Dietetic Technicians
29-2051.00Median wage $37,640/yr31,560 employed (US)Rank #290 of 923 scored · top 31% by substitution
Assist in the provision of food service and nutritional programs, under the supervision of a dietitian. May plan and produce meals based on established guidelines, teach principles of food and nutrition, or counsel individuals.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution34
Exposure31
Augmentation63
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
13 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
0%
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%32
panel mean rating 2.3/5 → substitution pressure 32/100
Technical feasibility todayw 20%30
panel mean rating 2.2/5 → substitution pressure 30/100
Cost vs. human wagew 15%34
panel mean rating 2.3/5 → substitution pressure 34/100
Adoption barriersw 20%inverted — strong barriers lower the score47
panel mean rating 3.1/5 (barrier strength) → substitution pressure 47/100
Sector adoption velocityw 10%25
panel mean rating 2.0/5 → substitution pressure 25/100
Task breakdown (13 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Determine food and beverage costs and assist in implementing cost control procedures.
62CI 52–72 · exposure 62 · augmentation 75 · importance 4.4/5 · click for rater detail
Determine food and beverage costs and assist in implementing cost control procedures.
62| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Healthcare food service and catering operations show moderate adoption of automated cost tracking and inventory management software, but full end-to-end displacement remains inconsistent. Larger healthcare systems and foodservice contractors have adopted these tools, but many smaller facilities and institutional kitchens still rely on manual or semi-manual processes. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Healthcare and institutional food service sectors are slower AI adopters compared to finance or tech, with cost-control tools often lagging behind more digitized industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assistants can significantly enhance a dietetic technician's productivity by automatically pulling cost data, calculating variances, flagging outliers, and suggesting cost-control actions, allowing the technician to focus on strategy and stakeholder communication rather than manual arithmetic and data gathering. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI spreadsheet and analytics tools can significantly speed up cost calculations, trend analysis, and identification of cost-saving opportunities while the technician retains decision-making responsibility. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can readily extract pricing data, calculate ingredient costs, analyze variance trends, and generate cost control recommendations by processing supplier invoices, inventory records, and menu systems. The task involves structured data analysis and rule-based cost procedures, which are highly automatable; minimal human judgment is required for routine cost calculation and monitoring. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can compute costs, analyze spreadsheets, and suggest cost control measures given clean data, but requires integration with facility-specific purchasing/inventory systems and human validation of context-specific tradeoffs. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers exist for automating cost calculation and analysis itself. However, organizational friction may arise if human oversight or sign-off is required for implementing cost control procedures, and some facilities may prefer technician involvement in policy decisions. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for cost calculations, though facility policies and financial accountability create some oversight friction favoring human involvement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | The inference cost for analyzing pricing data, calculating food costs, and monitoring expense variance is minimal compared to the loaded wage of a dietetic technician. Once integrated into existing food service systems, the per-task cost is a fraction of human labor. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-assisted spreadsheet/analytics tools are cheap to run, but data integration, verification, and on-site coordination still require paid technician time, making net savings moderate rather than dramatic. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed systems for food service inventory management, cost accounting, and procurement analytics already perform cost determination and generate control recommendations in production settings. Software used by food service operations, hospitals, and catering firms routinely automates cost tracking and flags overspend, though integration and customization may require setup. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Spreadsheet AI tools and food-service management software with cost analytics exist and are used, but fully automated end-to-end cost control implementation in dietetics settings is not common practice yet. |
Provide dietitians with assistance researching food, nutrition, or food service systems.
62CI 51–72 · exposure 62 · augmentation 88 · importance 4.0/5 · click for rater detail
Provide dietitians with assistance researching food, nutrition, or food service systems.
62| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Healthcare and food service sectors show moderate but growing AI adoption in administrative and research tasks. Large hospital systems and food service chains are piloting AI research tools, but production deployment remains inconsistent; adoption lags faster-adopting sectors like finance or tech. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Healthcare and food service settings, where dietetic technicians work, have historically been slower and more cautious adopters of AI tools compared to pure information-sector work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically assists research productivity by rapidly synthesizing nutrition literature, cross-referencing databases, and compiling food composition data while the dietitian retains judgment on clinical appropriateness and system design—transforming research efficiency while keeping humans in the loop. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools substantially speed up literature searches, summarization, and data compilation, letting technicians focus on verification and application rather than manual research. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can efficiently research nutritional databases, food compositions, and service system documentation with high accuracy and speed, achieving well over 50% time savings at equivalent quality. Dietitians can leverage LLMs and specialized nutrition databases to automate literature review, food composition lookup, and system analysis with minimal human oversight. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can rapidly synthesize nutrition literature, summarize studies, and compile food service data, covering much of the research legwork, though verification and context-specific application still require human judgment.4points from AI literature tools.5removed for accuracy risk in clinical nutrition contexts. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While research assistance faces moderate barriers (quality assurance, verification requirements, organizational protocols for clinical evidence), there are no legal licensure barriers to AI performing research work itself. Dietitians retain decision authority, but adoption requires internal validation workflows. |
| Adoption barriers | claude-sonnet-5 | 2/5 | This is a support task not requiring licensure itself, though final dietary recommendations from a licensed dietitian still require human sign-off, creating mild oversight friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-powered research tools (cloud LLMs, nutrition database subscriptions) cost pennies per query compared to the loaded wage of a human researcher or technician performing equivalent literature review and data compilation work, yielding a cost advantage of several orders of magnitude. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-based research assistance (search, summarization, drafting) costs a small fraction of a technician's hourly wage for comparable literature or data synthesis work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature products exist (LLMs, medical literature databases, nutrition software) that reliably perform nutrition research and food composition lookups in production. Some narrowness remains around highly specialized dietetic systems, but general research assistance is deployable and widely used by healthcare organizations. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | General-purpose AI research assistants and literature-summarization tools are deployed broadly, but no nutrition-specific product reliably handles this narrow research-support role in production at scale. |
Develop job specifications, job descriptions, or work schedules.
57CI 56–59 · exposure 50 · augmentation 75 · importance 4.4/5 · click for rater detail
Develop job specifications, job descriptions, or work schedules.
57| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Healthcare and HR technology sectors show moderate adoption of AI for administrative tasks like scheduling and documentation, with pilots common but full production automation still selective in smaller dietetic departments. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Healthcare/food service settings where dietetic technicians work show slower AI adoption for administrative tasks compared to fast-adopting sectors like finance or tech. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can significantly assist technicians by generating initial drafts, proposing schedule options, and flagging compliance considerations, allowing the technician to focus on context-specific refinement and organizational fit rather than drafting from scratch. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools substantially speed up drafting of job descriptions and schedules, letting the human focus on verifying accuracy and fit for specific staffing needs. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can draft job descriptions and schedules by extracting requirements and organizing them into templates, but developing nuanced job specifications requires understanding organizational context, legal compliance, and role-specific skill levels that typically need human review and revision. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft job specifications, descriptions, and schedules quickly given inputs about role requirements, but requires human review and contextual knowledge of the specific facility/team to finalize. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No legal requirement mandates human sign-off on job specifications or schedules for non-regulated positions; organizations can freely adopt AI tools, though some HR departments may prefer human judgment for cultural and compliance reasons. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for writing job specs or schedules; some organizational approval processes exist but no hard legal barrier prevents AI-assisted drafting. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-generated initial drafts cost pennies per task, while a technician would spend hours refining descriptions and schedules; even accounting for review overhead, AI is substantially cheaper for routine specification work. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Drafting text-based documents and schedule templates via AI is far cheaper than a technician or manager spending hours on manual drafting, though oversight adds some cost back. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Tools like ChatGPT and specialized HR software can generate job descriptions and basic schedules, but deployed products still have material limitations in handling complex compliance requirements and organizational-specific customization without human oversight. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | General-purpose LLMs and HR software already generate job descriptions and scheduling drafts in production, but dietetic-specific role requirements and staffing constraints still need human customization and validation. |
Plan menus or diets or guide individuals or families in food selection, preparation, or menu planning, based upon nutritional needs and established guidelines.
