Advertising Sales Agents
41-3011.00Median wage $64,820/yr91,700 employed (US)Rank #90 of 923 scored · top 10% by substitution
Sell or solicit advertising space, time, or media in publications, signage, TV, radio, or Internet establishments or public spaces.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution48
Exposure42
Augmentation74
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
20 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
20%
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%41
panel mean rating 2.7/5 → substitution pressure 41/100
Technical feasibility todayw 20%42
panel mean rating 2.7/5 → substitution pressure 42/100
Cost vs. human wagew 15%50
panel mean rating 3.0/5 → substitution pressure 50/100
Adoption barriersw 20%inverted — strong barriers lower the score64
panel mean rating 2.4/5 (barrier strength) → substitution pressure 64/100
Sector adoption velocityw 10%52
panel mean rating 3.1/5 → substitution pressure 52/100
Task breakdown (20 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.
Write sales outlines for use by staff.
81CI 79–84 · exposure 75 · augmentation 100 · importance 2.9/5 · click for rater detail
Write sales outlines for use by staff.
81| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Advertising and sales organizations are information-sector leaders in AI adoption; generative AI tools for sales enablement are already in pilot and production use across major agencies and ad tech firms. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Sales and advertising functions are digitized, tech-forward, and among the fastest adopters of generative AI writing tools, with many CRM and sales-enablement platforms embedding AI drafting features already. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI excels at rapidly generating multiple outline variants, personalizing by client segment, and surfacing competitive angles—transforming an agent's productivity by handling research and structure while the agent refines positioning and strategy. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI is highly effective as a drafting aid, letting a human sales agent quickly generate, refine, and customize outlines rather than starting from a blank page, significantly boosting productivity while the agent retains final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can generate structured sales outlines with key talking points, objection handling, and call-to-action frameworks that meet quality thresholds with significant time savings. However, task completion requires human review and customization for specific client contexts, preventing a full 5 rating. |
| Task automatability | claude-sonnet-5 | 4/5 | Drafting sales outlines is a text-generation task well within current LLM capabilities, requiring only prompt inputs like product info and target audience to produce usable drafts quickly.》 |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Sales outline writing is a pure knowledge task with no licensing requirement, legal signoff mandate, or regulatory barrier; organizational friction is minimal since outlines are internal planning documents rather than client-facing deliverables. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There is no licensing, liability, or regulatory requirement tied to drafting internal sales outlines, so nothing legally or structurally prevents AI-generated drafts from replacing human-written ones. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI inference cost per outline is negligible compared to the loaded cost of a human sales agent spending 30–60 minutes researching and drafting equivalent material, yielding at least an order-of-magnitude cost advantage. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Generating a written outline via AI costs fractions of a cent in compute versus the loaded hourly cost of a sales agent's time, making the cost differential very large. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (ChatGPT, Claude, specialized sales platforms with AI) reliably produce usable sales outlines in production environments, though they often need human refinement for pitch-specific details and industry nuance. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Products like ChatGPT, Jasper, and CRM-integrated AI writing assistants are already used in production by sales teams to generate outlines and scripts, though outputs often need light editing for accuracy and brand voice. |
Process all correspondence and paperwork related to accounts.
80CI 67–92 · exposure 78 · augmentation 88 · importance 4.3/5 · click for rater detail
Process all correspondence and paperwork related to accounts.
80| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Sales and marketing organizations are rapidly adopting document automation and CRM-integrated AI tools; this administrative task sits in a digitization-friendly, fast-adopting sector (professional services, information) with clear ROI. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Sales and advertising sectors are adopting CRM and AI email/document tools at a moderate pace, with pilots and partial deployment more common than full automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assistants (email summarization, auto-tagging, suggested responses, intelligent filing) substantially raise human productivity on correspondence and paperwork when a human remains in quality-control loop, especially for complex account escalations. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI tools like email drafting assistants, CRM auto-fill, and document generation significantly boost productivity for agents handling correspondence and paperwork while they remain in control of client relationships. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Processing correspondence and paperwork for accounts is highly structured, document-centric work involving routing, filing, data entry, and straightforward classification—tasks where current AI systems like document automation platforms, RPA, and large language models can achieve >50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 4/5 | Correspondence drafting, filing, and standard paperwork processing (contracts, invoices, order forms) are largely templated text and data-entry tasks that current AI and workflow automation can handle with substantial time savings. |
| Adoption barriers | claude-haiku-4-5-20251001 | 1/5 | There are no licensing, regulatory, or legal barriers preventing automation of correspondence and paperwork processing; no human sign-off or customer contact is inherently required for these back-office tasks. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for this administrative task, though contract accuracy and client relationship concerns create some organizational caution before full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | The all-in cost of AI-driven document processing and correspondence handling is at least an order of magnitude cheaper than a salary-bearing administrative employee, even accounting for setup and integration overhead. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-assisted document processing and correspondence tools cost a small fraction of a human's time for routine account paperwork, though some oversight cost remains. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (document automation, email-to-CRM pipelines, intelligent document processing) reliably handle routing, filing, and basic data extraction in production advertising and sales environments, though some nuance-dependent decisions still benefit from human oversight. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | CRM tools with AI email drafting, autofill, and document generation are deployed widely, but full end-to-end processing of varied account paperwork still requires human review for accuracy and exceptions. |
Prepare promotional plans, sales literature, media kits, and sales contracts, using computer.
79CI 75–84 · exposure 83 · augmentation 88 · importance 4.3/5 · click for rater detail
Prepare promotional plans, sales literature, media kits, and sales contracts, using computer.
79| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Sales and marketing functions are digitally mature and early adopters of AI content generation; many ad agencies and sales teams are already using AI tools for copy, collateral, and contract drafting in production workflows. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Advertising and marketing sectors are fast adopters of generative AI for content and sales collateral, with widespread production use of AI copywriting and design tools. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly boosts a sales agent's productivity by generating first drafts, template options, and layout suggestions that the human refines for brand voice and legal fit; this augmentative capability is already widely deployed and valued. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up drafting of plans, literature, and kits while the agent still finalizes messaging, contract terms, and client-specific customization. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | AI can now generate promotional plans, sales literature, and media kits with high quality and speed using generative models; creating sales contracts from templates is routine document automation. The entire workflow can be executed end-to-end with >50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 4/5 | Drafting promotional plans, media kits, and sales literature are largely generative writing/design tasks that current LLMs and design AI tools can produce quickly, requiring human review rather than creation from scratch, meeting the time-saving bar for most of the content. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal legal barriers exist for AI-generated promotional or sales materials; regulatory oversight is sector-dependent but not inherently restrictive. The main friction is organizational preference for brand review and legal sign-off, not a hard licensing requirement. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI-assisted drafting, though sales contracts may need review by authorized personnel for legal enforceability, creating mild friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI inference and integration costs are a small fraction of the loaded wage for a sales agent to manually draft these materials; even with human oversight, the per-task cost is likely 5–10× cheaper than full human labor. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Generating drafts of sales literature and media kits via AI tools costs a fraction of an agent's or copywriter's time, though some oversight and contract-specific legal review adds cost back. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature products (e.g., ChatGPT, Claude, specialized sales tools) reliably produce promotional content, kit layouts, and contract drafts in production; minor human review is typically required for brand compliance and legal nuance, but the technical capability is deployed at scale. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | AI writing assistants, presentation generators, and contract-drafting templates are widely deployed in marketing and sales workflows today, though sales contracts still often require legal/human customization. |
Write copy as part of layout.
73CI 61–84 · exposure 62 · augmentation 100 · importance 4.2/5 · click for rater detail
Write copy as part of layout.
73| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Marketing and advertising sectors are early movers in AI adoption, with agencies and in-house teams actively piloting copy generation tools. Many mid-to-large firms now use AI draft copy in production workflows, though full displacement remains limited. Adoption is faster in high-volume, lower-risk campaigns. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Advertising and marketing sectors have rapidly adopted generative AI copywriting tools, with widespread production use in agencies and sales teams. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI copy assistants substantially amplify human copywriter productivity by generating multiple drafts, variations, and quick ideation loops, allowing the human to focus on strategy, brand fit, and refinement rather than blank-page creation. This augmentation is already widely deployed and transformative for the task. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI copy tools are widely used to draft, brainstorm, and refine ad copy, significantly boosting sales agents' productivity while they retain control over final messaging and client fit. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can generate promotional copy and variant messaging at scale, but advertising copy typically requires brand voice alignment, subtle persuasion tuning, and creative direction that humans refine. Current AI systems handle initial drafting and variations effectively, meeting the time-saving threshold for parts of the workflow, though full end-to-end replacement with equal quality remains inconsistent. |
| Task automatability | claude-sonnet-5 | 4/5 | Modern LLMs can generate advertising copy tailored to layout/space constraints quickly, meeting or exceeding the 50% time-saving threshold for drafting, though final polish and client-specific nuance still need human review. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few hard regulatory or licensing barriers block copy generation automation. Weak barriers exist: clients often prefer human oversight, brand/legal review remains mandatory, and organizational friction around job displacement tempers adoption, but nothing legally requires a human to write it. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There is no licensing, legal, or regulatory requirement for a human to write ad copy; clients care about results, not authorship. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI inference and integration costs for copywriting are very low (dollars per 100+ variants), while copywriter labor is substantial. Even accounting for human oversight and refinement, the marginal cost of AI-assisted copy generation is roughly 5–10× cheaper than writing from scratch, favoring the AI decisively. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Generating copy via an LLM costs fractions of a cent to a few cents per piece versus a human copywriter's hourly wage, an order-of-magnitude or greater cost advantage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed products (ChatGPT, specialized copywriting tools, Adobe Express) demonstrably generate advertising copy, but they require human review, brand-specific prompting, and often fail on nuance, tone-matching, and legal/compliance details. Production use exists but typically in augmentation mode rather than fully autonomous generation. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Deployed products (Jasper, Copy.ai, ChatGPT-based tools integrated into ad platforms) routinely generate ad copy in production settings, though quality/consistency checks are still typically applied by humans. |
Provide clients with estimates of the costs of advertising products or services.
70CI 61–79 · exposure 62 · augmentation 88 · importance 4.5/5 · click for rater detail
Provide clients with estimates of the costs of advertising products or services.
70| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Advertising and sales technology sectors are digitized and early-to-mid adoption phase for AI-assisted quoting and CRM tools. Large agencies and tech-forward firms are already deploying automated proposal and estimation systems at scale. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Digital advertising and media sales are a fast-adopting, highly digitized sector with mature self-serve and automated pricing tools already widely deployed. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI dramatically assists sales agents by auto-populating rate cards, generating draft estimates, and surfacing comparable pricing, freeing the agent to focus on negotiation and client relationship. The human remains in control while productivity gains are substantial. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI-driven pricing calculators and CRM-integrated quoting tools substantially speed up and improve accuracy of estimates while agents retain relationship and negotiation roles. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can generate cost estimates based on standard pricing models, inventory, and historical data, but estimates often require human judgment on client-specific discounts, media mix complexity, and negotiation margins. Roughly half the estimation workflow could be automated with significant setup of rate cards and templates. |
| Task automatability | claude-sonnet-5 | 4/5 | Cost estimation for ad products is largely a structured, rules-based calculation (rate cards, media inventory pricing, package tiers) that AI/software can generate quickly given inputs, though final negotiation nuance may need human input. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or regulatory barriers exist; estimates are advisory and require no formal sign-off. Main friction is organizational (sales teams prefer human relationship-building) and customer expectation for personalized service, not hard legal requirements. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but some organizational friction exists as clients may expect a human relationship for negotiated custom packages. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven estimation (via SaaS tools or integrated agents) costs a fraction of the loaded wage for a sales agent to manually research rates, build proposals, and iterate on quotes. The cost savings are substantial once systems are deployed. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Automated pricing/quoting tools operate at near-zero marginal cost compared to a human sales agent's time spent building estimates. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist (CRM tools, pricing engines, proposal generators) that produce estimates, but they typically require human review and modification for accuracy and client context. Material error rates occur when AI misses industry-specific nuances or client-specific exceptions. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Ad platforms (Google Ads, Meta, programmatic DSPs) already generate automated cost estimates and pricing quotes at scale in production today, though bespoke/complex media packages still involve manual quoting. |
Deliver advertising or illustration proofs to customers for approval.
