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AI Scheduling for Broadcasters

Strategic AI prototype to modernise broadcast scheduling

Use case

Mid-sized broadcasters often rely on static weekly schedules, planned manually based on past performance and editorial judgement. Yet audience interest shifts rapidly, driven by breaking news, viral moments, or trends.

“We envisioned an AI system that reacts to real-time signals and helps forecast viewer demand, before the content airs.”

The challenge

Static schedules unable to respond to fast-changing viewer behaviour
No integration of real-world trends, search, or social sentiment
Heavy manual coordination between editorial, scheduling, and promo teams
Lack of predictive models tailored to local channel performance

The solution

As part of a Berkeley capstone project Ancast Intelligence proposed a prototype AI scheduling system capable of:

Forecasting hourly viewership using XGBoost & LSTM models
Incorporating real-time data (Twitter trends, search spikes, live logs)
Generating next-slot, +7 day, and +28 day demand predictions
Simulated retraining cycles (daily + intra-day updates)
Editorial override interface with feedback loop
Designed to plug into CMS or scheduling tools

Prototype features

Dynamic nowcasting based on real-time signals
Confidence scoring using MAPE/RMSE thresholds
“Explainable AI” layer for editorial trust
Human-in-the-loop scheduling design
Model drift alerts and override capture for editorial feedback

Validation (prototype metrics)

While not deployed in a live broadcast environment, the model proposed testing against 2 years of historical viewership data from real-world UK broadcasters:

Target MAPE: Reduced from ~20% → ~12% in simulations
Hypothetical uplift: +5% audience reach in high-signal test slots
Workflow simulation: ~20% potential reduction in manual hours
Editorial interface showed high usability in stakeholder testing

Results and impact

Project highlights

Developed as part of the Berkeley AI Strategy & Business Applications program
Structured as a strategic business case for AI-led broadcast transformation
Combined research, modelling frameworks, and stakeholder analysis
Reviewed by AI practitioners, media consultants, and academic faculty
Designed for realistic deployment within mid-sized broadcaster environments

Who this is for