automate large portions of data collection, cleaning, and statistical analysis of demographic and behavioral data, but framing research questions, sourcing proprietary data, and interpreting nuanced market context still require human judgment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal regulatory or licensing barriers prevent automation; the task is information-gathering and analysis without legal gatekeeping. Organizational friction (preference for human interpretation, integration with legacy systems) provides some friction but does not block adoption. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for this task, but organizational trust in data quality/interpretation and some reliance on proprietary or panel data create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-powered analytics platforms (including cloud ML services and BI automation) cost orders of magnitude less per analysis than hiring analysts for equivalent depth of data processing and pattern detection, though senior interpretation commands a premium. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-driven analytics tools can process large datasets far cheaper than manual analyst hours, though data acquisition, licensing, and human oversight for validity still add cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (analytics platforms with ML, BI tools with AI-driven insights, survey tools with automated coding) routinely perform demographic segmentation, preference clustering, and demand forecasting in production. Error rates on routine classification are low; edge cases and strategic interpretation still benefit from human review. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Deployed analytics and BI tools (e.g., AI-augmented survey platforms, customer analytics suites) reliably handle data aggregation and pattern detection, but full end-to-end market analysis with strategic interpretation is not yet fully automated in production. |
Forecast and track marketing and sales trends, analyzing collected data.
66CI 57–75 · exposure 62 · augmentation 100 · importance 4.0/5 · click for rater detail
Forecast and track marketing and sales trends, analyzing collected data.
66| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Marketing and sales functions operate in digitized, information-rich sectors with high cloud adoption. Many enterprises are already using predictive analytics and AI-driven dashboards for forecasting; adoption is rapid and visible in mid-to-large organizations. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Marketing and market research functions sit within fast-adopting sectors (professional services, tech-enabled marketing) where AI-driven analytics and forecasting tools are already widely deployed. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI significantly augments analyst productivity by automating data aggregation, visualization, and preliminary pattern detection, allowing humans to focus on interpretation, strategy, and narrative. This is a high-value augmentation scenario where human judgment and context remain essential while AI handles computational heavy lifting. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI significantly enhances an analyst's ability to process large datasets, generate visualizations, and surface patterns, dramatically speeding up trend analysis while the analyst retains interpretive and strategic control. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can analyze historical sales and marketing data, identify patterns, and generate trend forecasts with minimal human intervention. Tools like statistical models and large language models can handle data processing and trend spotting at scale, delivering 50%+ time savings on routine forecasting and tracking tasks. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can analyze structured data, identify trends, and generate forecasts, but reliable trend forecasting requires domain judgment, data cleaning, and contextual interpretation that still needs human oversight for most of the value chain.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or regulatory barriers exist to automating trend analysis itself. However, organizations often retain human analysts for strategic insight and stakeholder communication, and data privacy concerns around customer data can slow adoption in regulated industries. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human analyst, though organizations often want human accountability for strategic forecasts feeding into business decisions. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Cloud-based predictive analytics and AI services cost significantly less than analyst time when processing large datasets; ongoing inference and integration costs are typically one-tenth to one-fifth of a full analyst's loaded wage for routine forecasting. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools reduce analyst time on data crunching substantially, but licensing, integration, and required human validation of forecasts keep costs roughly comparable to a skilled analyst's marginal cost for complex trend work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple deployed products (Salesforce Einstein, Tableau with AI, predictive analytics platforms) reliably perform trend analysis and forecasting in production environments. Error rates on standard datasets are acceptable for tactical decision-making, though strategic forecasts still typically require human validation. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like predictive analytics platforms, BI tools with AI forecasting modules, and LLM-based data analysis exist and are used in production, but accuracy and reliability vary significantly depending on data quality and market volatility. |
Measure the effectiveness of marketing, advertising, and communications programs and strategies.
64CI 57–71 · exposure 55 · augmentation 100 · importance 4.1/5 · click for rater detail
Measure the effectiveness of marketing, advertising, and communications programs and strategies.
64| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Marketing and professional services sectors show strong, documented adoption of AI-powered analytics and measurement platforms. Major platforms have integrated AI measurement capabilities; early-stage displacement of routine measurement work is evident in marketing operations. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Marketing and analytics functions have seen fast, deep AI tool adoption via platforms like Google Analytics, Adobe Analytics, and AI-driven attribution modeling, consistent with broader adoption patterns in professional/marketing services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI markedly augments analyst productivity by automating data collection, statistical testing, and baseline reporting, freeing humans to focus on strategic interpretation, hypothesis generation, and cross-functional insights. This is a canonical augmentation scenario in digital marketing. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI dramatically enhances this task today by automating data collection, generating visualizations, running statistical models, and surfacing insights, letting analysts focus on strategic interpretation and recommendations. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automate parts of effectiveness measurement—data aggregation, statistical analysis of campaign metrics, A/B test evaluation, and report generation—but requires human judgment to interpret results, contextualize business impact, and refine strategy. This covers roughly half the task with significant setup. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can automate data aggregation, statistical analysis, and dashboard generation for campaign metrics, but interpreting results in business context and recommending strategic pivots still requires human judgment and domain knowledge. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing or legal requirement mandates human oversight of marketing effectiveness measurement. Organizational preference for human interpretation and strategic judgment provides some friction, but adoption barriers are low—automation is purely discretionary. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or regulatory requirements mandate human measurement of marketing effectiveness; this is a purely commercial analytical task with no legal barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI analytics tools and integrated platforms cost a fraction of hiring dedicated analysts for continuous measurement and reporting. The inference and integration overhead is modest compared to fully loaded analyst salaries, yielding substantial savings per unit of measurement work. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-powered analytics tools reduce time spent on data compilation and basic reporting substantially, but licensing costs for enterprise platforms plus required human oversight for interpretation keep overall costs roughly comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (analytics platforms, marketing mix modeling tools, AI-driven dashboards from Google, HubSpot, Adobe) reliably measure campaign metrics, attribution, and ROI at scale. Some limitations remain in causal inference and strategic interpretation, but core measurement is production-ready. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Marketing analytics platforms (Google Analytics, HubSpot, Tableau with AI features) reliably compute metrics like ROI, attribution, and conversion rates, but sophisticated multi-channel effectiveness measurement with causal inference remains error-prone and requires analyst oversight. |
Measure and assess customer and employee satisfaction.
62CI 57–66 · exposure 55 · augmentation 88 · importance 4.3/5 · click for rater detail
Measure and assess customer and employee satisfaction.
62| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Tech-forward and mid-market firms have widely adopted AI-powered survey and sentiment tools in production. Professional services, marketing, and large enterprises show strong adoption patterns for automated satisfaction measurement and reporting. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Marketing and HR analytics functions in mid-to-large firms show fast, deep adoption of AI-driven sentiment and satisfaction analysis tools, consistent with professional services adoption patterns. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI dramatically augments analyst productivity by automating survey administration, coding open-ended responses, and generating initial dashboards and trend summaries, freeing humans to focus on strategic interpretation and action planning. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially augments this task by automating survey analysis, text mining open-ended responses, and generating dashboards, letting analysts focus on interpretation and recommendations. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automate survey distribution, sentiment analysis on feedback, and basic statistical aggregation, achieving meaningful time savings. However, interpreting nuanced satisfaction drivers, designing custom survey instruments, and making strategic recommendations still require human judgment, limiting full end-to-end automation. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can automate survey design, sentiment analysis, and reporting on satisfaction data, but framing research questions, contextualizing findings, and driving strategic action still require human judgment.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing or legal requirement mandates human execution; organizations can fully automate satisfaction measurement if they choose. Barriers are mainly organizational preference for human interpretation and stakeholder trust in AI-generated insights, not regulatory or structural constraints. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for this task, though some industries impose data privacy rules and preference for human-led interpretation of sensitive employee feedback creates minor friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-powered survey platforms and sentiment analysis tools cost a fraction of manual coding, analysis, and reporting labor. Integration and oversight are modest, making AI-assisted assessment substantially cheaper than hiring analysts for routine satisfaction measurement. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools reduce analyst hours for data processing and sentiment tagging, but licensing costs, integration, and human oversight for interpretation keep the cost differential moderate rather than order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (Qualtrics, SurveySparrow with AI enhancements, sentiment analysis APIs) reliably perform sentiment extraction and automated reporting at scale. Some tools generate insights with acceptable accuracy, though human verification of strategic conclusions remains standard practice. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Qualtrics AI, Medallia, and various NLP sentiment tools are deployed in production for analyzing satisfaction data, but full end-to-end measurement design and interpretation still involve human analysts. |
Seek and provide information to help companies determine their position in the marketplace.
57CI 57–57 · exposure 50 · augmentation 88 · importance 4.0/5 · click for rater detail
Seek and provide information to help companies determine their position in the marketplace.
57| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Market research, marketing, and business intelligence teams operate in digitized, information-heavy sectors with rapid SaaS adoption and demonstrated AI tool penetration (platforms like Similarweb, Semrush, and GenAI-powered research assistants seeing widespread use). Adoption is accelerating across mid-market and enterprise organizations. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Marketing and professional services are among the faster-adopting sectors for AI tools, with widespread pilot and production use of AI for market research and competitive analysis. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly assists analysts by automating data collection, generating competitive summaries, identifying trends, and producing draft reports, materially raising analyst productivity while humans validate strategy, interpret nuance, and advise on positioning. The human remains central but AI-powered analysts accomplish far more research per hour. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially accelerates data gathering, summarization, and drafting of market positioning reports, letting analysts focus on interpretation and strategic recommendations. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automatically gather, aggregate, and summarize market data from public sources, competitive websites, and databases, achieving significant time savings on data collection and initial synthesis. However, the task requires judgment about market positioning strategy, context-specific interpretation, and client needs that typically demand human oversight, limiting full end-to-end automation. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can gather and synthesize competitive/market data and draft positioning analyses, but validating data quality, sourcing proprietary insights, and making strategic judgments still require significant human input.om time-savings are real but partial, not full end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers exist for AI-assisted market research; organizations can adopt AI tools with minimal friction. Customer preference for human insight and organizational skepticism about AI recommendations in strategic decisions present some adoption friction, but no hard legal requirement for human sign-off. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human perform this task, though organizational trust in strategic recommendations and reliance on proprietary/confidential data create some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI inference and data integration costs for market research tasks are now comparable to junior analyst labor for routine data collection and synthesis, though senior analyst judgment commands higher human costs. The all-in cost (including oversight and integration) roughly matches mid-level analyst productivity on standardized research. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools reduce research time substantially but still require subscription costs, data licensing, and human oversight to validate and contextualize findings, making the net cost savings moderate rather than order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed AI tools (web scraping, data aggregation platforms, and generative summarization) can reliably perform data-gathering and basic competitive analysis at scale. However, mature production systems for end-to-end market positioning analysis remain limited; most organizations still require human analysts to validate findings and contextualize results for strategic decision-making. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like AI research assistants and market intelligence platforms (e.g., Crayon, Klue, ChatGPT with browsing) exist and are used in production, but they still have material gaps in accuracy, freshness, and depth compared to skilled analysts. |
Develop and implement procedures for identifying advertising needs.
56CI 38–75 · exposure 50 · augmentation 88 · importance 3.1/5 · click for rater detail
Develop and implement procedures for identifying advertising needs.
56| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Marketing and media sectors show rapid, production-level adoption of AI-driven analytics, audience segmentation, and competitive intelligence; major enterprises and agencies deploy these tools routinely for needs assessment and campaign planning. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Marketing and market research is a digitized, professional-services-adjacent field with growing AI tool adoption, though procedural/strategic work lags behind content generation tasks., |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI substantially augments marketer productivity in needs identification by automating data aggregation, pattern recognition, and scenario modeling, allowing humans to focus on strategic interpretation, creative synthesis, and business alignment—this is actively deployed in most modern marketing organizations. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by analyzing market data, drafting procedural frameworks, and surfacing advertising need patterns, significantly speeding up the analyst's workflow while the human still designs and implements the procedure., |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI systems can now perform substantial portions of advertising needs identification through data analysis, audience segmentation, competitive monitoring, and trend detection with commercial tools like marketing analytics platforms and generative models. However, final strategic judgment about which needs to prioritize for business objectives typically requires human input, preventing a perfect 5. |
| Task automatability | claude-sonnet-5 | 2/5 | Developing and implementing organizational procedures requires judgment about business context, stakeholder alignment, and process design that current AI cannot fully own end-to-end, though AI can assist with parts of the analysis., |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or regulatory barriers exist for automating advertising needs identification; the task does not require licensing, and no mandatory human signoff is typically required. Some organizational preference for human marketing strategy oversight exists but is weak. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational buy-in, stakeholder trust, and the need for contextual judgment create moderate friction to full automation., |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-powered analytics and automated reporting cost a fraction of employing multiple analysts for the same volume of needs identification work; SaaS tools and inference costs are orders of magnitude cheaper than loaded salaries for equivalent coverage and speed. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Because the task involves procedure creation and implementation requiring human coordination and iteration, AI reduces some research costs but does not replace the bulk of labor cost, keeping cost ratios only modestly favorable., |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature products (Google Analytics, Meta Business Suite, HubSpot, marketing intelligence platforms) reliably perform data-driven audience and competitive analysis at scale in production. Generative AI can draft need identification frameworks and summaries, though interpretation and validation remain partially manual in most deployments. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product autonomously develops and implements advertising-needs identification procedures; existing tools support research and analysis but not the procedural design and organizational rollout., |
Devise and evaluate methods and procedures for collecting data, such as surveys, opinion polls, or questionnaires, or arrange to obtain existing data.