65CI 50–80 · exposure 55 · augmentation 63 · importance 4.3/5 · click for rater detail
Deliver advertising or illustration proofs to customers for approval.
65| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Advertising sales teams are moderately digitized but remain relationship-driven; adoption of proof-delivery automation is happening in larger, tech-forward firms but remains inconsistent across the sector. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Advertising and media sales sectors have rapidly adopted digital workflow and CRM/proofing tools, making automated proof delivery a common practice already. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by auto-formatting proofs, suggesting optimal delivery times based on customer behavior, and flagging missing approvals, improving sales agent efficiency without removing human oversight of client relationships. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-enabled tools can automatically generate, track, and manage the sending and approval status of proofs, greatly speeding up the agent's administrative workload while the agent retains the client relationship. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Delivering proofs to customers requires coordination with specific individuals, understanding approval workflows, and handling potential questions or revisions. While AI could draft emails or identify contact information, the task involves judgment about timing, format preferences, and relationship management that resists full automation today. |
| Task automatability | claude-sonnet-5 | 4/5 | Delivering proofs for approval is largely a digital file-transfer and notification task that can be handled via automated email/portal systems with e-signature or approval workflows, requiring minimal human judgment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are minimal legal or authorization barriers to automating proof delivery itself. Some organizational friction exists (sales agents may prefer human control, customers may prefer direct contact), but these are weak friction points rather than hard blockers. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There is no licensing, legal, or liability requirement mandating a human deliver proofs; this is a low-risk administrative task with no meaningful regulatory or trust barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | A simple automated email delivery with the proof attachment costs pennies in AI inference and integration, while the human sales agent labor (loaded wage ~$30–50/hour) makes AI dramatically cheaper for routine delivery. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated delivery and approval-tracking software costs a fraction of a human agent's time spent manually sending and following up on proofs, though some oversight and customer relationship costs remain. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Email and document delivery systems exist and can be partially automated, but production systems lack reliable handling of exceptions (wrong recipient, approval authority changes, special requests). The task involves human relationship nuances that deployed products handle inconsistently. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Proofing and approval platforms (e.g., PDF markup tools, client portals, project management software) are widely deployed in advertising and creative agencies today and reliably handle proof delivery and approval tracking. |
Obtain and study information about clients' products, needs, problems, advertising history, and business practices to offer effective sales presentations and appropriate product assistance.
54CI 50–59 · exposure 42 · augmentation 88 · importance 4.4/5 · click for rater detail
Obtain and study information about clients' products, needs, problems, advertising history, and business practices to offer effective sales presentations and appropriate product assistance.
54| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Advertising and sales organizations have adopted AI-powered research and CRM tools at moderate pace, with pilots common in larger agencies. However, the human relationship element and sales context means full automation of client understanding remains limited; most deployment is augmentative rather than substitutive. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Sales and advertising sectors are moderately fast adopters of AI tools for prospecting and research, with many CRM platforms integrating AI features, though full deployment across the industry remains uneven. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at rapid information synthesis, competitive analysis, and market research summary that a sales agent can then refine and personalize. Tools that aggregate client data, suggest relevant product angles, and flag business context significantly boost agent productivity in preparation for sales conversations. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI tools significantly speed up gathering and synthesizing client background information, letting agents spend more time on relationship-building and tailoring pitches rather than manual research. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can gather and summarize publicly available information about clients' products and basic business data, the task requires understanding nuanced client needs, problems, and business practices that typically emerge through dialogue and relationship context. Current AI lacks the ability to independently conduct the discovery conversations and relationship assessment that define effective sales preparation at equal quality. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can rapidly aggregate and summarize public information about a client's products, market position, and history, but understanding nuanced business needs and problems often requires direct conversation and relationship-based discovery that current AI cannot fully replace. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are minimal legal or regulatory barriers to AI assisting with or automating client research and information gathering. The main friction is organizational preference for human relationship-building and client expectations of personal engagement, which are adoption barriers rather than legal ones. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI-assisted research, though sales relationships often depend on human trust-building and there may be some organizational preference for personal contact in high-value accounts. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-powered research and data aggregation tools are inexpensive relative to sales agent labor for information gathering and initial analysis. The cost of deploying AI systems (research platforms, CRM integration) is low compared to the loaded wage of a sales agent spending hours on client research. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-driven research and summarization tools cost a small fraction of an agent's time-equivalent for gathering and synthesizing background information, though some human review keeps this from being a full order-of-magnitude savings in all cases. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | AI products can retrieve and compile client information from websites, financial reports, and public data sources reliably, but deployed systems struggle with the interpretive depth needed to truly 'understand' client problems and advertising history in strategic context. Some CRM and market research tools incorporate AI assistance, but none fully automate the holistic client study phase. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | CRM-integrated AI research tools and sales intelligence platforms (e.g., Clay, Apollo, ZoomInfo with AI summarization) are deployed today for prospect research, but they still require human verification and often miss qualitative context about client pain points. |
Locate and contact potential clients to offer advertising services.
