Pharmacy Aides
31-9095.00Median wage $37,680/yr39,530 employed (US)Rank #67 of 923 scored · top 7% by substitution
Record drugs delivered to the pharmacy, store incoming merchandise, and inform the supervisor of stock needs. May operate cash register and accept prescriptions for filling.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution54
Exposure55
Augmentation57
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
14 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
29%
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%56
panel mean rating 3.3/5 → substitution pressure 56/100
Technical feasibility todayw 20%51
panel mean rating 3.0/5 → substitution pressure 51/100
Cost vs. human wagew 15%55
panel mean rating 3.2/5 → substitution pressure 55/100
Adoption barriersw 20%inverted — strong barriers lower the score57
panel mean rating 2.7/5 (barrier strength) → substitution pressure 57/100
Sector adoption velocityw 10%43
panel mean rating 2.7/5 → substitution pressure 43/100
Task breakdown (14 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.
Prepare prescription labels by typing or operating a computer and printer.
94CI 92–95 · exposure 100 · augmentation 75 · importance 4.5/5 · click for rater detail
Prepare prescription labels by typing or operating a computer and printer.
94| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Pharmacy chains and large healthcare systems have broadly adopted automated label-printing systems over the past decade. This is not a cutting-edge AI application but rather a mature standard in the healthcare IT sector. |
| Sector adoption velocity | claude-sonnet-5 | 5/5 | Retail and institutional pharmacy is a highly digitized sector where automated label printing has already been adopted nearly universally for years. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-assisted label generation can flag potential issues (drug interactions, dosage anomalies) while a technician reviews, significantly boosting accuracy and productivity without removing human oversight. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Where manual entry still occurs, computer systems with autofill, barcode scanning, and templates substantially speed up and reduce errors in label preparation. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Typing prescription labels is a highly structured, rule-based task with minimal variation. Current OCR, structured data extraction, and label-printing software can fully automate this end-to-end, meeting the 50% time-saving bar with existing pharmacy management systems. |
| Task automatability | claude-sonnet-5 | 5/5 | Label generation from structured prescription data is a routine templated text/print task fully handled by existing pharmacy management software with minimal human input. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While some regulatory oversight applies to pharmacy operations, label generation itself is not a licensed or legally reserved task—technicians already perform it under pharmacist supervision. Integration into existing systems faces minor organizational friction but no hard legal barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | While pharmacist verification of the final label is often required, the actual typing/printing step itself has no licensing requirement and is already delegated to software in most pharmacies. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Once integrated into a pharmacy's existing management system, the marginal cost of automated label generation is negligible (minimal inference or API calls). This is substantially cheaper than the loaded wage of a pharmacy aide performing this task. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Automated label printing via existing software costs a tiny fraction of a cent per label compared to manual typing labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Pharmacy management and label-printing systems are mature, deployed products in widespread use across retail and hospital pharmacies today. Barcode generation, drug name lookup, and automatic label printing are production-standard functionality. |
| Technical feasibility today | claude-sonnet-5 | 5/5 | Pharmacy dispensing systems (e.g., from major chains and PBMs) already auto-generate and print prescription labels as standard production functionality. |
Operate cash register to process cash or credit sales.
92CI 92–92 · exposure 100 · augmentation 63 · importance 4.5/5 · click for rater detail
Operate cash register to process cash or credit sales.
92| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Retail and pharmacy sectors have rapidly and extensively adopted automated and self-checkout payment systems; major chains have implemented these technologies, representing high digitization and faster adoption patterns. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Retail and pharmacy chains have widely adopted self-checkout and automated payment systems over the past decade, representing fast, broad adoption though not universal in small independent pharmacies. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-powered POS systems assist pharmacy staff by streamlining transaction processing, flagging suspicious activity, managing inventory in real time, and reducing manual data entry, substantially raising productivity when the human remains in a supervisory role. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Where humans still operate registers, modern POS software assists with scanning, pricing, and transaction processing, offering moderate productivity gains rather than full transformation. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Current POS systems and AI-integrated checkout solutions (e.g., self-checkout kiosks, mobile payment processors) fully automate cash and credit transaction processing at scale with >50% time savings and equal or better accuracy compared to manual operation. |
| Task automatability | claude-sonnet-5 | 5/5 | Operating a cash register/POS terminal to process cash or credit sales is a routine transactional task fully handled by automated POS systems, self-checkout kiosks, and card readers today with no meaningful quality loss. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While self-checkout and automated payment systems exist and are widely adopted, some jurisdictions require human supervision of age-restricted sales (alcohol, tobacco) and regulatory requirements around cash handling create minor friction, though not a hard legal barrier to automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human cashier; some friction exists from theft/loss-prevention concerns, elderly customer preferences, and need for occasional human backup for exceptions. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Modern POS infrastructure and payment processing operate at pennies per transaction when amortized, orders of magnitude cheaper than the loaded hourly wage of a pharmacy aide. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Automated POS/self-checkout hardware and software cost far less per transaction than a human wage once amortized, making this an order-of-magnitude cheaper solution at scale. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Mature, production-grade systems for automated transaction processing are deployed across thousands of retail and pharmacy locations worldwide, demonstrating reliable performance in high-volume environments. |
| Technical feasibility today | claude-sonnet-5 | 5/5 | Self-checkout and automated payment terminals are deployed at massive scale across retail and pharmacy settings, reliably processing cash and credit transactions in production. |
Process medical insurance claims, posting bill amounts and calculating copayments.