38CI 25–51 · exposure 38 · augmentation 63 · importance 4.4/5 · click for rater detail
Plan menus or diets or guide individuals or families in food selection, preparation, or menu planning, based upon nutritional needs and established guidelines.
38| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption in healthcare and clinical nutrition settings remains slow; most organizations still rely on human dietitians and technicians for personalized guidance. Some consumer wellness apps use AI, but that is a different market segment and not the core occupational task. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Healthcare and food service settings employing dietetic technicians have been slower to adopt AI tools compared to fully digital sectors, with adoption mostly at pilot or consumer-app stage. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by generating candidate menus, analyzing nutrient profiles, and suggesting alternatives, reducing manual calculation burden. However, the human technician must retain control over final recommendations, client interaction, and safety verification, so augmentation is meaningful but not transformative. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can significantly speed up menu drafting and provide nutritional calculations, letting technicians focus on personalized counseling and verification. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can generate menu plans and nutritional analysis from guidelines, but the task requires ongoing adaptation to individual preferences, cultural contexts, medical contraindications, and behavioral coaching that demand human judgment and rapport. Current systems cannot reliably handle the full end-to-end personalization and guidance at equal quality. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can generate menu plans and dietary suggestions based on nutritional guidelines quite well, but individualized guidance requiring assessment of health conditions, preferences, and follow-up counseling still needs human judgment and interaction. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Dietetic technicians often require credentialing (Registered Dietitian Technician status) and work within clinical or institutional settings where liability for dietary advice is substantial. Regulatory and professional oversight, combined with legal exposure if AI-generated plans cause harm, creates strong adoption friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensure barrier for basic menu planning, but clinical dietary guidance tied to medical conditions may require oversight by credentialed professionals, creating moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The all-in cost of deploying AI (API calls, user interface, oversight by qualified staff, liability management) remains comparable to or exceeds the labor cost of a technician, especially when quality and safety guarantees are factored in. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-generated menu plans cost a fraction of a technician's time per plan, though oversight and personalization for special dietary needs add some cost back. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Nutrition planning apps and AI tools exist but are typically narrow in scope (calorie counting, meal suggestions), require significant human oversight to verify safety for special populations, and lack reliable integration with real clinical or behavioral outcomes. No mature production system fully replaces a dietetic technician's guidance. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Nutrition apps and AI meal planners (e.g., Cronometer, various diet apps with AI features) exist and are used by consumers, but professional-grade individualized dietetic guidance in clinical settings still relies on human technicians for accuracy and accountability. |
Analyze menus or recipes, standardize recipes, or test new products.
37CI 25–49 · exposure 38 · augmentation 75 · importance 3.6/5 · click for rater detail
Analyze menus or recipes, standardize recipes, or test new products.
37| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of automation in dietetics and food service is slow; many institutional kitchens and foodservice operations remain lower-tech and resistant to change. While large corporate foodservice and healthcare systems have adopted nutritional analysis tools, displacement of testing and recipe work remains minimal. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Healthcare and food service settings where dietetic technicians work have historically been slower to adopt AI tools compared to information-sector professions, though nutrition analysis software adoption is growing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI augmentation is strong: recipe calculation tools, nutrient database lookups, and menu analysis assistants significantly speed technician productivity without replacing human judgment on taste, safety validation, and recipe finalization. These systems are routinely deployed to assist rather than automate. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI nutrition analysis tools and recipe calculators significantly speed up the data analysis and standardization calculations, letting technicians focus more time on sensory testing and product development. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can partially automate menu analysis (nutrient calculation, ingredient matching) and recipe standardization (format conversion, scaling), but testing new products requires sensory evaluation, texture assessment, and safety validation that current systems cannot perform end-to-end. Automation covers perhaps 40-60% of a technician's work on these tasks. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can assist with nutritional analysis and recipe scaling calculations, but standardizing recipes and testing new products for taste, texture, and practical kitchen feasibility requires physical trial, sensory judgment, and hands-on iteration that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Food safety regulations (HACCP, FDA compliance) and product liability require documented human oversight and decision-making authority over recipes and testing. Many settings require a licensed nutritionist or registered dietitian to sign off, creating a legal/professional requirement for human involvement. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement strictly mandates a human for this specific task, though institutional quality control and food safety oversight create some organizational friction against fully automating recipe testing. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Nutritional analysis software has moderate upfront cost and ongoing licensing; integration and human oversight for interpretation add expense. The cost is comparable to or slightly less than a technician's wage, not substantially cheaper all-in. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can cheaply handle nutrient calculations, but the physical testing and sensory evaluation components still require paid staff time, so overall cost savings for the full task are modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed products exist for nutritional analysis (e.g., ESHA, Nutritionix APIs) and recipe scaling software, but they require human validation and handle only quantitative aspects. Product testing remains primarily human-dependent; no production system reliably automates sensory or safety evaluation. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Nutrition analysis software and AI-assisted recipe tools exist and are used in production, but they handle only the data/calculation portion; actual recipe testing and standardization in food service settings still relies on human technicians. |
Observe and monitor patient food intake and body weight, and report changes, progress, and dietary problems to dietician.
30CI 30–30 · exposure 25 · augmentation 50 · importance 4.7/5 · click for rater detail
Observe and monitor patient food intake and body weight, and report changes, progress, and dietary problems to dietician.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Healthcare adopts AI slowly outside radiology and billing; monitoring tasks in particular remain manual and technician-centric in most care settings. Pilot programs exist but production deployment of autonomous monitoring in clinical dietetics is uncommon. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Healthcare, especially nursing/allied health support functions, is a comparatively slow adopter of AI-driven monitoring tools relative to information-sector benchmarks, with pilots more common than production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by automating routine weight tracking, flagging statistical outliers, and summarizing intake logs, allowing technicians to focus on contextual observation and problem-solving rather than manual data entry—a meaningful but partial boost to productivity. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist by flagging weight trends, auto-populating intake logs from photos or sensor data, and drafting reports, meaningfully aiding the technician without replacing hands-on observation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can analyze digitized weight records and intake logs with near-perfect accuracy, the task critically requires direct observation of patient food intake and behavior—noticing eating patterns, difficulties, preferences, and contextual factors that demand human presence. AI cannot reliably substitute for the in-person monitoring aspect that ensures quality equivalent to human observation. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical observation, weighing, and interaction with patients cannot currently be automated end-to-end; only the data logging and reporting portions are automatable, leaving most of the hands-on task to humans.dc |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Patient privacy regulations (HIPAA), clinical accountability standards, and healthcare facility policies create meaningful friction; however, these are governance and oversight barriers rather than legal prohibitions on AI use. A dietician must still authorize dietary decisions based on reported data. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing strictly requires a human to observe and report, but clinical oversight norms, patient contact requirements, and liability for missed dietary problems create meaningful friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration of AI monitoring systems (cameras, sensors, data pipelines) plus necessary human oversight typically rivals or exceeds the loaded wage of a dietetic technician who performs the task directly, especially when accounting for false positives and clinical liability. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Sensors and monitoring hardware plus integration costs are significant relative to the low wage of a dietetic technician, and human observation/judgment is still needed for edge cases and food intake estimation. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Automated systems exist for weight tracking (scales, wearables) and can flag data anomalies, but no deployed product reliably performs end-to-end observation and reporting of patient food intake and behavioral changes at clinical quality. Most implementations remain hybrid, requiring human dietetic technicians to validate observations and contextualize findings. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some digital tools (smart scales, EHR intake logging, sensor-based tray monitoring) exist but are not widely deployed as complete solutions for continuous patient food/weight monitoring and reporting in most clinical settings. |
Conduct nutritional assessments of individuals, including obtaining and evaluating individuals' dietary histories, to plan nutritional programs.
