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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AI-Powered Questions
Intelligent follow-up questions based on responses
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Detailed Reports
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Template Overview
32
Questions
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Ready-to-Use
Launch in Minutes
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)
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)
Data privacy
Security
Safety
Fairness/bias
Reliability/availability
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)
Policies & governance
Monitoring & alerting
Model registry & inventory
Data governance for edge datasets
Risk & compliance processes
Tooling & automation
People, training & change management
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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