74CI 70–79 · exposure 75 · augmentation 63 · importance 4.2/5 · click for rater detail
Process medical insurance claims, posting bill amounts and calculating copayments.
74| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Healthcare and pharmacy operations have digitized rapidly; major pharmacy chains and PBMs are actively deploying claim automation and RPA. Production adoption is underway in information-intensive healthcare organizations, though smaller independent pharmacies lag. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Retail and institutional pharmacy is heavily digitized with near-universal use of e-claims and automated adjudication systems already embedded in workflows for years. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assists pharmacy aides by auto-populating claim fields, flagging discrepancies, and pre-calculating copayments, significantly speeding review and reducing manual data entry while the aide remains responsible for validation and exception handling. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Existing systems assist aides significantly in calculating and posting amounts, but human review remains needed for exceptions, denials, and patient communication. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can extract claim data from documents, validate insurance information, post amounts, and calculate copayments with high accuracy using OCR and structured data processing. While some edge cases or policy exceptions may require human review, the core task meets the ≥50% time-saving threshold with current systems. |
| Task automatability | claude-sonnet-5 | 4/5 | Insurance claim processing and copay calculation are largely rule-based, structured data tasks well-suited to automation via pharmacy management software and claims-adjudication systems, though edge cases (rejected claims, prior authorizations) need human handling. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Regulatory requirements around claim accuracy, audits, and pharmacy licensing create some friction; HIPAA compliance and potential liability for billing errors introduce oversight and governance overhead, but no hard legal requirement mandates human sign-off on every transaction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for this specific clerical task, though pharmacy oversight regulations and payer contract rules create some procedural friction requiring accurate handling. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven claim processing costs (per claim processed) are orders of magnitude cheaper than human pharmacy aides, especially at scale, with minimal oversight overhead once systems are validated and integrated. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated claims adjudication software processes thousands of transactions per second at a fraction of the cost of manual processing, though software licensing and integration costs keep it from being a full order-of-magnitude cheaper in all cases. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed RPA and healthcare AI platforms already handle insurance claim processing and payment calculations in production pharmacy settings. Solutions like robotic process automation and insurance claim software are mature, though integration requirements and policy variations may necessitate some manual oversight. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Pharmacy management systems (e.g., real-time claims adjudication with PBMs) already automate copay calculation and claims submission in production at most retail pharmacies today. |
Prepare, maintain, and record records of inventories, receipts, purchases, or deliveries, using a variety of computer screen formats.
74CI 72–75 · exposure 75 · augmentation 75 · importance 3.8/5 · click for rater detail
Prepare, maintain, and record records of inventories, receipts, purchases, or deliveries, using a variety of computer screen formats.
74| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Large pharmacy chains and hospital systems have already adopted automated inventory management and receipt processing at scale; adoption is fastest in digitized retail and healthcare settings, though independent and smaller pharmacies lag. Production deployment is common in major chains and institutional pharmacy. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Retail and healthcare pharmacy settings have moderate digitization with inventory software common in larger chains, but smaller independent pharmacies still rely on manual or semi-manual processes. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly assists pharmacy aides by auto-populating inventory records, flagging discrepancies, and suggesting reorder quantities, allowing the aide to focus on physical stock checks and exception handling rather than manual data entry. This substantially raises productivity while keeping the human in oversight and decision-making roles. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Existing inventory and pharmacy management software significantly streamlines record-keeping, reducing manual entry and errors while the aide still oversees exceptions and physical verification. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | This task involves highly structured data entry, inventory tracking, and record maintenance on standardized computer systems—work that current AI and RPA systems can perform end-to-end with significant time savings. Record preparation and inventory documentation are largely rule-based and repetitive, though some oversight may be needed for discrepancies or exceptions. |
| Task automatability | claude-sonnet-5 | 4/5 | Inventory tracking, recording receipts/purchases/deliveries and updating records via computer screens is a structured data-entry and reconciliation task well-suited to automation with existing inventory management and pharmacy software integrated with barcode/RFID scanning. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or authorization barriers exist for automating inventory records in pharmacy; however, some organizations impose oversight requirements for receipt reconciliation and the pharmacy may require human sign-off on critical discrepancies. Liability for inventory errors is typically organizational rather than personal, lowering friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for inventory record-keeping itself, though pharmacies may have internal controls or controlled-substance tracking regulations that require some human verification and accountability. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-powered inventory management and automated record-keeping cost substantially less per transaction than human data entry labor once deployed. Integration costs are modest relative to the wage of a pharmacy aide, making the cost ratio heavily favorable to automation. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated inventory systems and scanning technology cost far less per transaction than a human aide's wage for repetitive record-keeping, though initial software/hardware integration adds some cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed pharmacy management systems and inventory software already integrate with RPA and AI-assisted data entry tools; many hospitals and chains use automated inventory tracking and receipt processing. Products like inventory management platforms with OCR and automated logging are in production use, though some require human verification of edge cases. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Pharmacy inventory management systems (e.g., automated dispensing cabinets, pharmacy ERP software) are widely deployed in production and reliably track inventory, receipts, and deliveries today, though some manual reconciliation and exception handling remain. |
Perform clerical tasks, such as filing, compiling and maintaining prescription records, or composing letters.
66CI 60–72 · exposure 70 · augmentation 75 · importance 3.9/5 · click for rater detail
Perform clerical tasks, such as filing, compiling and maintaining prescription records, or composing letters.
66| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Pharmacies have adopted document management and e-prescription systems broadly, but clerical task automation (RPA, LLM-based letter composition) remains in pilot and early rollout phases rather than mature production at scale. Adoption is occurring but not yet industry-standard. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Healthcare/pharmacy support roles are a moderately digitized but conservative sector; clerical automation adoption in small retail pharmacies lags behind information/finance sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | Current AI tools meaningfully augment pharmacy aides on record organization, template-based letter drafting, and data entry suggestion, raising their per-task throughput substantially while they remain responsible for review and compliance sign-off. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully speed up filing organization, record compilation, and letter composition for pharmacy aides while a human still oversees accuracy and compliance. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Filing, record compilation, and prescription maintenance are highly structured, rule-based clerical tasks that current AI and document-processing systems can automate end-to-end. Letter composition is also well within reach of LLMs. This task easily meets the ≥50% time-saving threshold with standard RPA and language model tools. |
| Task automatability | claude-sonnet-5 | 4/5 | Filing, compiling records, and composing letters are largely routine document/data-management tasks that current AI (document management systems, LLM drafting tools) can handle with substantial time savings, though some integration with pharmacy-specific systems is needed. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Some regulatory constraints apply: certain prescription records require auditable human review and pharmacist sign-off under pharmacy law, and HIPAA compliance adds oversight friction. However, the automation itself is not legally barred—clerical automation is standard in healthcare IT, mitigating full barrier strength. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Prescription records involve some regulatory recordkeeping requirements and privacy considerations (e.g., HIPAA), creating moderate friction, but the clerical task itself isn't restricted to licensed personnel. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated document processing and LLM-based letter composition cost far less than pharmacy aide wages (typically $28k–$35k annual loaded cost) when amortized across tasks. API costs and integration overhead are modest relative to full-time clerical labor. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated filing and letter drafting via software/AI is far cheaper per unit of clerical output than paying a human aide's wage for the same volume of routine tasks. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature products (document management systems, OCR, RPA platforms, and LLMs) are already deployed in pharmacy settings for record-keeping and document generation, though integration and oversight remain necessary. Production deployments exist with manageable error rates on routine filing and standardized letter templates. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products exist for records management and letter drafting (e.g., pharmacy management software, AI writing assistants), but pharmacy-specific record compilation tied to regulated prescription data is not yet a mature, widely deployed end-to-end AI product. |
Receive, store, and inventory pharmaceutical supplies or medications, check for out-of-date medications, and notify pharmacist when inventory levels are low.