30CI 30–30 · exposure 25 · augmentation 63 · importance 4.5/5 · click for rater detail
Conduct nutritional assessments of individuals, including obtaining and evaluating individuals' dietary histories, to plan nutritional programs.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Healthcare and dietetics sectors show slow, cautious adoption of automation; most organizations continue to rely on human dietetic professionals; AI deployment in this domain remains limited to pilot programs or data-entry augmentation rather than production-scale substitution. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Healthcare and nutrition services are historically slow AI adopters relative to information/finance sectors, with pilots for AI-assisted dietary tracking but limited production-scale deployment for formal assessments. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by pre-analyzing dietary logs, flagging nutritional gaps, or generating initial program drafts that a dietetic technician refines; this support raises productivity on parts of the task without removing the human's central role in assessment and program design. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can streamline dietary history intake via structured questionnaires, food-logging apps, and data summarization, meaningfully speeding up the technician's information-gathering and documentation work while the human still evaluates and plans. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can analyze dietary data and generate basic nutritional recommendations from structured input, conducting full nutritional assessments requires complex clinical judgment, eliciting nuanced dietary histories through conversation, and adapting to individual circumstances—tasks where current AI falls short of the 50% time-saving threshold for equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Gathering dietary history involves interviewing individuals and interpreting nuanced verbal/behavioral information, and evaluation requires clinical judgment tied to physical exams and lab data that AI cannot independently access or verify end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Nutritional assessment falls under healthcare delivery with some regulatory oversight, though dietetic technicians (unlike registered dietitians) typically do not require licensure in many jurisdictions; organizational caution around liability and the preference for human dietary interaction create moderate friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing mandate universally requires a human for basic assessment, but many care settings require credentialed staff to conduct and sign off on nutritional assessments, plus patient trust and liability concerns create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI systems for dietary analysis require substantial integration, data curation, and clinical oversight to be usable, while dietetic technicians are relatively inexpensive labor; the all-in cost of AI deployment likely exceeds or approximates human-provided assessment. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI-assisted forms and questionnaires reduce some documentation cost, human interviewing, rapport-building, and clinical judgment still dominate labor costs, keeping AI only modestly cheaper for the full task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some tools exist for dietary logging and basic analysis, but no deployed product reliably performs end-to-end nutritional assessment and program planning without significant human oversight; most solutions are narrow, supplementary, or research-stage. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI diet-tracking and nutrition chatbots exist but are not deployed as reliable substitutes for a technician's structured assessment and history-taking in clinical settings; adoption remains narrow and supervised. |
Select, schedule, or conduct orientation or in-service education programs.
30CI 30–30 · exposure 25 · augmentation 75 · importance 3.8/5 · click for rater detail
Select, schedule, or conduct orientation or in-service education programs.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Healthcare and nutrition sectors show slower digitization for interpersonal training roles. Adoption of AI for content support exists, but displacement of educators in live settings remains minimal and tentative. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Healthcare and clinical support roles adopt AI more slowly than fully digital sectors, and training/education functions in these settings remain largely human-led. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially assist by generating content drafts, managing scheduling logistics, providing presentation visuals, and offering reference materials that enhance a human educator's productivity and scope without requiring the educator to leave the loop. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist in creating training materials, scheduling logistics, and drafting content for in-service education, improving efficiency while humans still lead delivery. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate educational content outlines and draft materials, the interactive facilitation and real-time adaptation required for effective orientation and in-service programs demand human presence. Scheduling and select elements can be partially automated, but end-to-end delivery with quality equivalence is not achievable today. |
| Task automatability | claude-sonnet-5 | 2/5 | Designing and delivering orientation/in-service education involves scheduling logistics, needs assessment, and interactive facilitation that AI can support but not fully replace end-to-end today.assessment |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Healthcare organizations typically expect qualified, licensed personnel to conduct professional development; while not a legal mandate universally, institutional policy and professional standards create meaningful friction against pure automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not licensed-specific, in-service education often requires institutional oversight, compliance with healthcare training standards, and interpersonal delivery that create moderate friction to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI systems for content generation and scheduling have moderate costs, but the need for human facilitators to actually conduct orientation means total automation is not cost-effective. The human wage advantage remains significant. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human facilitators are still needed for live training delivery and interpersonal engagement, so AI mainly reduces prep time rather than replacing the full cost of the task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Products exist for generating training materials and managing scheduling calendars, but no deployed system reliably conducts live orientation or in-service education with the interpersonal sensitivity and adaptive response expected in professional healthcare settings. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools can help draft training content or schedule sessions, but no deployed product autonomously selects, schedules, and conducts in-service education programs in clinical dietetic settings. |
Deliver speeches on diet, nutrition, or health to promote healthy eating habits and illness prevention and treatment.
