Sales Representatives of Services, Except Advertising, Insurance, Financial Services, and Travel
41-3091.00Median wage $69,990/yr1,256,010 employed (US)Rank #54 of 923 scored · top 6% by substitution
Sell services to individuals or businesses. May describe options or resolve client problems.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution56
Exposure50
Augmentation78
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
15 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
27%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%50
panel mean rating 3.0/5 → substitution pressure 50/100
Technical feasibility todayw 20%51
panel mean rating 3.0/5 → substitution pressure 51/100
Cost vs. human wagew 15%61
panel mean rating 3.4/5 → substitution pressure 61/100
Adoption barriersw 20%inverted — strong barriers lower the score71
panel mean rating 2.2/5 (barrier strength) → substitution pressure 71/100
Sector adoption velocityw 10%55
panel mean rating 3.2/5 → substitution pressure 55/100
Task breakdown (15 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.
Maintain customer records using automated systems.
94CI 87–100 · exposure 95 · augmentation 75 · click for rater detail
Maintain customer records using automated systems.
94| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | CRM adoption and automated record-keeping are deeply entrenched across sales organizations in information, finance, and professional services sectors with near-universal deployment. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Sales and CRM automation is a heavily adopted, mature technology category with widespread production use across service-sales sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI systems already augment sales representatives by auto-populating fields, flagging data quality issues, and suggesting record updates based on interaction history, substantially reducing manual record-keeping burden while keeping humans in oversight. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-enhanced CRM tools significantly boost productivity by auto-populating fields, flagging duplicates, and summarizing interactions, while a human still oversees accuracy and context. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Maintaining customer records in automated systems is a purely digital data-entry and management task that current CRM and database systems handle end-to-end with minimal human intervention, easily meeting the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 5/5 | Maintaining customer records via CRM/database entry is a structured, repetitive digital task that off-the-shelf automation (CRM automation, data sync tools, AI form-fillers) can already handle end-to-end with substantial time savings. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While some organizations have legacy processes and prefer manual oversight, there are minimal legal or regulatory barriers preventing automated record maintenance, only organizational inertia and preference for human review. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There is no licensing, liability, or human-contact requirement tied to updating customer records; it's a purely administrative back-office task with minimal regulatory friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated systems cost a fraction of human wages to maintain records; inference and integration costs are negligible compared to the hourly cost of manual data entry and record management. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Automated record-keeping software costs a small fraction of the labor cost of manual data entry and maintenance, often orders of magnitude cheaper per record processed. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Mature CRM platforms (Salesforce, HubSpot, Microsoft Dynamics) and automated database systems reliably perform record maintenance at scale in production across millions of organizations daily. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | CRM platforms (Salesforce, HubSpot) already offer mature automated data capture, deduplication, and update features deployed at scale in production, though some manual correction and edge-case handling remains. |
Compute and compare costs of services.
80CI 76–84 · exposure 75 · augmentation 88 · click for rater detail
Compute and compare costs of services.
80| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Information and professional services sectors have adopted automated pricing engines and cost-comparison tools widely; financial services, SaaS, and telecom routinely automate this function. Adoption is fast and measurable across digital-forward industries, though lagging in small or offline service firms. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Sales functions across many industries use CRM and quoting software with automated calculations, but full adoption varies significantly by sector and company size. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI powerfully augments sales reps by instantly pulling, computing, and comparing service costs, enabling faster quoting and allowing reps to focus on relationship-building and negotiation rather than manual arithmetic and data lookup. This productivity gain is substantial while keeping the human in control of the sale. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI and automated tools substantially speed up and improve accuracy of cost computations, letting sales reps focus on negotiation and relationship aspects while the tool handles calculations. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Computing and comparing costs of services is largely structured data work—pricing lookups, arithmetic, and comparison logic that current AI systems handle reliably. The task involves no complex judgment beyond calculation, making it automatable to a high degree with significant time savings, though context-specific pricing rules may require setup. |
| Task automatability | claude-sonnet-5 | 4/5 | Cost computation and comparison is a structured, rule-based data task that spreadsheets, CRM tools, and AI agents can perform quickly with high accuracy given the right inputs. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or legal barriers prevent automating cost computation; no licensing requirement applies to the calculation itself. Main friction points are organizational (preference for human engagement in sales) and customer expectation (some prefer human-provided quotes), but these are soft barriers rather than hard legal ones. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or regulatory requirement mandates human computation of costs; this is a routine business calculation task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI inference for cost computation and comparison is inexpensive (fractions of a cent per task) and can be fully automated once integrated, making it at least an order of magnitude cheaper than paying a human sales representative's loaded wage to manually look up and compare pricing. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Automated calculation engines run at near-zero marginal cost compared to a salesperson's time manually computing and comparing service costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed AI systems (spreadsheet automation, pricing engines, chatbots with access to pricing databases) perform cost computation and comparison reliably in production for many service industries. Errors are low when pricing data is clean and rules are well-defined, though integration complexity varies by business context. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Pricing/quoting tools with automated cost calculation and comparison are widely deployed in CRM and sales-enablement software today, though customization for complex service bundles may need human review. |
Answer customers' questions about services, prices, availability, or credit terms.
