Successfully Scaling AI and the Role of Trust
summary
A concrete claim: scaling AI fails when users can’t rely on outputs — a neuroprosthetic user chooses a simple, deterministic arm over a high‑tech one because it just works, showing trust beats novelty. Root causes cited: poor data foundation, models’ uncertainty and non‑explainability, no business impact measurement, missing engineering/ops for deployment, and talent/ways‑of‑working gaps. Remedies: solid data and target architecture, automated pipelines with quality gates, monitoring and adaptation, cross‑functional teams and servant leadership, measured by the VOTE framework and proven by 20 projects in production under trustworthy AI practices.
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