Broadcast Media: The Inside Track

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๐ŸŽ™๏ธ Reinventing Broadcast: AI, Content, and the Future of Media The media industry is evolving fastโ€”AI, automation, and digital transformation are reshaping broadcasting and content creation. Join Ben, a broadcast consultant & AI strategist, as he explores: โœ… AIโ€™s impact on media & content โœ… Expert insights & consulting case studies โœ… Practical strategies for staying ahead With a mix of AI-driven conversations, deep dives, and guest insights, this series is a must-listen for media professionals. ๐ŸŽง Subscribe now & explore more at Ancast.tv

  1. 3D AGO

    Nowcasting for Broadcast: From UC Berkeley Theory to Real-World Revenue

    ๐ŸŽ™๏ธ Episode 33 | Broadcast Media: The Inside Track In this episode, we go deeper into nowcasting than ever before โ€” 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. ๐Ÿ” What is nowcasting? Nowcasting is the practice of estimating what is happening right now and what is likely to happen in the immediate future โ€” 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. ๐Ÿ“ก What we cover in this episode: Why traditional broadcast operations built around predictability are incomplete for today's fast-moving viewing environment โ€” 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 โ€” high-variance, ad-funded environments where even small retention improvements translate directly into revenue uplift. How a realistic pilot works โ€” 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 โ€” aligning editorial expertise, data science capability and commercial strategy with proper governance and incentive structures. Why measurement discipline is non-negotiable โ€” holdout datasets, cross-validation techniques and clear separation between training and testing data to avoid overfitting. ๐Ÿ’ก Key takeaway: "Nowcasting is a disciplined way to use real-time or near-term behavioural signals to improve the next decision โ€” without disrupting long-term strategy." ๐Ÿ“ˆ 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 โ€” this episode lays out a practical, evidence-first roadmap. ๐ŸŽง Start the internal audit. Explore your data. Ask whether measurable signal exists. Start small and build deliberately โ€” because in today's media environment, standing still is still a decision. ๐Ÿ”— Find out more: www.ancast.co.uk or connect with Ben on LinkedIn #BroadcastAI #Nowcasting #FAST #StreamingMedia #AIStrategy #BroadcastMedia #TheInsideTrack #AncastLimited #OTT #AdTech #BroadcastOptimisation

    14 min

  2. FEB 4โ€„ยทโ€„BONUS

    ๐ŸŽ™๏ธ BONUS EPISODE: Multi-Agent AI Transforming Live Broadcast

    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. โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” ๐Ÿ“Š WHAT'S INSIDE: ๐Ÿ”ง The Architecture โ€” Why three specialized AI agents outperform single monolithic systemsโ€ข Metadata Tracking Agent (detects anomalies in real-time)โ€ข Buffer Management Agent (predicts spikes before they happen)โ€ข Configuration Agent (monitors device interactions at scale) โšก Real Deployment Timeline โ€” Months, not years, from pilot to production (when you get governance right) ๐Ÿ›ก๏ธ Human-in-the-Loop Governance โ€” Every critical decision validated, never automated away ๐ŸŽ“ The Knowledge Gap โ€” Why SMPTE ST 2110 adoption is the bottleneck before adding AI ๐Ÿ’ก Live Use Cases โ€” Edge computing + IP workflows + cloud orchestration โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” ๐Ÿ‘ค ABOUT TEJU MULAGADA Technical Program Manager | AI Strategist | Growth Leader @ Alphacord Media Group 10-year IT background โ†’ 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). ๐Ÿ”— Connect with Teju: https://www.linkedin.com/in/tejaswi-mulagada/ ๐Ÿ“ฐ Watch her SMPTE 2025 presentation: https://www.youtube.com/watch?v=MuUKUWZZqK0&list=PLzxtgAAyZWThbz7RYpbnPdqdwqX1PyGR4 โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” ๐Ÿ’ญ 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." โ€” Ben, Ancast Intelligence โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” #BroadcastAI #SMPTE2110 #MultiAgentAI #AIOrchestration #BroadcastEngineering #LiveProduction #MediaTransformation #AIImplementation #BroadcastTechnology #IPWorkflows #SMPTE #BroadcastMedia #MediaTech #Automation

    30 min

  3. JAN 21

    Why Broadcast AI Fails: Not Technology, It's Change Management

    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: ๐ŸŽ™๏ธ A Broadcast Technology Leader Confesses"Our AI is being rolled out everywhere. But nothing tangible is happening. We're spinning our wheels." โš™๏ธ The Change Management CrisisWhy engineers don't trust AI recommendations. Why approval chains collapse. Why the systems get ignored. Why governance is missing entirely. โŒ Why Centers of Excellence Aren't Being BuiltThe hard truth about why broadcast organizations avoid coordinated AI strategyโ€”and what that avoidance really costs them. ๐Ÿ“ˆ 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. ๐Ÿ’ก 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: Do you have unified operational data across your broadcast divisions?Do you have business processes designed for AI-assisted decision making?Do you have governance so humans actually trust the system?If you're answering noโ€”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

