Employee Comfort Delegating Tasks to AI Agents
Measures how comfortable employees are handing off specific work to AI agents, what's currently delegated, how closely output gets checked, and what would build more trust. Built for HR and AI-adoption teams rolling out agentic tools, with an AI follow-up interview that digs into the real reasons behind hesitation or over-reliance rather than generic attitude scores.
샘플 질문
템플릿에 포함된 내용을 미리 확인해 보세요. 모든 질문은 설문 공개 전에 자유롭게 수정할 수 있습니다.
In the last 30 days, which of these tasks have you delegated to an AI agent (not just a chatbot for quick questions, but something that completed a task with less oversight)?
- Scheduling or calendar management
- Drafting emails or documents
- Summarizing meetings or reports
- Data analysis or reporting
- Customer-facing responses
- Writing or reviewing code
- Research or information gathering
- I haven't delegated any tasks to an AI agent
How comfortable are you delegating each type of task to an AI agent, even if you haven't tried it yet?
- Administrative or scheduling tasks
- Drafting communications on your behalf
- Data analysis or reporting
- Customer-facing responses
- Decisions that affect budget or priorities
- 외 1개
Overall, how much do you trust an AI agent to complete a delegated task correctly without you double-checking it?
When an AI agent completes a task for you, how often do you review its output before acting on it or sending it forward?
- Always review it fully
- Usually skim it
- Occasionally spot-check
- Rarely review it
- Never — I use it as-is
Rank these concerns from biggest to smallest when it comes to delegating tasks to AI agents.
- Errors or inaccurate output
- Losing my own skills or judgment
- Not knowing who's accountable if something goes wrong
- Data privacy or security
- Impact on my job security
- Quality dropping below my own standard
Which of these would do the most, and the least, to make you more comfortable delegating tasks to AI agents?
- Clear logs of what the AI agent did and why
- An easy way to undo or override its actions
- Proof it's been tested on tasks like mine
- Training on how to supervise it well
- Clear rules on who's accountable for mistakes
- Seeing colleagues use it successfully first
- Starting with low-stakes tasks only
- A human always reviewing before anything goes out
Probe the real story behind this person's comfort level with AI agents. Ask them to describe one specific recent time they delegated a task and one time they held back or double-checked everything — what made the difference. If they picked a top concern (like accountability or errors), get a concrete example of when that concern was realized or nearly was. If they said they never review output, gently check whether that's confidence or just not having noticed a problem yet.
How satisfied are you with the training and support your company has provided for working with AI agents?
Which best describes your role level?
- Individual contributor
- People manager
- Senior leader / executive
- Prefer not to say
How long have you been with the company?
- Less than 1 year
- 1–3 years
- 4–7 years
- 8+ years
- Prefer not to say
That's everything — thank you! Your responses will be pooled with your colleagues' to guide training, guardrails, and which tasks we roll AI agents out to next.
포함된 기능
AI 후속 질문
정형화된 설문이 놓치는 세부 내용을, 주관식 답변에 맞춰 AI가 심층 질문으로 끌어냅니다.
주의력 확인 장치
성의 없는 답변과 저품질 응답자를 걸러내는 내장 안전장치입니다.
AI가 작성한 문안
문구, 질문 순서, 분기 로직까지 AI가 연구 목표에 맞춰 작성합니다.
자동 리포트
응답이 모이면 주요 주제, 인용문, 이해하기 쉬운 요약이 자동으로 작성됩니다.
이 템플릿을 선택하는 이유
이 템플릿의 설계 목적을 소개합니다. 다른 설문 도구에서는 직접 비교할 만한 템플릿을 찾지 못했습니다.
차별화 포인트
- Includes an AI follow-up interview that probes the real reasons behind hesitation or over-reliance, rather than stopping at a generic attitude score
- Combines a matrix question on comfort level by task type with a multiple-choice question on what's actually been delegated in the last 30 days, so results tie stated comfort to real behavior
- Uses a ranking question and a max-diff exercise to force trade-offs on concerns and trust-building actions, giving HR and AI-adoption teams prioritized, decision-ready data instead of flat rating averages
- Captures oversight behavior directly (how often output gets reviewed before use) alongside satisfaction with training/support, so the report can connect trust, checking behavior, and enablement gaps
자주 묻는 질문
“Employee Comfort Delegating Tasks to AI Agents” 템플릿에는 어떤 질문이 포함되어 있나요?
바로 사용할 수 있는 질문 12개가 포함되어 있으며, 처음 질문은 다음과 같습니다: “Thanks for taking a few minutes on this! We're trying to understand how comfortable people feel delegating tasks to AI a…” · “In the last 30 days, which of these tasks have you delegated to an AI agent (not just a chatbot for quick questions, but…” · “How comfortable are you delegating each type of task to an AI agent, even if you haven't tried it yet?”. 전체 질문은 위에서 미리 볼 수 있고 모두 수정 가능합니다.
이 설문을 완료하는 데 얼마나 걸리나요?
응답자는 보통 질문 12개를 약 7분 안에 완료합니다.
템플릿을 수정할 수 있나요?
네. 설문을 공개하기 전에 모든 질문, 답변 옵션, 순서를 자유롭게 수정할 수 있습니다. 질문을 추가·삭제하거나 AI 편집기에 연구 목표에 맞춘 재구성을 요청할 수도 있습니다.
이 템플릿은 무료인가요?
네. 편집기에서 바로 열어 수정을 시작할 수 있습니다. 체험에는 계정이 필요 없으며, 무료 플랜으로 설문을 공개할 수 있습니다.
설문을 공개할 준비가 되셨나요?
이 템플릿을 편집기에서 열어 보세요. 첫 응답자가 보기 전에 모든 부분을 원하는 대로 바꿀 수 있습니다.
관련 템플릿
비슷한 주제의 다른 설문을 만나 보세요.
Shift Scheduling Satisfaction Survey for Hourly Teams
Measures how predictable, swappable, and fair hourly employees find their work schedules, plus early burnout signals like clopening shifts and exhaustion. Includes an AI follow-up that reconstructs a specific recent scheduling incident — a denied swap or a last-minute change — instead of relying on general impressions.
템플릿 보기Willingness to Delegate Subscriptions to AI Agents
Measures how comfortable people are letting an AI agent renew, cancel, reorder, or renegotiate their subscriptions on their own — and what safeguards would make that trust possible. Built for product teams evaluating agentic commerce features, with an AI follow-up that digs into why trust differs by task type and what a bad experience would look like.
템플릿 보기AI Agent Output Review Burden and Trust Calibration Survey
Measures how much time and cognitive effort employees spend checking AI agent outputs, where trust is over- or under-calibrated, and what triggers a full manual re-check. An AI follow-up probes the last time output was wrong or nearly acted on unchecked.
템플릿 보기AI Agent Autonomy, Escalation & Control Preferences Survey
Measures user expectations for AI agent autonomy, preferred escalation and handoff mechanisms, permissible actions, spending thresholds, and risk concerns. Designed for UX researchers and product teams building agentic AI workflows.
템플릿 보기AI Monitoring of Work Quality: Employee Attitudes Survey
Measures how employees feel about AI-based monitoring and quality-scoring tools at work — covering comfort, trust, fairness, and behavior change — with an AI follow-up that reconstructs one specific moment monitoring affected them instead of abstract opinions. Built for HR, legal, and workplace-technology teams evaluating or rolling out AI monitoring practices.
템플릿 보기Workplace AI Adoption & Compliance Assessment
Measures employee AI tool usage patterns, shadow AI risks, policy awareness, and training needs to inform governance and safe-adoption strategies across the organization.
템플릿 보기