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Edge AI Governance & Monitoring Maturity Assessment

Assesses organizational readiness across edge AI governance, monitoring, risk, and MLOps practices. Designed for AI/ML leaders, DevOps, and compliance stakeholders to benchmark maturity and prioritize investment.

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Template Overview

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This professionally designed survey template helps you gather valuable insights with intelligent question flow and automated analysis.

Sample Survey Items

Q1
Chat Message
Welcome to the Edge AI Governance & Monitoring Maturity Assessment. This survey takes approximately 7–10 minutes and asks about your organization's current edge AI practices, governance, monitoring, and risk posture. There are no right or wrong answers—we are interested in your honest assessment of current practices. Your participation is entirely voluntary, and you may stop at any time. All responses are confidential and will be reported only in aggregate to inform governance and operations priorities. Please click 'Next' to begin.
Q2
Dropdown
What is your current level of involvement with edge AI models in your organization?
  • Owner/accountable
  • Contributor
  • Aware/consulted
  • Not involved
Q3
Dropdown
What scope best describes the practices you will be reporting on in this survey?
  • Organization-wide
  • Multiple sites or teams
  • Single site or team
  • Unsure
Q4
Dropdown
What is your organization's current stage with edge AI models?
  • Not using edge AI models
  • Exploring / proof of concept
  • Pilot in limited locations
  • Production in multiple sites
  • Retiring or suspending edge AI
Q5
Dropdown
If your organization is not yet in broad production with edge AI, when do you expect to begin or expand a pilot?
  • Less than 3 months
  • 3–6 months
  • 6–12 months
  • 12+ months
  • No plans
  • Not applicable — already in production
Q6
Multiple Choice
Which of the following edge AI use cases are most relevant to your organization over the next 12 months? (Select all that apply)
  • Quality inspection (vision)
  • Predictive maintenance
  • Safety monitoring
  • On-device personalization
  • Text classification (NLP)
  • Voice/audio processing
  • Object detection/classification (vision)
  • Edge demand forecasting
  • Fraud detection at POS/kiosks
  • Undecided / not defined
  • Other
Q7
Multiple Choice
Which model types are currently in scope for edge deployment in your organization? (Select all that apply)
  • Computer vision
  • Time-series forecasting
  • Anomaly detection
  • Natural language processing (NLP)
  • Speech/voice
  • Recommendation
  • Control/optimization
  • Other
Q8
Multiple Choice
Which edge environments are most relevant to your organization? (Select all that apply)
  • IoT sensors/devices
  • Industrial equipment/robots
  • On-premises servers/gateways
  • Mobile devices/tablets
  • Vehicles/fleets
  • Retail POS/kiosks
  • Medical/clinical devices
  • Other
Q9
Dropdown
How formalized are your organization's policies for the edge model lifecycle?
  • Written, organization-wide policies
  • Written, team-specific policies
  • Informal guidelines only
  • None in place
Q10
Opinion Scale
How would you rate the level of governance control your organization applies during model development and training for edge deployments?
Range: 1 7
Min: No controlMid: NeutralMax: Rigorous, enforced control
Q11
Opinion Scale
How would you rate the level of governance control your organization applies during model deployment and release for edge environments?
Range: 1 7
Min: No controlMid: NeutralMax: Rigorous, enforced control
Q12
Opinion Scale
How would you rate the level of governance control your organization applies during ongoing monitoring and maintenance of edge models?
Range: 1 7
Min: No controlMid: NeutralMax: Rigorous, enforced control
Q13
Dropdown
Does your organization maintain a model registry or inventory that includes edge deployments?
  • Yes — unified across cloud and edge
  • Yes — but partial coverage
  • No — planned within 6 months
  • No
Q14
Multiple Choice
If your organization maintains a model registry, which of the following does it track for edge models? (Select all that apply)
  • Model version and lineage
  • Training data provenance
  • Performance metrics