54CI 41–66 · exposure 42 · augmentation 88 · importance 4.5/5 · click for rater detail
Locate and contact potential clients to offer advertising services.
54| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | B2B and B2C sales organizations are rapidly adopting AI-driven lead generation, intent detection, and outreach tools in production. Information, finance, and professional services sectors show particularly high velocity. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Sales and marketing functions have moderate AI tool adoption (CRM-integrated AI, lead scoring) but full replacement of human sales agents remains rare; pilots and augmentation are more common than full automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically augments sales agents by automating lead discovery, list building, and draft outreach while the agent focuses on qualified conversation and closing. This is a textbook assistive use case with proven productivity gains across the industry. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools substantially help agents identify prospects, personalize outreach, and manage pipelines, meaningfully boosting productivity while the human remains central to closing deals. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can autonomously handle lead research, database matching, and initial contact via email or messaging, achieving significant time savings on prospecting. However, the persuasion, relationship-building, and deal negotiation components typically require human judgment and credibility, preventing full automation at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Prospecting and outreach can be partially assisted by AI (lead generation, email drafting), but locating genuinely qualified clients and building initial rapport/trust still requires human judgment and relationship-building that current AI cannot fully replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or regulatory barriers exist to automating prospecting; the main friction is organizational preference for human relationship-building at first contact and desire to maintain sales team workflows. Customer trust in human outreach provides some resistance but is not a hard blocker. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but customer preference for human interaction in sales and relationship-based trust creates moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI prospecting tools cost $100–500/month per user and can surface 5–10x more qualified leads than manual research alone, substantially undercutting the fully-loaded cost (~$40–80/hour) of a human researcher doing equivalent lead discovery. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-assisted prospecting tools reduce time spent on research and outreach drafting significantly, but human oversight, calls, and relationship management remain costly, keeping overall cost roughly comparable to a human-driven process with AI augmentation. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature products (LinkedIn Sales Navigator, 6sense, HubSpot) reliably perform lead identification and outreach in production. Email and message generation is automated at scale, though human-in-the-loop remains standard practice for quality assurance. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Sales intelligence and outbound automation tools exist and are used in production, but they mostly generate leads and drafts rather than reliably closing or even initiating effective client contact autonomously. |
Draw up contracts for advertising work, and collect payments due.
51CI 46–55 · exposure 50 · augmentation 75 · importance 4.3/5 · click for rater detail
Draw up contracts for advertising work, and collect payments due.
51| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Advertising agencies and media companies have adopted contract and payment automation tools, but adoption remains uneven—small firms and independent agents lag, and pilots of agent-based systems are still common rather than deep production deployment. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Sales and advertising sectors have moderate AI adoption for document automation and billing, with pilots and partial deployment common but full end-to-end automation still uneven across firms. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI drafting tools and contract intelligence platforms substantially assist agents by pre-populating terms, flagging risks, and automating payment reminders; this augmentation significantly boosts productivity even as a human sales agent retains negotiation and client relationship authority. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools significantly speed up contract drafting via templates and clause suggestions and can automate invoice generation and payment reminders, meaningfully boosting agent productivity while humans retain final approval and client contact. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | Contract generation can be partially automated using templates and extraction of client details, but review, negotiation of terms, and addressing non-standard requests typically require human judgment. Payment collection can be automated for routine invoicing but manual follow-up on overdue accounts remains common, yielding roughly 40-50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 3/5 | Contract drafting from templates and payment collection workflows can be substantially automated with AI-assisted document generation and automated invoicing/collections systems, though negotiation nuances and client relationship handling remain human-dependent. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Contracts may involve legal review depending on jurisdiction and contract complexity, and payment collection has dispute-resolution requirements; however, no hard licensing barrier mandates human sign-off, and many firms already use template-based systems with supervisory oversight. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for drafting standard sales contracts, but liability concerns about contract errors and the need for relationship-based payment collection create moderate organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for contract generation and payment automation carry monthly licensing costs and integration overhead; combined with required human review, the all-in cost approaches or slightly exceeds a lower-wage sales support role, especially for smaller transaction volumes. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-assisted contract templates and automated payment systems reduce cost meaningfully, but integration with CRM/billing systems and human oversight for contract accuracy keep total cost roughly comparable to a well-tooled human process rather than order-of-magnitude cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist for contract automation (DocuSign, Ironclad, contract generation tools) and payment processing systems (Stripe, Square), but they operate at narrower scope than the full task and often require human oversight on contract terms and payment disputes, limiting reliability in production to standard cases only. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Contract lifecycle management and automated billing/collections products exist and are deployed in many sales organizations, but tailored advertising contract terms and dispute handling still often require human review. |
Determine advertising medium to be used, and prepare sample advertisements within the selected medium for presentation to customers.
51CI 46–55 · exposure 42 · augmentation 75 · importance 4.1/5 · click for rater detail
Determine advertising medium to be used, and prepare sample advertisements within the selected medium for presentation to customers.
51| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Marketing and advertising sectors are adopting AI tools for content generation and analytics, but sales-facing automation remains slower—many agencies and companies still rely on human judgment for client pitch strategy and medium selection, with AI used mainly as an assistive tool rather than replacement. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Advertising and marketing sectors are adopting generative AI tools quickly for content creation, though full sales-agent workflows still show pilots more than widespread production replacement. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly assists by generating multiple ad copy variations, visualizing concepts, and recommending media channels based on data, allowing sales agents to present richer options faster and spend more time on client consultation and relationship management rather than creative iteration. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools significantly speed up creating sample ads across mediums (copy, images, mockups), letting agents present more options faster while retaining control over strategy and client relationships. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can generate sample advertisements and suggest media channels (email, social, display) based on customer profile and budget, but the strategic determination of medium typically requires understanding customer goals, brand positioning, and market context that usually needs human judgment. Partial automation is feasible but end-to-end replacement without human oversight is unreliable. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft sample ads and suggest media options, but final medium selection requires client relationship knowledge, budget negotiation, and judgment that current systems can't fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers exist for AI-generated ad suggestions; however, sales relationships and client trust in human expertise create organizational friction, and liability for poor ad placement choices (brand safety, audience targeting) often rests with the agent. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but client trust, sales relationship, and creative sign-off create some organizational friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI-assisted ad generation and channel recommendation cost relatively little (under $1 per task with bulk APIs), but the human sales agent's loaded cost ($30–50/hour) is still competitive when accounting for integration, fact-checking, customization, and client-specific strategy work that AI cannot fully replace. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-generated sample ads are cheap to produce, but the overall task still requires a human sales agent for client consultation and strategic medium choice, keeping blended costs moderate rather than dramatically lower. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products like generative AI ad creators (Jasper, Copy.ai, Adobe Express) exist and can produce sample ads; marketing automation platforms suggest channels. However, these tools have material limitations in understanding nuanced customer requirements and often require significant human editing and validation before presentation. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Generative AI tools (image/copy generators, ad platforms) are deployed and used by agencies to create sample ads and mockups, though medium selection strategy still relies on human sales judgment and client interaction. |
Inform customers of available options for advertisement artwork, and provide samples.
46CI 37–55 · exposure 42 · augmentation 75 · importance 4.2/5 · click for rater detail
Inform customers of available options for advertisement artwork, and provide samples.
46| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Digital advertising and e-commerce sectors are experimenting with AI-driven product recommendations and design suggestions, but adoption remains mostly in pilots and backend support; few organizations have fully displaced sales agent roles with pure automation. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Advertising and media sales are moderately digitized with growing AI tool adoption for creative generation and CRM assistance, but many agents still rely on traditional consultative sales processes. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially augment this task by rapidly generating multiple design variations, organizing options by customer segment, and pre-populating sample galleries, allowing agents to focus on relationship-building and strategic consultation rather than routine sample assembly. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Generative AI tools significantly speed up creating and presenting sample artwork options, letting agents rapidly show more variations to customers while still handling the relationship and final decisions themselves. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can generate sample advertisements and organize product catalogs, but the task fundamentally requires understanding customer context, preferences, and strategic needs—judgments that typically demand human interaction and customization. Full end-to-end automation with equal quality would require extensive customer discovery that AI systems still struggle to conduct reliably. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can generate and present artwork options and samples via generative image tools and chatbots, but personalized client consultation and negotiation still benefit from human judgment, so only partial time savings are achievable end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Sales agents must often maintain established client relationships and provide consultative expertise that organizations and customers expect from a human; however, there are no hard legal or licensing barriers preventing AI-assisted or delegated sample generation and option presentation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human for this task, though customer relationship preferences and brand trust create some friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI inference and sample generation are cheap, but the integration overhead, human review cycles, and the need for custom adjustments to match customer requirements keep total costs comparable to or higher than direct human communication for personalized sales scenarios. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-generated sample artwork is cheap to produce, but integration, curation, and customer-facing communication still require human oversight, keeping total cost roughly comparable rather than a clear order-of-magnitude win. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Chatbots and content-generation tools can present predefined options and generate sample artwork, and some sales platforms incorporate AI to suggest designs; however, these systems often produce generic or misaligned outputs and require significant human review before customer-facing use. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like generative design tools and AI sales chatbots exist and are used in ad agencies, but reliable end-to-end deployment for this specific customer-facing informing task is still narrow and often paired with human review. |
Recommend appropriate sizes and formats for advertising, depending on medium used.
46CI 32–59 · exposure 38 · augmentation 75 · importance 4.2/5 · click for rater detail
Recommend appropriate sizes and formats for advertising, depending on medium used.
46| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Ad tech is digitized and adoption-ready, but sales organizations have been slow to replace account management roles given the relationship-driven nature of client interactions. AI-assisted tools see broader adoption than full automation. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Digital advertising and media sales have moderate AI tool adoption (e.g., automated bidding, format recommendations) but human-led consultative sales remains common for many segments. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can effectively assist sales agents by instantly surfacing format and size options, compliance requirements, and format-specific performance data, enabling faster and more comprehensive recommendations while the agent maintains client relationship and strategic judgment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools significantly help agents by suggesting optimal formats/sizes based on data, letting agents focus on client relationships and creative strategy. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can retrieve technical specifications for ad sizes and formats across media types, but this task requires contextual judgment about audience, campaign goals, and medium-specific constraints that demand human strategic input. Only narrow, lookup-driven aspects can be automated without significant human review. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can analyze media specs and audience data to recommend ad sizes/formats, but final recommendations often require client-specific negotiation and judgment that current tools don't fully replicate end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Sales relationships and client trust are significant but not legally binding barriers; clients often expect a human relationship with their sales agent. However, no hard licensing requirement prevents AI deployment, creating moderate but surmountable friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but client relationships and trust in sales agents' judgment create some friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI infrastructure and oversight costs for a recommendation system are non-trivial, and the task demands human validation to avoid costly client dissatisfaction. The cost advantage is modest at best, and human judgment remains essential for quality assurance. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated recommendation engines are cheap to run at scale compared to a human sales agent's time, though integration and oversight costs remain a factor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While ad specifications databases and size-recommendation tools exist, no deployed product reliably performs this task end-to-end with the strategic judgment required. Systems can suggest standard specs but lack the contextual understanding of client goals and competitive positioning needed for consistent, business-critical recommendations. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Ad platforms (e.g., Google Ads, programmatic tools) already suggest formats and sizes based on placement, but these are narrow, platform-specific tools rather than comprehensive agent-like advisors across all media types. |
Gather all relevant material for bid processes, and coordinate bidding and contract approval.
