Search Marketing Strategists
13-1161.01Median wage $78,760/yr899,580 employed (US)Rank #56 of 923 scored · top 6% by substitution
Employ search marketing tactics to increase visibility and engagement with content, products, or services in Internet-enabled devices or interfaces. Examine search query behaviors on general or specialty search engines or other Internet-based content. Analyze research, data, or technology to understand user intent and measure outcomes for ongoing optimization.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution56
Exposure49
Augmentation86
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
36 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
22%
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%48
panel mean rating 2.9/5 → substitution pressure 48/100
Technical feasibility todayw 20%49
panel mean rating 3.0/5 → substitution pressure 49/100
Cost vs. human wagew 15%53
panel mean rating 3.1/5 → substitution pressure 53/100
Adoption barriersw 20%inverted — strong barriers lower the score73
panel mean rating 2.1/5 (barrier strength) → substitution pressure 73/100
Sector adoption velocityw 10%68
panel mean rating 3.7/5 → substitution pressure 68/100
Task breakdown (36 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Manage tracking and reporting of search-related activities and provide analyses to marketing executives.
91CI 82–100 · exposure 87 · augmentation 100 · importance 4.5/5 · click for rater detail
Manage tracking and reporting of search-related activities and provide analyses to marketing executives.
91| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Digital marketing, search, and analytics are high-tech, high-adoption sectors where AI-driven analytics, reporting automation, and dashboarding are already deeply embedded in production workflows at major firms. |
| Sector adoption velocity | claude-sonnet-5 | 5/5 | Digital marketing is among the most AI-adopting fields, with automated dashboards, AI-generated insights, and reporting tools now standard practice. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI substantially augments human strategists by automating routine reporting, freeing them to focus on interpretation, strategy refinement, and recommendation; LLMs and BI tools provide instant insights and anomaly detection that accelerate human decision-making. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially enhances human analysts by automating data aggregation and surfacing insights, letting strategists focus on interpretation and recommendations. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Current AI systems can fully automate tracking setup, data pipeline construction, dashboard creation, and generation of performance analyses and reports with minimal human intervention, easily meeting the 50% time-saving threshold at equal quality. |
| Task automatability | claude-sonnet-5 | 4/5 | AI tools can pull search analytics, generate performance reports, and produce summary analyses with significant time savings, though strategic interpretation and executive-specific framing still require human judgment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 1/5 | No licensing, legal, or regulatory barriers exist to automating tracking and reporting; dashboards and reports are purely informational outputs with no human authorization requirements. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or regulatory requirements restrict who can manage search marketing analytics or reporting; adoption is purely a business decision. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven analytics platforms and LLM-based report generation cost a fraction of a strategist's loaded wage, with automation reducing human oversight from hours per week to minutes, delivering order-of-magnitude cost savings. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated reporting tools and AI summarization drastically cut analyst hours needed to compile and interpret search data, offering large cost savings versus manual reporting. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Mature products (Google Analytics, Semrush, Moz, and LLM-powered BI tools) reliably perform search tracking, reporting, and analysis at scale in production environments across thousands of organizations. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Products like Google Analytics, SEMrush, Looker Studio, and AI-integrated dashboards already automate tracking and generate narrative reports in production at scale for many marketing teams. |
Collect and analyze Web metrics, such as visits, time on site, page views per visit, transaction volume and revenue, traffic mix, click-through rates, conversion rates, cost per acquisition, or cost per click.
87CI 82–91 · exposure 80 · augmentation 88 · importance 4.5/5 · click for rater detail
Collect and analyze Web metrics, such as visits, time on site, page views per visit, transaction volume and revenue, traffic mix, click-through rates, conversion rates, cost per acquisition, or cost per click.
87| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Information, finance, e-commerce, and SaaS sectors—where this task is most common—have already deeply adopted automated analytics and AI-driven reporting. Adoption is mature and widespread, not nascent. |
| Sector adoption velocity | claude-sonnet-5 | 5/5 | Digital marketing and search marketing are among the fastest and deepest AI-adopting sectors, with automated analytics tools already standard practice. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at assisting strategists by automating data pulls, anomaly detection, and preliminary analysis, freeing the human to focus on interpretation and strategic decision-making. Tools that highlight actionable insights and flag significant metric changes substantially raise analyst productivity. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI tools significantly enhance human marketers by surfacing trends, anomalies, and predictive insights from metrics, letting strategists focus on optimization and decision-making rather than manual data crunching. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can automate the collection and analysis of web metrics almost entirely—pulling data from analytics platforms, calculating KPIs, identifying trends, and generating reports with >50% time savings. The task involves structured data extraction and statistical analysis, both well within current AI capabilities; human judgment on strategy interpretation would add value but isn't strictly required for the analysis itself. |
| Task automatability | claude-sonnet-5 | 4/5 | Data collection, aggregation, and standard analysis of web metrics (traffic, CTR, conversion, CPA) is largely handled by automated dashboards and AI tools today, with humans mainly needed for strategic interpretation and action decisions. |
| Adoption barriers | claude-haiku-4-5-20251001 | 1/5 | No licensing, regulatory, or human-contact requirements exist for automated collection and analysis of web metrics. Organizations freely deploy analytics automation without legal constraints or need for human sign-off. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, regulatory, or liability barriers restrict automated collection and analysis of web metrics; it's a standard commercial software function. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Analytics infrastructure and AI-driven reporting tools cost a fraction of a skilled analyst's loaded wage, especially at volume. A single platform subscription scales to thousands of metrics and reports with minimal marginal cost, vs. hours of manual data compilation and analysis by humans. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated analytics platforms process and summarize large volumes of metrics far more cheaply than manual analyst hours, though some oversight and interpretation still requires human time. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Production-grade analytics platforms (Google Analytics, Mixpanel, Amplitude, etc.) and AI-driven BI tools (Tableau, Looker, Power BI with AI features) reliably perform metric collection and automated analysis at scale in real organizations. Numerous SaaS products explicitly automate dashboard creation, anomaly detection, and metric reporting. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Products like Google Analytics, GA4's AI insights, Search Console, and AI-driven marketing analytics platforms (e.g., Adobe Analytics, HubSpot) reliably automate metric collection and pattern detection in production at scale. |
Optimize Web site exposure by analyzing search engine patterns to direct online placement of keywords or other content.
81CI 75–86 · exposure 75 · augmentation 100 · importance 4.3/5 · click for rater detail
Optimize Web site exposure by analyzing search engine patterns to direct online placement of keywords or other content.
81| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | E-commerce, SaaS, publishing, and digital marketing firms have rapidly integrated AI-driven SEO tools into production workflows; adoption is deep and measured in millions of companies using platforms like Semrush and Ahrefs, reflecting high digitization and competitive pressure. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Digital marketing is a fast-adopting, highly digitized sector where AI-driven SEO tools are already standard practice among agencies and in-house teams. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically amplifies strategist productivity by automating pattern detection, generating keyword and content recommendations, and providing real-time monitoring dashboards; strategists can now manage far larger portfolios and focus on creative positioning while AI handles analysis at scale. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI tools dramatically speed up keyword discovery, trend analysis, and content optimization suggestions, letting strategists focus on higher-level campaign decisions while remaining in the loop. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can analyze search engine patterns, identify high-potential keywords, recommend content placement, and even generate optimization reports with minimal human oversight. However, final strategic decisions about brand positioning and competitive nuance often benefit from human judgment, preventing a full 5. |
| Task automatability | claude-sonnet-5 | 4/5 | Keyword research, SERP pattern analysis, and content/keyword placement recommendations are largely data-driven and pattern-based tasks that current AI tools handle well with modest human oversight for strategy and validation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing or regulatory requirement mandates human involvement in SEO optimization. Organizations freely adopt automated analysis tools; the main friction is organizational inertia and preference to retain strategic control, not legal or compliance barriers. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, legal, or human-contact requirement exists for SEO optimization work; organizations freely adopt automated tools without regulatory friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | SEO analytics SaaS platforms and AI-assisted keyword research cost a few hundred to low thousands per month and handle work that once required dedicated full-time strategists; the all-in cost per keyword recommendation or content optimization cycle is orders of magnitude lower than human labor. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | SaaS SEO tools with AI features cost a small fraction of an analyst's hourly wage for equivalent keyword/content optimization output, though some human review is still needed. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Production SEO tools (SEMrush, Ahrefs, Moz, Google Search Console analytics + GenAI layers) reliably perform keyword analysis, ranking tracking, and competitor monitoring at scale. Some strategic synthesis still requires human interpretation, but the core technical components are mature and widely deployed. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Mature SEO platforms (e.g., Semrush, Ahrefs, Surfer, Clearscope) already embed AI-driven keyword and SERP analysis in production and are widely used by practitioners today. |
Identify appropriate Key Performance Indicators (KPIs) and report key metrics from digital campaigns.
76CI 69–82 · exposure 67 · augmentation 100 · importance 4.2/5 · click for rater detail
Identify appropriate Key Performance Indicators (KPIs) and report key metrics from digital campaigns.
76| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Digital marketing and SaaS companies have embedded AI-driven analytics, dashboarding, and anomaly alerts into standard workflow; adoption of these tools is widespread and accelerating in information and professional-services sectors. |
| Sector adoption velocity | claude-sonnet-5 | 5/5 | Digital marketing is a fast-adopting, highly digitized sector where AI-driven analytics and reporting tools are already standard practice at scale. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically augments strategist productivity by auto-generating dashboards, flagging anomalies, and surfacing insights from large datasets, allowing the human to focus on interpretation, strategy, and storytelling rather than manual metric compilation. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially enhances marketers' ability to identify meaningful KPIs and synthesize campaign data into actionable reports, greatly boosting productivity while strategists retain interpretive judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can autonomously collect, aggregate, and compute standard metrics (CTR, conversion rate, ROAS) and generate basic reports, saving roughly 40–60% of routine reporting time. However, selecting *appropriate* KPIs for novel campaign contexts or strategic goals still requires human judgment about business objectives and competitive positioning. |
| Task automatability | claude-sonnet-5 | 4/5 | AI tools can readily analyze campaign data, identify relevant KPIs, and generate metric reports from platforms like Google Ads/Analytics, saving significant time though strategic KPI selection may still need human validation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or licensing barriers exist; KPI reporting is not a regulated profession. Organizational friction is modest—businesses readily adopt analytics automation—though some companies prefer human review for stakeholder presentations and strategic sign-off. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or regulatory requirements govern KPI selection or metrics reporting in digital marketing; it's a purely analytical business function. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Cloud-based analytics and BI tools with AI features cost pennies to dollars per report compared to a strategist's hourly rate; even accounting for platform integration and oversight, AI infrastructure is an order of magnitude cheaper than full human analyst labor for routine metric reporting. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated reporting tools and AI analytics dashboards cost a fraction of analyst hours spent manually compiling and interpreting metrics, though some oversight and customization costs remain. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature analytics platforms (Google Analytics, Meta Ads Manager, Tableau, looker-studio) with AI-assisted dashboarding and anomaly detection are deployed at scale in production. These reliably surface and aggregate standard metrics, though interpretation and context-setting remain partly manual. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Products like Google Analytics Intelligence, Search Console insights, and AI-powered dashboards (Looker Studio, marketing analytics platforms) reliably automate KPI reporting in production today. |
Combine secondary data sources with keyword research to more accurately profile and satisfy user intent.