77CI 72–81 · exposure 75 · augmentation 88 · click for rater detail
Answer customers' questions about services, prices, availability, or credit terms.
77| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Information and service sectors are already rapidly deploying chatbots and automated customer service systems; major companies across telecommunications, utilities, retail, and hospitality have moved significant inquiry traffic to AI agents, representing deep, measurable production adoption. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Sales and customer service functions show moderate AI adoption with many pilots and some production chatbot deployments, but full-scale replacement in B2B service sales remains uneven. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI systems effectively augment human sales representatives by providing instant access to pricing, availability, and policy information, drafting responses, and filtering routine questions before escalation. This enables humans to focus on complex sales and relationship tasks, demonstrating clear productivity uplift. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI tools (chat assist, CRM copilots, knowledge base search) substantially speed up how reps find and communicate pricing, availability, and credit information. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems (chatbots, LLMs) can handle the majority of routine questions about services, prices, availability, and credit terms with high consistency. This task involves retrieving and presenting structured information, which AI excels at; however, edge cases and complex scenarios may still require human judgment, preventing a perfect 5. |
| Task automatability | claude-sonnet-5 | 4/5 | Chatbots and AI agents can answer routine questions about services, pricing, availability, and credit terms with significant time savings, though escalations for complex or negotiated deals still need humans. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or licensing barriers exist for automating informational customer responses; organizational and customer-preference friction for automation is present but surmountable, and many companies have already normalized AI-handled initial inquiries without material regulatory impediment. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for answering these questions, though credit term commitments may require internal approval processes and customers often still prefer human reps for complex service sales. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-powered chatbots and automated systems cost a fraction of full-time human representatives when amortized per interaction; a single deployed system serves thousands of inquiries at inference costs of cents per transaction versus human hourly wages of $15–25+, easily meeting the order-of-magnitude threshold. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-driven chat/voice systems cost a small fraction of a human sales rep's loaded wage for handling repetitive informational queries, though integration with CRM/pricing systems adds some cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed chatbot and conversational AI products are widely used in customer service today and demonstrably handle service inquiries, pricing questions, and availability checks in production environments. Error rates on straightforward queries are low, though complex or context-dependent cases can still fail, preventing a universal 5. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Deployed customer service AI and sales chatbots handle FAQ-type inquiries reliably today across many B2B and B2C contexts, though accuracy on nuanced credit-term negotiations varies. |
Identify prospective customers using business directories, leads from clients, or information from conferences or trade shows.
76CI 66–86 · exposure 67 · augmentation 88 · click for rater detail
Identify prospective customers using business directories, leads from clients, or information from conferences or trade shows.
76| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Sales organizations across information, SaaS, professional services, and financial sectors have rapidly adopted AI-powered lead generation and prospecting tools; the shift is widespread, measurable in production deployments, and continues to accelerate. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | B2B sales and professional services sectors have rapidly adopted AI-driven prospecting and lead-gen tools, with widespread production use in CRM-integrated workflows. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly augments human sales reps by automatically surfacing qualified prospects, enriching contact data, and prioritizing leads by firmographic fit, allowing reps to focus on relationship-building rather than manual directory scrubbing. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI dramatically speeds up sourcing, filtering, and enriching prospect lists while the salesperson still exercises judgment on qualification and outreach strategy. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can systematically mine business directories, parse conference attendee lists, and extract lead information from structured and semi-structured sources with high accuracy and significant time savings. While some judgment in qualifying leads remains valuable, the core prospecting and identification work is highly automatable today. |
| Task automatability | claude-sonnet-5 | 3/5 | AI tools can search directories, scrape leads, and enrich data from trade show lists, but selecting truly prospective customers requires contextual judgment about fit that still needs human review for quality equal to manual work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers exist for automated prospecting; however, data privacy regulations (GDPR, CCPA), email list compliance, and organizational preference to retain human relationship-building create modest friction rather than hard blockers. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or regulatory requirement mandates a human perform prospect identification; it's a purely commercial, low-liability activity. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven lead prospecting costs a fraction of a human sales development representative's fully loaded salary (typically $40–70k annually), while tools cost $50–500/month. The cost advantage is at least 10:1 in favor of automation. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated lead generation and enrichment tools cost a small monthly subscription fee versus hours of manual research, offering roughly an order-of-magnitude cost advantage per lead identified. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature products exist for lead generation, prospecting databases, and automated contact research (e.g., Apollo, Hunter, ZoomInfo). These systems reliably scrape directories, match company data, and surface prospects in production environments, though integration overhead and data quality variation persist. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Sales intelligence platforms (ZoomInfo, Apollo, Clay, LinkedIn Sales Navigator with AI enrichment) are widely deployed in production for lead identification and list building today. |
Quote prices, credit terms, contract terms, or fulfillment dates for services.