55CI 55–55 · exposure 50 · augmentation 75 · importance 4.3/5 · click for rater detail
Devise and evaluate methods and procedures for collecting data, such as surveys, opinion polls, or questionnaires, or arrange to obtain existing data.
55| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Market research and analytics firms are actively experimenting with AI-assisted survey design and data collection tooling, with some production pilots, but widespread displacement remains limited by quality concerns and methodological conservatism in the sector. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Marketing and research functions are moderately fast adopters of AI tools for drafting and ideation, but full methodological design workflows still show pilot-stage rather than deep production adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly assists by rapidly generating survey variants, identifying potential biases in questionnaire design, and organizing existing data sources, allowing analysts to focus on validation and strategic methodology choices rather than boilerplate drafting. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly speeds up drafting survey questions, suggesting sampling frames, and summarizing existing datasets, meaningfully boosting analyst productivity while humans retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can generate survey designs, questionnaire templates, and evaluation frameworks with significant time savings, but typically requires human review and domain expertise to finalize methodology. Arranging existing data acquisition still involves substantial human negotiation and validation that AI cannot fully automate. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft survey instruments, suggest sampling methodologies, and identify existing data sources quickly, but designing valid methodology requires domain judgment, stakeholder alignment, and often fieldwork arrangement that AI cannot fully replace. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal barriers exist, though institutional review boards (IRBs) and compliance requirements mean human researchers must approve final methodology; customer and organizational preference for human methodological judgment provides modest friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this task, though some organizational trust and expertise requirements slow full reliance on AI-generated methodologies, especially for high-stakes market decisions. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI inference and survey generation are cheap, but integration into existing data systems, validation workflows, and human review of methodology add non-trivial costs that approach or match the hourly wage of a specialist for typical tasks. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools reduce time spent drafting survey questions and identifying data sources, but human analysts still must validate methodology and manage data collection logistics, keeping overall costs only moderately reduced. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed tools (GPT-based systems, Qualtrics API integrations) can draft surveys and suggest evaluation procedures, but error rates in survey design logic and sampling methodology remain material; production use still requires human oversight and iteration. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like ChatGPT-based survey builders and platforms (e.g., Qualtrics AI features) assist with question drafting and methodology suggestions today, but they don't reliably design complete, validated data collection procedures without human oversight. |
Conduct research on consumer opinions and marketing strategies, collaborating with marketing professionals, statisticians, pollsters, and other professionals.
35CI 32–38 · exposure 25 · augmentation 75 · importance 4.4/5 · click for rater detail
Conduct research on consumer opinions and marketing strategies, collaborating with marketing professionals, statisticians, pollsters, and other professionals.
35| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Marketing and analytics organizations pilot AI-driven data tools and sentiment analysis widely, but deep production adoption of autonomous or semi-autonomous research workflows remains uneven. Larger firms adopt faster; small and mid-market adoption lags. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Marketing and market research sectors are moderately fast adopters of AI analytics tools, though full collaborative workflows still rely heavily on human coordination. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI substantially augments researcher productivity by automating survey logistics, data cleaning, preliminary analysis, pattern detection, and report scaffolding. Researchers can focus on strategy, interpretation, and stakeholder collaboration while AI handles routine analytical heavy lifting, meaningfully raising output per analyst. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly aids in synthesizing consumer data, generating insights, and drafting reports, meaningfully boosting analyst productivity while humans manage collaboration and judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with data collection, analysis of existing datasets, and literature synthesis, the task's core—conducting original research, interpreting nuanced consumer opinions, and collaborating meaningfully with diverse professionals—requires human judgment, relationship management, and strategic decision-making. AI cannot autonomously execute the full end-to-end research cycle at 50% time savings with equal quality today. |
| Task automatability | claude-sonnet-5 | 2/5 | The collaborative, cross-functional design and strategic interpretation aspects resist full automation, though AI can accelerate survey design, data collection, and initial analysis components. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Moderate barriers exist: client relationships and trust favor human experts, and professional standards in market research value human judgment and accountability. However, no hard licensing requirement mandates human execution, allowing gradual AI augmentation and some task shifting. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational reliance on cross-functional teams and stakeholder trust in human-led collaboration creates moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for data collection and basic analysis are inexpensive, but the human analyst remains essential for design, collaboration oversight, and interpretation. The all-in cost of AI-assisted versus purely human research is roughly comparable, as overhead for integration and human review remains substantial. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cut costs on data processing subtasks, but the overall collaborative research process still requires substantial human labor, keeping total cost comparable to human-only approaches. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Products exist for specific subtasks (survey distribution, basic sentiment analysis, trend spotting in social media), but no deployed system reliably performs original consumer research design, expert collaboration, and strategic interpretation as a unified task. Material gaps remain in qualitative insight generation and cross-functional coordination. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools exist for survey analysis and sentiment mining, but coordinating multi-stakeholder research projects with statisticians and pollsters in production remains largely human-led. |
Attend staff conferences to provide management with information and proposals concerning the promotion, distribution, design, and pricing of company products or services.