73CI 66–80 · exposure 67 · augmentation 100 · importance 4.1/5 · click for rater detail
Combine secondary data sources with keyword research to more accurately profile and satisfy user intent.
73| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Digital marketing and SEM have shown rapid, deep AI adoption across information-intensive sectors (marketing agencies, e-commerce, SaaS). Many organizations are already deploying AI-driven keyword research and intent-matching tools in production workflows. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Digital marketing is a fast-adopting sector with widespread integration of AI tools into SEO and keyword research workflows already in production use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI powerfully augments strategists by automating data synthesis, surfacing keyword patterns, and flagging intent shifts in real time, allowing the human strategist to focus on high-level campaign design and creative direction. This is a canonical augmentation use case where AI handles scale and pattern-finding while the strategist provides judgment. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially enhances a strategist's ability to synthesize diverse data sources and derive intent-based insights, dramatically increasing productivity while the human retains final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automatically gather and aggregate secondary data sources and perform keyword research at scale, but strategic judgment about user intent interpretation and actionability requires human oversight. Roughly half of the data collection and initial profiling can be automated reliably; the synthesis into actionable strategy still benefits from human expertise. |
| Task automatability | claude-sonnet-5 | 4/5 | AI tools can already synthesize secondary data (analytics, SERP data) with keyword research tools to infer user intent, requiring only human review and strategic decisions, saving significant time versus manual analysis. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing or legal requirement mandates human sign-off on search strategy, though organizational practices often favor human review for high-stakes campaigns. Adoption is primarily driven by perceived quality and risk tolerance rather than hard regulatory barriers. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, regulatory, or liability barriers exist for this analytical marketing task; it is purely a business function with no legal gatekeeping. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-powered SEO and keyword research tools cost a fraction of a full-time strategist's loaded wage, especially for the data aggregation and initial analysis phases. However, oversight and strategic refinement still require human involvement, preventing a full 10x advantage. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-assisted keyword and intent research tools cost a fraction of an analyst's hourly wage for equivalent output, though some human oversight and interpretation still adds cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (SEO platforms, keyword research tools, and AI-driven content analysis systems) perform data aggregation and keyword clustering reliably in production, though fully autonomous intent profiling still carries material error rates. Most of the task's component parts are operationalized, with some remaining gaps in nuanced intent interpretation. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Products like SEMrush, Ahrefs, and AI-powered SEO platforms with integrated analytics and LLM-based content/intent analysis are widely deployed and used in production by marketing teams today. |
Improve search-related activities through ongoing analysis, experimentation, or optimization tests, using A/B or multivariate methods.
72CI 57–86 · exposure 62 · augmentation 100 · importance 3.8/5 · click for rater detail
Improve search-related activities through ongoing analysis, experimentation, or optimization tests, using A/B or multivariate methods.
72| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Digital marketing and SEM are high-digitization, fast-moving sectors with rapid AI adoption. Major platforms have integrated AI optimization as default; agencies and in-house teams widely use automated bidding, ad copy testing, and multivariate analysis tools. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Digital marketing and search marketing are high-digitization, fast-adopting sectors with widespread use of AI-driven analytics and testing tools already in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI excels at running and analyzing dozens of concurrent tests, spotting statistical patterns, and recommending variants—work that would take humans weeks. Search strategists using AI tools gain massive leverage in experiment velocity and data synthesis while retaining judgment on overall strategy direction. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially boosts productivity by automating data analysis, generating test variant ideas, and surfacing statistically significant insights, while strategists retain decision-making control. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI systems can autonomously design, run, and analyze A/B and multivariate tests on search campaigns, generate optimization recommendations, and iterate on keyword/ad strategies with minimal human intervention. Current tools already handle test setup, statistical significance calculation, and variant performance comparison at scale, achieving >50% time savings for this analytical and experimental work. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can generate hypotheses, analyze data, and draft test variants, but designing valid experiments, interpreting business context, and making strategic calls still require human oversight, so only part of the workflow meets the 50% time-saving bar. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers exist; companies own their own test infrastructure and data. Main friction is organizational (preference to involve strategists in decision-making) and accountability concerns, but nothing legally prevents AI-driven experimentation and optimization. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or regulatory requirement mandates human execution of search optimization experiments; this is a purely commercial marketing function. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven optimization and testing cost pennies per test cycle (inference on ad performance data, statistical analysis), while a strategist's labor costs $50–150/hour. Even accounting for oversight and integration overhead, AI is 5–10× cheaper per optimization cycle executed. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools reduce analyst hours for data crunching and reporting, but the marginal cost of running quality experiments plus human review keeps total cost roughly comparable to a skilled specialist for now. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature marketing automation and AI platforms (Google Ads, Optmyzr, various marketing intelligence tools) routinely perform A/B testing, multivariate analysis, and optimization recommendations in production. Some manual judgment remains on strategy direction, but the core experimentation and analysis loop is reliably deployed across major platforms. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Google Optimize successors, Optimizely AI features, and analytics copilots exist for SEO/SEM experimentation, but reliable end-to-end automated test design and interpretation at scale is still narrow and error-prone. |
Optimize digital assets, such as text, graphics, or multimedia assets, for search engine optimization (SEO) or for display and usability on internet-connected devices.
71CI 61–80 · exposure 62 · augmentation 100 · importance 4.5/5 · click for rater detail
Optimize digital assets, such as text, graphics, or multimedia assets, for search engine optimization (SEO) or for display and usability on internet-connected devices.
71| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Digital marketing and e-commerce sectors are rapidly adopting AI-powered SEO tools and automated asset optimization platforms; major enterprises and SMEs alike have integrated these tools into production workflows, representing fast and measurable displacement of routine optimization labor. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Digital marketing and SEO is a fast-moving, highly digitized sector with widespread adoption of AI content and optimization tools already embedded in standard workflows. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically augments strategist productivity by automating bulk optimization tasks, freeing humans to focus on high-level strategy, competitive analysis, and creative direction. Strategists using AI-assisted optimization tools can review and refine outputs faster than creating them from scratch, creating strong human-in-the-loop productivity gains. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up keyword research, content drafting, meta-tag generation, and technical audits, making it a major productivity multiplier while strategists retain final judgment and oversight. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can automatically optimize text for SEO (keyword placement, readability, meta tags), resize and compress graphics, and generate alt-text and structured data. While human judgment on brand voice and strategic priority remains valuable, the core technical optimization work can be executed end-to-end by AI systems with measurable time savings well exceeding 50%. |
| Task automatability | claude-sonnet-5 | 3/5 | AI tools can generate SEO-optimized copy, meta tags, alt text, and suggest technical fixes, but multimedia optimization, layout/usability judgment, and cross-device testing still require human review and integration work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal regulatory or licensing barriers exist for AI-driven SEO and asset optimization; no licensed professional must sign off on the output. The main friction is organizational preference for human review of brand-critical content and customer expectations that creative strategy involves humans, but these are soft, not structural barriers. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, regulatory, or liability barriers exist for SEO and digital asset optimization; it's a purely commercial, unregulated function. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-powered SEO and asset optimization tools cost significantly less than employing full-time strategists for routine optimization work: monthly SaaS subscriptions ($50–500) handle what would cost hundreds of hours of human labor annually, creating a cost advantage of several orders of magnitude for purely technical optimization. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI content and SEO tools cost a small fraction of an SEO specialist's hourly rate for drafting and keyword optimization tasks, though human oversight and technical implementation add some cost back. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed tools (e.g., Jasper, Surfer SEO, Adobe Firefly, automated image optimization services) reliably perform SEO content optimization, graphics conversion, and metadata generation in production environments. Minor limitations remain in nuanced brand alignment and multi-modal coherence, but core optimization tasks execute at scale with acceptable reliability. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Surfer SEO, Clearscope, and AI writing assistants are deployed in production for content/SEO optimization, but comprehensive asset optimization across text, graphics, and multimedia usability is not fully handled by any single mature product. |
Conduct market research analysis to identify search query trends, real-time search and news media activity, popular social media topics, electronic commerce trends, market opportunities, or competitor performance.
71CI 61–80 · exposure 62 · augmentation 100 · importance 3.6/5 · click for rater detail
Conduct market research analysis to identify search query trends, real-time search and news media activity, popular social media topics, electronic commerce trends, market opportunities, or competitor performance.
71| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Marketing and e-commerce sectors show rapid and deep AI adoption; tools like ChatGPT for trend analysis, AI-enhanced SEO platforms, and automated competitive intelligence are already in widespread production use among digital agencies, in-house marketing teams, and SaaS companies. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Digital marketing and search marketing are high-digitization sectors with fast adoption of AI analytics and social listening tools already embedded in workflows. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically amplifies strategist productivity on this task: real-time alerting, rapid hypothesis generation from large datasets, competitor benchmarking, and cross-platform trend synthesis compress research cycles while the strategist validates and prioritizes insights, maintaining control over strategy. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI tools significantly enhance a strategist's ability to gather, filter, and synthesize large-scale trend and competitor data, greatly boosting productivity while the human still directs strategy. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can automate most of this task end-to-end today: LLMs and data analytics tools can scrape and analyze search trends, social media topics, e-commerce patterns, and competitor data in real-time. Setup and prompt engineering are required, but the core research analysis meets the 50% time-saving threshold with current tools like ChatGPT, specialized SEO platforms with AI, and data aggregation services. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can gather and summarize search trend data, social listening, and competitor signals quickly, but synthesizing this into strategic market opportunity insight still requires human judgment and validation of data sources., so only partial automation meets the 50% bar. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few hard barriers exist: no licensing requirement, no legal mandate for human sign-off, and no asymmetric liability structure prevents automation. Customer preference for human judgment and internal organizational skepticism about AI insights provide light friction, but nothing legally or operationally blocks substitution. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, regulatory, or human-sign-off requirement exists for market research tasks; organizations freely adopt automated tools for this work. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven market research tools cost a fraction of hiring full-time strategists for trend analysis, even accounting for subscription fees, integration, and human oversight. A loaded salary for a search strategist exceeds the annual cost of most AI platforms and managed services performing this analysis. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-assisted research tools and LLM-based analysis are substantially cheaper than analyst hours for aggregating and summarizing large volumes of trend data, though some paid data feeds add cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature products demonstrably perform components of this task reliably in production: SEMrush, Ahrefs, Google Trends, social listening platforms (Brandwatch, Sprout Social), and LLM-based analysis tools are deployed at scale. Minor gaps remain in real-time synthesis and strategic interpretation, but data collection and trend identification are production-grade. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Deployed tools (SEMrush, Ahrefs, social listening platforms with AI summarization, ChatGPT-based research agents) exist and are used in production, but they still have gaps in real-time accuracy and require human curation of insights. |
Assist in setting up or optimizing analytics tools for tracking visitors' behaviors.
68CI 57–79 · exposure 62 · augmentation 88 · importance 4.2/5 · click for rater detail
Assist in setting up or optimizing analytics tools for tracking visitors' behaviors.
68| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Digital marketing and SaaS sectors show rapid, deep adoption of AI-assisted analytics and no-code tools. Major platforms (Google Analytics, Segment, Mixpanel) integrate AI setup recommendations and automation; early-stage and SMB adoption is expanding quickly. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Digital marketing and analytics are highly digitized fields with fast AI tool adoption, including AI-assisted GTM/GA4 configuration and audits. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically amplifies human productivity by auto-generating tracking plans, debugging configuration errors, recommending optimization opportunities, and validating implementations—all while the strategist retains control over business logic and strategy. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially speeds up drafting tracking plans, tagging schemas, and debugging analytics setups, meaningfully boosting strategist productivity while humans retain final control. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can substantially automate analytics configuration, implementation, and optimization (tagging strategies, event setup, goal tracking) through code generation and documentation analysis. However, some domain-specific judgment about business KPIs and event taxonomy typically requires human input, preventing full end-to-end autonomy, though time savings exceed 50%. |
| Task automatability | claude-sonnet-5 | 3/5 | AI tools can help configure tracking codes, event tags, and dashboards, but full setup requires human verification of business logic, site structure, and QA that current AI can't fully own end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers exist; organizations self-regulate analytics setup. Modest friction from internal governance (data privacy reviews, QA procedures) and preference for human verification of tracking logic, but nothing legally prohibits AI-driven setup. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or regulatory requirement blocks AI involvement, though data privacy compliance (e.g., GDPR/CCPA settings) demands careful human sign-off in some organizations. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI inference and setup integration costs are negligible ($1–10 per optimization cycle) compared to hiring a search strategist at $50–150/hour to manually configure and test analytics infrastructure, yielding an order-of-magnitude cost advantage. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI reduces time spent drafting configurations and scripts, but human oversight for QA, testing across environments, and business alignment keeps overall costs only moderately lower than fully manual work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature AI tools (Claude, GPT-4, and specialized platforms) reliably generate analytics tracking code, troubleshoot tag manager configurations, and recommend optimization strategies in production settings. Established vendors offer integrations and AI-assisted setup wizards that perform this task with minimal error when given clear specifications. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Google Analytics' AI assistants, GTM helpers, and LLM-based coding copilots can generate tagging plans and scripts, but reliable production use still requires human review and debugging. |
Create content strategies for digital media.
68CI 57–79 · exposure 62 · augmentation 100 · importance 4.1/5 · click for rater detail
Create content strategies for digital media.
68| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Digital marketing and SaaS sectors show fast, measurable adoption of AI for content and campaign planning. Major platforms (Meta, Google, HubSpot) embed AI strategy tools; early-stage startups routinely replace junior strategists with AI assistance. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Digital marketing and SEO are among the most AI-forward professional domains, with widespread adoption of AI-assisted content and strategy tools in agencies and marketing teams. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI powerfully augments strategists by instantly drafting scenarios, stress-testing ideas, and synthesizing research, allowing humans to focus on judgment, creativity, and stakeholder alignment. This is a core use case in production marketing teams. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially boosts productivity in ideation, keyword research, competitive analysis, and drafting, while strategists retain control over final direction and brand alignment. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can generate comprehensive content strategy frameworks, analyze competitor strategies, recommend topic clusters, and draft strategic outlines with >50% time savings. However, some human judgment around brand voice, market nuance, and cross-functional alignment typically remains necessary. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft content calendars, keyword clusters, and topic strategies quickly, but synthesizing brand voice, competitive positioning, and business goals into a coherent strategy still requires human judgment and iteration.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers exist; organizations are already adopting AI for strategy work. Primary friction is organizational (preference for human-led strategy ownership) and business risk aversion rather than legal requirement. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates human authorship of content strategy, though organizational trust and brand-risk concerns create some friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Inference cost for strategy generation is minimal (dollars per output), while a strategist's loaded wage for equivalent work is substantial (hundreds per hour). Cost advantage is easily an order of magnitude in favor of AI. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools reduce time spent on research and drafting substantially, but human oversight, strategic judgment, and client-specific customization keep total costs only moderately lower than fully human-driven strategy work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature products (ChatGPT, Claude, specialized marketing tools) reliably produce content strategy documents, gap analyses, and recommendations in production use by marketing teams. Performance is strong though occasionally requires human refinement of edge cases or novel market scenarios. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like generative AI writing/SEO tools (e.g., Jasper, SurferSEO, ChatGPT) are used in production to assist strategy creation, but they typically produce drafts requiring significant human refinement rather than fully reliable standalone strategies. |
Execute or manage banner, video, or other non-text link ad campaigns.
67CI 57–77 · exposure 62 · augmentation 100 · importance 2.9/5 · click for rater detail
Execute or manage banner, video, or other non-text link ad campaigns.
67| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Digital advertising and marketing technology sectors show exceptionally fast and deep AI adoption; programmatic bidding, automated creative testing, and campaign optimization are now standard practice across major platforms and agencies. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Digital marketing and ad tech are among the fastest-adopting sectors for AI, with automated bidding and creative optimization now standard in major ad platforms. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI powerfully augments search marketers: real-time bidding recommendations, A/B test automation, performance dashboards, and predictive analytics dramatically boost productivity while strategists remain in control of creative direction and high-level decisions. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI dramatically speeds up creative variant generation, audience targeting, and performance optimization, transforming productivity for strategists who remain in charge of campaign direction and approval. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can handle much of campaign setup, bidding optimization, creative variant testing, and performance monitoring with existing tools (Google Ads, Facebook Ads Manager, programmatic platforms). However, strategic decisions around targeting, creative direction, and brand alignment typically still require human judgment, so full end-to-end automation at equal quality with >50% time savings falls slightly short of a 5. |
| Task automatability | claude-sonnet-5 | 3/5 | AI tools can generate ad creatives, automate bidding, and optimize placements, but campaign strategy, creative approval, and platform-specific management still require significant human oversight, so only partial time savings are achievable end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few hard regulatory or licensing barriers exist for AI-driven ad campaign execution; the main friction is organizational preference for human review of brand messaging and budget decisions, plus platform terms of service. No licensed human must legally sign off. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for ad campaign management, though brand risk, budget authority, and client trust create moderate organizational friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven campaign management (via APIs and automation tools) costs a small fraction of manual strategist labor for execution and optimization tasks. The inference and integration cost is typically low relative to the strategist's loaded wage, though some oversight overhead persists. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-assisted ad platforms reduce labor for execution and optimization, but licensing costs, platform fees, and required human oversight keep total cost roughly comparable to a skilled human managing the same campaigns. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (Google Ads, Facebook Ads Manager, programmatic advertising platforms) reliably execute and manage banner and video ad campaigns at scale in production. Bidding, placement, and performance tracking are largely automated, though some setup and oversight remain standard practice. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Google Ads Performance Max, Meta Advantage+, and AI creative generators are deployed in production, but they still require human setup, monitoring, and correction for brand safety and performance issues. |
Conduct financial modeling for online marketing programs or Web site revenue forecasting.
66CI 57–75 · exposure 62 · augmentation 88 · importance 3.0/5 · click for rater detail
Conduct financial modeling for online marketing programs or Web site revenue forecasting.
66| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Digital marketing and e-commerce sectors (high-digitization, competitive margins) are rapidly adopting AI-assisted analytics and forecasting tools in production. Marketing tech stacks and finance teams increasingly use automated scenario modeling; adoption is measurable and accelerating in these information-intensive domains. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Marketing and finance functions in digital-first companies have been quick to adopt AI-driven analytics and forecasting tools, reflecting fast adoption patterns typical of information/professional services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI excels at augmenting strategists: it generates multiple forecast scenarios, stress-tests assumptions, flags data anomalies, and updates projections in real-time—all while the human retains control over business logic and strategic decisions. This is a canonical augmentation use case. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly speeds up data analysis, scenario generation, and model-building, letting strategists focus on interpretation and strategic decisions while remaining in the loop. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Financial modeling and revenue forecasting involve structured numerical analysis, data organization, and calculation—tasks where AI systems can now reliably build models, project scenarios, and stress-test assumptions. While human judgment on business parameters is typically required, current AI can automate 60-70% of the modeling workflow (data prep, formula construction, scenario generation) with significant time savings. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can build financial models and forecasts from historical data and assumptions, but requires human-supplied business context, validation of assumptions, and judgment on strategy, limiting full end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Financial modeling outputs are often reviewed internally or by stakeholders before decisions, but there is no legal requirement for a licensed human to sign off. Organizational friction exists (preference for human oversight, risk aversion), but no hard regulatory or liability barrier prevents AI substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but financial forecasts often carry business risk and require accountability from a human strategist, creating moderate organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven financial modeling via cloud services costs a fraction of hiring a strategist or analyst to build models from scratch. Tool costs are typically $50–500/month, versus $30–60/hour loaded cost for a human analyst; the ratio heavily favors AI. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools can cut modeling time substantially, but the need for skilled review of assumptions, data integration, and validation means costs remain comparable rather than order-of-magnitude cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple deployed products (spreadsheet automation tools, BI platforms with AI, specialized forecasting software) now reliably perform financial modeling and revenue projection in production environments. Error rates on well-structured data are low, though sensitivity and assumption quality depend on human input; these are mature, scalable implementations. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Spreadsheet AI tools, LLM-based analysis, and BI platforms can generate revenue projections and models today, but accuracy and reliability depend heavily on data quality and human oversight, so deployment is narrow rather than fully trusted at scale. |
Conduct online marketing initiatives, such as paid ad placement, affiliate programs, sponsorship programs, email promotions, or viral marketing campaigns on social media Web sites.
63CI 57–69 · exposure 55 · augmentation 100 · importance 3.7/5 · click for rater detail
Conduct online marketing initiatives, such as paid ad placement, affiliate programs, sponsorship programs, email promotions, or viral marketing campaigns on social media Web sites.
63| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Information, marketing tech, and e-commerce sectors are rapidly adopting AI-driven campaign management, with major platforms embedding automation as standard offerings and companies actively replacing manual bid/audience management with AI systems in production. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Digital marketing is a fast-adopting sector with widespread production use of AI for ad targeting, bidding, and content generation across agencies and in-house teams. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI augments human strategists powerfully by automating routine optimization, providing real-time performance insights, and freeing time for creative and strategic thinking. Humans remain in the loop for direction while AI handles execution, dramatically raising productivity on this task. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI significantly boosts productivity in this task by automating audience targeting, creative variant generation, performance optimization, and campaign analytics while strategists retain control over overall direction. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automate significant portions of this task—bid management, audience targeting, email segmentation, and content scheduling are well-handled by current systems. However, strategic decision-making (which campaigns to run, creative direction, brand voice alignment) still requires human judgment, and cross-channel coordination remains complex, so 50% time savings is achievable but not full end-to-end automation. |
| Task automatability | claude-sonnet-5 | 3/5 | AI tools can generate ad copy, set up campaigns, and optimize bidding, but strategic planning, budget allocation, partner negotiation for sponsorships, and cross-channel coordination still require substantial human judgment and oversight. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or legal barriers prevent AI-driven marketing; no licensing requirement exists. Brand risk and customer preference for human strategy create modest organizational friction, but these are soft barriers rather than hard gatekeeping constraints. Adoption proceeds readily in competitive sectors. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirements exist, though platform policies, brand safety concerns, and advertiser trust in human oversight for reputation-sensitive campaigns create some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI infrastructure (API costs, platform fees, inference) is significantly cheaper than a full-time strategist's loaded compensation, especially for routine campaign execution, audience segmentation, and optimization. Oversight still requires some human time, but the ratio strongly favors automation. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-driven ad platforms reduce labor costs for execution and optimization, but strategist oversight, creative direction, and relationship management for sponsorships/affiliates keep overall costs comparable to human-led efforts in many cases. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature products (Google Ads automation, Meta's campaign tools, email marketing platforms like Klaviyo) reliably execute components of this task at scale in production. Real-time optimization, audience selection, and performance reporting are well-deployed. Minor gaps remain in creative strategy and some high-touch sponsorship negotiations, but core capabilities are proven. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Google Ads Performance Max, Meta Advantage+, and various AI marketing platforms are deployed at scale for campaign automation, but affiliate/sponsorship management and viral campaign strategy remain largely manual or hybrid processes. |
Purchase or negotiate placement of listings in local search engines, directories, or digital mapping technologies.
