Operations & data survey templates

Assesses how prepared staff feel to communicate during a crisis — protocol awareness, spokesperson clarity, channel reliability, and drill experience — with an AI follow-up that reconstructs a real or simulated crisis moment to surface exactly where confusion or delay actually occurs.

Captures the origin, destination, cargo specifics, timing, and special handling needs a freight broker or carrier needs to price a shipment accurately. An AI follow-up clarifies any vague or incomplete cargo details — like unclear dimensions or hazmat class — before the quote goes to the routing team.

For employees requesting to onboard a new vendor or supplier. Captures the business need, estimated spend, urgency, risk factors, and alternatives already considered so procurement and operations teams can triage requests quickly. The AI follow-up interview probes why existing approved vendors won't meet the need and surfaces risk details a form alone would miss.

Measures how thoroughly people prepare for meetings they lead and attend — agendas, objectives, materials, and roles — and where preparation habits break down. An AI follow-up interview reconstructs a specific under-prepared meeting to surface the real friction behind missed prep steps, for operations teams trying to improve meeting quality.

Maps when employees are genuinely available and willing to meet, how their meeting time actually breaks down, and where scheduling friction eats into focus work. An AI follow-up interview digs into one specific recent meeting that went wrong and what would have fixed it. Built for ops, IT, and people teams auditing meeting culture.

Measures how well meetings are planned, run, and followed up on — cadence, agenda discipline, and the biggest time-wasters — for teams and ops leaders auditing their meeting culture. An AI follow-up interview reconstructs a recent unproductive meeting to surface the specific root cause behind the numbers.

Measures how people actually schedule, reschedule, and coordinate meetings today — time spent, satisfaction with current tools, and which features would save the most time. Built for product and operations teams evaluating or building meeting planning software. An AI follow-up interview reconstructs a specific recent scheduling breakdown instead of relying on a generic satisfaction score.

Evaluates how well your organization sources from certified diverse-owned suppliers — spend levels, barriers, and category priorities — for procurement and sourcing leaders. An AI follow-up interview reconstructs a real recent sourcing decision to surface what actually helped or blocked a diverse supplier from winning the business.

Captures how easy and effective it is for internal teams to reach and work with a vendor's point of contact — responsiveness, accuracy of contact info, and follow-through. An AI follow-up digs into the most recent real interaction to surface what actually happened when things went well or went wrong, beyond the rating.

Measures how much customers trust your supply chain claims, which transparency details (sourcing, labor, environmental impact, certifications) actually matter to them, and what they'd pay for verified information — with an AI follow-up that digs into the real story behind a specific trust or mistrust moment.

Assesses how a vendor or supplier is performing on quality, reliability, pricing, and support, and gauges internal appetite for renewal. Built for procurement teams and vendor managers running periodic supplier reviews, with an AI follow-up interview that digs into the specific incident or driver behind a respondent's satisfaction score instead of stopping at a number.

Measures how a vendor performs on quality, delivery, pricing, and support, plus renewal intent and what factors matter most in the relationship. Built for procurement and operations teams running periodic vendor reviews, with an AI follow-up interview that digs into the specific incident behind a low or high score instead of leaving it as a number.

Diagnoses friction in a specific internal process — where time gets lost, which steps break down, and what to fix first — built for ops leads, IT, or process owners running a targeted improvement review. A trade-off ranking surfaces the top pain point, and the AI follow-up interview digs into a concrete recent example instead of a generic complaint.

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.

Pinpoints which internal processes waste the most employee time, how often people route around official steps with workarounds, and which tasks are the best candidates for automation. Built for ops, IT, or PMO teams doing a process-improvement audit; the AI follow-up reconstructs the actual workaround steps behind the numbers so you know exactly what to fix first.

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.

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.

Measures how reps actually experience your sales tech stack — CRM friction, tool overlap, hours lost to admin, and which tools they'd cut or keep — for sales ops leaders auditing their stack. An AI follow-up reconstructs the specific moment a tool slowed a rep down, instead of collecting generic complaints.

Measures how employees experience IT support — ticket resolution speed, first-contact resolution, technician communication, and whether self-service resources actually deflect tickets. An AI follow-up reconstructs what really happened on the respondent's most recent ticket instead of a generalized impression, surfacing the specific breakdowns behind low satisfaction scores.

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.

Collects structured partner feedback on reliability, delivery, communication, and value over a 90-day period to generate actionable supplier performance scorecards for operations optimization.

Measures B2B professionals' trust in supplier provenance claims, identifies data gaps and verification practices, and surfaces priorities for improving supply-chain transparency. Designed for procurement, quality, compliance, and supply-chain roles.

