AI shapes curation workflows
AI and machine learning are now central to content curation and editorial workflows across entertainment platforms — from recommender systems to story‑editing tools — changing how projects are discovered and how development teams prioritize concepts. (editorialge.com)
More than 80% of shows watched on Netflix in a recent two‑year window were discovered through the service’s recommendation algorithms rather than search, a figure the company has highlighted in public comments. (qz.com) Warner Bros. struck a deal on Jan. 8, 2020 to adopt Cinelytic’s AI project‑management platform, which produces territory‑level revenue forecasts and assigns monetary “value” to stars to inform greenlight and marketing allocation decisions. (hollywoodreporter.com) ScriptBook, founded in Antwerp in 2014, sells screenplay analysis and box‑office prediction models to studios and producers and has raised roughly $1.15 million in disclosed funding to refine AI-driven script scoring used in development pipelines. (scriptbook.io) Epagogix, operating since 2003 in the U.K., applies neural‑network forecasting to screenplays and has worked confidentially with major studios as an early commercial attempt to quantify box‑office potential. (en.wikipedia.org) AI video and trailer platforms — including makers listed in recent industry roundups and automation vendors such as Shotstack — now generate test trailers and localized cuts at scale, with industry guides noting traditional trailer production typically costs $50,000–$200,000 and can be dramatically shortened and down‑priced by AI workflows. (imagine.art) Netflix maintains a small cohort of human “taggers” (about 30 employees) who apply granular metadata—drawn from thousands of data points and over 3,000 tags—to titles so recommendation models and personalized rows can surface content more effectively than simple match percentages. (businesstimes.com.sg)