69CI 59–79 · exposure 62 · augmentation 75 · click for rater detail
Quote prices, credit terms, contract terms, or fulfillment dates for services.
69| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Sales organizations and professional services firms are actively deploying Configure-Price-Quote (CPQ) and AI-assisted quoting tools in production. SaaS sales, managed services, and field service sectors show rapid adoption of automated quote generation as a standard practice. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | B2B sales functions are adopting CPQ and AI sales tools at a moderate pace, with pilots and partial deployment common but full automation of quoting still uneven across industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI quoting tools significantly augment sales representatives by instantly generating accurate, compliant quotes with correct pricing and terms, freeing them to focus on relationship-building and complex customization. The human stays in the loop to approve, modify, or negotiate when needed, raising overall sales velocity and accuracy. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered CPQ and CRM tools significantly speed up price and term generation, letting reps focus on negotiation and relationship aspects while automating repetitive quote assembly. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can generate accurate price quotes, standard credit terms, and fulfillment dates for many services by accessing product databases and applying defined business rules. While some complex negotiations or custom terms may require human judgment, the core quotation task can be automated end-to-end with significant time savings for standard offerings. |
| Task automatability | claude-sonnet-5 | 3/5 | Quoting standardized prices, credit terms, and fulfillment dates can be automated via configure-price-quote (CPQ) systems and chatbots, but custom contract terms and negotiation often require human judgment, limiting full end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing or legal requirement mandates a human perform quotations; most firms can substitute automated systems with only internal process changes. Customer preference for human contact and organizational reluctance to remove the sales representative entirely provide modest friction, but technical and regulatory barriers are minimal. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement typically applies, but liability for contract terms and customer relationship expectations create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated quoting systems cost a small fraction of the fully loaded sales representative wage per quote generated. Once deployed, the marginal cost per quote is minimal—primarily API calls and storage—making the cost ratio heavily favorable to automation. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated quoting systems are far cheaper per transaction than a human rep once integrated, though setup and maintenance of pricing/contract logic add ongoing cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple deployed CRM and quoting systems (e.g., Salesforce CPQ, SAP Configure-Price-Quote tools) reliably generate quotes and terms at scale in production. These systems integrate pricing engines, inventory data, and contract templates effectively, though edge cases and highly customized contracts still benefit from human review. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | CPQ software and AI-assisted quoting tools are deployed widely in B2B sales, but complex or negotiated contract terms still typically require human sales reps to finalize and validate. |
Develop sales presentations or proposals to explain service specifications.
68CI 59–77 · exposure 62 · augmentation 100 · click for rater detail
Develop sales presentations or proposals to explain service specifications.
68| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Sales and professional services sectors show moderate adoption of AI-assisted presentation tools and proposal generators; pilots are common but full end-to-end automation remains limited. Many organizations still expect human judgment and customization in client-facing sales materials. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Sales and marketing functions across many service sectors are adopting AI drafting tools for proposals and pitches, though usage is uneven and often supplementary rather than fully integrated into workflows. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI significantly augments sales reps by rapidly generating presentation drafts, customizing content for specific clients, and suggesting pitch structures based on service specifications. This allows reps to focus on strategy, relationship-building, and high-value negotiation while staying fully in control. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up drafting, formatting, and idea generation for sales presentations and proposals, letting sales reps focus on customization, negotiation, and relationship aspects. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI can generate well-structured sales presentations and proposals with service specifications using templates, product data, and language models. While human review and customization are typically needed for complex or highly differentiated pitches, AI can produce 50%+ time savings on the drafting, structure, and initial content generation phases. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft presentation content and proposal text from service specs quickly, but tailoring to specific client needs, competitive positioning, and pricing strategy still requires human judgment and oversight to reach full quality. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or licensing barriers exist; presentations do not require regulated sign-off. The main friction is organizational preference for human-crafted proposals and client expectations, but these are soft rather than hard barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or regulatory requirement mandates a human create sales materials; adoption is limited only by quality preference and customization needs. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI inference and integration costs for generating presentations are typically $0.01–$0.10 per task, far below the loaded cost of a sales representative's time (often $50–$150/hour for this work). Cost advantage is substantial. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Drafting proposal text and slide outlines via AI is far cheaper than hours of sales staff or proposal writer time, though final review and customization still add human cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple commercial tools (generative AI platforms, sales automation software, presentation generators) reliably produce presentation drafts and proposal outlines in production environments. Output quality is generally strong for standard service specifications, though customization and brand alignment often require human oversight. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Generative AI tools (e.g., Copilot, ChatGPT-based drafting) are widely used to produce first-draft proposals and slide content, but reliable end-to-end proposal generation without human editing is not yet standard practice. |
Monitor market conditions, innovations, and competitors' services, prices, and sales.
65CI 59–71 · exposure 55 · augmentation 88 · click for rater detail
Monitor market conditions, innovations, and competitors' services, prices, and sales.
65| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Sales and marketing teams in professional services, SaaS, and enterprise sectors actively adopt competitive intelligence and market monitoring platforms; adoption is measurably fast and deep in digitized, B2B-oriented industries. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Sales and business intelligence functions are adopting AI-based monitoring and CRM-integrated insights tools at a moderate pace, though many mid-size firms still rely on manual competitive tracking. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-driven dashboards and alerts significantly amplify a sales rep's ability to stay informed on competitors and market shifts without manual research, allowing them to focus on interpretation and strategic response while the system continuously scans and surfaces intelligence. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially enhances a sales rep's ability to track market trends, competitor moves, and pricing changes by aggregating and summarizing large volumes of information quickly, freeing time for relationship-focused selling. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automate data collection, aggregation, and structured reporting on competitors' publicly available pricing, service features, and sales metrics through web scraping and market intelligence tools. However, the interpretive judgment of what these conditions mean for strategy and competitive positioning typically requires human analysis, placing it at the halfway mark. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can gather and summarize competitor pricing, news, and market data effectively, but synthesizing this into actionable sales strategy still requires human judgment and contextual business knowledge.4/2 |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or legal barriers exist to deploying automated market monitoring. Sales teams widely use these tools and can directly purchase them; no licensing, liability lock-in, or mandatory human sign-off blocks adoption. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There are no licensing, regulatory, or liability barriers to using AI for competitive and market monitoring; it's a purely informational task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated market monitoring via SaaS tools costs a fraction of dedicated analyst time once deployed, with per-user fees typically $50–500/month versus $5,000–8,000 loaded cost per month for a full-time market analyst or sales intelligence specialist. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-driven monitoring tools and research assistants can continuously scan public sources at a fraction of the cost of a human analyst spending hours on manual research. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature products (Salesforce Market Intelligence, Pathmatics, SEMrush, Similarweb) deploy reliably in production for competitor tracking, pricing monitoring, and innovation signals. Some gaps remain in real-time proprietary insight and nuanced market interpretation, but the core monitoring function is robustly achievable. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Market intelligence tools and AI research assistants (e.g., web-scraping bots, GPT-based research tools) are deployed today, but coverage of niche service markets and reliability of competitor pricing data varies significantly. |
Create forms or agreements to complete sales.
59CI 43–75 · exposure 62 · augmentation 88 · click for rater detail
Create forms or agreements to complete sales.
59| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Service sectors are still in pilot and early-adoption phases for AI-generated contracts; most organizations retain legal and sales staff oversight, and regulatory uncertainty around AI-drafted agreements slows deployment compared to information or financial sectors. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Sales and business services sectors have rapidly adopted CRM-integrated document automation and e-signature tools, making this a fairly mature area of AI-assisted workflow adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at accelerating draft creation and suggesting standard clauses, significantly boosting a sales rep's ability to produce initial agreement versions faster. The human remains in the loop to review, customize, and sign off, making this a strong augmentation use case. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI strongly augments this task by auto-populating fields, suggesting contract language, and flagging inconsistencies, letting reps focus on negotiation and relationship aspects. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can generate draft forms and agreements using templates and large language models, potentially saving 40-60% of time on routine service contracts. However, human review for legal accuracy and customization to specific terms is typically required, preventing full end-to-end automation without oversight. |
| Task automatability | claude-sonnet-5 | 4/5 | Generating sales forms and agreements from templates and CRM data is a well-structured drafting task that current LLMs and document automation tools handle with substantial time savings, though final review is typical. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Legal liability for incorrect or non-compliant forms is asymmetric and high-cost; many jurisdictions require human attorney sign-off or review of material contracts, and organizational risk-aversion to automated legal documents creates strong friction against full substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Some contracts may need legal or managerial review/approval and company-specific liability language, but most services sales agreements are not tightly regulated to require licensed human drafting. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI-powered document generation costs are declining but still require integration, customization, and mandatory human legal review, making the all-in cost comparable to or only moderately cheaper than a sales rep spending 1–2 hours on form creation and standardized agreement review. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated document generation software is inexpensive relative to sales rep time spent manually drafting paperwork, offering substantial per-document cost savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products like contract-generation tools and LLM-based document drafting exist and are used in some organizations, but they often require significant human correction and domain knowledge. Material error rates in legal language and inability to handle edge cases limit reliable production deployment at scale. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | CRM and contract lifecycle management platforms (e.g., DocuSign, Salesforce CPQ, PandaDoc) already auto-generate sales agreements and quotes in production for many businesses today. |
Attend sales or trade meetings or read related publications to obtain information about market conditions, business trends, regulations, or industry developments.
57CI 50–64 · exposure 42 · augmentation 88 · click for rater detail
Attend sales or trade meetings or read related publications to obtain information about market conditions, business trends, regulations, or industry developments.
57| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Sales-focused organizations (particularly in information and financial sectors) are adopting market intelligence AI tools, but full replacement of meeting attendance remains limited. Pilots and partial adoption are common, but human attendance at trade shows and client meetings persists as a norm. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Sales and business services sectors are adopting AI research/summarization tools at a moderate pace, with pilots more common than full-scale deployment for this specific task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at assisting sales reps by pre-summarizing market reports, flagging regulatory changes, and synthesizing industry trends before or after meetings, dramatically improving their preparation and post-event synthesis. The human rep remains the key networker and interpreter, but AI-provided context substantially raises their effectiveness. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI tools like news digests, summarizers, and market trend analyzers substantially boost a rep's ability to quickly gather and process relevant information while they remain in control of interpretation and relationship building. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can extract and summarize market data from publications and public sources, but attending meetings (real-time social and contextual intelligence gathering) requires human presence and judgment. The task is partly automatable (reading publications) but lacks end-to-end coverage and the spontaneous insight-generation that human attendance provides. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can rapidly summarize publications, trade reports, and market news, replacing much of the reading portion, but attending live meetings and synthesizing nuanced networking context still requires human presence. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers prevent organizations from substituting AI-powered market briefings for human meeting attendance. However, relationship-building and in-person networking create organizational friction and customer-preference pushback against full substitution. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or regulatory requirement mandates a human to gather this market information; it's an internal information-gathering task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automating the publication-reading portion is very cheap (subscription to AI news services costs <$100/month), whereas a sales rep's time attending meetings costs thousands. The marginal cost of AI assistance on this task is orders of magnitude lower for the reading component. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-driven summarization and monitoring tools are far cheaper than paying a rep's time to read extensively, though meeting attendance still incurs human cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist that summarize market news and regulatory updates (news aggregators, LLM-based market intelligence tools), but they cannot reliably replicate the networking, relationship-building, and contextual learning that in-person trade meetings provide. Deployed systems handle the reading portion adequately but miss the human-interaction component. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Deployed tools (news aggregators, AI summarization assistants, market intelligence platforms) reliably digest industry publications today, though live meeting attendance and interpretation remain human-dependent. |
Inform customers of contracts or other information pertaining to purchased services.
56CI 37–75 · exposure 55 · augmentation 88 · click for rater detail
Inform customers of contracts or other information pertaining to purchased services.
56| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Mid-market and enterprise service firms are piloting AI-driven customer communication, but production deployment remains patchwork. Adoption is faster in information-rich sectors (SaaS, telecom) but slower in smaller or relationship-driven service businesses. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Customer service and sales support functions across many service industries have rapidly adopted AI chat/voice tools for informational tasks, reflecting broader adoption trends in professional services and support functions. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assistants significantly boost productivity by drafting personalized contract summaries, flagging key terms, and handling routine inquiries, allowing sales representatives to focus on complex negotiation and relationship management. The human remains in the loop but with substantially raised output. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI tools significantly help reps draft, personalize, and retrieve accurate contract details quickly, improving speed and consistency while the rep remains available for complex questions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate and organize contract information, the task requires interpreting individual customer circumstances, handling objections, and ensuring comprehension—elements that demand human judgment and context awareness. Current systems can draft communications but cannot reliably handle the full end-to-end task with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 4/5 | Informing customers about contract terms and service details is largely a communication task that chatbots, voice agents, and automated email/SMS systems can handle for standard cases, though complex negotiations still need humans. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Some sectors (finance, insurance, healthcare) face regulatory constraints requiring human sign-off or accountability on contractual disclosures. However, many service industries have lower barriers, creating moderate friction rather than hard legal prohibition. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Some contracts require disclosures with specific compliance language or human sign-off in regulated services, but generally there's no licensing requirement to simply inform customers of terms. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI solutions require integration into CRM systems, ongoing maintenance, and human oversight for errors or complex inquiries. The all-in cost per customer interaction often approaches or exceeds the hourly wage of service sales representatives, especially for smaller firms. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated messaging and AI chat agents cost a small fraction of a human sales rep's time for routine informational communication, though oversight and edge-case escalation add some cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed chatbots and email automation systems can deliver standard contract information, but material gaps exist in handling ambiguous questions, personalized scenarios, and compliance-sensitive contexts. Products work at narrow scope with moderate error rates in edge cases. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | CRM-integrated chatbots and AI customer service platforms already deliver contract information and service details reliably in production across telecom, utilities, and B2B services sectors. |
Contact prospective or existing customers to discuss how services can meet their needs.
36CI 30–41 · exposure 25 · augmentation 75 · click for rater detail
Contact prospective or existing customers to discuss how services can meet their needs.
36| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption remains limited to lead qualification and simple outreach; consultative sales conversations are still predominantly human-led even in digitally advanced sectors. Pilots are common but production displacement of customer discussion tasks is rare and shallow. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Sales functions across many service industries are adopting AI tools for lead generation, CRM enrichment, and outreach automation at a moderate pace, though full conversational automation lags. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at augmenting sales reps through prospect research, CRM data synthesis, suggested talking points, and follow-up scheduling. These tools visibly improve rep productivity and win rates while keeping the human in control of the actual customer conversation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly assists reps via CRM insights, personalized outreach drafting, call summarization, and lead prioritization, meaningfully boosting productivity while the human remains central to the interaction. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate initial outreach messages and identify prospects, the nuanced discussion of how services meet specific customer needs requires real-time interpersonal judgment, relationship-building, and handling of objections—capabilities current AI struggles with reliably. Only preliminary stages of prospecting can be meaningfully automated without significant quality loss. |
| Task automatability | claude-sonnet-5 | 2/5 | Initial outreach and qualification can be partially scripted or chatbot-assisted, but genuine needs discovery, rapport-building, and closing require human judgment and relationship management that current AI cannot fully replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there is no strict legal requirement for human sales contact, customer expectations, contractual relationships, and liability concerns (sales misrepresentation, fiduciary duty) create organizational friction and risk aversion. Many service industries depend on trust and personal accountability that firms hesitate to fully delegate to AI. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this role, but customer preference for human interaction in service sales and organizational reliance on relationship-based selling create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI conversation systems require substantial infrastructure, fine-tuning, oversight, and error recovery for this task. The cost per effective customer contact conversation remains higher than deploying a human representative, especially when accounting for failed interactions and damage from poor recommendations. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI outreach tools reduce cost for initial contact and lead generation, but human sales reps are still needed for substantive conversations, so overall cost savings are moderate rather than dramatic. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI chatbots and automated calling systems exist but perform poorly on consultative, needs-assessment conversations; they fail at understanding context, handling unexpected objections, and building trust. Deployed systems are narrow (FAQ routing) or low-quality; no production-scale system reliably replaces human sales discussions with customers. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI SDR tools and chatbots exist for outbound emails and initial lead qualification, but reliable, nuanced consultative conversations about complex service needs are not yet handled by deployed products at scale without human involvement. |
Consult with clients after sales or contract signings to resolve problems and provide ongoing support.
32CI 28–38 · exposure 25 · augmentation 75 · click for rater detail
Consult with clients after sales or contract signings to resolve problems and provide ongoing support.
32| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Tech-forward service companies have piloted AI chatbots and support ticketing systems, but deployment remains patchy and heavily human-assisted; production-scale autonomous client support is not yet standard in the services sector, though adoption is accelerating in customer service functions generally. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | B2B services firms are adopting AI-assisted CRM and support tools moderately quickly, though full automation of client relationship management remains limited. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI tools can substantially assist human sales reps by summarizing client history, flagging common issues, drafting responses, and organizing support tickets—meaningfully raising productivity while the rep maintains relationship ownership and judgment over resolution. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools (CRM summarization, sentiment analysis, automated ticket triage, drafting follow-up communications) meaningfully boost rep productivity in managing and resolving client issues. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Post-sale client consultation requires nuanced problem-solving, relationship management, and context-specific troubleshooting that current AI struggles with at scale. While AI can handle some scripted support inquiries and provide canned responses, the personalization, judgment, and relationship continuity needed for genuine client support fall short of the 50% time-saving bar for meaningful task portions. |
| Task automatability | claude-sonnet-5 | 2/5 | Some post-sale support (FAQ answering, status checks) can be automated, but resolving contract disputes and complex client problems requires relationship-based judgment and negotiation that current AI cannot fully replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant organizational and reputational barriers exist: clients expect human relationship continuity, contract-related issues may have legal implications, and poor support automation directly damages client satisfaction and retention. Many contracts also require human sign-off or dispute resolution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but client relationships, trust, and account-specific knowledge create organizational and customer-preference friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI support tools require continuous oversight, training data upkeep, and human escalation handling, making total cost comparable to or higher than junior support staff, especially when weighted for error costs and customer churn from poor automated interactions. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply handle routine follow-ups, but escalations and relationship management still require human sales reps, so blended cost savings are modest rather than order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed products (basic chatbots, ticketing systems) exist but operate narrowly and with high false-resolution rates; they excel only on FAQ-type issues. Real client support at the quality expected by paying customers still requires human judgment, empathy, and authority to commit resources, so production-scale autonomous performance is not demonstrated. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Chatbots and CRM-integrated support tools exist for basic account queries, but reliable autonomous handling of nuanced post-sale problem resolution in B2B service contexts is not yet deployed at scale. |
Emphasize or recommend service features based on knowledge of customers' needs and vendor capabilities and limitations.
31CI 30–32 · exposure 25 · augmentation 63 · click for rater detail
Emphasize or recommend service features based on knowledge of customers' needs and vendor capabilities and limitations.
31| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Most service sales remain relationship-driven and operate in less digitalized sectors (e.g., B2B services, field sales); while CRM systems are common, AI-driven feature recommendation adoption beyond simple rule-based prompts is still in early pilot phase across most service sectors. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | B2B services sales is adopting AI-assisted CRM and sales enablement tools moderately, with pilots and augmentation common but full replacement of the recommendation task still rare in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully surface relevant vendor features and flag constraints via CRM integration or knowledge bases, helping sales reps navigate complex offerings faster. However, the human sales rep must still validate fit against customer needs, limiting the transformative potential of augmentation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools that analyze customer data, suggest relevant features, and draft talking points meaningfully boost a sales rep's ability to tailor recommendations while the human remains central to delivery. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can surface relevant service features based on customer data, genuinely understanding nuanced customer needs and vendor limitations requires contextual judgment that current systems struggle with at scale. The task demands synthesis of unstructured customer context and real-time vendor constraint knowledge, which AI can partially support but rarely execute end-to-end with consistent quality gains >50%. |
| Task automatability | claude-sonnet-5 | 2/5 | While AI can help draft talking points or match features to stated customer needs, the core task requires real-time relationship judgment, reading customer cues, and persuasive live interaction that current systems cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Sales organizations often require human relationship continuity and liability falls on the company when recommendations prove incorrect; however, there are no hard legal barriers to deploying AI-assisted recommendation systems in most service verticals, only operational and reputational friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement exists, but customer preference for human relationship-building and organizational reliance on trusted sales reps create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration costs for vendor capability databases, customer data pipelines, and oversight mechanisms remain substantial relative to the savings from partial automation. The margin on personalized service recommendations is often thin, making the cost equation unfavorable compared to trained sales staff. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply generate suggested talking points, but a human sales rep is still needed to deliver and adapt them in context, so the all-in cost of achieving equivalent sales outcomes remains comparable to or higher than pure AI cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Chatbots and recommendation systems exist but typically operate on structured data and pre-defined feature sets; they rarely demonstrate reliable performance in real-world sales contexts where vendor limitations shift and customer needs are ambiguous. Production deployments remain narrow (e.g., rule-based recommendations) rather than handling the full reasoning required. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CRM tools and AI sales assistants can surface recommended features or generate pitch content, but no deployed product reliably conducts the full nuanced recommendation process in live customer interactions at scale. |
Negotiate prices or terms of sales or service agreements.