28CI 25–30 · exposure 25 · augmentation 75 · importance 3.9/5 · click for rater detail
Deliver speeches on diet, nutrition, or health to promote healthy eating habits and illness prevention and treatment.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Healthcare organizations and public health departments have adopted AI-assisted content creation and scheduling, but autonomous AI-delivered health speeches remain rare in practice. Adoption is limited to support roles (drafting, scheduling) rather than replacement of the delivery function. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Healthcare and community health education sectors adopt AI slowly for public-facing communication tasks, with pilots more common than production use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly augments dietetic technicians by generating evidence-based content, personalizing messages for different audiences, drafting scripts, and creating multimedia assets. This raises technician productivity and reach while preserving human credibility and judgment in health communication. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly help dietetic technicians prepare speech content, visuals, and talking points, improving efficiency and quality of the human-delivered speech. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate nutritional content and speeches, delivering effective health messaging requires dynamic audience engagement, credibility-building, and adaptive response to real-time feedback that current AI systems cannot reliably replicate end-to-end. The persuasive and interpersonal dimensions of health promotion fall short of the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can draft speech content and slides, but live in-person delivery, audience engagement, and answering spontaneous questions require human presence, limiting full end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Health promotion and medical nutrition advice often fall under scope-of-practice regulations requiring licensed or certified personnel (dietitian or dietetic technician credentialing). Liability and professional accountability for health claims create legal and organizational barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement to give a health talk, but audience trust, credibility of the presenter, and organizational preference for human interaction create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI speech generation and content creation tools are inexpensive, but integrating them into a credible health promotion program still requires dietetic technician oversight, video production, and distribution infrastructure. The all-in cost remains comparable to or exceeds direct human delivery for this trust-sensitive task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Content generation is cheap, but live delivery still requires a paid human presenter or expensive video/avatar production, keeping overall costs comparable to human delivery. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably delivers health speeches autonomously in production; AI can draft scripts and provide talking points, but human dietetic technicians must deliver and adapt the message. Text-to-speech and video generation exist but lack the professional credibility, audience rapport, and clinical judgment required for health communication. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Text-to-speech and AI presentation tools exist but are not reliably deployed to replace human speakers delivering health talks to real audiences in clinical/community settings. |
Supervise food production or service or assist dietitians or nutritionists in food service supervision or planning.
