73% of AI projects never make it to production. We're mapping the real blockers — from security gaps to organizational fear — to build better solutions for engineering and product teams.
73%
POCs never ship
13
Questions
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🔒 Your individual responses are strictly confidential and will never be shared. Only anonymized, aggregate findings will be published once we have sufficient responses.
01
Question 1 of 13 · Context
What's your role?
Pick the one that fits best.
ML / AI Engineer
Data Scientist
MLOps / Platform Engineer
CTO / VP Engineering
Product Manager
Security / Compliance
Founder
Other
Enter ↵ to continue
02
Question 2 of 13 · Context
Company size?
Helps us understand which AI deployment challenges are tied to org scale.
A
1–10 (Startup / Solo)
B
11–100 (Early stage)
C
100–500 (Growth)
D
500–5,000 (Mid-market)
E
5,000+ (Enterprise)
03
Question 3 of 13 · Context
What industry are you in?
💻 SaaS / Tech
🏦 Fintech / Banking
🏥 Healthcare
🛍️ Retail / E-commerce
🏭 Manufacturing
💳 Payments
🏛️ Insurance
🎓 Education
⚖️ Legal
Other
04
Question 4 of 13 · Status
Where is your org on the AI journey?
🔬
Still experimenting / building POCs
🚧
POCs built, stuck before production
🚀
1–2 things in production, want to scale
✅
Multiple AI systems running in prod
🤔
Haven't started yet
05
Question 5 of 13 · The Gap
How many AI projects are stuck before production right now?
Include POCs, pilots, and internal tools that never shipped.
A
None — everything ships or gets cancelled cleanly
B
1–2 projects
C
3–5 projects
D
6–10 projects
E
10+ — it's a graveyard
06
Question 6 of 13 · Core Blockers
What are your top 3 blockers to production?
Select up to 3. We want the real ones, not the polite ones.
✦ Your top 3 blockers — drag or click to select
Drag items here or click below to add
✓ Select up to 3
0 / 3 selected
🔴 Security & Vulnerabilities
🔒
Model vulnerability — prompt injection, jailbreaks, adversarial inputs
🧬
Training data poisoning or supply chain risk
🕵️
No way to red-team or pentest AI systems before launch
🔵 Trust & Explainability
🧩
Can't explain AI decisions to stakeholders or customers
🎲
Model behavior is unpredictable / non-deterministic
📡
No monitoring or observability for model drift in production
🟡 Compliance & Legal
📋
Regulatory uncertainty — EU AI Act, GDPR, HIPAA, SOC2
⚖️
Legal liability — who's responsible when AI makes a mistake?
📊
Data governance — PII exposure, consent, lineage gaps
🟣 Organizational & Cultural
🏢
No executive buy-in or leadership fear of AI failures
👥
Talent gap — no MLOps, AI security, or governance expertise in-house
🎭
No clear owner — everyone disagrees on who's responsible
What are you using today to address AI production risks?
Select all that apply — even partial or experimental use counts.
✦ Tools you're using — drag or click to select
Drag items here or click below to add
✓ Select all that apply
0 selected
🔵 Explainability & Interpretability
🔍
SHAP (SHapley values)
🔍
LIME
🔍
Captum (PyTorch)
🔍
InterpretML (Microsoft)
🔍
What-If Tool (Google)
🔍
Fiddler AI
🟢 Model Monitoring & Observability
📈
Arize AI
📈
Evidently AI
📈
WhyLabs
📈
Datadog ML monitoring
📈
Grafana + custom dashboards
📈
Weights & Biases (W&B)
🔴 AI Security & Red-teaming
🛡️
Garak (LLM vulnerability scanner)
🛡️
HackAPrompt / PromptBench
🛡️
Pyrit (Microsoft)
🛡️
Lakera Guard / Guardrails AI
🛡️
Rebuff / NeMo Guardrails (NVIDIA)
🛡️
External red-team / pen-test engagement
🟢 MLOps & Deployment Pipelines
⚙️
MLflow
⚙️
Kubeflow / KServe
⚙️
Metaflow (Netflix)
⚙️
BentoML / Seldon
⚙️
SageMaker / Vertex AI / Azure ML
⚙️
Homegrown CI/CD pipelines
🟣 LLM Evaluation & Testing
🧪
LangSmith (LangChain)
🧪
Promptfoo
🧪
Braintrust
🧪
Ragas (RAG evaluation)
🧪
Eleuther AI lm-evaluation-harness
🧪
Deepeval
🟡 Governance & Compliance
📁
Internal AI policy docs / risk registers
📁
NIST AI RMF framework
📁
ISO 42001 (AI management)
📁
OneTrust AI Governance
📁
Credo AI
📁
Manual audits / third-party assessments
⬜ None of the above
🚫
Nothing formal yet — we're winging it
🤷
Not sure what my org uses
11
Question 11 of 13 · Budget
Is there budget for AI governance / security tooling this year?
A
Yes — dedicated budget line item exists
B
Sort of — it's buried in security/compliance budgets
C
No — but I could make the case for it
D
No — budget is frozen / org isn't there yet
E
I'm not involved in budget decisions
12
Question 12 of 13 · Sentiment
How does your leadership talk about AI risk internally?
😶
They mostly ignore it — "ship fast, fix later"
😰
Actively worried — risk conversations slow everything down
🙈
In denial — "our AI is different / special"
🏗️
Building processes — we're taking it seriously
😵
Paralyzed — too scared to do anything
🎭
It depends on who you ask — no alignment
13
Question 13 of 13 · The Real Story
Describe the last AI project that got stuck before production.
What happened? What killed it? What would it have taken to ship? Be as specific as you're comfortable.
Ctrl + Enter to continue
14
Last Step · Your Details
Almost done — who are you?
Your response will be attributed to you and kept confidential — used for research purposes only.
Enter ↵ to submit
✦
You're awesome.
Your responses will help us understand the real cost of AI stuck in limbo — and build tools that actually fix it.
Your individual data is strictly confidential and will never be shared. Once we have sufficient responses, we'll publish anonymized, aggregate findings in the 2025 State of AI Production Readiness report — and we'll email you when it drops.
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AI security · explainability · production readiness