65CI 55–75 · exposure 67 · augmentation 75 · importance 4.1/5 · click for rater detail
Receive, store, and inventory pharmaceutical supplies or medications, check for out-of-date medications, and notify pharmacist when inventory levels are low.
65| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Major retail chains (CVS, Walgreens), hospital systems, and mail-order pharmacies have invested heavily in automated inventory and dispensing systems over the past decade. Adoption is particularly deep in large, digitized organizations, though smaller independent pharmacies lag behind, putting overall velocity in the fast-to-moderate range. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Retail and hospital pharmacy chains have adopted inventory automation at a moderate pace, though many independent pharmacies still rely on manual or semi-manual processes. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-driven inventory dashboards, automated alert systems, and real-time stock visibility tools substantially amplify a pharmacy aide's productivity by eliminating manual counting and reducing time spent on manual record-keeping. Aides can focus on receiving, verification, and problem-solving while the system handles routine checks and notifications. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Automated inventory systems significantly help pharmacy aides track stock levels, flag expirations, and generate reorder alerts, improving efficiency while humans still perform physical tasks and verification. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Inventory management, stock tracking, and expiration date checking are highly routine and rule-based tasks that current AI-enabled systems (barcode scanners, RFID, inventory software with computer vision) can largely automate end-to-end. Low notification thresholds require minimal human judgment, though quality verification and physical handling remain human-dependent elements that prevent a full 5. |
| Task automatability | claude-sonnet-5 | 3/5 | Inventory tracking, expiration date checks, and low-stock alerts can be substantially automated via barcode/RFID scanning and inventory management software, but physical receiving/stocking still requires human handling. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Pharmacy operations are regulated by state boards and the DEA, with controlled-substance handling and chain-of-custody requirements that impose compliance burden. However, inventory and expiration tracking itself is not legally restricted to licensed humans—automation is permitted provided it is auditable and validated, so barriers are moderate rather than hard. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for inventory management itself, though pharmacy regulations around controlled substance tracking create some compliance overhead and record-keeping requirements. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated inventory systems (hardware + software + integration) have front-loaded capital costs but deliver substantial per-transaction savings over time compared to pharmacy aide labor, particularly in high-volume settings. The ongoing inference and oversight cost is typically much lower than the loaded wage of a full-time aide per equivalent output. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Software licensing and hardware (scanners) costs are moderate; savings on labor are real but the physical component still requires paid staff time, keeping cost roughly comparable to partial automation savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed pharmacy management systems, automated dispensing cabinets, barcode verification software, and inventory tracking platforms are mature and widely in production use. Computer vision for expiration date recognition and RFID-based stock management are operationally reliable in many retail and hospital pharmacies today, though some smaller facilities still rely on manual processes. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Pharmacy inventory management systems with automated reorder alerts and expiration tracking are widely deployed in retail and hospital pharmacies today. |
Greet customers and help them locate merchandise.
52CI 35–69 · exposure 45 · augmentation 63 · importance 4.7/5 · click for rater detail
Greet customers and help them locate merchandise.
52| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Retail and pharmacy sectors show slow, cautious adoption of fully autonomous customer-facing AI; pilots and kiosks exist but most pharmacies still rely on human greeters due to customer experience and organizational inertia. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Retail/pharmacy settings are slower to adopt AI-driven customer interaction for physical tasks compared to purely digital service sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can assist human aides by providing real-time inventory lookup and product recommendations, significantly boosting their ability to serve customers faster and more accurately while they maintain the human interaction element. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Store apps, digital signage, and AI-assisted inventory lookups can help aides quickly find product locations, improving efficiency while the aide still interacts with the customer. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI chatbots and retail assistants can handle customer greetings and product location through natural language and inventory databases; this would save >50% of time for routine inquiries, though exceptional cases (complex requests, accessibility needs) may require human intervention. |
| Task automatability | claude-sonnet-5 | 2/5 | Physically greeting customers and guiding them to items in a store requires presence, mobility, and spatial navigation that current AI cannot perform end-to-end; digital kiosks/apps offer only partial substitution.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Customer preference for human greeting and store policy can delay adoption, but no legal mandate requires a human aide to greet; regulatory barriers are minimal. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for this specific task, but customer service expectations and physical store logistics create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-powered greeter systems have minimal marginal inference cost ($0.01–0.10 per interaction) compared to pharmacy aide wages ($15–18/hour loaded), making automation orders of magnitude cheaper. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Kiosk/app-based directory systems are cheap to run but don't fully replace the interpersonal greeting and physical guidance task, so cost comparison favors the human for full task completion. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Chatbot systems and automated kiosks exist in pharmacies and retail, but performance varies on non-standard requests and spatial navigation; they are deployed in some chains but not yet universally reliable or widespread for full task coverage. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some retail apps and store maps offer wayfinding assistance, but no deployed product reliably replaces a human physically greeting and escorting customers in a pharmacy setting. |
Accept prescriptions for filling, gathering and processing necessary information.