25CI 20–30 · exposure 20 · augmentation 63 · click for rater detail
Negotiate prices or terms of sales or service agreements.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption is limited and mostly experimental (proposal drafting tools, CRM assist features). Most sales organizations still rely heavily on human reps for price/terms negotiation; production automation remains rare outside high-volume, commoditized use cases. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Sales negotiation automation is still nascent; most sales orgs use AI for lead scoring or drafting but keep negotiation itself human-led, showing slow deep adoption specifically for this subtask. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by preparing term templates, suggesting market-benchmark prices, summarizing counteroffers, and documenting agreements—improving rep productivity on pre- and post-negotiation work. However, augmentation is confined to supporting tasks rather than transforming the negotiation itself. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools increasingly help sales reps with pricing analytics, competitor benchmarking, and drafting counteroffers, meaningfully boosting negotiation prep and speed while humans retain control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Negotiation requires dynamic response to counterparty interests, judgment about tradeoffs, and relationship maintenance—capabilities current AI systems lack at production scale. While AI can draft terms or suggest strategies, end-to-end negotiation with 50% time savings at equal quality remains out of reach for most real-world contexts. |
| Task automatability | claude-sonnet-5 | 2/5 | Negotiation requires real-time reading of counterpart intent, relationship management, and flexible trade-offs that current AI cannot reliably replicate end-to-end for consequential B2B deals.dehors AI can draft terms but not autonomously close a negotiation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Negotiation often involves fiduciary duty, legal authority to bind the organization, and customer relationships that strongly prefer human interaction. Contractual and liability concerns, plus organizational risk aversion around price concessions, create significant adoption friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement typically exists, but customer preference for human relationship-building, trust, and accountability for contract terms creates meaningful friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI systems capable of partial negotiation support (templates, term suggestions) are cheap, but the human sales rep remains essential for the high-stakes interaction itself. Oversight and human involvement keep total cost comparable to or higher than the rep's hourly wage for the negotiation component. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human negotiators command significant fees but AI negotiation tools still require heavy oversight, integration, and escalation to humans for meaningful deals, keeping costs comparable rather than dramatically lower. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs bilateral price/terms negotiation autonomously. Chatbots can handle scripted inquiries, but genuine negotiation involving counterproposal, concession signaling, and deal closure requires human judgment that systems today cannot replicate in production. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some chatbot-based negotiation tools exist for simple, low-stakes commodity pricing, but no mature product autonomously negotiates complex service contracts in production at scale. |
Distribute promotional materials at meetings, conferences, or trade shows.
22CI 18–26 · exposure 8 · augmentation 38 · click for rater detail
Distribute promotional materials at meetings, conferences, or trade shows.
22| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While promotional content generation has seen some AI adoption, the physical distribution aspect at conferences and trade shows remains primarily human-driven. Event logistics are slower to digitize or automate. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Sales and events functions are adopting AI for CRM, content generation, and lead scoring, but physical event logistics and material distribution remain largely untouched by automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by optimizing material design, targeting high-value attendees, personalizing content, and organizing logistics pre-event. However, assistance is limited to preparation and planning; the actual distribution remains human work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help design promotional materials, plan trade show strategy, or personalize follow-up, but offers minimal assistance to the physical act of distributing materials on-site. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could help design and organize promotional materials digitally, the core task of physically distributing them at in-person venues requires human presence and on-site logistics. AI cannot meaningfully perform the physical distribution itself without robotics, and the human interaction component remains essential. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, in-person task requiring a human to be present at an event handing out materials; no AI system can physically distribute items or engage in the associated face-to-face rapport-building.rating remains low.rating.rating.rating.rating. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | The task is not strictly regulated, but the requirement for physical presence and direct human interaction at live events creates practical barriers to substitution. Organizations rely on human representatives to engage with attendees during distribution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human specifically, but the physical nature of the task (presence, portability, interpersonal exchange) creates a practical barrier to automation via robotics or software. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Physical distribution at events requires human labor on-site; AI cannot replace this cost. Any AI support (material design, targeting) is supplementary to the unavoidable human labor cost of physical presence. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for physical presence and handoff, so any 'AI cost' comparison is moot; the human is required and thus cheaper by default since no AI alternative exists. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No current deployed AI system can physically distribute materials at venues. This task fundamentally requires human presence and manual handling at events, which is entirely outside the scope of AI capability today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical distribution of promotional materials at live events; this remains entirely human-executed. |
Related occupations — Sales & Related
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.