37CI 25–50 · exposure 33 · augmentation 63 · importance 4.0/5 · click for rater detail
Gather all relevant material for bid processes, and coordinate bidding and contract approval.
37| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Advertising and sales organizations show moderate digital maturity but bid coordination and contract approval remain largely human-driven; pilots of automation exist but production displacement is limited compared to pure information work. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Media and advertising sales are moderately digitized with growing AI tool adoption for proposal generation and CRM, though full workflow automation adoption remains uneven. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by auto-organizing RFP materials, flagging missing documents, and drafting contract summaries, which would speed a human agent's preparation and review cycle without removing them from decision-making. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully speed up gathering research, drafting bid materials, and tracking approval status, giving agents substantial productivity gains while they retain control of negotiations and sign-off. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with gathering and organizing materials (document retrieval, categorization), the task requires complex judgment about bid relevance, contract nuance, and coordination with multiple stakeholders—activities that cannot reliably be fully automated end-to-end with the 50% time-saving threshold met today. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can gather documents, extract key terms, and draft bid packages, but coordinating approvals across stakeholders and negotiating still requires human judgment and relationship management, limiting full end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Contract approval typically requires authorized human sign-off, and bidding processes often involve legal review and client relationship stewardship that cannot be delegated; organizational and compliance friction is substantial. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational approval chains, contract liability, and stakeholder trust create moderate friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for document management and coordination are relatively low-cost, but the overhead of integration, training, and human oversight for bid and contract work approaches or matches the cost of human handling of these high-stakes processes. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools can cut time on material gathering and drafting significantly, but human oversight for coordination and approvals keeps blended costs only moderately below fully manual processes. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Products exist for document management and basic workflow automation, but coordinating multi-party bidding and contract approval requires human oversight at critical decision points; no mature system reliably handles the full task autonomously in production. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Document assembly and CRM/workflow tools with AI assistance exist, but no mature product autonomously manages the full bid-gathering-to-approval coordination pipeline in production for ad sales specifically. |
Identify new advertising markets, and propose products to serve them.
37CI 32–41 · exposure 25 · augmentation 75 · importance 3.9/5 · click for rater detail
Identify new advertising markets, and propose products to serve them.
37| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Marketing and advertising firms are experimenting with AI-assisted market analysis and lead generation, but adoption of autonomous market identification and product proposal remains in the pilot phase rather than production at scale. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Advertising and media sectors are moderately fast adopters of AI for analytics and targeting, though strategic market identification remains a human-led, slower-adopting activity. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can significantly assist by analyzing market trends, competitor data, and audience segments, then drafting product proposals that the sales agent refines and pitches. This augmentation meaningfully raises productivity while keeping strategic judgment and client relationship ownership with the human. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can significantly enhance market research, trend spotting, and idea generation, giving sales agents strong support while they retain ownership of final strategy and pitches. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with market analysis and data gathering, but the task requires creative product ideation and strategic judgment that integrate business context, competitive landscape, and client needs—capabilities that current systems handle inconsistently. End-to-end automation with 50% time savings at equal quality is not yet reliable. |
| Task automatability | claude-sonnet-5 | 2/5 | Market research and trend synthesis can be AI-assisted, but identifying novel markets and crafting viable new advertising products requires creative judgment, relationship insight, and strategic risk-taking that current AI cannot fully replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While not legally required, human judgment and accountability for market strategy carry organizational and reputational weight. Client relationships and strategic buy-in typically demand human-led proposals, creating some friction to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational trust in strategic business judgment and client relationships creates moderate friction against full AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI inference and data integration are inexpensive, but human oversight and strategic validation remain essential. The all-in cost (AI + human review time) is roughly comparable to a sales agent spending time on market research and ideation alone. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply produce data summaries and market scans, but the human synthesis, pitching, and creative product design still require significant paid human time, keeping overall cost comparable to human-led work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can generate market reports and product suggestions via existing tools, no deployed product reliably identifies *new* markets and proposes fit-for-purpose products at production scale. Results tend to be generic, lack strategic depth, and require substantial human refinement. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI market research and analytics tools exist and are used for identifying trends, but no deployed product reliably generates novel market opportunities and product proposals autonomously in production. |
Explain to customers how specific types of advertising will help promote their products or services in the most effective way possible.
35CI 32–38 · exposure 25 · augmentation 75 · importance 4.5/5 · click for rater detail
Explain to customers how specific types of advertising will help promote their products or services in the most effective way possible.
35| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Digital marketing and sales technology adoption is moderate to moderately high in information and advertising sectors, but actual displacement of human sales agents explaining advertising strategies remains limited; most adoption is in the form of assistive tools rather than substitution. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Media and advertising sales sectors are adopting AI tools for lead scoring, content generation, and CRM augmentation, though live sales pitching remains human-led in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at augmenting this task: it can rapidly generate campaign ideas, suggest targeting strategies, provide competitive intelligence, and draft talking points, allowing sales agents to prepare more sophisticated pitches and respond faster to customer questions while remaining the final decision-maker. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can generate customized pitch materials, ad performance data insights, and talking points that significantly enhance an agent's ability to explain advertising value propositions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate product descriptions and basic marketing messaging, explaining advertising strategy to customers requires understanding their unique business context, competitive landscape, and needs—tasks that demand judgment and customization beyond what current systems reliably do end-to-end. Partial automation of pitch preparation is feasible, but full replacement achieving 50% time savings at equal quality is not yet deliverable. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires live persuasive, relationship-driven conversation tailored to a specific client's business context, objections, and trust-building, which current AI cannot fully replicate end-to-end despite drafting help.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Sales-relationship dynamics and customer preference for human interaction create moderate adoption friction, though no hard legal or licensing requirement mandates human involvement. Organizational sales cultures also tend to resist full automation of client-facing explanation and persuasion. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but customer preference for human rapport and trust in sales relationships creates real friction against full substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI tools that assist with sales content are relatively inexpensive, but when factored with required human oversight, integration, and the need for a human to finalize and deliver the pitch, the all-in cost remains comparable to or slightly cheaper than direct human labor, not substantially cheaper. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human sales agents carry commission-based variable cost, but the AI still requires significant human oversight, CRM integration, and relationship management, limiting cost savings versus a fully autonomous replacement. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some AI tools can draft marketing copy and suggest ad strategies, but no deployed product reliably performs the full task of explaining customized advertising solutions to customers with the persuasion and contextual adaptation required. Existing solutions are narrow (templated pitches) and require significant human oversight and revision. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI chatbots and sales-enablement tools can generate pitch content but no deployed product autonomously conducts full client-facing persuasive sales conversations reliably at scale. |
Consult with company officials, sales departments, and advertising agencies to develop promotional plans.