  • Deployment location/device
  • Hardware/resource requirements
  • Owner/team accountability
  • Compliance or approval status
  • Not applicable — no registry
  • Other
Q15
Opinion Scale
In the last 6 months, how well defined and enforced were data governance controls for edge datasets in your organization?
Range: 1 7
Min: Not at all definedMid: NeutralMax: Fully defined and enforced
Q16
Multiple Choice
Which of the following signals has your organization monitored on edge deployments in the last 30 days? (Select all that apply)
  • Data drift
  • Concept drift
  • Data quality checks
  • Latency/throughput
  • Accuracy/precision/recall
  • Hardware resource usage
  • Privacy/security events
  • Safety constraint violations
  • Human-in-the-loop feedback
  • None of the above
Q17
Opinion Scale
How mature are your organization's service-level objectives (SLOs) or service-level agreements (SLAs) for edge model performance?
Range: 1 7
Min: No SLOs/SLAs definedMid: NeutralMax: Fully defined, measured, and enforced
Q18
Multiple Choice
What tooling does your organization use to observe and alert on edge models? (Select all that apply)
  • Built-in device logs/metrics
  • Centralized monitoring (e.g., Prometheus, Grafana)
  • MLOps platform
  • Custom scripts/agents
  • Commercial APM/observability tool
  • Data observability tool
  • Not sure
  • Other
Q19
Dropdown
What is the approximate average time to detect a production edge AI incident in the last 90 days?
  • Less than 5 minutes
  • 5–15 minutes
  • 16–60 minutes
  • 1–4 hours
  • 4–24 hours
  • More than 24 hours
  • Don't know / not tracked
Q20
Dropdown
Approximately how many edge AI deployments were rolled back in your organization in the last 90 days?
  • 0
  • 1–2
  • 3–5
  • 6–10
  • More than 10
  • Don't know / not tracked
Q21
Ranking
Rank the following risk areas for edge AI from highest to lowest priority for your organization.
Drag to order (top = most important)
  1. Data privacy
  2. Security
  3. Safety
  4. Fairness/bias
  5. Reliability/availability
  6. Regulatory compliance
Q22
Dropdown
How often does your organization conduct formal risk assessments before edge AI deployments?
  • Every release
  • Major changes only
  • Ad hoc
  • Never
  • Planned within 6 months
Q23
Dropdown
Do any of your organization's edge AI models currently process sensitive personal data?
  • Yes, regularly
  • Sometimes
  • Unsure
  • No
Q24
Long Text
What are the top two or three gaps currently blocking edge AI governance and monitoring in your organization?
Max chars
Q25
AI Interview
We'd like to explore your thoughts on edge AI governance and readiness in more depth. An AI moderator will ask you up to 2 follow-up questions based on your earlier responses.
AI InterviewLength: 2Personality: [Object Object]Mode: Fast
Reference questions: 6
Q26
Ranking
Rank the following areas by how urgently they need investment to improve your organization's edge AI readiness.
Drag to order (top = most important)
  1. Policies & governance
  2. Monitoring & alerting
  3. Model registry & inventory
  4. Data governance for edge datasets
  5. Risk & compliance processes
  6. Tooling & automation
  7. People, training & change management
  8. Deployment/rollback processes
Q27
Long Text
Based on your responses in this survey, is there anything else you believe should be considered for edge AI governance or monitoring?
Max chars
Q28
Dropdown
What is your primary role?
  • Executive/VP
  • Director/Manager
  • Data science/ML
  • Software/IT/DevOps
  • Product/Operations
  • Security/Compliance/Risk
  • Quality/Manufacturing
  • Other
Q29
Dropdown
Which function do you primarily belong to?
  • Engineering/IT
  • Data/AI
  • Product
  • Operations
  • Manufacturing/Supply chain
  • Security/Risk/Compliance
  • Finance
  • HR
  • Other
Q30
Dropdown
How many years of experience do you have in data, AI, or analytics?
  • 0–1
  • 2–4
  • 5–9
  • 10+
Q31
Dropdown
Which region best describes your primary work location?
  • North America
  • Europe
  • APAC
  • Latin America
  • Middle East & Africa
  • Multiple regions
Q32
Chat Message
Thank you for completing this survey! Your input will help prioritize edge AI governance and monitoring improvements across your organization. Results will be shared in aggregate form.

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