51CI 45–56 · exposure 50 · augmentation 75 · importance 4.5/5 · click for rater detail
Accept prescriptions for filling, gathering and processing necessary information.
51| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Large pharmacy chains and healthcare systems have actively deployed automated prescription intake, routing, and data-entry systems; adoption is deep in corporate retail and hospital settings, though slower in independent pharmacies. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Retail and healthcare pharmacy settings have adopted e-prescribing and automated systems steadily, but full automation of intake remains uneven across pharmacy chains and independent pharmacies. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-assisted prescription intake (OCR scanning, form auto-fill, insurance verification suggestions) materially speeds aide workflow and reduces transcription errors while the aide remains responsible for validation, flagging issues, and patient communication. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted intake systems, OCR for handwritten scripts, and automated insurance verification meaningfully speed up the gathering/processing workflow for aides who still review and finalize submissions. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | A pharmacy AI system could automate receipt, initial validation, and data entry of prescriptions with 50%+ time savings, but manual verification of ambiguous prescriptions, insurance coverage issues, and patient contact for clarifications would require human oversight, limiting full end-to-end automation. |
| Task automatability | claude-sonnet-5 | 3/5 | Intake and data entry portions (capturing prescription details, patient info, insurance data) can be automated via OCR, e-prescribing integration, and chatbots, but verifying legitimacy, handling ambiguous handwriting, and patient interaction still require human involvement. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Pharmacy operations face regulatory requirements (e.g., pharmacist verification, HIPAA compliance, DEA rules) and liability concerns that mandate human review and sign-off; patient preference for human contact and state licensing rules create organizational friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While pharmacy aides are not typically licensed pharmacists, prescription handling is regulated (controlled substances, HIPAA, verification requirements) and often requires oversight by a pharmacist, creating moderate compliance friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Prescription processing via automated systems costs significantly less than human aide labor per task when amortized across high-volume pharmacies; infrastructure is inexpensive relative to pharmacy aide wages, though integration and oversight add overhead. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Software licensing and integration costs are moderate relative to a low-wage aide's pay, and human oversight is still needed for exceptions, keeping cost savings only moderate rather than an order of magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed pharmacy management systems and OCR-based prescription intake tools exist and handle routine prescriptions reliably, but material error rates persist with handwritten or complex prescriptions, and edge cases still require human judgment in production settings. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | E-prescribing systems and pharmacy management software already automate much of the data capture and processing, but exceptions (illegible scripts, insurance issues, controlled substances) still routinely require staff intervention. |
Compound, package, and label pharmaceutical products, under direction of pharmacist.
49CI 16–81 · exposure 58 · augmentation 38 · importance 4.3/5 · click for rater detail
Compound, package, and label pharmaceutical products, under direction of pharmacist.
49| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Hospital and chain retail pharmacies have rapidly deployed automation over the past 10–15 years, with production systems in widespread use. Compounding and dispensing robots are now standard in major healthcare systems and large pharmacy chains, representing deep, fast adoption in digitized healthcare settings. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Healthcare and pharmacy settings, especially smaller retail pharmacies, have been slow to adopt automation for physical dispensing tasks compared to information-sector AI adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI and automation augment pharmacists' oversight (e.g., error-checking, real-time inventory), but for the aide role itself (mixing, labeling, packaging), systems primarily replace rather than assist human effort. Minimal augmentation of the aide's own task performance. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI/software can help with label accuracy, dosage calculations, and inventory tracking, providing modest assistance, but does not transform the core physical compounding and packaging work. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | The task consists of well-defined, repetitive physical and information-handling steps (measuring, mixing, packaging, labeling) that robotic systems can perform end-to-end with significant time savings. Modern pharmacy automation (dispensing robots, counting machines, label printers) already achieve >50% time savings at equal or higher accuracy compared to manual work. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical compounding, packaging, and labeling requires manual dexterity, handling of physical materials, and precise physical execution that current AI systems cannot perform end-to-end; software can assist with labeling accuracy checks but not the physical task itself.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Pharmacist oversight is legally required for compounding and dispensing; compounds must meet state pharmacy board regulations and USP <797> standards. Liability asymmetry and regulatory approval of the automation itself create moderate-to-substantial friction, though automation is permitted and widely adopted under pharmacist supervision. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Pharmaceutical compounding and dispensing are subject to strict regulatory oversight, licensing, and mandatory pharmacist supervision, creating strong legal and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Pharmacy automation equipment has significant capital costs but amortized per unit, the per-dose cost of automation is substantially lower than the loaded wage of a pharmacy aide ($15–18/hour). Over 3–5 years, automation typically reaches 5–10× cost advantage per task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic compounding/dispensing systems exist in some large pharmacies but are capital-intensive and still require human oversight, making all-in AI/automation costs comparable to or higher than aide wages in most settings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Deployed robotic pharmacy systems (by companies like Omnicell, Baxter, and others) demonstrably perform compounding, packaging, and labeling in hospital and retail pharmacy production at scale today, with high reliability and regulatory approval. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product independently compounds or packages pharmaceuticals; this remains a physical, hands-on task performed by humans under pharmacist supervision. |
Answer telephone inquiries, referring callers to pharmacist when necessary.