32CI 32–32 · exposure 25 · augmentation 63 · importance 4.0/5 · click for rater detail
Consult with company officials, sales departments, and advertising agencies to develop promotional plans.
32| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Advertising and marketing agencies have piloted AI-assisted planning tools, but full deployment in sales consultancy remains limited. Adoption is uneven—larger agencies experiment more than smaller firms, and the human relationship component slows production-level automation. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Advertising and sales sectors show moderate AI adoption for content generation and analytics, but adoption for the consultative/relationship core of this task remains a pilot phase. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by generating promotional concepts, market analysis, and draft plans, which helps agents iterate faster with stakeholders. However, augmentation is partial because the core task—negotiating and building consensus across multiple parties—remains firmly human-driven. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by generating campaign concepts, market research summaries, and draft proposals that agents use to prepare for and enrich these consultations. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft promotional plans and analyze data, the task fundamentally requires real-time stakeholder consultation, relationship building, and negotiation across multiple parties. Current AI systems cannot reliably conduct these live, context-sensitive conversations or make binding decisions, limiting time savings to <50%. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a collaborative, relationship-driven consultative task requiring real-time judgment, negotiation, and reading organizational dynamics that current AI cannot fully replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Advertising sales are relationship-driven and face moderate organizational friction; clients often prefer direct human contact for trust-building. However, no strict licensing or legal requirement prevents AI assistance, making barriers moderate rather than hard. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but strong organizational and relational friction exists since clients expect human judgment, trust-building, and accountability in strategic sales conversations. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI consultation tools are still costly to deploy and maintain relative to the loaded wage of a sales agent when accounting for oversight, error correction, and the human labor needed to conduct actual stakeholder meetings and finalize plans. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human relationship management and negotiation still dominate the value here, so AI assistance reduces some drafting time but doesn't replace the labor cost of the consultative process itself. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably conducts multi-party consultation and plan development end-to-end. AI can assist with research and drafting, but deployed systems lack the conversational depth, real-time stakeholder coordination, and judgment refinement required for production use in actual sales processes. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools can draft talking points or generate campaign ideas, but no deployed product independently conducts consultative meetings with company officials to develop promotional plans. |
Prepare and deliver sales presentations to new and existing customers to sell new advertising programs and to protect and increase existing advertising.
31CI 25–38 · exposure 25 · augmentation 63 · importance 4.6/5 · click for rater detail
Prepare and deliver sales presentations to new and existing customers to sell new advertising programs and to protect and increase existing advertising.
31| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Advertising sales remains heavily relationship-driven and human-centric. While companies experiment with AI-assisted prospecting and content generation, autonomous presentation delivery is not yet common in production, and adoption velocity remains low. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Sales and advertising sectors are adopting AI tools for content generation and CRM insights at a moderate pace, with pilots common but full presentation delivery still human-led. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by drafting personalized pitch decks, identifying prospect pain points from data, and suggesting talking points—raising human agent productivity. However, the human agent remains essential for delivery, negotiation, and closing. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly aids agents by drafting slides, tailoring pitches with customer data, and generating talking points, meaningfully boosting productivity while the human still delivers and closes the sale. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft presentation slides and generate talking points, the task requires real-time audience adaptation, relationship-building, negotiation, and closing—activities that demand human judgment and interpersonal skill. Current AI systems cannot reliably replicate the persuasive, context-sensitive delivery needed to win new business. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft presentation content and slides, but the live relationship-building, negotiation, and persuasive delivery to specific clients remains largely human-driven and doesn't meet the 50% end-to-end time savings bar. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Client relationships and trust are central to advertising sales; customers often demand face-to-face or video presentations with a human agent. Organizational inertia, liability concerns for failed closes, and customer preference for human contact create strong adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but customer preference for human interaction, trust-building, and relationship-based selling create moderate organizational and market friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Automating presentation delivery would require custom AI agents, integration with CRM systems, and ongoing oversight—likely comparable to or exceeding the cost of a junior sales agent, especially when factoring in the risk of lost deals from impersonal delivery. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply produce presentation materials, but the actual selling process still requires a paid human sales agent for delivery, negotiation, and relationship management, so overall cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably handles end-to-end sales presentations autonomously. AI can assist with content creation and scheduling, but production systems do not generate and deliver persuasive, customer-specific presentations at scale without heavy human involvement. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products exist for generating sales decks and pitch content (e.g., generative AI slide tools), but no deployed product reliably conducts client-facing sales presentations or manages account relationships at scale. |
Maintain assigned account bases while developing new accounts.