25CI 20–30 · exposure 20 · augmentation 50 · importance 4.5/5 · click for rater detail
Supervise food production or service or assist dietitians or nutritionists in food service supervision or planning.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Food service sectors remain relatively low-tech and labor-intensive with slow AI adoption. Healthcare and institutional food services show modest digitization; automation in this context is limited to back-office tasks rather than operational supervision roles. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Institutional food service (hospitals, schools) is a slower-adopting sector for AI-driven operational management compared to information/finance industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist supervisors by automating menu calculations, generating compliance checklists, predicting waste, and supporting scheduling decisions, though the human supervisor remains essential for real-time judgment and regulatory sign-off. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help with scheduling, inventory forecasting, menu compliance checks, and documentation that supports the supervisor, offering moderate productivity gains. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could assist with menu planning, scheduling, and nutritional calculations, the task requires real-time supervision of food production, quality control, and staff coordination that demands human presence and judgment. Current AI cannot reliably oversee physical food service operations end-to-end with the required quality assurance. |
| Task automatability | claude-sonnet-5 | 2/5 | Supervision involves real-time coordination of kitchen staff, quality checks, and physical presence that AI cannot perform end-to-end; only planning/documentation sub-components are automatable today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Food safety regulations (HACCP, health codes) typically require licensed personnel or supervisors to certify compliance and food quality, creating legal liability barriers. Health department inspections and food handling certifications create hard requirements for human accountability in food service supervision. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement for supervision itself, but food safety regulations, liability for service quality, and need for on-site human judgment create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for menu planning and nutritional analysis have moderate adoption costs, but the core supervision and decision-making functions still require human oversight, making full cost replacement unfavorable compared to human supervisors' wages. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Since AI cannot substitute for the supervisory function, organizations still need a human at comparable or added cost for software licensing, making AI not clearly cheaper for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI systems can support scheduling and nutritional analysis, but no deployed product reliably supervises food production or validates compliance with health/safety standards in real kitchens. The task requires embodied oversight that current systems cannot perform independently in production settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product supervises food production or service staff; existing AI tools only assist with menu planning or nutrient analysis, not the supervisory task itself. |
Refer patients to other relevant services to provide continuity of care.
21CI 16–25 · exposure 17 · augmentation 50 · importance 4.2/5 · click for rater detail
Refer patients to other relevant services to provide continuity of care.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Healthcare sectors show cautious, slow adoption of AI for patient-facing clinical decisions; most referral processes remain human-driven with minimal automation despite digital infrastructure, reflecting regulatory and liability concerns. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Healthcare, especially allied health support roles like dietetic technicians, has lagged in AI-driven care coordination automation compared to information-sector professions. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by surfacing relevant service options, consolidating referral network databases, or flagging clinical flags—supporting faster, more complete referral decisions while the technician retains final judgment. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help surface relevant service options, summarize patient records, and draft referral communications, aiding but not replacing the technician's judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires identifying appropriate external services and making referrals tailored to individual patient needs, which involves clinical judgment, contextual knowledge, and discretion that current AI systems cannot reliably perform end-to-end without human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | Referral decisions require clinical judgment about patient needs, coordination context, and interpersonal communication that current AI cannot reliably replace end-to-end, though it can help identify appropriate referral options. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Healthcare referral decisions carry liability and regulatory responsibilities; clinical oversight requirements, patient safety standards, and organizational accountability create substantial friction against full automation, even if technical capacity existed. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Referrals for continuity of care often require clinical authorization, documentation compliance, and interprofessional judgment tied to licensure and liability, limiting full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for referral decision support or resource lookup are available but require integration, validation, and human oversight that offsets their nominal inference cost relative to a technician's time on complex, individualized referral decisions. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human dietetic technicians must still evaluate and execute referrals with accountability; AI assistance reduces some documentation time but doesn't replace the core judgment and coordination costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can assist in retrieving referral resources or suggesting relevant services, no deployed system reliably determines appropriate referrals independent of human clinical decision-making; any attempt at full automation would carry significant liability risk in healthcare. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some clinical decision-support tools suggest referrals or flag care gaps, but no deployed product independently manages patient referrals reliably in production without clinician oversight. |
Prepare a major meal, following recipes and determining group food quantities.