    16 min

  4. JAN 6

    Orchestrate, Don't Automate: Your 2026 Broadcast AI Roadmap

    Agent autonomy is so last year's hype. What broadcast leaders are actually building in 2026: orchestrated systems that work reliably under human oversight. In this conversation, Ben Anchor (Ancast Intelligence) and RaIAna explore the gap between AI agent hype and operational reality. From MCP protocols to A2A standards, from nowcasting to real-time sports production, discover why orchestration beats autonomyโ€”and why broadcast operators have a genuine competitive advantage heading into 2026. ๐ŸŽฏ WHAT YOU'LL LEARN ๐Ÿ“Š Why Agent Autonomy Failed and what actually works instead๐Ÿ”Œ MCP & A2A Standards that eliminate custom middleware integrationsโšก Your Data Infrastructure is a Moat (ratings, CDN, metadata)๐ŸŽฌ Real Production Examples: Sports detection to distribution๐Ÿง  System 2 Thinking & when to allocate expensive reasoning๐Ÿ” Ethics Pipeline for avoiding bias at broadcast scale๐Ÿ“ˆ Three-Phase Implementation: 12-week proof of concept to scaling๐Ÿ† First-Mover Advantage in Q1 2026 ๐ŸŽ™๏ธ EPISODE HIGHLIGHTS MCP & A2A: The Infrastructure LayerModel Context Protocol standardizes how agents access tools. Agent-to-Agent protocols let independent agents coordinate without hard-coded integrations. Broadcast's Hidden Competitive AdvantageYou already have ratings, CDN infrastructure, metadata systems, and real-time audience analytics. Most AI teams in other industries are building this from scratch. You're starting 18 months ahead. Sports Production as Real-World OrchestrationLive match โ†’ Autonomous cameras โ†’ Highlight detection โ†’ Real-time encoding โ†’ Metadata tagging โ†’ Statistics generation โ†’ Distribution. Multiple systems coordinating in real-time under human oversight. Scientific Acceleration in BroadcastAI systems testing hypotheses about audience behavior, proposing experiments, interpreting results. Humans make final decisions armed with deep analysis. That's augmented reasoning, not replacement. Ethical AI Isn't OptionalBias in training data compounds at broadcast scale. Building with transparency (SHAP, LIME tools) becomes engineering requirement, not compliance checkbox. ๐Ÿ“š RESEARCH & SOURCES Human in the Loop (Andreas Horn) - Scientific acceleration thesis, model bifurcation, 2026 predictionshttps://www.humanintheloop.online/ Maven: AI Agents & Agentic Workflows (Sara Davison & Tyler Fisk) - Tinkerer-to-implementer progression, orchestration frameworkshttps://maven.com/ SMPTE ER 1011:2025 - Official broadcast AI standards on MCP/A2A, data infrastructure, ethical implementationhttps://www.smpte.org/ ๐ŸŽฏ KEY TAKEAWAYS โœ… Orchestration > Autonomy โ€” Systems where AI and humans work together reliably win โœ… Standards Are Coming โ€” MCP and A2A frameworks mean early movers get competitive advantage โœ… Your Data is Real Advantage โ€” BARB, CDN, metadata = signal richness for nowcasting and prediction โœ… Ethics is Engineering โ€” Bias testing and transparency are foundational to system performance โœ… Timeline is NOW โ€” Start POC in Q1 2026, get 6-9 month lead on competitors ๐Ÿ’ฌ PERFECT FOR ๐Ÿ“บ Broadcast engineers exploring AI integration๐Ÿ’ผ Streaming and FAST platform operators๐ŸŽฏ Content leaders and programming teams๐Ÿข Operations and technology executives๐Ÿ“Š Audience analytics teams๐Ÿค– Anyone building broadcast AI systems ๐Ÿ”— EXPLORE FURTHER Ancast - Broadcast AI Consulting8-12 week proof of concept programs with clear ROI measurement and human-in-the-loop implementation.https://www.ancast.co.uk/ MCP Framework: https://modelcontextprotocol.io/ #BroadcastAI #AIAgents #2026Roadmap #MCP #A2A #Nowcasting #BroadcastTech #AIOrchestation #SMPTE #HumanInTheLoop #Maven #BroadcastLeadership #MediaTech #ResponsibleAI #BroadcastInnovation Hosted by Ben Anchor with AI co-host RaIAna. Perfect for commute listening or pre-strategy meeting research. Press play, take notes, start your proof of concept. The first-mover window is still open.

