{ "platformId": "chatgpt-work", "taskSlug": "task-18872", "directAnswer": "Use ChatGPT Work to assemble a recruiting or retention program decision packet from approved workforce evidence: make the problem definition, affected population, constraints, alternatives, owners, measures, risks, and decision gates visible. It can structure a program and expose unanswered questions; it must not infer protected traits, diagnose why an individual may leave, decide eligibility, rank employees or candidates, promise an outcome, change a benefit, or contact anyone. The attached figures are projected role-task priors, not measured ChatGPT adoption by human resources specialists, so they support a controlled planning trial rather than a claim about current practice.", "topUseCases": [ { "interventionId": "task-18872:task-18872-scaffold:assist", "taskId": "task-18872", "task": "Advise management on organizing, preparing, or implementing recruiting or retention programs.", "workflowId": "task-18872-scaffold", "authorityMode": "assist", "centralityScreen": { "value": 0.05166990465, "importance": { "value": 0.6825, "sourceField": "role-tasks-kv:role-13-1071-00.tasks[slug=task-18872].importance" }, "occupationalRelevance": { "value": 0.9002, "sourceField": "role-tasks-kv:role-13-1071-00.tasks[slug=task-18872].relevance / 100" }, "frequency": { "kind": "modal-response-share", "modalResponseSharePct": 8.41, "collectionDate": "2026-06-03", "sourceField": "role-tasks-kv:role-13-1071-00.tasks[slug=task-18872].frequency" } }, "agentFit": { "value": 0.9025, "sourceField": "rule:digital-feasibility-and-capability-fit-single-count.v2" }, "distribution": { "value": 0.9, "sourceField": "roles-kv:role-13-1071-00.aiPlaybook.release" }, "evidenceConfidence": "weak", "withinGateScore": 0.0419688800519625, "gate": { "status": "limited", "reason": "Authority remains below an approval-required intervention." }, "capabilityIds": [ "connected-apps-files", "deliverable-generation" ], "expectedArtifact": "Recruiting or retention program decision packet with evidence ledger, population boundaries, options, measures, risks, and approval gates", "humanReviewPoint": "HR, legal, finance, and the accountable business owner validate evidence, equity implications, feasibility, and every program decision before implementation.", "riskLevel": "medium" } ], "workflows": [ { "id": "task-18872-scaffold", "goal": "Turn approved workforce evidence and management constraints into a decision-ready recruiting or retention program packet without deciding who receives an opportunity or benefit.", "inputs": [ "Approved workforce, recruiting, or retention evidence with dates and population definitions", "Business objectives, operating constraints, budget ranges, and existing policy", "Named decision owners, legal or labor constraints, and success measures" ], "connectedSystems": [ "Read-only HR or recruiting export", "Approved policy and document repository", "Read-only workforce analytics or survey evidence" ], "steps": [ "Inventory the supplied evidence by source, population, period, and missingness; do not infer individual motives or protected traits.", "Frame the stated workforce problem, scope, constraints, and alternatives; distinguish measured findings, stakeholder reports, assumptions, and decisions still required.", "Produce a decision packet with option-specific eligibility boundaries, implementation dependencies, cost ranges, equity and privacy questions, measures, risks, and named approval gates.", "Stop at the packet: employee or candidate treatment, policy change, compensation or benefit commitment, communication, and system updates remain human actions." ], "expectedArtifact": "Program decision packet with source ledger, scope, options, equity questions, implementation gates, and unresolved assumptions", "starterPrompt": "Using only the workforce evidence, policies, constraints, and owners I provide, prepare a recruiting or retention program decision packet. Separate sourced findings, stakeholder reports, assumptions, and decisions; define the population and eligibility questions for each option; include cost ranges, dependencies, equity and privacy questions, measures, risks, and approval gates. Do not infer why any named person may leave or decide who receives an opportunity or benefit.", "humanReviewPoint": "The accountable HR, legal, finance, and business owners verify the population, evidence, fairness and policy implications, cost, and implementation decision before any program action.", "commonFailure": "Treating survey sentiment or attrition patterns as an explanation of individual intent, or converting a program option into an unreviewed eligibility rule, promise, or change to employment terms." } ], "setupRecommendations": [ { "item": "Use minimum-necessary, preferably aggregated or de-identified workforce and recruiting evidence through read-only sources; connect approved policy and planning documents, but disable HRIS writes, compensation or benefits actions, candidate or employee messaging, and eligibility automation.", "rationale": "Program design requires