AI in broadcasting: efficiency with purpose

While technology is exciting, the primary driver for AI adoption in media is economic. There are several key motivations:

• Reduced Manual Labor: Automating repetitive tasks allows teams to focus on creativity rather than rote data entry or file management.
• Increased Production Output: With the same headcount, organizations can deliver content faster (critical for news) or produce more volume (critical for scaling).
• Content Monetization: AI helps process "unhandled data", such as usage logs or archival footage—that would otherwise be too labor-intensive to invoice or resell.
• Multi-Platform Reach: Efficiently versioning content for TV, VOD, and social media (TikTok, Facebook, X) ensures maximum audience reach without a linear increase in costs.

AI isn't a single tool; it's a series of specialized engines applied across the "Input-Processing-Output" chain.

1. Ingest & Metadata

The goal here is creating timed metadata.

• Speech-to-Text (Transcription): Converting voice to searchable text.
• Diarization: Distinguishing between different speakers.
• Face Recognition: Identifying key figures in news, sports, or historical archives to make assets findable.

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2. Production & Storytelling

AI assists in the "localization" and "internationalization" of content.

• Automatic Subtitling & Dubbing: Using translation and voice cloning to reach local markets.
• Ad Insertion: Using AI to understand the narrative flow and identify the perfect "slice points" for advertisements without disrupting the story.
• Visual Enhancements: Upscaling HD to Ultra HD or colorizing black-and-white archives.

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3. Delivery & Versioning

Different platforms require different treatments.

• Vertical vs. Horizontal: AI can automate the "crop and pan" process to turn 16:9 TV footage into 9:16 TikTok clips.
• Text Shortening: Condensing a full article into a 160-character post for X (formerly Twitter).

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Choosing the right environment depends on scale, budget, and security:

For government or high-security projects (like parliamentary archives), on-premises AI is often the only viable option due to data sovereignty requirements.

One of the most valuable insights from the session was the distinction between AI and Business Logic.

• AI/Deep Learning: Best for complex, "human-like" tasks like recognizing a face or understanding the sentiment of a story.
• Business Logic: A simple set of rules and constraints.

You don’t need to "AI-washing" every process. For example, you don't need a creative AI to tell a transcoder how to format a video for Facebook. You already know the requirements; you just need a standard rule set. Using AI for simple logic is like hiring a translator to read a stop sign—it’s slow, expensive, and unnecessary.