18CI 7–28 · exposure 8 · augmentation 75 · importance 3.6/5 · click for rater detail
Attend staff conferences to provide management with information and proposals concerning the promotion, distribution, design, and pricing of company products or services.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While marketing teams use AI for analytics and content generation, actual conference attendance and strategic proposal delivery remain human-centric. Adoption of AI for this specific task is minimal, with tools supporting preparation rather than replacement. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Marketing and professional services sectors show fast AI tool adoption for analysis and content generation, but meeting attendance itself remains untouched. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially augment this task by rapidly analyzing market data, generating proposal drafts, summarizing competitor information, and creating data visualizations before the conference, allowing the analyst to present more compelling and data-driven insights. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can strongly assist by drafting proposals, generating data-backed insights, and preparing talking points ahead of the conference, materially boosting the analyst's productivity in preparation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Attending conferences and presenting information requires physical presence and real-time interpersonal engagement. While AI can help prepare materials and analyses beforehand, the core task of attending, listening, and dynamically responding to management discussion cannot be meaningfully automated end-to-end today. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically attending and participating in live staff conferences requires human presence, real-time verbal interaction, and situational judgment that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong organizational and social barriers exist: management expects human experts to be physically present, provide real-time strategic judgment, and be accountable for proposals. Corporate culture and decision-making norms heavily favor human participation in strategic meetings. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but organizational norms and the need for a human representative in meetings create moderate friction against full substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI can assist with pre-conference research and proposal generation at low cost, but cannot replace the human attendance and presentation itself, making the all-in cost still dominated by the human labor required. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Since AI cannot perform the attendance/presentation itself, there is no comparable AI cost basis to replace the human task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can autonomously attend staff conferences, participate in real-time discussions, or adapt proposals based on live managerial feedback. The task fundamentally requires human presence and judgment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for a human attending meetings and presenting proposals in real time; AI is at best a prep tool, not a participant. |
Direct trained survey interviewers.
16CI 0–32 · exposure 13 · augmentation 50 · importance 3.5/5 · click for rater detail
Direct trained survey interviewers.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Even in digitally advanced market research firms, directing human interviewers remains a human management function. There is no measurable displacement or production AI-agent adoption in survey workforce supervision. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Market research firms are moderately digitized and using AI tools for survey design and analytics, but direct management of interviewer teams remains largely manual with pilots only in workflow tools. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can assist a human supervisor with scheduling analytics, quality-control flagging, or performance dashboards, but these are narrow supports to a fundamentally human leadership task rather than transformative augmentation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by tracking interviewer performance metrics, flagging quality issues, optimizing schedules, and providing real-time coaching prompts, boosting managerial productivity. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Directing trained survey interviewers requires real-time human judgment, interpersonal coaching, performance feedback, and adaptive leadership—capabilities that current AI cannot perform end-to-end. The task involves managing people, not processing data or text. |
| Task automatability | claude-sonnet-5 | 2/5 | Directing and managing human interviewers involves real-time coordination, motivation, quality control, and interpersonal management that current AI cannot fully replicate end-to-end.assign, monitor, and coach staff. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Employment law, liability for worker treatment, duty-of-care obligations, and organizational governance require a human manager to legally direct and oversee employee performance and conduct. AI cannot assume fiduciary responsibility for workers. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but organizational friction and the need for human judgment in managing personnel create moderate barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Automating managerial oversight of human workers would require AI systems with supervisory authority, liability, and accountability—all of which are far more expensive than the human supervisor's loaded wage when feasibly deployed at all. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can support scheduling and monitoring dashboards cheaply, but the supervisory/managerial function still requires human oversight, keeping costs comparable to or above human management costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product directs or manages human interviewers today. This is a fundamentally human supervisory and mentorship function that falls outside the scope of current AI automation offerings. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some workforce management and scheduling tools exist, but no deployed product autonomously directs and supervises human interviewers in production at scale. |
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.