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The EU AI Act mandates human oversight anddecision confidence documentation for AI managing critical infrastructure,yet current O-RAN automation lacks the calibrateduncertainty needed for confidence-aware autonomous operationunder human oversight. We address this through conformalprediction combined with learned confidence signals, validatedon three European production networks (500, 8,000, and 12,000cells). Our key contribution is demonstrating that cell similarityis necessary for measured confidence. For accessibility, efficiency,and reliability Key Performance Indicators (KPIs), similarityderivedsignals (neighbor agreement, similarity strength) are theonly source of confidence discrimination. Baseline magnitudealone provides little or no useful discrimination for these KPIcategories. Learned signal combinations achieve up to 13× discriminationbetween reliable and unreliable predictions, enablingoperators to document expected accuracy at any automationthreshold. Conformal prediction achieves empirical 94.5–95.0%coverage at target 95% without distributional assumptions.Automating the top 70% by confidence incurs only 51% of totalerror. Practitioner heuristics (same-site, same-band) perform onlymarginally better than random selection, confirming the needfor data-driven similarity. Each decision carries traceable confidenceevidence supporting regulatory audit. The combinationof calibrated bounds and similarity-derived confidence providesa practical foundation for confidence-aware O-RAN automationwith documented risk trade-offs.