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Understanding how automation changes work — task by task, with the evidence shown and the uncertainty admitted.

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Translator / InterpreterBank tellerCopywriterContent moderatorCustomer service representativeAdministrative assistantSoftware tester / QA engineerData entry clerkGraphic designerParalegalVideo editorAccountant / BookkeeperMarketing specialistFrontend developerTax preparer / tax agentVoice actorMedical coderData analystInsurance claims handlerTechnical writer / documentation engineerJunior software developerInsurance underwriterHR / recruiterLoan officer / credit officerFinancial analystProcurement / supply chain specialistJournalistSales / account managerReal estate agentNetwork engineerIllustratorTravel agent / travel advisorIT support specialist / helpdeskAuditorSupply chain plannerManagement consultantPhotographerBackend developerAI researcherProduct / UX designerBusiness systems ownerE-commerce operations specialistActuaryAnimator and VFX artistWriter and authorPayroll specialist / payroll clerkRadiologistData engineerProject managerData scientistCourt reporterEditor and proofreaderLawyerMedical assistant / clinic assistantQuantity surveyorContent creatorQuantitative analystMachine learning engineerExperienced software engineerDevOps engineer / platform engineer / SREBusiness analystMusician and composerGIS analyst / cartographerProperty appraiser / valuerProperty managerProduct managerDietitian / nutritionistEconomistPharmacistPartnerships / channel managerSecurity analyst (SOC)Financial adviser / financial plannerInterpreterPathologistChip design engineerGrant writer / grants officerCompliance officerLibrarianDrafter / CAD technicianArchitectFirst-line manager / team supervisorCounsellor / psychotherapistRetail salesperson / shop assistantCivil / structural engineerElectrical engineerIndustrial engineerStatisticianSecurity guardInterior designerUniversity lecturerPsychologistBus driverInsurance agentMechanical engineerFashion designerOptometristNotarySchool teacherGeneral practitioner / primary care doctorAirline pilotDriving instructorUrban plannerEmergency dispatcherWaiter / restaurant serverRadiographer / radiologic technologistLand surveyorAuto mechanic / vehicle technicianPolice officerSonographerAircraft maintenance technicianDental hygienistAnaesthesiologist / anaesthetistBuilding inspectorSpeech-language pathologist / speech and language therapistNurse practitionerAir traffic controllerVeterinarianBartender / baristaPhysiotherapist / rehabilitation therapistDentistSurgeonParamedic / EMTConstruction workerSocial workerJudge / magistrateRegistered nurseMassage therapistMidwifeCare worker / nursing assistantPlumberAI implementation lead
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Enter as:I have a jobI am studyingI run a companyI am building something
On this pageFilesEmbed the mapThree rules when you use itLicence and how to cite

Open data

Everything this site publishes about AI and work, in a shape a spreadsheet, a script or a newsroom can use: every occupation, every task judgement with what it rests on, and every verified evidence record with its source.

Files#

volo-occupations.csv

162 rows · 22 KB

One row per occupation: sector, impact index with its confidence and assessment date, the O*NET code it maps to and how well, task counts, page URL.

volo-tasks.csv

825 rows · 343 KB

One row per task judgement: direction, basis (evidenced or inferred), weight, technologies, what the judgement does not establish, the evidence behind it, URL.

volo-evidence.csv

982 rows · 1137 KB

One row per verified evidence record: what happened and when, its stage, the source and its tier, where it applies, URL.

volo-open-data.json

1969 rows · 1902 KB

All three in one file, with the licence and the usage notes attached.

volo-ontology.json

18 object types · 24 link types · 26 action types · 49 KB

The model behind all of it: what kinds of objects the site recognises, how they link, who may change them and how, and which read-only tools AI agents get. Modelled on Palantir's Ontology.

See it drawn as one diagram →

volo-ontology.jsonld

56766 statements · 3912 KB

The same model with every object and link filled in, as linked data (JSON-LD): occupations, tasks, judgements, evidence, rules, O*NET work activities and the official classifications they map to, with each fit written as a W3C SKOS match.

Regenerated from the site's content whenever a record is added, and checked against it byte for byte before every release. English only; each page carries the full text in six languages.

Embed the map#

The homepage map as a static image, regenerated with the data: every occupation where it stands today, coloured by sector, with the confidence, the assessment dates and the source written into the image itself. Use it in an article or a slide.

How AI is changing 162 occupations, task by task — VOLO

To embed it, paste this into your page — the link back is the credit the licence asks for:

<a href="https://flyvolo.ai/en"><img src="https://flyvolo.ai/data/volo-map.svg" alt="How AI is changing 162 occupations, task by task — VOLO" width="1200" height="900" style="max-width:100%;height:auto"></a>

Three rules when you use it#

  1. The impact index is not a probability of losing a job. Quote it only with its confidence and assessment date.
  2. Task direction, evidence stage and the impact index are separate readings. Do not add or average them into one score.
  3. Every task carries what its judgement does not establish (does_not_establish). Carry it with the claim.

Licence and how to cite#

CC BY 4.0 — https://creativecommons.org/licenses/by/4.0/

Credit: VOLO (flyvolo.ai) — please credit and link to https://flyvolo.ai/en/data

Cite the page that carries a claim where you can — a task lives at /careers/<occupation>#task-<id>, a record at /changes/<id>; both URLs are stable and are in the files. The O*NET codes come from the U.S. Department of Labor's O*NET 31.0 (CC BY 4.0).

For agents, the same content is a public MCP server at mcp.flyvolo.ai/mcp — no key.