전략·혁신

Captures why employees are requesting changes to their work schedule — shifted hours, compressed weeks, remote days, or reduced hours — and how urgent and job-critical the need is. Built for HR and People teams shaping flexible-work policy, with an AI follow-up that digs into the real constraint behind the request and what a workable compromise would look like.

Gauges how a specific proposed change request lands with the people it affects — awareness, perceived risk, resource readiness, and willingness to support it — built for change managers, PMOs, and transformation leads preparing a rollout or approval decision. The AI follow-up interview digs into each respondent's single biggest concern and what would turn hesitation into active support.

Measures whether employees actually noticed, understood, and trust a recent payroll change notice (e.g., new pay schedule, provider, or deduction) and how confident they are their next paycheck will be correct — with an AI follow-up that digs into exactly what's still unclear or worrying to the people who need the most help. Built for HR, payroll, and change-management teams rolling out a payroll transition.

Tracks how employees understand, feel about, and are coping with an active organizational change — reorg, merger, system rollout, or leadership shift. Built for HR and transformation leads, with an AI follow-up that surfaces the concrete 'why' behind sentiment scores instead of generic complaints.

Helps hosts plan a game day watch party guests will actually love — covering food, activities, budget trade-offs, and timing. An AI follow-up interview digs into what made past game day parties memorable (or forgettable) so the host can prioritize the details that matter most, not just the ones that are easiest to plan.

Gathers the bridal party's real preferences on budget, activities, destination, and trip length before you lock in plans — with an AI follow-up that surfaces the tradeoffs attendees would actually accept when priorities collide (like budget vs. the bride's dream activity).

Captures how guests actually experienced an event — venue, food, entertainment, timing, and communication — plus where they'd reallocate next year's budget. An AI follow-up interview reconstructs the single moment that made or broke the experience, giving planners a concrete story behind the ratings, not just a score.

Gauges whether employees or leaders understand the organization's strategic priorities, believe resources and daily work are aligned to them, and feel confident execution will succeed — with an AI follow-up that digs into the real barriers behind low-confidence or low-alignment answers.

Measures how employees actually experience your stated values day-to-day — trust, psychological safety, recognition, and manager role-modeling — plus an AI follow-up interview that surfaces the specific moments behind low-trust or low-safety scores. Built for HR, People Ops, and leadership teams running annual or pulse culture audits.

특정 팀 또는 프로세스가 실제로 AI 에이전트를 중심으로 업무를 재설계할 준비가 되어 있는지를 진단합니다 — 현재 사용 현황, 데이터 및 역량 준비 상태, 재설계 우선순위, 변화의 장애물을 다룹니다. 다음 파일럿을 어디에서 진행할지 범위를 정하는 운영, 혁신, IT 리더를 위해 설계되었으며, AI 후속 인터뷰는 일반적인 '변화 관리'라는 답변을 그대로 받아들이는 대신 가장 큰 단일 장애물을 깊이 파고듭니다.

A short pulse check for executive and senior leadership teams that surfaces where AI strategy is genuinely shared versus quietly contested — covering clarity, priorities, budget trade-offs, and adoption stage. An AI follow-up interview digs into the specific decision or disagreement behind the lowest-rated alignment statement instead of settling for a vague 'somewhat aligned' score.

Benchmarks how customers and prospects rate you against specific named competitors on the criteria that actually drive their choice — combining a max-diff prioritization, a head-to-head perception battery, and an AI follow-up that digs into the exact moment a competitor won or nearly won the deal.

Captures what went well, what broke, and why on a just-completed project, then surfaces the process changes the team would realistically adopt. An AI follow-up reconstructs the actual sequence of events behind the biggest setback instead of settling for a vague 'communication issues' answer. Built for project leads and PMOs running structured retros.

Measures the gap between the values a company says it holds and the values employees actually experience in day-to-day decisions, priorities, and leadership behavior. Built for HR and leadership teams auditing culture health, with an AI follow-up that reconstructs a real recent moment where a decision either honored or contradicted a stated value.

Collects input from every level of the organization on top priorities, resourcing tradeoffs, biggest risks, and the bold bets people would make for next year. An AI follow-up presses into the reasoning, blockers, and resources behind each person's proposed bet, so planning starts with real signal instead of guesses.

Assesses how ready employees are for digital transformation across tool adoption willingness, skill confidence, process pain points, and change fatigue. Built for operations, IT, and transformation leaders sequencing a rollout. An AI follow-up digs into the specific reason behind hesitation or exhaustion that scale scores alone can't explain.

A mid-cycle pulse for teams running OKRs (or similar goal frameworks) that measures understanding, buy-in, achievability, and cross-team dependency clarity. The AI follow-up digs past the confidence number to find exactly which key result is shaky, why, and whether the risk sits inside the team or with a dependency. Built for team leads and strategy/ops functions running quarterly check-ins.

Before a reorg, tooling migration, or new process lands, measure whether people understand it, believe in it, and feel equipped for it — the three things that decide adoption. The AI interviewer surfaces the concerns people won't put in a comment box, anonymously.

Replace single-point guesses with honest uncertainty: experts build a probability distribution over outcomes instead of naming one number, and the AI interviewer elicits the assumptions behind their shape. Ideal for sales forecasts, launch estimates, and planning reviews.

Give stakeholders 100 points and make them spend it: constant-sum allocation across initiatives forces the prioritization that agree-scale surveys hide. Follow-up questions capture what they'd cut entirely, and the AI interviewer pressure-tests their biggest bet.