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.
Execs want AI capability. IT wants governance. Security wants controls. Legal wants compliance. Finance wants ROI. They all say yes and mean completely different things.
The CISO enters the conversation in week 10. Every concern they surface in week 10 was knowable in week 1 — if anyone had asked.
Engineering measures tokens per second. Business measures cost savings. Legal measures risk reduction. They run the same pilot and reach opposite conclusions.
The AI market builds tools. Nobody helps the team agree to adopt them. The gap between "impressive demo" and "signed contract" is pure stakeholder alignment.
AI vendors have built world-class technology and mediocre tools for helping buying committees make decisions about it. The bottleneck isn't the product — it's the process of deciding.
Turn "everyone says yes" into "everyone means yes."
The Probe Method generates intentionally imperfect proposals. Stakeholders correct what's wrong — revealing what they actually think.
Quantify consensus across 8 Universal Criteria. See where Security and Engineering agree — and where they diverge in silence.
Resolve blockers before the pilot stalls. Move from evaluation to deployment with actual cross-functional alignment.
The same eight criteria that derail every complex decision are present in every AI evaluation — and nobody is measuring them.
Security: "Zero tolerance for data exposure"
Engineering: "Ship and iterate"
Legal: "Compliance first"
CEO: "Live in Q2"
Engineering: "Integration = 6 months"
Legal: "Compliance review = 90 days"
Business: "Cost savings"
Engineering: "Performance metrics"
Legal: "Risk reduction"
Finance: "Budget approved for pilot"
Engineering: "Need 3 engineers, 4 months"
IT: "Infrastructure cost unaccounted"
CEO: "Competitors are moving now"
CTO: "Architecture review needed"
CISO: "Pen test first"
Legal: "Any data residency issue"
Engineering: "If latency > 200ms"
Finance: "If ROI < 3x year one"
IT: "We own vendor selection"
Engineering: "We own tech stack"
Legal: "We approve all AI tools"
Champion: "Weekly pilot updates"
CISO: "Needs formal briefings"
CFO: "Monthly ROI review"
Real scenarios where lucix surfaces the hidden disagreement derailing enterprise AI adoption.
Multiple providers pass technical eval. The real disagreement is cost ceiling, data residency, and integration ownership.
Engineering picked the winner. IT wants the one they already manage. Security has concerns about neither option.
Product wants automation. Sales wants personalization. Finance wants cost reduction. Three roadmaps, one budget.
CISO says no. Legal says maybe. Engineering says it's fine. The actual risk threshold is undiscussed and unknown.
New architecture is better. Migration effort is real. Stakeholders disagree on cost, timeline, and acceptable downtime.
Technology deployed. Adoption stalling. Hidden concerns about job impact and workflow disruption never surfaced.
Hidden disagreement killed a $400K annual AI adoption. Here's exactly what lucix found.
Technical evaluation passed. Pilot metrics were strong. Champion reported "internal consensus." Deal moved to legal review. Then it stalled — for 4 months.
"Data processing agreements don't cover our EU customers. I wasn't in the pilot scope."
"Pilot costs don't include integration engineering. Real cost is 2.4x the contract value."
"We'd need to rebuild our data pipeline. That's 3 engineers for 4 months."
"Model versioning policy doesn't match our compliance obligations. We need SLA guarantees on model behavior."
4-month stall → no decision
Concerns surface after legal engagement. Deal dies.
3 weeks → hybrid approach agreed
CISO and CFO concerns addressed in pilot design. Deal closes modified.
Stop losing AI deals to invisible disagreement. See how lucix surfaces what's blocking your next enterprise AI decision.