    21 min

  5. 12/16/2025

    From UC Berkeley to DevStream Labs: Building AI-Powered Products

    ๐ŸŽฎ When Berkeley classmates become co-founders: Michael joins Bruce on an unexpected journey to accelerate video game development through AIโ€”and what they're building has massive implications for broadcast and media. THE STORY: Three UC Berkeley Applied AI cohort members reunite on the podcast. Michaelโ€”a behavioral economist and lifelong sales expert (from selling hotdogs at his mom's stand to selling buildings)โ€”has just joined DevStream Labs as co-founder. Bruce, the founder's technical partner, is a former Shell Oil automation engineer turned UC Berkeley data scientist turned Applied AI instructor. Their unexpected collaboration offers a masterclass in how AI actually creates value in creative industries. WHAT DEVSTREAM LABS SOLVES: Video game development drowns in bottlenecks. A small code change can cascade through massive collaborative systems, breaking everything. Multiple departments (art, sound, design, code) work in siloed friction. DevStream Labs applies manufacturing principles to complex software development: smaller batches, contained changes, rapid iteration cycles. The result? Teams ship faster, catch bugs earlier, and maintain creative momentum. IN THIS EPISODE YOU'LL DISCOVER: ๐ŸŽฏ Michael's unconventional founder storyโ€”from building "Higher AI" (a voice analytics platform for sales performance) to angel investing in Bruce's company to suddenly becoming co-founder in just weeks ๐ŸŽฏ How behavioral economics intersects with AI strategyโ€”understanding human motivation is just as critical as the technology itself ๐ŸŽฏ Bruce's technical journey: Shell Oil optimization engineer โ†’ recognizing ML was the future โ†’ UC Berkeley master's in data science โ†’ teaching applied AI โ†’ launching DevStream Labs with Bungee/Avid founder Brent ๐ŸŽฏ Why the Skydeck competition matteredโ€”DevStream Labs placed in the top 50 out of 4,200+ applicants, attracting Michael's family investment ๐ŸŽฏ The manufacturing-to-software-development analogyโ€”why smaller batches work in complex collaborative projects just like they worked at Shell Oil, and how this prevents catastrophic project failures ๐ŸŽฏ The trifecta of foundersโ€”how complementary skill sets (technical depth + domain expertise + business vision) drive successful startups ๐ŸŽฏ Why Jevons Paradox reshapes the AI conversationโ€”during the Industrial Revolution, cheaper coal led to MORE demand, not less. Same principle applies to AI: better tools = higher demand for expert humans to guide those tools ๐ŸŽฏ The urgent broadcast problem: 3D studio recreation from still photos, world models creating simulated broadcast environments, real-time virtual production becoming increasingly accessible ๐ŸŽฏ Why the future demands human-in-the-loop augmentationโ€”not replacement automationโ€”to maintain creative control and quality ๐ŸŽฏ The productivity paradoxโ€”AI tools don't eliminate expertise; they multiply the value of expert knowledge and free creative teams for higher-impact storytelling work ๐ŸŽฏ What's next: aviation as the next frontier for complex software development optimization THE BROADCAST ANGLE: If you're in media, this matters. DevStream Labs is building tools that could transform how broadcast studios manage complex collaborative workflows. 3D environment recreation, world models, real-time asset managementโ€”all accelerated by AI while keeping humans in creative control. RESOURCES & LINKS: ๐ŸŒ DevStream Labs: www.devstreamlabs.com ๐Ÿ’ผ DevStream Labs LinkedIn: Available for 12 Days of Christmas campaign https://www.linkedin.com/company/dev-stream-labs/ ๐ŸŽฎ Free Tools: Unity build error detection augmentation available now ๐ŸŽ™๏ธ Broadcast Media: The Inside Track on Spotify, Apple Podcasts, YouTube www.ancast.co.uk #AI #Broadcasting #GameDevelopment #Augmentation #HumanInTheLoop #StartupJourney #UCBerkeley #CreativeTechnology #ProductInnovation #AITransformation #SoftwareDevelopment #MediaTechnology #Entrepreneurship #VirtualProduction