30CI 28–32 · exposure 25 · augmentation 75 · importance 4.5/5 · click for rater detail
Maintain assigned account bases while developing new accounts.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Sales organizations are actively piloting AI-assisted lead generation and CRM automation, but adoption remains in the "augmentation and pilot" phase rather than widespread replacement. Some forward-moving firms use AI tools, but the sector has not yet shifted to autonomous account management at scale. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Sales and advertising functions are adopting AI-assisted CRM, lead generation, and personalization tools at a moderate pace, with pilots common but full agentic account management still rare. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist sales agents by automating lead qualification, identifying upsell opportunities, scheduling outreach, and summarizing account history, allowing agents to focus on relationship and negotiation. Current tools demonstrably raise productivity for agents who remain in the loop. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI meaningfully augments prospecting (lead identification, personalized outreach drafts, account analytics) and administrative account upkeep, boosting agent productivity while humans retain relationship ownership. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with account identification and basic outreach, the core task requires relationship management, negotiation, and trust-building with clients—activities that remain heavily dependent on human judgment and interpersonal presence. No current AI system can autonomously maintain existing accounts or develop new ones end-to-end at 50% time savings without significant human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | This task blends relationship management, prospecting, negotiation, and trust-building that requires human judgment and interpersonal rapport; AI can support but not replace the core relational work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Client relationships, contract negotiations, and account stewardship typically require authorized human representation and direct customer contact. Legal accountability and trust requirements in sales agreements strongly favor human agents as the accountable party, creating high organizational and liability barriers. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but strong organizational and client preference for human relationship managers, plus accountability for revenue targets, creates real friction against full substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for account management (CRM integrations, lead scoring) are available but represent an additional cost layer; they do not yet undercut the cost of a dedicated sales agent managing their own accounts. Full end-to-end replacement is not economically viable today. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools reduce some administrative burden but the core sales activity still requires a paid human agent; AI is an add-on cost rather than a wage replacement at this stage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some CRM tools and lead-generation systems exist with AI capabilities, but deployed products still require extensive human engagement to qualify leads, manage relationships, and close deals. Error rates and narrow scope prevent production-scale autonomous account management. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CRM tools with AI features (lead scoring, next-best-action suggestions) exist but no product autonomously maintains or grows an account base without a human sales agent driving relationships. |
Arrange for commercial taping sessions, and accompany clients to sessions.
11CI 5–16 · exposure 0 · augmentation 38 · importance 3.8/5 · click for rater detail
Arrange for commercial taping sessions, and accompany clients to sessions.
11| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The advertising and media production sectors show no meaningful automation of client accompaniment tasks; this remains a core human relationship and logistics function with minimal AI displacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Advertising sales is a moderately digitized sector but this specific in-person, hands-on task has seen little AI-driven displacement so far. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with scheduling suggestions, reminder systems, or pre-session logistics (location mapping, vendor contacts), but these are narrow support functions that don't materially transform the core requirement of human presence and relationship management. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with scheduling, calendar coordination, and logistics planning around taping sessions, improving efficiency even though the human still attends and manages the client relationship. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical presence, real-time coordination with multiple parties, and relationship-based decision-making. Current AI systems cannot autonomously arrange sessions, travel with clients, or manage the interpersonal dynamics of in-person commercial shoots. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical presence, logistical coordination, and in-person client relationship management during recording sessions, which current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: the task requires human presence as a client-facing agent, relationship continuity, and real-time decision-making. Client preferences for direct human interaction and the need for on-site judgment present significant friction against substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but strong client-relationship and physical-presence expectations create organizational and customer-preference friction against substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI cannot perform this task at all today, making cost comparison meaningless. The task inherently requires a human agent's time and physical presence. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI could reduce scheduling and logistics overhead cheaply, but the core deliverable (physical accompaniment and relationship management) still requires a human, so overall cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can autonomously arrange and accompany clients to taping sessions. This requires physical embodiment, real-time negotiation, and presence at a specific location—capabilities that do not exist in current systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product accompanies clients physically to taping sessions or manages the in-person coordination this task requires; it is fundamentally a physical, relational task. |
Attend sales meetings, industry trade shows, and training seminars to gather information, promote products, expand network of contacts, and increase knowledge.
4CI 0–7 · exposure 0 · augmentation 38 · importance 3.8/5 · click for rater detail
Attend sales meetings, industry trade shows, and training seminars to gather information, promote products, expand network of contacts, and increase knowledge.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task is inherently dependent on human participation, so adoption of AI has been negligible. Sales organizations continue to require their agents to attend events personally, reflecting no measurable shift toward automation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Sales roles are adopting AI for CRM, lead generation, and research, but the specific in-person networking/attendance activity sees little to no AI substitution. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist by scheduling meetings, identifying relevant attendees beforehand, or summarizing event content post-attendance, but it offers limited assistance for the core activity of in-person networking and relationship building itself. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help prepare for meetings, summarize trade show content, suggest contacts, and follow up on notes, providing moderate productivity assistance around the core in-person task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Attending meetings, trade shows, and seminars in person requires physical presence, real-time relationship-building, and spontaneous networking—tasks that current AI cannot perform end-to-end. AI cannot substitute for the human presence and interpersonal dynamics that define these events. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical or live presence, in-person networking, and relationship-building at events, which current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong organizational and practical barriers exist: the task fundamentally requires human presence, real-time relationship-building, and authentic interpersonal engagement. Sales networks depend on trust and personal credibility, which demand human contact and cannot be delegated to automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Attending events and building personal relationships inherently requires human presence and social capital; there's no regulatory barrier but a strong physical/human-contact requirement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of an AI system that could attend events and network would far exceed the cost of sending a human sales agent, making automation economically infeasible even before considering the lack of technical capability. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for physical attendance and networking, so cost comparison favors the human by default since AI cannot perform the task at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can physically attend events, engage in genuine networking conversations, or build relationships autonomously. These activities require embodied presence and authentic human interaction that is not yet technologically feasible. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product attends meetings, trade shows, or seminars in a human's place; this remains entirely a human activity. |
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.