73% of AI pilots stall in evaluation—not from bad technology, but from hidden disagreement. lucix surfaces what's blocking alignment before the decision never happens.
Everyone says "yes" to AI pilots. Nobody agrees on what success looks like. The cost isn't saying no—it's never deciding at all.
"Pilot LLM infrastructure"
"Ship AI features fast"
"Ensure SOC 2 compliance"
"Prove ROI first"
"Mitigate liability risks"
Then the pilot stalls for 12 weeks while stakeholders silently disagree about priorities, timelines, and success criteria.
The market builds tools. Nobody helps teams agree to adopt them.
Everyone builds AI tools. Nobody helps teams agree to adopt them.
Engineering picks tools → Security blocks deployment
Product prioritizes features → Finance questions ROI
Everyone says "yes" → Requirements change mid-pilot
73% of AI pilots die from hidden disagreement, not technical failure.
The 8 dimensions where stakeholders disagree in silence
Engineering: "We can pilot in sandbox"
Security: "Data leaves our infrastructure"
Legal: "Customer data in training sets?"
Product: "Ship feature in Q2"
Engineering: "Need Q3 for infrastructure"
Security: "Audit takes 90 days"
Product: "User engagement"
Finance: "Cost per query"
Engineering: "Latency < 2 seconds"
Finance: "$50K pilot budget"
Engineering: "Need 2 FTEs for 6 months"
Product: "Designer + PM allocation?"
CEO: "Move fast, competitors shipping"
Security: "Penetration test required"
Legal: "Terms of service review"
Product: "If accuracy < 85%"
Finance: "If cost > $0.02/query"
Engineering: "If latency > 3s"
Product: "We own AI roadmap"
Engineering: "We approve vendors"
Security: "We have final say"
Product: "Weekly stakeholder sync"
Engineering: "Slack updates sufficient"
Finance: "Monthly business review"
Turn "everyone says yes" into "everyone means yes"
AI-powered Probe Method generates intentionally imperfect evaluation positions.
Quantify consensus across the 8 Universal Factors applied to AI decisions.
Resolve blockers before pilots stall, move from evaluation to production faster.
Real scenarios where lucix surfaces hidden disagreement
Engineering wants OpenAI. Finance wants lower cost. Security wants on-premise. Product wants best accuracy. lucix reveals priorities before contracts are signed.
Technical teams pilot 3 vendors. Ops wants managed service. Engineering wants self-hosted. Finance compares unit economics. Hidden cost/control tradeoffs surface.
Product has 10 AI feature ideas. Engineering can ship 2. Finance wants proven ROI. Customer Success wants different features than Sales. Alignment before development.
Security blocks GenAI due to data leakage concerns. Product needs it for roadmap. Legal unclear on liability. Engineering proposes workarounds. Risk tolerance alignment.
Move from Sagemaker to Databricks. Engineering wants full control. Finance wants lower TCO. MLOps wants proven stability. Timeline disagreements hidden.
Deploy AI tools to 500 employees. Product owns rollout. IT handles provisioning. Training wants 3 months. Sales wants next quarter. Hidden timeline conflict.
How hidden disagreement killed a $400K/year AI adoption decision
Company: B2B SaaS platform, 800 employees
Decision: Adopt LLM for customer support automation
Stakeholders: Product, Engineering, Security, Finance, Customer Success
Timeline: 4 months (planned: 6 weeks)
Product championed LLM adoption. Everyone said "yes" in kickoff. Engineering started pilot. Then… silence. No deployment for 4 months. Competitors shipped first.
"lucix revealed we needed a hybrid: fine-tuned open-source model for sensitive data, API for general queries. Security got control, Finance got cost efficiency, Product got speed. We shipped in 6 weeks."
— VP Product, B2B SaaS Platform
See how lucix surfaces hidden AI evaluation concerns and accelerates adoption decisions.