38CI 25–51 · exposure 38 · augmentation 63 · importance 4.4/5 · click for rater detail
Answer telephone inquiries, referring callers to pharmacist when necessary.
38| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Most pharmacies retain human call screening; adoption of AI call routing in healthcare settings is slow due to risk aversion, regulatory uncertainty, and reliance on human judgment for triage. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Retail pharmacy and healthcare support roles have historically been slower to adopt AI-driven call automation compared to finance or tech, though call-center AI is growing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist aides by suggesting scripts, categorizing common questions, or flagging keywords that warrant escalation, moderately raising throughput without removing the human decision-maker from the loop. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI call-routing and transcription tools can meaningfully assist pharmacy aides by pre-screening calls, drafting responses, and flagging urgent issues for pharmacist attention. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can classify simple inquiries (refills, hours), most pharmacy calls require judgment about when to escalate to a pharmacist, understanding context, and handling complex patient concerns—tasks where current AI lacks reliability for safe delegation without human oversight. |
| Task automatability | claude-sonnet-5 | 3/5 | AI phone/chat systems can triage routine calls and route pharmacist-required queries, but reliably distinguishing which calls need pharmacist escalation in a healthcare context requires judgment and verification, limiting full end-to-end automation today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulatory and liability barriers are substantial: pharmacy regulations require licensed personnel for medication-related advice, and misrouting or misunderstanding a caller's needs carries legal and patient-safety risk; organizational preference for human judgment in medical contexts is strong. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement to answer phones, but health-information handling triggers privacy/compliance concerns (HIPAA) and liability risk if routing errors delay urgent care, creating moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI infrastructure, maintenance, and oversight labor may approach pharmacy aide wages but does not undercut them significantly; the cost of errors (missed serious inquiries) adds overhead. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI voice/chat answering systems cost a fraction of an hourly wage per call handled, though integration with pharmacy systems and compliance oversight adds some cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Chatbots and IVR systems exist but struggle with call routing decisions, natural conversation, and knowing when escalation is medically necessary; no mature product reliably replaces pharmacy telephone intake at scale. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Automated phone systems and IVR/AI voice agents are deployed in pharmacies and call centers, but many pharmacies still rely on human aides for nuanced routing and patient reassurance, so deployment is partial and inconsistent. |
Unpack, sort, count, and label incoming merchandise, including items requiring special handling or refrigeration.
31CI 19–44 · exposure 28 · augmentation 50 · importance 4.0/5 · click for rater detail
Unpack, sort, count, and label incoming merchandise, including items requiring special handling or refrigeration.
31| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Pharmacies are digitized but traditionally conservative in automation of inventory tasks. Adoption of full automation is slow; most use hybrid models with barcode scanning and human physical work. Large chain pharmacies pilot more, but deployment at scale remains limited. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Retail pharmacy back-office logistics is a low-digitization, physically intensive sector with minimal AI/robotics adoption for this kind of manual task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-assisted barcode scanning, automated inventory tracking, and labeling systems significantly boost pharmacy aide productivity by reducing manual counting and documentation errors. Computer vision can flag items needing special handling, streamlining the aide's prioritization and decision-making. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with inventory tracking, barcode scanning suggestions, or generating labels, but the core physical unpacking and sorting sees little productivity transformation from AI tools. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | Physical unpacking and sorting require robotic manipulation systems, which exist but are not yet standard in pharmacy settings. Counting and labeling of standard items can be partially automated with vision systems, but special handling and refrigeration requirements introduce variability that current systems handle inconsistently, limiting full end-to-end automation. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical unpacking, sorting, and handling of items (especially those requiring refrigeration) requires manual dexterity and mobility that current AI systems cannot perform; only the labeling/data-entry portion could be assisted digitally.: |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Pharmacy operations are regulated (controlled substances, temperature monitoring, traceability), and many organizations require human verification of received items. However, the task itself does not mandate a licensed professional—only oversight—creating moderate friction rather than hard legal barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | While handling controlled substances or special refrigeration items may involve some regulatory tracking requirements, the physical task itself has no strict licensing requirement, though inventory accuracy matters for compliance. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Barcode readers and labeling systems are relatively inexpensive, but the robotics needed for physical unpacking and sorting remain capital-intensive. The all-in cost (hardware, integration, maintenance, oversight) currently exceeds the loaded wage of a pharmacy aide in most settings. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Physical automation (robotics) for unpacking and sorting variable merchandise is far more expensive than human labor for this low-wage task, given current robotics costs and lack of standardization. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While barcode scanning for counting is deployed widely, full automation of unpacking, sorting, and conditional labeling based on storage requirements remains largely at pilot stage in pharmacies. Existing systems struggle with the diversity of pharmaceutical packaging and special-handling rules. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed general-purpose robotic products reliably performing pharmacy receiving/unpacking/sorting tasks in production settings today. |
Restock storage areas, replenishing items on shelves.
26CI 19–33 · exposure 20 · augmentation 25 · importance 4.0/5 · click for rater detail
Restock storage areas, replenishing items on shelves.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Pharmacy operations remain relatively low-digitization, physical-task-heavy environments with limited adoption of robotic or AI restocking systems. Large retail chains have piloted some automation, but deployment is laggard relative to information or finance sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Pharmacy retail and physical stocking tasks are in a low-digitization, labor-intensive sector with minimal AI/robotics adoption for this specific function. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by predicting stock-outs or optimizing restocking routes, but the core task is manual shelf placement; current systems offer minimal productivity lift to a human aide already performing the physical work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-driven inventory management systems can help predict restocking needs and flag low-stock items, offering some planning assistance, but do not assist the physical act of shelving itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While robotic systems can physically move and place items, current off-the-shelf AI lacks reliable integration with pharmacy inventory systems, variable shelf layouts, and fragile item handling in a way that meets the 50% time-saving bar. Robots exist but are specialized and expensive to deploy in the chaotic pharmacy environment. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical restocking requires manipulation of objects, shelving, and inventory placement, which current general-purpose AI cannot perform end-to-end; only narrow robotic pilots exist in warehouse contexts, not pharmacy settings.imestamp |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Pharmacies operate under regulatory oversight and liability rules that could complicate automated restocking (inventory tracking, controlled substance areas), and human oversight is typically expected. However, no explicit legal barrier prevents AI/robotic assistance in non-controlled areas. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically bars automation of shelf restocking, but physical environment constraints (narrow spaces, fragile/controlled substances, safety protocols) create meaningful practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic systems capable of pharmacy restocking (perception, arm control, integration) cost tens of thousands to hundreds of thousands of dollars upfront, with ongoing maintenance, versus paying an aide minimum wage (~$25k–30k annually). The economics do not favor automation for this lower-wage task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic solutions for this specific physical task would require costly specialized hardware and integration far exceeding the low wage cost of a human aide performing this manual task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Automated inventory restocking is mostly research or pilot stage in pharmacies; deployed warehouse robots exist elsewhere but are not standard in pharmacy operations. No mature, widely-used product reliably handles pharmacy-specific restocking at scale in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical shelf restocking in pharmacy aide roles; robotic shelf-stocking remains largely experimental or limited to large retail warehouses, not pharmacies. |
Maintain and clean equipment, work areas, or shelves.