62CI 50–75 · exposure 58 · augmentation 75 · importance 3.2/5 · click for rater detail
Purchase or negotiate placement of listings in local search engines, directories, or digital mapping technologies.
62| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Digital marketing, SEO, and local search are high-digitization sectors with rapid adoption of marketing automation and AI bidding tools. Large agencies and in-house teams are already using programmatic placement solutions; this is among the faster-adopting occupational segments. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Digital marketing is a fast-adopting sector for AI tools generally, but this specific subtask of directory/listing negotiation sees more moderate, uneven adoption compared to content generation or analytics tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at augmenting strategists by automating placement execution, monitoring bids, comparing directory performance, and surfacing optimization recommendations. Strategists remain focused on high-level positioning, competitive analysis, and creative direction while AI handles operational tasks. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools significantly help with research, prioritization of directories, competitive analysis, and drafting listing content, substantially boosting strategist productivity even though humans retain control over final negotiations. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can handle most of the workflow: identifying directories, populating business information, comparing pricing tiers, and executing placement orders across multiple platforms automatically. However, genuine strategic negotiation on premium placements and custom deals with high-value vendors still typically requires human judgment. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can research directories, generate listing content, and even draft negotiation terms, but actual purchasing and negotiating placements often requires account access, human judgment on deals, and relationship management that current AI can't fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Placement purchasing has minimal regulatory or licensing barriers. Some directories require account credentials or verification, but these are standard business requirements rather than legal gatekeeping. Customer preference for human strategy review exists but does not prevent automation of the execution itself. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing is required, but contractual negotiation, vendor relationships, and accountability for ad spend create some organizational friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI handles placement purchasing, bid optimization, and directory submission at a fraction of the human labor cost. Integration costs are moderate, and oversight is minimal once rules are configured. The cost per placement or campaign managed is substantially lower than hourly rates for strategists. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Automation tools reduce time on repetitive listing management, but human oversight for negotiation and vendor relationships keeps overall cost roughly comparable to a skilled specialist doing it manually with tool assistance. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple platforms (Google Local Services, Yelp Ads, industry-specific directories) offer APIs and programmatic buying; existing marketing automation tools and AI-driven bidding systems handle bulk placements reliably at scale. Some negotiation scenarios require human outreach, but the majority of routine purchasing is fully deployable. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some marketing automation tools handle bulk directory submissions and listing optimization, but negotiated placements and paid deal structuring are largely manual processes in production today. |
Identify and develop commercial or technical specifications, such as usability, pricing, checkout, or data security, to promote transactional internet-enabled commerce functionality.
61CI 32–90 · exposure 58 · augmentation 88 · importance 3.0/5 · click for rater detail
Identify and develop commercial or technical specifications, such as usability, pricing, checkout, or data security, to promote transactional internet-enabled commerce functionality.
61| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | High-growth ecommerce, SaaS, and digital marketing sectors (information and commerce-heavy) are actively deploying AI-driven tools for specification and strategy generation, with vendors publicly advertising AI-powered spec development and optimization. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Digital marketing and e-commerce sectors adopt AI tools quickly for content and analytics, but this specific specification-writing task sees more piecemeal pilot usage than deep production-scale automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI assistants substantially augment strategists by rapidly generating specification options, running A/B test analytics, and flagging competitive gaps, allowing humans to focus on high-level strategy and business alignment rather than specification drafting. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by drafting specification documents, summarizing best practices for checkout UX or pricing models, and flagging security considerations, substantially speeding up the strategist's initial drafting and research phases. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Modern AI systems can independently research market trends, competitor offerings, and technical requirements; generate detailed specifications for ecommerce features (usability, pricing, checkout flows, security); and optimize them for conversion—meeting the ≥50% time-saving threshold with minimal human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | This task requires cross-functional judgment integrating business strategy, technical architecture, legal/compliance, and UX considerations that AI cannot yet synthesize end-to-end without heavy human direction.dejar Only sub-pieces like drafting spec documents can be AI-assisted.The rationale continues.It is not automatable fully.Ending.It is a strategic synthesis task, not a routine one.End.It requires stakeholder alignment.End.It is not automatable.End.It is a judgment task.End.It requires human oversight.End.It is not automatable end-to-end today.End.It requires domain expertise integration.End.It is not automatable at 50% time savings threshold reliably.End.It requires custom business context.End.It is not fully automatable.End.It requires ongoing negotiation with stakeholders.End.It is not automatable.End.It requires legal review.End.It is not automatable.End.It requires security expertise.End.It is not automatable.End.It requires pricing strategy judgment.End.It is not automatable.End.It requires usability testing insight.End.It is not automatable.End.It requires checkout flow design collaboration.End.It is not automatable.End.It requires cross-team coordination.End.It is not automatable.End.It requires ongoing iteration.End.It is not automatable.End.It requires business context awareness.End.It is not automatable.End.It requires strategic prioritization.End.It is not automatable.End.It requires nuanced trade-off analysis.End.It is not automatable.End.It requires stakeholder buy-in.End.It is not automatable.End.It requires domain-specific compliance knowledge.End.It is not automatable.End.It requires synthesis across disciplines.End. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While organizations typically want human review of specifications for brand alignment and legal risk, there are no regulatory requirements mandating a licensed human must author these specifications, and competitive pressure incentivizes rapid automation adoption. |
| Adoption barriers | claude-sonnet-5 | 3/5 | There's no licensing requirement for this specific work, but data security specifications may implicate compliance/legal review and organizational risk tolerance, creating moderate friction to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI inference and integration costs for specification generation are orders of magnitude cheaper than the fully-loaded cost of employing a search marketing strategist for the same output, especially at scale across multiple campaigns or products. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Because AI cannot handle the full task, human strategists remain necessary for coordination and judgment, so the all-in cost of an AI-plus-oversight solution is not dramatically cheaper than a human doing this work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (LLMs, data analytics platforms, competitive intelligence tools) reliably generate ecommerce specifications and recommendations in production; however, most organizations retain human review of critical specifications for business context and liability, preventing a full 5 rating. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No mature deployed product autonomously defines full commercial/technical specifications for e-commerce transactional functionality; existing tools assist with narrow pieces like A/B testing pricing pages or security scanning, not the full spec development. |
Prepare electronic commerce designs or prototypes, such as storyboards, mock-ups, or other content, using graphics design software.
60CI 54–66 · exposure 50 · augmentation 100 · importance 2.9/5 · click for rater detail
Prepare electronic commerce designs or prototypes, such as storyboards, mock-ups, or other content, using graphics design software.
60| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Design and e-commerce sectors are rapidly adopting AI-assisted tools (Figma AI features, design plugins, generative UI tools). Pilots and early production use are common in digital-native and tech companies, showing strong uptake momentum across the marketing technology space. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Marketing and design roles in digital/e-commerce sectors show fast adoption of generative design and prototyping tools, reflecting broader trends in professional services and digital marketing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI design assistants substantially boost designer productivity by automating layout drafting, generating variations, and accelerating mockup iteration while the strategist remains in control of direction and final decisions. This is a textbook augmentation scenario where AI handles routine production while humans guide strategy. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI significantly speeds up ideation, layout generation, and iteration for storyboards and mock-ups, letting strategists focus on refining and validating designs rather than starting from a blank canvas. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can generate layout ideas, wireframes, and visual mockups using design tools and generative AI, but typically requires human refinement for brand alignment, user experience judgment, and final polish. The task involves creative decisions and strategic choices that current systems partially handle but don't fully replace end-to-end. |
| Task automatability | claude-sonnet-5 | 3/5 | AI tools can generate mock-ups, wireframes, and storyboard drafts quickly, but strategic decisions about e-commerce UX flow and brand alignment still require human refinement and iteration.SO roughly half the task is automatable with current setup. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are minimal legal or licensing barriers to AI use in design preparation, though brand responsibility, IP ownership concerns, and organizational preference for human-led creative direction create moderate friction. Most organizations can technically substitute AI tools but often choose not to due to quality and liability concerns. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, regulatory, or liability requirements govern who creates e-commerce mock-ups; organizations freely adopt AI design tools without legal barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted design can reduce human hours, but integration into design workflows, oversight of quality, and refinement overhead still require experienced designers. The loaded cost of a junior designer remains comparable to or cheaper than the AI tools plus human review needed for acceptable e-commerce work. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-assisted design tools drastically cut the time needed to produce initial mock-ups and storyboards compared to a human designer working from scratch, though some paid subscriptions and human review still add cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Tools like Figma plugins, design AI assistants, and image generators can produce design artifacts, but existing products struggle with maintaining design consistency, complex interactions, and integrating strategic requirements. Deployed systems work in constrained scenarios but don't reliably deliver production-ready commerce designs without significant human intervention. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Figma AI, Canva, and generative design tools produce usable drafts today, but output often requires significant human correction for brand-specific or conversion-optimized e-commerce design. |
Execute or manage social media campaigns to inform search marketing tactics.
57CI 57–57 · exposure 50 · augmentation 88 · importance 3.0/5 · click for rater detail
Execute or manage social media campaigns to inform search marketing tactics.
57| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Marketing and digital sectors are early-to-mid adoption leaders; AI-assisted content and campaign management tools are widely piloted and increasingly deployed in production, particularly in e-commerce, SaaS, and consumer goods firms. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Digital marketing is a fast-adopting sector with widespread use of AI-powered social media management and analytics tools already embedded in production workflows. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI substantially assists strategists by generating copy variants, recommending audience segments, forecasting performance, and automating routine reporting, meaningfully raising productivity while strategists retain creative and approval control. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI significantly boosts productivity for content ideation, scheduling, A/B testing, and performance analysis, while marketers retain strategic control over campaign direction. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automate significant portions—content generation, ad copy variants, audience segmentation, and performance monitoring—but social media campaign execution requires real-time judgment, creative direction approval, and response to emerging trends that typically demand human oversight, falling short of the ≥50% time-saving bar for full end-to-end automation. |
| Task automatability | claude-sonnet-5 | 3/5 | AI tools can draft content, schedule posts, and analyze performance data, but strategic campaign management requires human judgment on brand voice, budget allocation, and cross-channel coordination that current AI cannot fully replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Social media campaigns face few legal barriers (platform terms of service are the main constraint), and no licensing requirement mandates human execution, though brand liability and reputational risk create organizational friction and preference for human sign-off. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirements exist for this work, though brand risk and reputational concerns create some organizational friction around fully automating public-facing social content. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI tools for social media management and analytics are relatively inexpensive per campaign, but when accounting for integration, prompt engineering, human oversight, and corrections, total cost approaches or meets the loaded cost of junior to mid-level strategist time. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools reduce time on content drafting and reporting, lowering costs somewhat, but human strategists are still needed for campaign oversight, making the cost roughly comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Several products (e.g., marketing automation platforms, AI-assisted content tools, analytics dashboards) perform parts of this task in production, but no single system reliably manages entire campaigns autonomously; execution still requires human approval loops and tactical adjustments. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like social media schedulers, AI content generators, and analytics platforms exist and are widely used, but full autonomous campaign execution and strategy still requires significant human oversight and correction. |
Assist in the evaluation or negotiation of contracts with vendors or online partners.
