The Al Trust Gap & How to Fix It. The numbers are crazy 👇 88% of companies use AI. Only 6% trust it to run critical processes. That gap? That’s where most AI programs go to die. I just came across this whitepaper from GoodData on the “AI Trust Gap” and it’s one of the most practical reads I’ve seen on why enterprise AI fails at production. The core argument is sharp: the problem isn’t the model. It’s the missing layer between your data and your outputs, the one that carries business definitions, governance rules, and traceability. Three trust killers they identify that I see constantly in the wild: 1️⃣ Everyone has a different definition of “revenue” 2️⃣ Governance built for humans breaks under AI speed 3️⃣ Polished outputs that look right but can’t be verified The fix isn’t better prompting. It’s treating trust as infrastructure, not an afterthought. There’s also a clean 4-stage framework to move from AI experimentation to production-ready intelligence. No fluff, just actionable steps. If you’re in data, AI strategy, or just tired of your AI pilots never graduating to real business use, worth 10 minutes of your time. 🔗 Link to the white paper: https://lnkd.in/erXfUFqg #ai #data
Andrew Royal
1w
In practice, trust only holds if outputs are tied to a named owner, validation happens before action, and every decision is auditable in real time.
Well put Eduardo, those “implicit rules” are really organizational politics and unspoken constraints. AI fails not because it’s inaccurate, but because it doesn’t understand the unwritten logic humans rely on. That’s the real trust gap.
Treating trust as infrastructure is a smart approach.
Omkar S.
1w
Great find Eduardo Ordax Addressing the trust gap and treating it as infrastructure is key to moving AI from experimentation to reliable business solutions.
The revenue definition problem is the one I'd pull forward. I've been in rooms where three different dashboards showed three different revenue numbers — all technically correct, all using different business logic — and nobody could agree which one the AI should use. That's not a data quality problem. That's a semantic layer problem. And no amount of prompt engineering fixes it. The 6% figure is the real story here. 88% experimenting, 6% trusting it in production. That gap doesn't close with better models. It closes when organizations treat shared business definitions and governance as infrastructure investments, not cleanup work they'll get to later. Most don't. Which is why most pilots stay pilots.
Alex Rogov
1w
The gap between AI adoption (88%) and AI trust for critical processes (6%) perfectly mirrors what I see in software architecture. Most teams bolt AI onto existing workflows without architectural preparation. No wonder trust is low — the AI operates in a black box with no guardrails. In my TypeScript/Node.js projects, I've found that trust comes from structure: • CLAUDE.md files that give AI agents explicit boundaries and context • Clean Architecture layers that isolate AI decisions from core business logic • Domain-driven design that makes AI outputs verifiable against business rules The companies in that 6% likely didn't just "adopt AI" — they architected for it. Trust isn't a feeling, it's an engineering outcome. What's been your experience — does better architecture lead to more trust in AI outputs?
That 88% usage vs. 6% trust stat is telling. I saw this firsthand while developing a crypto recommendation engine prototype with a team. We used social media data to predict price movements. The model was good at finding patterns, but the trust gap was the input data itself. How do you account for bot farms or coordinated sentiment campaigns? Without that verifiable layer, the output is just a high-tech guess. It's not a model problem. It is a data integrity problem.
Paul Iusztin
1w
Most teams validate models on accuracy, but not on business consistency. That’s how you end up with systems that ‘pass tests’ but fail in real decisions. A practical shift is to define evals around business invariants
The 6% trusting AI for critical processes isn't all signal. Some of them are wrong to do so and won't know until a failure surfaces. The gap worth closing isn't 88% to 100%. It's identifying which of the current 6% are running the right processes and which just got lucky.
I'm a driven 𝐃𝐚𝐭𝐚…•673 followers
1wThis is exactly the problem I’m trying to solve with BRIX Protocol (my open-source library)👇 ⭐ GitHub: https://github.com/Serhii2009/brix-protocol Deterministic rules on top of probabilistic models (LLMs). Full audit trail on every decision. A single metric that tells you whether your reliability configuration is actually working in production – or quietly broken. And when something risky is detected – real actions are taken 🚩Better prompting. ✅ Infrastructure. Would love to hear thoughts on this approach – and thank you for the post, it confirmed I’m solving the right problem))