17CI 10–24 · exposure 8 · augmentation 13 · importance 3.5/5 · click for rater detail
Maintain and clean equipment, work areas, or shelves.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Pharmacy automation adoption focuses on dispensing, inventory, and labeling; physical cleaning remains performed by human staff. Retail and healthcare settings show low adoption of general-purpose cleaning robots due to cost and reliability concerns. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Physical maintenance and cleaning tasks in retail/healthcare settings show minimal AI or robotic adoption, as this sector for physical tasks lags significantly behind digital work automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance for routine physical cleaning and maintenance; scheduling or inventory-tracking tools can help plan tasks, but do not materially amplify the core cleaning activity itself. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for physical cleaning and organizing tasks; this is not a cognitive or information-processing task that current AI tools can meaningfully support. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Cleaning and maintaining physical spaces requires dexterous manipulation, spatial navigation, and adaptation to variable environments. Current AI lacks reliable embodied robotics for general cleaning tasks; specialized industrial robots exist only in highly controlled settings, not general pharmacy environments. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical cleaning and organizing of equipment, work areas, and shelves requires manual dexterity and mobility that current AI systems, absent robotics, cannot perform. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Pharmacies have health and safety regulations (sanitation standards) that currently expect human oversight and accountability, though automation could theoretically satisfy standards if proven compliant. Liability for contamination or equipment damage adds organizational friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for cleaning tasks, though pharmacy environments may have hygiene/safety protocols; the barrier is primarily physical/technological rather than regulatory. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized cleaning robots capable of operating in pharmacy settings cost tens of thousands of dollars upfront plus ongoing maintenance, far exceeding the loaded hourly wage of pharmacy aides for similar output. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute for this manual task at comparable cost; specialized cleaning robots exist but are far more expensive and less flexible than human labor for this scope. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No mainstream deployed product performs general pharmacy equipment cleaning and area maintenance reliably today. Cleaning robots operate only in narrow, pre-mapped domains and lack the flexibility to handle pharmacy-specific equipment, shelf organization, and surface variability. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product cleans pharmacy equipment or shelves; this remains a purely physical task performed by humans or occasionally simple robotic cleaners, not AI systems generally available. |
Deliver medication to treatment areas, living units, residences, or clinics, using various means of transportation.
14CI 5–23 · exposure 13 · augmentation 38 · importance 3.8/5 · click for rater detail
Deliver medication to treatment areas, living units, residences, or clinics, using various means of transportation.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Pharmacy aide roles remain predominantly in healthcare organizations (hospitals, clinics, long-term care) that have been slow to automate this task. Most facilities continue using human aides; pilot programs exist but production-scale deployment of autonomous medication delivery remains rare, indicating slow sector-wide adoption. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Healthcare facility logistics and physical delivery tasks are a low-digitization, physical-labor sector with minimal AI/robotic adoption for this specific task at present. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted route optimization, inventory tracking, and delivery scheduling can improve pharmacy aide productivity on planning and logistics aspects of the task. However, the physical delivery and interpersonal handoff components limit augmentation's scope, making it useful for partial task improvement rather than transformative productivity gains. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help optimize delivery routes or track inventory, but it offers little direct assistance to the physical act of transporting medication to treatment areas. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While route planning and tracking could be partially automated, the task requires physical delivery to varied locations (treatment areas, residences, clinics) and navigating real-world obstacles. Current autonomous delivery systems exist for controlled environments but lack the flexibility and reliability needed for healthcare settings where medication security and proper handoff procedures are critical. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical delivery of medications requires locomotion and handling of physical objects through real-world environments, which current AI systems cannot perform without embodied robotics infrastructure that is not generally deployed for this purpose.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Medication delivery involves regulatory compliance (Chain of Custody, DEA regulations, patient verification), potential liability for lost or misdelivered controlled substances, and security requirements that create strong legal and operational barriers to full automation. Healthcare facilities also face liability concerns and regulatory scrutiny around autonomous medication handling. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Medication handling and delivery is subject to safety, chain-of-custody, and often regulatory requirements ensuring correct medications reach the correct patients, requiring accountable human or tightly controlled systems. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Autonomous delivery systems and robots currently have high upfront capital and maintenance costs that exceed the loaded wage of pharmacy aides in most settings. Integration, cybersecurity, and oversight infrastructure add further expense, making full automation more costly than human delivery in typical healthcare organizations. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no general AI system that can substitute for a human delivering medication, so any comparison would require expensive robotic hardware and infrastructure exceeding the cost of human labor for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Autonomous delivery robots exist in limited healthcare deployments, but they are not yet reliably deployed at scale for medication delivery across diverse real-world locations. Current systems struggle with variable indoor/outdoor navigation, security requirements, and the need to interface with human recipients in compliance with pharmacy protocols. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product physically transports medications to treatment areas or residences today; this remains a physical logistics task performed by humans or occasionally specialized robots in narrow hospital settings. |
Related occupations — Healthcare Support
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.