This paper provides a meta-analysis of macroeconomic, quantitative studies that seek to identify the causal impact of automation and artificial intelligence (AI) on employment in the European Union (EU). We contribute an EU-focused synthesis that concentrates on macro-level evidence, complementing a literature dominated by micro task-based analyses and broader cross-country discussions. Using an AI-assisted workflow for search, screening, and extraction with human oversight, we map the available evidence and report clear methodological and knowledge gaps. A central finding is that the automation and robotics literature is comparatively developed, while credible causal macro evidence on AI itself remains sparse and fragmented. We treat this scarcity as a result that motivates a concrete research agenda. Across the core empirical studies and selected grey literature, the net employment effect appears modestly positive on the order of 1.5 to 2.0 percent, but the distributional pattern is uneven. Risks concentrate among low-skilled and routine workers, older cohorts, and lagging regions, pointing to policy priorities in reskilling, education, and digital infrastructure. Methodologically, the paper demonstrates how LLM-assisted procedures can improve transparency, consistency, and scalability in early-stage evidence synthesis where conventional meta-regression is premature.