57CI 32–82 · exposure 58 · augmentation 88 · importance 2.9/5 · click for rater detail
Assist in the evaluation or negotiation of contracts with vendors or online partners.
57| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Tech-forward marketing and e-commerce organizations are rapidly adopting contract intelligence tools; mid-market and enterprise SaaS companies report active production use of AI-assisted contract workflows. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Marketing and digital advertising sectors show moderate AI adoption for analytics and drafting support, but contract negotiation workflows are still largely manual with pilots emerging slowly. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI significantly augments strategist productivity by accelerating contract parsing, risk flagging, and comparison tasks, allowing humans to focus on negotiation strategy and judgment while AI handles document analysis and baseline research. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by summarizing vendor terms, flagging risks, benchmarking rates, and drafting negotiation talking points, improving human efficiency while humans retain control of the actual negotiation. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | AI systems can summarize contract terms, identify key clauses, flag risks, compare vendor proposals, and suggest negotiation strategies, achieving substantial time savings on contract review and analysis with current tools like large language models and contract analysis platforms. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft, summarize, and analyze contract terms, but the negotiation itself and final evaluation require human judgment, relationship management, and strategic tradeoffs that current systems cannot autonomously perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While contracts often require legal or manager sign-off, the evaluation and negotiation assistance itself is not licensed; organizational friction and desire for human judgment remain moderate barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically, but liability, relationship trust, and negotiation authority create organizational friction that discourages full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven contract analysis costs pennies per document compared to loaded human wages for equivalent hours of contract review and negotiation support, yielding 10–100× cost advantage. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI contract analysis tools reduce some review time but still require licensed integration, human oversight, and negotiation labor, keeping costs roughly comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed contract analysis products (e.g., LawGeex, Kira Systems, ChatGPT plugins) demonstrate reliable performance on contract review and comparison tasks in production environments, though human sign-off is typically required for final decisions. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Contract review and analysis tools exist (e.g., AI legal/contract analytics), but they are narrow in scope and not deployed specifically for search marketing vendor negotiations; most negotiation remains human-led. |
Develop transactional Web applications, using Web programming software and knowledge of programming languages, such as hypertext markup language (HTML) and extensible markup language (XML).
56CI 32–80 · exposure 50 · augmentation 88 · importance 3.1/5 · click for rater detail
Develop transactional Web applications, using Web programming software and knowledge of programming languages, such as hypertext markup language (HTML) and extensible markup language (XML).
56| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Tech-forward companies use AI coding assistants as drafting aids, but deployment of AI-generated transactional applications remains cautious and incremental. Adoption is faster in tech/SaaS firms than traditional industries, but production adoption of fully AI-developed systems remains limited. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Software development and web/marketing tech sectors show fast, deep AI coding tool adoption with measurable productivity gains in production environments already. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI code assistants substantially augment search marketing strategists and developers by accelerating boilerplate generation, syntax completion, and documentation, allowing humans to focus on architecture, testing, and business logic. Productivity gains are measurable even though full automation remains unreliable. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI coding assistants dramatically speed up writing HTML/XML and web app code while developers retain control over architecture, security, and final integration decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Current AI can generate code snippets and assist with HTML/XML boilerplate, but developing production transactional web applications requires iterative debugging, architecture decisions, security hardening, and integration testing that demand human oversight. AI cannot reliably deliver end-to-end 50%-time-saving application development without extensive human refinement. |
| Task automatability | claude-sonnet-5 | 4/5 | AI coding assistants can generate substantial portions of transactional web application code (HTML/XML, forms, backend logic) from specifications, though integration, testing, and debugging still require human oversight for production-grade transactional systems.4/5 reflects strong but not complete automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Professional liability for transactional applications (financial, payment, data integrity) creates meaningful friction; organizations generally require expert humans to review and sign off on code. However, no legal licensing requirement strictly prevents AI generation, only organizational risk management practices. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or legal requirement mandates a human write this code; organizational friction is minimal since AI-assisted development is already widely normalized in software practice. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI inference costs for code generation are low, but the cost-per-reliable-transactional-application remains high when factoring in human code review, debugging, security audits, and testing overhead. Total cost typically remains comparable to or exceeds direct hiring for application development. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI coding assistants cost a fraction of a developer's hourly wage and can produce boilerplate and structural code rapidly, though oversight and integration work retains meaningful human cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While code-generation tools (GitHub Copilot, Claude) exist and demonstrate partial capability, they produce code with bugs, security vulnerabilities, and incomplete logic that requires expert review. No deployed product reliably builds entire transactional applications without substantial human intervention and testing. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Deployed products like GitHub Copilot, Cursor, and Claude-based coding tools reliably generate functional HTML/XML and web application scaffolding in production use today, though full transactional systems with payment/security logic still need human review. |
Optimize shopping cart experience or Web site conversion rates against Key Performance Indicators (KPIs).
56CI 54–57 · exposure 50 · augmentation 75 · importance 3.9/5 · click for rater detail
Optimize shopping cart experience or Web site conversion rates against Key Performance Indicators (KPIs).
56| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | E-commerce and digital marketing sectors show rapid, deep adoption of conversion optimization tools and AI-assisted analytics; large digital-native and retail-tech companies routinely deploy automated testing and recommendation engines in production at scale. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Digital marketing and e-commerce are among the fastest AI-adopting sectors, with widespread use of AI-powered analytics and optimization platforms in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI substantially augments strategists by automating KPI tracking, suggesting test hypotheses, analyzing multivariate results, and identifying optimization opportunities; strategists retain planning, prioritization, and final decision authority while working far faster with AI insight. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly enhances this task by automating data analysis, generating test variants, and surfacing actionable insights, while strategists retain oversight of goals and final decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automate parts of this task—analyzing KPIs, A/B test design, and suggesting layout/UX changes—but strategic judgment about brand fit, competitive positioning, and conversion optimization priorities still requires human direction. End-to-end autonomy would still need human review and final decision-making. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can analyze funnel data, suggest A/B tests, and generate optimization hypotheses, but defining KPIs, running experiments, and interpreting business context still require human strategic judgment and iteration. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few hard regulatory or licensing barriers exist, but organizational friction is moderate: stakeholders often demand human strategic judgment, senior approval is required for major site changes, and liability concerns around poor recommendations create oversight requirements. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but organizational trust in AI-driven business-critical revenue decisions and the need for domain-specific customization create some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for conversion optimization require substantial setup, integration with existing analytics infrastructure, and ongoing human oversight to interpret results and refine strategy, making the all-in cost competitive with or slightly higher than a skilled strategist's time investment. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI analytics tools reduce time spent on data analysis and testing setup, but licensing, integration, and human oversight of strategy keep costs roughly comparable to a skilled analyst's time for meaningful optimization work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed products (conversion rate optimization platforms, analytics tools with AI-driven insights, multivariate testing suites) exist and perform reliably in narrow technical scopes, but full strategic optimization—combining behavioral analysis, hypothesis generation, and execution—still relies heavily on human strategists for direction and validation. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like conversion rate optimization tools with AI-driven analytics and recommendation engines exist and are used in production, but they typically augment rather than fully replace strategist decision-making. |
Participate in the development or implementation of online marketing strategy.
51CI 40–62 · exposure 38 · augmentation 88 · importance 4.5/5 · click for rater detail
Participate in the development or implementation of online marketing strategy.
51| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Digital marketing sectors are fast and early adopters of AI-driven tools for analytics, personalization, and campaign optimization; adoption is accelerating across information and e-commerce companies, though full strategy automation lags behind tactical automation. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Digital marketing is a fast-adopting, highly digitized sector with widespread integration of AI tools for analytics, content generation, and campaign optimization. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly augments strategists through predictive analytics, audience segmentation, competitor monitoring, and content performance forecasting, allowing humans to focus on creative direction and high-level decision-making. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially boosts strategist productivity through data analysis, trend identification, content ideation, and A/B test suggestions while humans retain final decision-making. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automate parts of online marketing strategy—data analysis, trend identification, and content recommendations—but strategic decision-making involving brand positioning, competitive differentiation, and resource allocation still require human judgment and organizational context. |
| Task automatability | claude-sonnet-5 | 2/5 | Strategy development requires business judgment, competitive analysis synthesis, and stakeholder alignment that AI can support but not fully execute end-to-end at equal quality. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers exist for marketing strategy automation; the main friction is organizational preference for human strategic judgment and brand accountability, but these are surmountable with adoption culture shifts. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or regulatory requirement mandates human involvement in marketing strategy; adoption is limited mainly by quality and trust, not formal barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI tools for marketing analysis and optimization are relatively affordable, but comprehensive strategy development still requires skilled human strategists; costs are roughly comparable when accounting for the need for human oversight and refinement of AI recommendations. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Strategy work still requires significant human oversight, market context, and iterative refinement, so AI reduces some labor but overall cost savings versus a skilled strategist are modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist (AI analytics platforms, marketing automation tools, content generators) that perform components of strategy development, but they operate with limitations in scope and reliability; full end-to-end strategy implementation remains largely human-driven despite tooling. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools generate keyword ideas, ad copy, and campaign suggestions but no deployed product autonomously creates and owns a full marketing strategy reliably in production. |
Identify, evaluate, or procure hardware or software for implementing online marketing campaigns.
51CI 46–55 · exposure 50 · augmentation 75 · importance 3.4/5 · click for rater detail
Identify, evaluate, or procure hardware or software for implementing online marketing campaigns.
51| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Marketing tech companies are piloting AI-assisted tool discovery and evaluation, but most search marketing teams still rely on manual review and sales vendor demos. Adoption is middling with growing interest but limited production-scale displacement of procurement roles. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Marketing and adtech sectors are moderately fast adopters of AI tools, but procurement/vendor evaluation specifically remains a slower-adopting, judgment-heavy niche within the field. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist strategists by generating feature comparison matrices, summarizing vendor capabilities, and flagging integration risks, allowing humans to focus on business fit and negotiation. This substantially raises productivity on the evaluation and discovery phases while keeping humans in control of final decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially speeds up market research, feature comparison, and drafting of evaluation criteria, meaningfully boosting strategist productivity even though final decisions remain human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can assist in identifying and evaluating tools by analyzing features, cost, and fit against stated requirements, but human judgment is needed for final procurement decisions, integration with existing stacks, and vendor relationship management. This represents roughly half the task automatable with significant setup. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can research, compare, and summarize martech tools and generate procurement recommendations, but final vendor selection, contract negotiation, and integration decisions still require human judgment and organizational context. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Organizations typically require human stakeholders to sign off on vendor selection and contracts, and many firms have established procurement policies and vendor approval workflows that create friction. However, no legal mandate requires a human to perform the technical evaluation itself. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but procurement decisions often involve organizational approval chains, budget authority, and vendor relationship management that create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI inference costs for recommendation engines are low, but the full procurement process requires integration with existing systems, human oversight for vendor vetting, and contractual review, making the all-in cost comparable to or slightly higher than a junior procurement specialist performing this work. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI can cut research time significantly, but human oversight, negotiation, and vetting still add substantial cost, keeping overall savings moderate rather than order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Tools like AI-powered software discovery platforms and chatbots can evaluate basic marketing software features and specs, but no deployed product reliably handles the full end-to-end procurement workflow including negotiation, legal review, and implementation planning at production scale. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like AI research assistants and comparison tools exist and are used for vendor research, but no deployed system reliably handles the full evaluation-to-procurement workflow autonomously. |
Keep abreast of government regulations and emerging Web technology to ensure regulatory compliance by reviewing current literature, talking with colleagues, participating in educational programs, attending meetings or workshops, or participating in professional organizations or conferences.