Collects structured stakeholder feedback on SLA adherence, incident resolution quality, and improvement priorities over a 90-day window to identify service gaps and guide operational improvements.

Measures employee phishing detection confidence, reporting behavior, and security practices to identify organizational risk gaps and prioritize awareness training investments.

Measures safety behaviors, reporting confidence, environmental hazards, and leadership support across e-commerce fulfillment teams to identify targeted risk-reduction priorities.

Measures warehouse workers' safety perceptions, incident exposure, training adequacy, and improvement priorities related to collaborative robots. Designed for operations and safety teams managing human-cobot work environments.

Assesses internal stakeholders' experiences with vendor onboarding speed, process friction, and compliance steps over the past 6 months to identify bottlenecks and prioritize process improvements.

Diagnoses bottlenecks, clarity gaps, and cycle-time issues across procurement request and approval workflows. Designed for internal operations teams seeking data-driven improvements to purchasing processes based on employee feedback.

A company-wide pulse survey assessing employee readiness for post-merger integration across culture alignment, process documentation, tooling access, and communication effectiveness. Designed for fielding in the weeks before or after a merger close to surface urgent gaps.

Measures customer satisfaction across three core last-mile dimensions—tracking accuracy, delivery timing, and handoff quality—to identify specific improvement priorities for retail delivery operations.

Measures internal stakeholder satisfaction with operations support across resolution speed, communication quality, and professionalism, with NPS benchmarking. Designed for recurring deployment to track service improvement over time.

Assesses how employees find, evaluate, and trust internal knowledge resources—measuring findability, content quality, ownership clarity, and tool effectiveness to prioritize knowledge management improvements.

Measures employee preparedness for internal audits, identifies gaps in guidance, tooling, and training, and surfaces priority areas for process improvement. Designed for organizations seeking to standardize audit readiness across departments.

Measures incident impact, communication effectiveness, and trust recovery across stakeholder roles. Designed for internal post-incident reviews to identify systemic improvement priorities.

Evaluates the timeliness, clarity, channel effectiveness, and overall quality of incident communications from stakeholders' perspectives to identify actionable improvements in crisis response.

Evaluates stakeholder perceptions of incident communication timeliness, clarity, frequency, and helpfulness to identify priority improvements in crisis communication workflows.

Measures employee satisfaction with HR service quality across responsiveness, clarity, and fairness dimensions. Designed for quarterly internal pulse checks to identify improvement priorities and track service trends.

Measures cost visibility, ownership clarity, and trade-off decision-making across engineering teams. Designed for engineering leaders seeking to benchmark FinOps maturity and identify tooling or process gaps.

Measures employee satisfaction with facilities maintenance requests—from submission clarity to resolution speed—helping workplace operations teams identify service bottlenecks and communication gaps.

Measures employee perceptions of facility cleanliness, environmental comfort, and maintenance responsiveness over a 30-day recall period to help facility and operations teams identify and prioritize improvement areas.

Assesses the operational effort, data challenges, and improvement priorities suppliers face during customer-requested ESG audits. Designed for manufacturing and supply chain professionals involved in ESG compliance processes.

Measures consumer trust, noise tolerance, adoption likelihood, and barriers for retail drone delivery. Designed for market researchers evaluating residential readiness for aerial last-mile logistics.

Captures stakeholder expectations for data product availability, freshness, and quality to inform internal SLO/SLA definitions. Designed for data consumers across engineering, analytics, and business teams.

An internal assessment for measuring employees' data literacy, self-service analytics confidence, tool adoption, and support needs — designed to surface skill gaps, trust issues, and barriers that inform data-enablement strategy.

Measures data practitioners' confidence in lineage accuracy, impact analysis efficiency, and tooling gaps. Designed for data engineering, analytics, and platform teams to identify high-priority improvements to lineage infrastructure and workflows.

Diagnoses catalog discoverability, ownership clarity, and data trust across teams. Designed for internal data practitioners to identify governance gaps and prioritize catalog improvements using NPS and behavioral metrics.

Measures how easily analytics teams can find, evaluate, and trust data assets across the organization, revealing gaps in metadata, documentation, and lineage practices. Designed for data and analytics professionals; estimated 6–8 minutes.

Measures customer perceptions of clarity, timeliness, and trust in crisis and outage communications. Designed for B2B operations teams seeking to benchmark and improve incident response communication.

Measures adoption rates, usability barriers, perceived safety outcomes, and data trust related to IoT wearables and site sensors among construction workers. Designed for safety managers and operations teams evaluating wearable technology programs on active job sites.