🎙️ Episode 33 | Broadcast Media: The Inside Track

In this episode, we go deeper into nowcasting than ever before — moving well beyond the concept into practical, market-ready application for broadcasters and streamers.

What started as an academic framework during Ben's UC Berkeley AI Strategy programme has evolved into something far more powerful. By mapping nowcasting onto real broadcast data, real scheduling decisions and real commercial constraints, the idea has shifted from theory into a genuine market opportunity.

🔍 What is nowcasting?

Nowcasting is the practice of estimating what is happening right now and what is likely to happen in the immediate future — using live or near-term signals. While forecasting asks what will happen next quarter, nowcasting asks what is most likely to happen in the next few minutes or hours. That distinction sounds subtle, but in media it is significant. Audiences switch platforms instantly. Devices fragment engagement. External events change viewing behaviour within minutes. Relying solely on lagging indicators leaves optimisation opportunities untapped.

📡 What we cover in this episode:

Why traditional broadcast operations built around predictability are incomplete for today's fast-moving viewing environment — and what to do about it.

How economists inspired a broadcast-specific approach. They use shipping movements, credit card transactions and mobility data to estimate GDP before official figures land. The same logic applies to using behavioural signals to refine scheduling decisions.

Why promos are the natural low-risk entry point. Broadcasters invest heavily in promotional assets, yet placement decisions often rely on experience rather than granular behavioural analysis. Nowcasting enables a more precise question: given the signals present at that moment, was there a more effective option?

Why FAST channels are the ideal proving ground — high-variance, ad-funded environments where even small retention improvements translate directly into revenue uplift.

How a realistic pilot works — analysing a month of historical data for a specific channel, isolating break types, simulating alternative content choices and quantifying predicted retention uplift. No need to rebuild playout systems. Start as a contained desktop exercise. Validate signal before scaling.

The organisational dynamics that matter just as much as the technology — aligning editorial expertise, data science capability and commercial strategy with proper governance and incentive structures.

Why measurement discipline is non-negotiable — holdout datasets, cross-validation techniques and clear separation between training and testing data to avoid overfitting.

💡 Key takeaway: "Nowcasting is a disciplined way to use real-time or near-term behavioural signals to improve the next decision — without disrupting long-term strategy."

📈 Why incremental matters: A 1% improvement in retention across hundreds of breaks accumulates quickly. Media markets are competitive and margins are tight. Nowcasting succeeds when positioned as disciplined optimisation rather than dramatic overhaul.

Whether you're a CTO exploring AI implementation, a commercial head looking for revenue uplift, a product manager evaluating optimisation tools, or an industry leader shaping strategy — this episode lays out a practical, evidence-first roadmap.

🎧 Start the internal audit. Explore your data. Ask whether measurable signal exists. Start small and build deliberately — because in today's media environment, standing still is still a decision.

🔗 Find out more: www.ancast.co.uk or connect with Ben on LinkedIn

#BroadcastAI #Nowcasting #FAST #StreamingMedia #AIStrategy #BroadcastMedia #TheInsideTrack #AncastLimited #OTT #AdTech #BroadcastOptimisation

Metadata drift. Buffer overflows. Configuration chaos.

Live broadcast infrastructure just got an AI upgrade. But not the hype kind.

In this episode, Ben Anchor sits down with Teju Mulagada (Alphacord Media Group) to explore how multi-agent AI is turning SMPTE ST 2110 workflows from reactive firefighting to orchestrated intelligence.

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📊 WHAT'S INSIDE:

🔧 The Architecture — Why three specialized AI agents outperform single monolithic systems• Metadata Tracking Agent (detects anomalies in real-time)• Buffer Management Agent (predicts spikes before they happen)• Configuration Agent (monitors device interactions at scale)

⚡ Real Deployment Timeline — Months, not years, from pilot to production (when you get governance right)

🛡️ Human-in-the-Loop Governance — Every critical decision validated, never automated away

🎓 The Knowledge Gap — Why SMPTE ST 2110 adoption is the bottleneck before adding AI

💡 Live Use Cases — Edge computing + IP workflows + cloud orchestration

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👤 ABOUT TEJU MULAGADA

Technical Program Manager | AI Strategist | Growth Leader @ Alphacord Media Group

10-year IT background → broadcast transformation specialist. Her research paper, "Leveraging Multi-Agent AI Systems for SMPTE ST 2110 Broadcast Automation," was presented at SMPTE 2025 in Pasadena and is being published in the SMPTE Motion Imaging Journal (May 2026 edition).

🔗 Connect with Teju: https://www.linkedin.com/in/tejaswi-mulagada/

📰 Watch her SMPTE 2025 presentation: https://www.youtube.com/watch?v=MuUKUWZZqK0&list=PLzxtgAAyZWThbz7RYpbnPdqdwqX1PyGR4

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💭 KEY TAKEAWAY:

"Broadcast isn't failing because AI technology doesn't work. Broadcast is failing because adoption, governance, and change management are hard. This conversation is about how to actually implement the future."

— Ben, Ancast Intelligence

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#BroadcastAI #SMPTE2110 #MultiAgentAI #AIOrchestration #BroadcastEngineering #LiveProduction #MediaTransformation #AIImplementation #BroadcastTechnology #IPWorkflows #SMPTE #BroadcastMedia #MediaTech #Automation

Your broadcast organization has AI pilots running everywhere. Different vendors in each division. Vendors are promising transformation. But nothing tangible is happening. You're spinning your wheels.

The problem isn't the technology. It's change management.

Ben explores the uncomfortable truth: organizations aren't even attempting coordinated AI leadership. News division trying one solution. Playout engineering trying another. Advertising running its own pilot. Facilities looking at something else. Zero reference point. Zero best practice. Zero joined-up roadmap. Zero governance.

And the reason? Nobody's prepared the people who actually operate these systems to trust, understand, or work with AI.

IN THIS EPISODE:

🎙️ A Broadcast Technology Leader Confesses"Our AI is being rolled out everywhere. But nothing tangible is happening. We're spinning our wheels."

⚙️ The Change Management CrisisWhy engineers don't trust AI recommendations. Why approval chains collapse. Why the systems get ignored. Why governance is missing entirely.

❌ Why Centers of Excellence Aren't Being BuiltThe hard truth about why broadcast organizations avoid coordinated AI strategy—and what that avoidance really costs them.

📈 The Disillusionment Phase ExplainedPeak hype crashes into reality. Most organizations quit. Some become cynical. The smart ones climb toward enlightenment. You're probably in this phase right now.

💡 The Market Window75% of broadcasters haven't started. 25% are in the disillusionment trough. First-movers who fix the fundamentals win the next five years.

THREE QUESTIONS FOR YOU:

1. Do you have unified operational data across your broadcast divisions?
2. Do you have business processes designed for AI-assisted decision making?
3. Do you have governance so humans actually trust the system?

If you're answering no—that's your roadmap.

FEATURING: Insights from PwC's Global CEO Survey, Mohamed Kande's leadership diagnosis, and real conversations with broadcast technology leaders navigating the AI chaos.

This is the conversation about broadcast AI that matters.

Reach out at Ancast.co.uk or find Ben on LinkedIn to explore whether your broadcast is ready to move from disillusionment to enlightenment.

#BroadcastAI #ChangeManagement #AITransformation #BroadcastTech #DigitalStrategy #AIStrategy #Leadership #MediaInnovation #Podcast #BroadcastMedia