population-level evidence, policy context, and a review trail, while individual treatment, employment terms, and communication carry fairness, privacy, legal, and relationship consequences that must remain under accountable human control.", "capabilityIds": [ "connected-apps-files", "approval-enterprise-controls" ] } ], "prompts": [ { "id": "task-18872-starter", "title": "Build a recruiting or retention decision packet", "prompt": "Here are the approved workforce findings, policies, constraints, and decision owners. Build a program decision packet that labels each statement as sourced, reported, assumed, or still to decide; defines the population and eligibility questions; compares options with cost ranges, dependencies, equity and privacy questions, measures, risks, and approval gates. Do not infer individual intent or recommend who should receive an opportunity or benefit.", "expectedArtifact": "Review-ready program decision packet with evidence ledger and approval gates", "humanReviewPoint": "HR, legal, finance, and the accountable business owner decide program scope, eligibility, terms, funding, and implementation." } ], "humanOwnedResponsibilities": [ "Population law: the accountable owner defines the purpose, population, evidence scope, eligibility question, and lawful basis before program analysis begins", "No-inference law: protected traits, health, family status, age, origin, disability, and individual motives or intent are neither inferred nor converted from aggregate patterns into personal facts", "Fairness law: program options require human review for disparate treatment, disparate impact, accessibility, accommodation, privacy, labor, and policy implications", "Terms law: compensation, benefits, eligibility, recruiting promises, employment terms, and exceptions remain authorized human decisions", "Relationship law: candidate and employee contact, manager guidance, program enrollment, HRIS changes, and implementation communications remain human-controlled" ], "risks": [ { "id": "task-18872-scaffold-risk", "level": "medium", "description": "A polished program proposal can turn aggregate attrition or survey evidence into a story about individual employees, hide who is excluded, normalize a proxy criterion, or make an unapproved policy, benefit, or recruiting promise look ready to implement.", "mitigation": "Use source, population, and missingness labels; keep individual data minimum-necessary; require an explicit eligibility and equity review for every option; record policy, legal, finance, and owner approvals; and keep all HRIS changes, communications, enrollment, and employment-term actions outside the workflow." } ], "sources": [ { "id": "occupation-human-resources-specialists", "title": "Human Resources Specialists occupational render model", "url": "https://singulariki.com/roles/human-resources-specialists", "publisher": "gold.onet_role_starter_surface", "lastReviewedAt": "2026-06-03" }, { "id": "openai-chatgpt-work", "title": "ChatGPT Work and Codex", "url": "https://help.openai.com/en/articles/20001275-chatgpt-work-and-codex", "publisher": "OpenAI", "lastReviewedAt": "2026-07-11" }, { "id": "openai-work-deliverables", "title": "Creating and editing documents, spreadsheets, and presentations with ChatGPT Work", "url": "https://help.openai.com/en/articles/20001278-creating-and-editing-documents-spreadsheets-and-presentations-with-chatgpt-work", "publisher": "OpenAI", "lastReviewedAt": "2026-07-11" }, { "id": "openai-work-cloud-browser", "title": "Using cloud browser in ChatGPT", "url": "https://help.openai.com/en/articles/20001280-using-cloud-browser-in-chatgpt", "publisher": "OpenAI", "lastReviewedAt": "2026-07-11" }, { "id": "openai-work-desktop-browser", "title": "Using the built-in browser in the ChatGPT desktop app", "url": "https://help.openai.com/en/articles/20001277-using-the-built-in-browser-in-the-chatgpt-desktop-app", "publisher": "OpenAI", "lastReviewedAt": "2026-07-11" }, { "id": "openai-chatgpt-skills", "title": "Skills in ChatGPT", "url": "https://help.openai.com/en/articles/20001066", "publisher": "OpenAI", "lastReviewedAt": "2026-07-11" }, { "id": "openai-chatgpt-scheduled-tasks", "title": "Scheduled Tasks in ChatGPT", "url": "https://help.openai.com/en/articles/10291617-tasks-in-chatgpt", "publisher": "OpenAI", "lastReviewedAt": "2026-07-11" }, { "id": "openai-workspace-agents", "title": "ChatGPT Workspace Agents for Enterprise and Business", "url": "https://help.openai.com/en/articles/20001143", "publisher": "OpenAI", "lastReviewedAt": "2026-07-11" } ], "freshness": { "platformVersion": "2026-07-09-launch", "platformReviewedAt": "2026-07-11", "occupationalDataVersion": "O*NET 30.3", "occupationalDataGeneratedAt": "2026-06-03T05:05:52.989984+00:00" }, "quality": { "uniqueOccupationalOrTaskEvidence": false, "validPlatformSpecificWorkflow": false, "clearOutputArtifact": false, "realCapabilityMatch": true, "meaningfulHumanReviewSection": false, "sourcesAndFreshness": false, "distinctInternalRelationships": false, "noUnresolvedFactualOrProductVersionFailure": false, "indexable": false } }