19CI 19–19 · exposure 16 · augmentation 50 · importance 4.5/5 · click for rater detail
Prepare a major meal, following recipes and determining group food quantities.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Meal preparation automation is nascent in institutional settings; most adopters remain in hospitality research or niche high-end venues. Mainstream healthcare and institutional kitchens show minimal displacement by automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Food service and institutional kitchens are a low-digitization, physically intensive sector with minimal AI/robotic adoption for actual meal preparation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist through portion-size calculation, recipe scaling, inventory suggestions, and menu planning. These tools improve technician efficiency but do not transform hands-on cooking productivity since the physical execution remains human-dependent. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with recipe scaling, quantity calculations, and menu planning for group meals, improving efficiency in the planning portion even though physical cooking is unaffected. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate recipes and calculate quantities, executing the full meal preparation requires physical manipulation of ingredients, heat control, timing coordination, and sensory feedback. Current AI systems cannot physically cook or reliably adapt to real-time ingredient variations. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical food preparation for a major meal requires manual dexterity, cooking skill, and real-time sensory judgment (taste, texture) that current AI cannot perform; AI can only assist with planning aspects like scaling recipes.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Food safety regulations and health code compliance create some friction, though not absolute legal bars to automation. Institutional kitchens may face licensing and liability concerns but no prohibition on mechanized cooking. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not licensed like clinical work, food safety regulations, kitchen equipment handling, and institutional food service standards create real operational and safety barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic cooking systems remain prohibitively expensive compared to paying dietetic technicians or kitchen staff. Hardware, maintenance, and integration costs far exceed human labor for meal preparation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI system to substitute for the physical cooking labor, so AI cost comparison is not applicable and the human remains far cheaper than any hypothetical robotic solution. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably prepares complete meals end-to-end; robotic kitchens exist only in research or highly controlled settings. Production systems cannot match the dexterity, environmental adaptation, and quality consistency required. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously prepares physical meals at scale; this remains a human manual/culinary task with no robotic kitchen product in mainstream production use for institutional dietetics. |
Attend interdisciplinary meetings with other health care professionals to discuss patient care.
4CI 0–7 · exposure 0 · augmentation 38 · importance 4.1/5 · click for rater detail
Attend interdisciplinary meetings with other health care professionals to discuss patient care.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This is a fundamentally synchronous, in-person interpersonal task where human presence and judgment are mandatory by law and practice standards. Adoption of AI replacement is not occurring because substitution is not legally or professionally permissible. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Healthcare delivery settings adopt AI slowly for interpersonal clinical collaboration tasks, though administrative AI tools (transcription, scheduling) are seeing some uptake. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide limited support such as pre-meeting preparation summaries, documentation of key decisions, or real-time reference retrieval, but the core task—active professional participation in patient care meetings—remains entirely human-driven and offers minimal augmentation potential. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist by transcribing meetings, summarizing patient records beforehand, or generating discussion notes, meaningfully supporting preparation and follow-up even though it doesn't replace attendance. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Attending and meaningfully participating in interdisciplinary meetings requires real-time interpersonal engagement, consensus-building, and contextual judgment about patient care. Current AI cannot substitute for the required human presence and decision-making in a meeting setting. |
| Task automatability | claude-sonnet-5 | 1/5 | Attending live interdisciplinary meetings to discuss patient care requires physical/virtual presence, real-time judgment, and interpersonal collaboration that current AI cannot substitute for end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Healthcare regulations and institutional policy require a licensed or credentialed human dietetic professional to be present in clinical discussions affecting patient care decisions. Patient privacy (HIPAA), liability, and the need for professional accountability create hard barriers to any non-human substitute. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Patient care discussions typically require credentialed professional judgment and accountability, and institutional/regulatory norms expect human clinical staff participation, though not always a strict licensure mandate for a technician's attendance. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI cannot perform this task end-to-end, so direct cost comparison is not applicable. Any AI support (transcription, note-taking) would supplement rather than replace the human's meeting attendance. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Since AI cannot perform the core task of participating in meetings, there is no viable AI substitute cost to compare against human labor for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs the full task of attending and actively participating in clinical meetings as a substitute for a human professional. AI can draft meeting agendas or summarize notes, but cannot replace the attendee. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product attends and participates in clinical care meetings on behalf of a dietetic technician; AI is at most a note-taking or transcription aid. |
Related occupations — Healthcare Practitioners & Technical
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.