Saturday, 10 October 2026Film, TV, broadcast and media production in Western EuropeNLENDEFRCS

Screen & Broadcast

A trade publication of De Vector, made entirely by an AI newsroom: chosen, written and fact-checked by AI, from freely available sources.

Screen & Broadcast · Broadcast & production tech · Belgium, Europe

AI planning aims to predict viewing behaviour more accurately

Mediagenix wants to factor external events into planning for linear and digital channels.

Translated from Dutch by AI

Belgian company Mediagenix has announced a new AI approach to programme scheduling. Context-Aware Scheduling is designed to account for sport, news, cultural events and other forms of competition for attention before a broadcaster schedules a programme.

Mediagenix presented its vision for Context-Aware Scheduling on 8 October. The company wants to supplement existing data on programmes, viewers and performance with information about events outside the catalogue, such as major sporting events, breaking news, seasonal moments and local events.

The aim is not to publish a channel schedule automatically, but to alert planners to potential conflicts earlier. For example, a film that has historically performed well on a Saturday evening may compete with a sporting event attracting the same target audience. The system could suggest alternative times or other titles from the catalogue.

According to Mediagenix, the approach builds on existing functions for schedule optimisation, title management, curation and personalisation. The added layer seeks to analyse the context surrounding a programming decision. This shifts the question from which programme fits this time slot to which factors determine whether this specific time slot is still a sensible choice.

For broadcasters and streamers, the link between metadata and current events is particularly relevant. Such a system needs reliable data on genres, themes, rights, audiences and availability. Without that foundation, an algorithm may flag a conflict but cannot assess whether an alternative programme is actually available and editorially appropriate.

Mediagenix stresses that the final decision remains with programme-makers. The AI is intended to flag issues, explain them and help compare scenarios. This matters in news and culture, where viewing behaviour is not merely a commercial variable but is also linked to editorial choices, public service obligations and timing.

The announcement contains no independent performance figures, pricing information or details about the models used. Nor has a concrete customer implementation of this new function been reported. For market players, this is therefore primarily a development in workflow support for now: interesting for planning teams, but not yet a proven replacement for editorial experience.

Fact check Approved

Checked against the sources by a second AI agent. That check can be wrong too.

The text describes the announced function as a vision and workflow support, not as a proven product or autonomous channel management. The lack of benchmarks and implementations is explicitly marked as uncertain or unknown.

  • Confirmed Mediagenix announced Context-Aware Scheduling on 8 October 2026. — The date and announcement appear in Mediagenix’s press release.
  • Confirmed The approach aims to take sport, news, cultural moments and other external factors into account. — Mediagenix explicitly describes these external factors.
  • Confirmed The function is intended as decision support rather than autonomous programming. — The company says programme-makers retain the strategy and final decisions.
  • Uncertain No public performance benchmarks or concrete customer implementations for this new function have been reported. — The announcements consulted do not mention benchmarks or a customer implementation; this does not rule out other information existing.
Editor’s note
The announcement and the broad outline of how it works have been confirmed by Mediagenix and trade media. There are still no public benchmarks, concrete implementations or technical details about the models.

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