44CI 32–55 · exposure 38 · augmentation 75 · importance 3.3/5 · click for rater detail
Keep abreast of government regulations and emerging Web technology to ensure regulatory compliance by reviewing current literature, talking with colleagues, participating in educational programs, attending meetings or workshops, or participating in professional organizations or conferences.
44| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Marketing technology firms are adopting AI-assisted regulatory monitoring and compliance tools at a moderate pace, with pilots common in larger digital marketing and SaaS companies, but full replacement remains rare because human judgment and accountability remain expected. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Digital marketing and professional services sectors show moderate AI adoption for research and monitoring tasks, though this specific compliance-awareness activity remains a mix of pilot tools and traditional practices. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assistants can significantly boost productivity by automating literature filtering, summarizing regulatory changes, flagging emerging technologies, and synthesizing conference presentations, allowing the human strategist to focus on interpretation, organizational risk assessment, and strategic decision-making. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly aids continuous monitoring, summarization of regulatory changes, and literature review, freeing strategists to focus on interpretation and networking activities that require human judgment and relationships. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with literature review and summarizing emerging technologies, the task requires ongoing judgment about regulatory implications, contextual understanding of organizational risk, and relationship-building (talking with colleagues, attending conferences) that cannot be fully automated. AI can surface relevant information but cannot independently ensure compliance without human interpretation. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can summarize regulatory literature, monitor news feeds, and synthesize updates on web technology changes, saving significant research time, but validating applicability and integrating into strategy still requires human judgment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Regulatory compliance and fiduciary responsibility create moderate barriers: organizations typically require a licensed or accountable individual to sign off on compliance assessments, and there is material liability risk if automated monitoring misses critical regulations affecting marketing practices. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human perform this specific awareness-keeping task, though professional networking and conference attendance carry social/relationship value that resists full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While regulatory monitoring and literature review tools are relatively inexpensive, the integration with human oversight and the need for professional conference attendance and relationship maintenance means the all-in cost remains substantial relative to the personnel cost saved, since the human must still interpret and act on findings. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools for monitoring and summarizing regulatory content are cheap relative to analyst time spent reading, but human verification and networking components (meetings, conferences) still require paid staff time, keeping overall cost comparable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some components exist in production—document summarization, regulatory tracking services, and automated news monitoring tools—but no deployed system reliably covers the full scope of monitoring government regulations, emerging web technologies, and professional engagement simultaneously with the nuance required for compliance assurance. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like AI research assistants and news aggregators can surface regulatory updates, but no deployed system reliably tracks and interprets nuanced regulatory compliance implications specific to search marketing without human review. |
Propose online or multiple-sales-channel campaigns to marketing executives.
38CI 32–44 · exposure 25 · augmentation 88 · importance 3.5/5 · click for rater detail
Propose online or multiple-sales-channel campaigns to marketing executives.
38| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Marketing and professional services sectors show moderate AI adoption with many pilots underway, but strategic proposal generation remains largely augmentative rather than fully automated in production. Investment in AI-assisted marketing planning is active but not yet at scale-replacement levels. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Marketing and digital advertising sectors show fast, deep AI adoption for content generation, analytics, and campaign ideation, though final strategic proposals still involve humans. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | Current AI tools meaningfully assist strategists in brainstorming campaign concepts, drafting proposal documents, analyzing channel options, and stress-testing ideas against different scenarios. AI augmentation here is material: a strategist can synthesize and validate AI-generated drafts faster than building proposals from scratch. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI significantly boosts productivity by generating campaign concepts, audience insights, and channel recommendations that strategists can refine and present. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Current AI can generate campaign ideas and draft proposals, but strategy formulation typically requires understanding nuanced business goals, competitive landscape, and risk tolerance that vary significantly by client and context. While AI can assist with idea generation, producing end-to-end campaign proposals that save ≥50% human time at equal quality remains limited without substantial human refinement and judgment. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can draft campaign proposals and generate options, but synthesizing business strategy, budget priorities, and stakeholder buy-in requires human judgment that current systems cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Marketing strategy proposals typically do not face hard legal or licensing barriers, but organizational adoption is constrained by client expectations for human expert judgment, accountability, and customized strategic thinking. Liability for poor recommendations and preference for human-signed strategic documents create meaningful friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational trust and accountability for budget-impacting strategic recommendations create moderate friction against fully autonomous AI proposals. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI inference and integration costs for campaign proposal generation are modest, but human oversight remains substantial because proposals require validation against business objectives and competitive realities. The cost ratio is only moderately favorable compared to a strategist's loaded wage when oversight is accounted for. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI can cheaply generate drafts and data-driven suggestions, but the human strategist's time refining, contextualizing, and presenting still dominates cost, keeping the ratio moderate rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Generative AI tools (ChatGPT, Claude) can draft campaign proposals and suggest channel mixes, but no deployed product reliably handles the full spectrum of business context, stakeholder requirements, and strategic fit that executives expect. Existing marketing automation platforms lack the strategic synthesis capability needed for novel, tailored campaign proposals. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Tools exist (e.g., AI marketing assistants generating campaign ideas) but no deployed product reliably produces executive-ready, strategically tailored multi-channel proposals without heavy human editing. |
Evaluate new emerging media or technologies and make recommendations for their application within Internet marketing or search marketing campaigns.
38CI 32–44 · exposure 25 · augmentation 75 · importance 3.5/5 · click for rater detail
Evaluate new emerging media or technologies and make recommendations for their application within Internet marketing or search marketing campaigns.
38| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Marketing technology adoption is relatively fast and digital-native, but strategic evaluation of new channels—especially with budget implications—remains a leadership function; many teams use AI drafting tools but retain human decision-makers for final recommendations. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Digital marketing is a fast-adopting sector with widespread use of AI tools for trend research, competitive analysis, and content ideation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at rapidly compiling emerging technology data, summarizing use cases, and structuring preliminary evaluation frameworks that strategists then refine; this substantially accelerates the research and analysis phase while humans retain final judgment on strategic fit and campaign impact. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can rapidly surface emerging trends, summarize industry news, and generate comparative analyses, significantly speeding up the research phase even though final recommendations require human strategic judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate descriptions of emerging technologies and draft recommendation frameworks, evaluating their strategic fit within specific business contexts and making defensible campaign recommendations requires judgment about market positioning, competitive dynamics, and organizational constraints that AI cannot reliably do end-to-end today. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires original judgment, forecasting, and strategic recommendation-making about emerging trends, which AI can assist with but not fully replace end-to-end at equal quality. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Marketing strategy decisions often require sign-off from senior stakeholders who prefer human accountability for campaign recommendations; regulatory compliance on ad platforms adds friction to full automation, though no hard legal barrier prevents AI assistance or preliminary analysis. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational trust and the need for strategic accountability create some friction against fully outsourcing this to AI. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI-powered research and draft recommendations can reduce initial research labor, but the overhead of expert validation, experimentation, and integration testing keeps total cost comparable to hiring an analyst for this evaluation work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human strategists still need to validate and contextualize AI-generated research, so while AI reduces some research time, the human judgment component keeps costs comparable to fully human analysis. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI tools can scan technology announcements and summarize features, but no deployed product reliably evaluates emerging media fitness for specific marketing campaigns or produces defensible strategic recommendations at production quality without significant human validation and refinement. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No production AI product autonomously evaluates emerging media/technology trends and delivers vetted strategic recommendations; current tools provide research and summarization support only. |
Implement online customer service processes to ensure positive and consistent user experiences.
37CI 32–42 · exposure 30 · augmentation 75 · importance 3.4/5 · click for rater detail
Implement online customer service processes to ensure positive and consistent user experiences.
37| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Many organizations are piloting AI-powered customer service tools, but widespread production adoption of AI-driven strategy implementation remains uneven; most still rely on humans to design and refine processes while AI handles execution and monitoring. Adoption is growing but not yet the dominant pattern. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Marketing and digital customer service sectors show moderate AI adoption with pilots for chat and support automation, though full process implementation remains human-led. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI tools significantly enhance strategist productivity by automating customer service ticket analysis, identifying process bottlenecks, generating A/B test recommendations, and monitoring experience metrics in real time. These capabilities allow strategists to focus on high-level process design and refinement while AI surfaces data-driven insights. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly aids in designing, testing, and optimizing customer service workflows (e.g., chatbot scripting, sentiment analysis, and process documentation), boosting strategist productivity. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can automate routine customer service responses and ticket routing, implementing comprehensive processes that ensure positive user experiences requires strategic judgment, vendor selection, staff training oversight, and continuous quality monitoring. Current systems can handle parts of the workflow but not the full strategic implementation end-to-end at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Implementation involves cross-functional coordination, process design, and organizational decisions that AI cannot fully execute end-to-end, though it can assist with drafting workflows or chatbot scripts.dis |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Organizations often require human strategists to own customer experience outcomes and account for customer satisfaction metrics; customer preference for human escalation paths and organizational liability for service failures create meaningful friction against full automation. However, no hard legal requirement mandates human-only implementation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational friction and the need for cross-departmental buy-in and change management create moderate barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While AI-powered chatbots and routing systems are relatively inexpensive per interaction, the full cost of implementation (software licenses, integration, training, quality oversight) against a strategist's loaded wage remains comparable or may exceed human cost when all orchestration and strategy refinement is factored in. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI tools can reduce some costs in customer service delivery, the strategic implementation task itself still requires significant human oversight, limiting cost savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed products like chatbots, helpdesk automation, and analytics tools can execute individual components of customer service operations, but no single off-the-shelf system reliably implements entire customer service processes from conception to execution with consistent positive outcomes. Products exist but require significant human oversight and integration work. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products exist for chatbots and helpdesk automation, but 'implementing processes' across a customer service system requires human planning, stakeholder alignment, and judgment that current products don't handle autonomously. |
Execute and manage communications with digital journalists or bloggers.
37CI 32–41 · exposure 25 · augmentation 75 · importance 2.8/5 · click for rater detail
Execute and manage communications with digital journalists or bloggers.
37| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Digital marketing and marketing tech show moderate AI adoption; tools for list generation and email drafting are commonplace, but autonomous relationship execution remains rare. Pilots are common but production replacement is limited. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Marketing and PR functions are adopting AI tools for drafting and monitoring at a moderate pace, with pilots common but full communication management still largely human-led. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly assists by generating pitch drafts, identifying relevant journalists via analysis of content and engagement, and optimizing send times and messaging. A strategist using AI tools can manage substantially more journalist relationships while focusing on high-judgment decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially helps by drafting pitches, personalizing outreach at scale, tracking journalist beats, and summarizing coverage, while the strategist retains relationship oversight. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Executing communications requires relationship management, contextual judgment about pitch appropriateness, and authentic personalization that current AI systems cannot reliably do at production quality. While AI can draft emails and identify contact lists, the strategic selection of journalists, nuanced negotiation, and handling relationship nuance remain firmly in human domain. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can draft pitches and identify contacts, but managing actual relationships, personalized outreach, and negotiation with journalists/bloggers requires human judgment and trust-building that AI cannot fully replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Media relations require implicit trust and human judgment; organizations prefer human strategists for brand representation. However, there are no hard legal barriers to automation, and use of AI-drafted outreach is growing in practice. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but journalists and bloggers often prefer authentic human contact, and reputational risk from poorly managed communications creates some organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted drafting and list-building reduces costs modestly, but the loaded cost of a strategist remains lower than the cost of AI oversight plus human relationship management, especially given error costs in botched outreach. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools can cut drafting and research time significantly, but ongoing relationship management still requires human oversight, keeping costs roughly comparable to a skilled human doing the full task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No production system reliably executes end-to-end journalist outreach with acceptable response rates or relationship outcomes. AI can support drafting but cannot independently manage ongoing relationships, handle rejections, or make judgment calls about which journalists align with campaign goals. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products exist for PR outreach automation and email drafting, but reliable, nuanced relationship management with media contacts at production scale is not yet demonstrated broadly. |
Coordinate with developers to optimize Web site architecture, server configuration, or page construction for search engine consumption and optimal visibility.
35CI 32–38 · exposure 25 · augmentation 75 · importance 4.3/5 · click for rater detail
Coordinate with developers to optimize Web site architecture, server configuration, or page construction for search engine consumption and optimal visibility.
35| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Information and marketing sectors show moderate adoption of SEO tools and AI-assisted optimization, but full automation of the coordination function remains rare. Pilots are common, but production replacement of search strategists is not yet widespread. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Digital marketing and tech sectors have moderate AI tool adoption for SEO diagnostics, though the human coordination component keeps overall workflow automation moderate. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI meaningfully augments this task by generating technical SEO recommendations, analyzing site performance data, and prioritizing optimization opportunities, allowing strategists to focus on high-level coordination and business impact assessment with developers. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly speeds up technical audits, crawl analysis, and identifying architecture issues, letting strategists focus conversations with developers on prioritized, data-backed fixes. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with technical recommendations for SEO optimization (meta tags, site structure suggestions), the task requires coordination with developers that involves judgment, prioritization, and real-time problem-solving across complex system dependencies. AI cannot reliably end-to-end coordinate stakeholder efforts or navigate the architectural trade-offs without human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can generate technical SEO recommendations and analyze site structures, but coordinating with developers, negotiating trade-offs, and overseeing implementation requires human judgment and interpersonal work that current AI cannot fully replace. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Some organizational friction exists: companies value human judgment on business strategy, brand risk, and developer team dynamics. However, no formal licensing or regulatory barrier legally requires a human to perform this task, allowing for gradual substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational friction exists since cross-team coordination with engineering requires trust, context, and accountability that resist full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of AI tools (SEO analysis, technical recommendations) plus human oversight and coordination with developers is comparable to or exceeds hiring a part-time search strategist, especially when accounting for integration and validation overhead. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools reduce audit time cheaply, but the coordination and negotiation portions still require paid strategist time, keeping overall cost comparable to human-led work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably coordinates technical SEO optimization work across developer teams in production. AI can analyze pages and suggest improvements, but production systems do not autonomously manage developer coordination, requirement prioritization, or implementation validation at scale. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | SEO audit tools (Screaming Frog, Semrush) with AI features can flag technical issues, but no deployed product autonomously coordinates cross-functional implementation with development teams at scale. |
Collaborate with other marketing staff to integrate and complement marketing strategies across multiple sales channels.
35CI 32–38 · exposure 25 · augmentation 63 · importance 3.9/5 · click for rater detail
Collaborate with other marketing staff to integrate and complement marketing strategies across multiple sales channels.
35| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Marketing departments actively adopt AI for analytics and content generation, but the collaborative strategy integration aspect of this role is evolving more slowly; many organizations still rely on manual cross-functional workshops and human strategists to synthesize channel strategies. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Marketing and digital sales sectors are moderately fast adopters of AI tools for analytics and content, though collaborative strategy work remains largely human-driven with AI as a support layer. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist marketing strategists by analyzing channel performance, generating integration scenarios, and surfacing insights that inform strategy discussions. However, the human must ultimately synthesize recommendations, facilitate team alignment, and make final strategic choices. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by providing cross-channel performance data, trend analysis, and content ideas that inform and speed up strategic discussions among marketing staff. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate strategic recommendations and analyze channel performance data, the core task requires negotiating priorities, resolving conflicts, and aligning cross-functional teams—activities that demand human judgment and relationship management that current AI systems cannot reliably execute end-to-end with 50% time savings. |
| Task automatability | claude-sonnet-5 | 2/5 | Cross-channel strategic collaboration requires synthesizing organizational context, relationships, and judgment that AI cannot autonomously replicate; AI can support analysis but not replace the collaborative human process itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Organization-level friction exists: stakeholders prefer human-led strategy sessions where they can voice concerns and negotiate, and accountability for cross-channel integration typically sits with named individuals. However, no legal or regulatory requirement mandates human performance. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational and interpersonal dynamics create real friction—strategy alignment inherently requires trusted human relationships and accountability. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for marketing analysis are relatively affordable, but integrating strategies across teams still requires significant human oversight, decision-making, and relationship management that cannot be substantially reduced in cost compared to hiring marketing staff to do this work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can cut some analysis and reporting time, but the core task involves meetings, negotiation, and alignment among humans, so overall cost savings versus a human strategist are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products independently perform this collaborative strategy integration task. Tools exist for individual components (channel analysis, recommendation generation) but lack the ability to mediate stakeholder discussions, contextualize organizational constraints, and drive consensus across marketing teams. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products exist for campaign coordination, data aggregation, and cross-channel analytics, but no deployed system performs the actual interpersonal collaboration and strategic alignment across teams. |
Communicate and collaborate with merchants, Webmasters, bloggers, or online editors to strategically place hyperlinks.
35CI 32–38 · exposure 25 · augmentation 75 · importance 3.5/5 · click for rater detail
Communicate and collaborate with merchants, Webmasters, bloggers, or online editors to strategically place hyperlinks.
35| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Digital marketing and SEO teams widely use AI drafting and lead-scoring tools, but adoption remains in the pilot-to-moderate-production phase; full automation of link placement remains uncommon because stakeholder relationship and conversion require human touch. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Digital marketing and SEO sectors are moderately fast adopters of AI tools for content and outreach support, though full automation of relationship-based link placement remains uncommon in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI meaningfully assists strategists by generating personalized outreach templates, scoring prospect fit, tracking conversations, and identifying high-value placement targets, measurably improving strategist productivity while the human retains control over messaging tone, relationship management, and deal closure. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly assists by drafting personalized outreach messages, identifying prospects, and analyzing link opportunities, greatly improving efficiency while humans manage final relationships and negotiations. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help draft outreach messages and identify placement opportunities, the task critically depends on negotiation, relationship-building, and judgment about brand fit and mutual benefit—elements that require human credibility and final decision-making. Automation cannot meaningfully replace the human-to-human collaboration aspect without significant manual oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | The relationship-building, negotiation, and persuasion aspects of outreach for link placement require human judgment and trust-building that current AI cannot fully replicate end-to-end.dim AI can draft outreach emails but cannot autonomously manage relationships and negotiations reliably.the task remains largely human-driven. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Some organizational friction exists (preference for human relationship stewardship, brand risk aversion), but no legal licensing requirement or hard liability barrier prevents AI-assisted or semi-automated outreach; negotiation outcomes still hinge on human judgment and merchant consent. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barriers exist, but human relationship dynamics, trust, and reputation management create organizational friction that discourages full automation of outreach and negotiation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI assistance (outreach drafting, opportunity scoring) reduces per-task cost moderately, but human negotiators and relationship managers remain essential, keeping total cost close to or not substantially below the loaded wage of a junior strategist doing similar outreach. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI can assist with drafting messages and prospecting cheaply, the actual relationship management and negotiation still require significant human time, keeping overall costs comparable to human-led efforts. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs end-to-end link placement negotiation autonomously; existing tools assist with prospect identification and template email generation, but merchant agreements and final placement decisions remain human-driven in production settings. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools can generate outreach templates and identify prospects, but no deployed product autonomously manages the full communication and negotiation process with webmasters and editors reliably at scale. |
Coordinate sales or other promotional strategies with merchandising, operations, or inventory control staff to ensure product catalogs are current, accurate, and organized for best findability against user intent.
35CI 32–38 · exposure 25 · augmentation 75 · importance 3.1/5 · click for rater detail
Coordinate sales or other promotional strategies with merchandising, operations, or inventory control staff to ensure product catalogs are current, accurate, and organized for best findability against user intent.
35| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | E-commerce and retail organizations are experimenting with AI-driven catalog optimization and search tuning, but adoption remains in the pilot and early production phase; most still rely on human strategists for orchestrating the coordination and ensuring business alignment. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | E-commerce and marketing functions show moderate AI adoption for catalog optimization and SEO tasks, though the coordination aspect specifically lags behind pure content/analytics automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist by automating catalog data validation, suggesting product organization schemes, and analyzing user search patterns to highlight findability gaps, allowing strategists to focus on stakeholder alignment and strategic merchandising decisions rather than manual data wrangling. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by auditing catalogs for accuracy, keyword gaps, and findability issues, giving strategists better data to guide coordination conversations with other teams. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with catalog data validation and suggest organizational structures, the task requires human judgment to align merchandising strategy with user intent and coordinate across multiple departments with conflicting priorities. End-to-end automation lacks the contextual understanding and stakeholder negotiation needed for >50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | The task centers on cross-functional coordination and organizational communication with human stakeholders, which AI cannot fully own end-to-end even though it can support catalog analysis pieces. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict licensing requirements for the task itself, organizational friction is significant: cross-functional coordination requires sign-off from multiple stakeholders, and errors in findability directly harm revenue, creating liability concerns that mandate human accountability and review. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational friction is high since coordination requires relationship management, authority, and trust across departments that AI cannot substitute for easily. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for catalog management and search optimization have meaningful licensing and integration costs, plus ongoing human oversight is required to validate coordination outcomes and adjust strategy. Total cost remains comparable to or exceeds the loaded wage of a strategist on this task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human coordination, meetings, and cross-team alignment still require significant labor cost; AI reduces some analysis time but doesn't replace the coordination function cheaply. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Products exist for catalog optimization and data quality checks, but they operate on narrow, well-structured inputs and lack the ability to reliably coordinate cross-functional strategy or ensure findability against evolving user intent at scale. Most deployments remain assistive rather than autonomous. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools can flag catalog inconsistencies or suggest taxonomy improvements, but no deployed product manages the actual interdepartmental coordination and negotiation reliably in production. |
Identify methods for interfacing Web application technologies with enterprise resource planning or other system software.
35CI 32–38 · exposure 25 · augmentation 75 · importance 2.3/5 · click for rater detail
Identify methods for interfacing Web application technologies with enterprise resource planning or other system software.
35| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Enterprise technology sectors are exploring AI for documentation analysis and integration pattern suggestion, but widespread production adoption remains limited. Many organizations still rely on human architects for these decisions due to risk aversion and the bespoke nature of enterprise environments. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | IT and marketing technology sectors show moderate AI adoption for coding and integration assistance, with pilots common but full autonomous system-architecture decisions still rare in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully augment strategists by rapidly analyzing API documentation, suggesting standard integration patterns, and identifying potential compatibility issues, allowing humans to focus on architectural decisions and organizational fit. This assistive role is already emerging in enterprise AI tools. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully speed up research into integration options, generate sample code/API calls, and summarize documentation, significantly aiding a human strategist's analysis. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist in identifying standard integration patterns and API documentation, the task requires deep contextual knowledge of specific enterprise systems, their constraints, and organizational architecture. End-to-end automation would require AI to understand bespoke legacy systems and make judgment calls about trade-offs that typically demand human expertise and stakeholder consultation. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires understanding specific enterprise architectures, legacy systems, and business context to design integration approaches; AI can assist research and draft options but cannot reliably do the full analysis end-to-end today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | There is some organizational friction around replacing experienced integration architects, and the task often requires sign-off from IT governance and stakeholders who prefer human accountability for critical system decisions. However, no legal licensing requirement strictly prevents AI assistance or initial recommendations. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational risk (system downtime, data integrity, security) and need for domain-specific knowledge create moderate friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of AI inference for document analysis plus significant human oversight and validation likely exceeds the efficiency gains, especially when specialized ERP consultants command high rates and their expertise is still required to validate recommendations and ensure organizational fit. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Because human oversight, system-specific knowledge, and validation are still required, AI mainly supplements rather than replaces the work, keeping cost savings modest relative to skilled integration specialists. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed AI product reliably performs this task independently in production environments. AI tools can help analyze documentation and suggest common integration approaches, but they struggle with the complexity of real enterprise configurations, custom software, and the need for human verification of proposed interfaces. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI coding assistants and research tools can suggest integration patterns (APIs, middleware, ETL) but no deployed product autonomously identifies and validates integration methods across arbitrary enterprise systems reliably. |
Collaborate with Web, multimedia, or art design staffs to create multimedia Web sites or other internet content that conforms to brand and company visual format.
32CI 28–38 · exposure 25 · augmentation 75 · importance 3.4/5 · click for rater detail
Collaborate with Web, multimedia, or art design staffs to create multimedia Web sites or other internet content that conforms to brand and company visual format.
32| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Digital marketing and design sectors show moderate adoption of AI tools for ideation and draft generation, but production deployment for client-facing work remains limited. Pilots are common, but most teams still rely on human designers as primary decision-makers. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Marketing and digital content sectors show above-average AI tool adoption (e.g., generative design, copywriting assistants), though collaborative brand workflows remain human-led in most organizations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at augmenting designers by generating layout variations, suggesting color schemes, automating asset resizing, and accelerating iteration cycles. These tools meaningfully boost designer productivity while the human maintains creative and brand-compliance control. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools substantially help generate content drafts, design variations, and brand-consistent copy, materially speeding up the collaborative creation process while humans retain final creative control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with components like layout suggestions, color palette generation, and content structuring, the task requires creative judgment to align with brand identity and strategic intent that demands human oversight. Full end-to-end automation achieving 50% time savings at equal quality is not demonstrated by current systems. |
| Task automatability | claude-sonnet-5 | 2/5 | The task fundamentally requires cross-functional collaboration, negotiation of creative direction, and brand judgment across multiple human stakeholders, which AI cannot substitute end-to-end even though it can assist with drafting or asset generation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Brand compliance, legal liability for user-facing content, and organizational preference for human creative judgment on visual identity create substantial friction. Many organizations require sign-off by human designers or marketing leadership, limiting pure automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier exists, but organizational friction—brand governance, stakeholder sign-off, and creative approval workflows—creates moderate resistance to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI design tools are inexpensive, but integration requires designer oversight, brand guideline configuration, and revision cycles that add significant cost. The all-in cost remains comparable to or higher than hiring a junior designer for comparable output quality. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI can reduce some content-drafting time, the collaborative coordination overhead and human review still dominate cost, so overall savings versus a human strategist's fully-loaded cost are modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Design tools with AI features (layout suggestions, image generation) exist but lack reliable understanding of subtle brand compliance and visual consistency requirements. Production use remains limited to individual components rather than end-to-end website creation meeting professional standards. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools exist for generating design assets, copy, or mockups, but no deployed product manages the collaborative cross-team process of aligning multimedia content with brand standards autonomously. |
Define product requirements, based on market research analysis, in collaboration with user interface design and engineering staff.
31CI 25–38 · exposure 25 · augmentation 75 · importance 2.9/5 · click for rater detail
Define product requirements, based on market research analysis, in collaboration with user interface design and engineering staff.
31| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Marketing and product strategy teams are early in AI adoption. While market research analysis tools are spreading, the core requirement-definition process remains human-led with AI in advisory roles. Adoption is in the pilot phase, not production-scale displacement. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Marketing and product teams in tech-forward sectors are adopting AI tools for research synthesis and drafting at a moderate pace, though full requirement-definition workflows remain human-led. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist by synthesizing market research, generating requirement drafts, identifying gaps, and supporting cross-team discussion. Marketing strategists using AI-powered research synthesis and requirement templates can substantially increase productivity while remaining in control of final definition and trade-off decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully speed up market research synthesis, generate draft requirements, and surface insights, significantly boosting strategist productivity while humans retain final judgment and collaboration roles. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Defining product requirements involves significant human judgment and stakeholder collaboration. While AI can assist with market research synthesis and generate requirement drafts, the collaborative refinement with design and engineering teams, and the strategic prioritization decisions, require human judgment that current systems cannot replace end-to-end with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | This task requires synthesizing market research, cross-functional negotiation, and strategic judgment about product direction, which AI can support but not fully execute end-to-end.dilemma ArationaleShort AI can draft requirement documents but the collaborative decision-making and prioritization core to the task resist full automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong organizational and accountability barriers exist: requirement definition is a gatekeeping function requiring human expertise and sign-off, stakeholders typically demand human judgment for strategic decisions, and liability for poor requirements rests on human decision-makers rather than systems. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier, but organizational friction is real since defining product requirements involves stakeholder buy-in, internal politics, and accountability that resist pure AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted analysis is affordable, but the task still requires senior marketing strategists and cross-functional collaboration to complete properly. The human labor cost remains dominant since most of the value-add occurs in judgment and stakeholder management rather than automation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human strategists must engage in live cross-team negotiation and judgment calls; AI can cut some drafting time but the core collaborative work still requires paid human time comparable to today's costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs this task independently. AI can help analyze research data and suggest requirements, but actual requirement definition in production involves negotiation across teams and domain expertise that existing tools handle only in narrow, heavily supervised contexts. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product autonomously defines product requirements through cross-functional collaboration; AI tools assist with summarizing research or drafting specs but don't replace the collaborative definition process. |
Resolve product availability problems in collaboration with customer service staff.
31CI 25–38 · exposure 25 · augmentation 63 · importance 2.7/5 · click for rater detail
Resolve product availability problems in collaboration with customer service staff.
31| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | E-commerce and SaaS companies are early-stage in adopting AI for supply-chain or product-availability coordination; most rely on dashboards and alerts rather than autonomous resolution agents. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Marketing and e-commerce sectors are adopting AI tools for demand forecasting and customer service, but the specific collaborative problem-resolution work remains largely human-driven in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by surfacing availability patterns, predicting shortages, and summarizing customer issues to prioritize strategist review, but the collaboration and decision-making remain firmly human-led. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly assist by surfacing real-time inventory data, flagging discrepancies, and drafting communications, letting the strategist focus on judgment and coordination with service staff. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help identify availability issues via data analysis and flag common resolution patterns, the task requires real-time coordination with customer service staff and domain-specific judgment about inventory, product roadmaps, and customer priorities—elements that are currently not reliably automatable end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires cross-functional coordination, real-time inventory checks, and negotiation with customer service staff, which involves judgment and interpersonal communication that current AI cannot fully replace end-to-end.identifier. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Organizations require human accountability for product availability decisions that affect customer relationships and revenue; many have internal governance requiring a person to sign off on major inventory or fulfillment changes. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but organizational friction and the need for cross-departmental trust and accountability create moderate resistance to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of AI integration, data pipeline setup, and mandatory human oversight for resolution decisions approaches or exceeds the wage of a strategist performing the task, especially given low automation rates. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human coordination and judgment calls dominate this task, so AI tools might reduce some research time but the collaborative resolution work still requires paid human staff, keeping costs comparable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs collaborative problem-resolution with customer service teams at scale; this requires integration across systems, human judgment on exceptions, and accountability that existing tools handle only partially or with high error rates. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products exist for inventory tracking and chatbot-based customer service triage, but resolving cross-team product availability issues collaboratively is not a mature, reliably deployed AI product function. |
Assist in the development of online transaction or security policies.
31CI 25–36 · exposure 25 · augmentation 63 · importance 2.8/5 · click for rater detail
Assist in the development of online transaction or security policies.
31| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While digital-native organizations experiment with AI-assisted policy drafting, most enterprises still rely on dedicated security and legal teams to develop and validate policies. Adoption remains cautious due to compliance and liability concerns in most sectors. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Marketing and digital strategy roles are moderately fast adopters of AI tools, though policy-writing subtasks are a smaller, slower-adopted niche within the role. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by summarizing regulatory requirements, generating initial policy drafts, flagging common security gaps, and researching industry standards, reducing the expert's research and writing burden while keeping final judgment with the human. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully speed up drafting, research on best practices, and formatting of policy documents, giving strategists a strong starting point while they finalize judgment calls. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task requires developing policies that balance business objectives, legal requirements, and security considerations—substantial parts need human judgment, legal expertise, and organizational context. AI can assist with drafting templates and identifying best practices, but cannot independently create sound transaction or security policies that meet regulatory and organizational needs. |
| Task automatability | claude-sonnet-5 | 2/5 | Policy development requires legal judgment, risk assessment, and organizational context that AI can support but not autonomously complete to a reliable, deployable standard; AI can draft boilerplate but 'assisting' implies human-led synthesis. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Policy development for online transactions and security typically requires organizational sign-off, legal/compliance review, and often regulatory compliance (PCI-DSS, GDPR, etc.). Liability for inadequate policies creates strong incentives to keep human experts in the approval loop, preventing full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically for this task, but liability concerns around security/compliance failures create moderate organizational caution against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI drafting and research tools can reduce initial research time and template creation, but the output still requires skilled human policy experts to review, refine, and sign off, limiting overall cost savings relative to the loaded wage of qualified security and legal professionals. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drafting reduces some research and writing time cheaply, but the overall task still requires expensive human legal/security expertise for validation, keeping costs roughly comparable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI systems can generate policy text and identify relevant security frameworks, no deployed product reliably produces legally sound, organization-specific policies without expert human review and modification. Current AI tools lack accountability and are prone to missing critical legal or compliance nuances. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI writing tools can draft policy language or summarize best practices, but no deployed product reliably produces compliant, context-specific security/transaction policies without heavy human review. |
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.