    22 min

  6. 12/09/2025

    YouTube Light Years Ahead: Broadcasters and AI Implementation

    Every broadcaster is starting an AI initiative. Only five percent are actually doing something tangible. The rest are tinkering at the edges with expensive pilots nobody uses. The problem isn't the technology. It's change management. Most organizations approach AI like just another technology integration. Wrong. The first conversation should be about revenue opportunities and operational savings, not tech stacks. Because at the first hurdle, it gets handed to the IT team. Now it's an IT project. And those fail. Compare that to YouTube. Light years ahead. Not better engineersโ€”they embedded change management into their DNA from day one. Every product decision aligned with their recommendation engine. That's the operating system difference that separates winners from everyone else. ๐ŸŽฏ THE THREE FAILURE MODES: 1๏ธโƒฃ Strategic Misalignment: Starting with technology instead of the business problem. Where are we leaving money on the table? What if we could recover three to five percent revenue through better efficiency? 2๏ธโƒฃ Technical Mismatch: Organizations buy the wrong AI tool. A vendor pitches ChatGPT for a scheduling problem that actually needs predictive data science. Guaranteed failure. 3๏ธโƒฃ Operational Discontinuity: Systems get deployed but nobody uses them. The workflow doesn't fit how people actually work. So they revert to what they know. โœ… THE FOUR-STEP FIX: STEP ONE: Build a coalition of operators, not just executivesSTEP TWO: Crystal clarity on changeโ€”this is AI augmentation, not replacementSTEP THREE: Move people up the value chain, not out of jobsSTEP FOUR: Implement gradually in low-risk, high-reward areas first โฐ Timeline: Six to nine months for genuine business value. The competitive risk: broadcasters moving first through proper change management will be operating on a different system by 2026. For FAST channels using nowcasting? That's three to five percent annual revenue recovery. First-mover advantage. Real money. The window is closing. ๐Ÿ”ง IF YOU'RE READY: Don't start with technology. Start with a conversation. Get your ops, finance, and IT teams together. Ask: where are we losing money or efficiency today? Pick one problem. Figure out if AI helps. Then design the change management approach. Then build the solution. That's how the five percent do it. Ancast Intelligence works with broadcasters on the implementation side. We offer: AI Discovery Sprint (two-week audit), Roadmap & R&D Advisory (four to six weeks), Fractional Delivery Lead (live projects), and Bespoke Projects (full implementations like Nowcasting). You're not buying AI. You're buying the certainty that it will actually work and drive the value you expect. ๐ŸŽ™๏ธ Ben Anchor (Broadcast Consultant & AI Strategist) with RaIAna Not hype. Just 20 years of broadcast experience and the hard truth about what actually works. ๐ŸŒ ancast.co.uk #BroadcastAI #ChangeManagement #AIImplementation #Nowcasting #FASTChannels #MediaTech #DigitalTransformation #BroadcastIndustry

    14 min

  7. 12/02/2025โ€„ยทโ€„BONUS

    ๐ŸŽฏ From IBC Networking to Production AI: Amira Labs Special

    ๐Ÿš€ BONUS EPISODE: The Technical Deep-Dive That Separates Real Broadcast AI from the Hype Remember when everyone at IBC 2024 was talking about AI revolutionizing broadcast? Most of it was buzzword bingo. But then Ben met Kyle Seuss and Stefan Cardenas from Amira Labsโ€”and they were actually shipping solutions to real broadcasters while building serious R&D. Fast forward to 2025: with AI agents and thinking models dominating every conference conversation, we reconnected to ask the hard questions: What's actually working? What's still vaporware? And why do most broadcast AI projects fail before they even start? This bonus episode is the real dealโ€”no hype, just two engineers and a broadcast consultant breaking down the operational, technical, and business realities of AI in broadcast. ๐Ÿ”ฅ What You'll Discover: The Brutal Truth About Broadcast AI: MIT study says 95% of AI deployments failโ€”Stefan explains exactly why in broadcast specificallyThe data accessibility crisis: Most broadcasters can't even access their own operational data for AI to work with. Think about that. You can't automate what you can't see or measure.Why top-down "AI mandates" from executives almost always fail when they don't integrate with existing workflowsThe missing ingredient in 90% of vendor pitches: actual engagement with the engineers, operators, and technical staff who'll use the system dailyReal Production Examples: Language Sense: Watch how Amira Labs is automating language identification for international distribution. This one feature transformed a full day of manual work (checking thousands of audio tracks by holding up a phone to a screen) into a 2-3 minute automated scan with proactive exception monitoring. Error reduction, speed multiplier, operational sanityโ€”all in one workflow.A top three US broadcaster centralizing master control facilitiesโ€”and how Amira Labs architected solutions that scale across hundreds of channels simultaneouslyThe Engineering Deep-Dive: Stefan's take on why agents won't be production-ready until 2030 (and what has to happen first)Thinking models explained: How they'll actually work in broadcast (spoiler: diagnosing why channel 45 has wrong audio AND suggesting three solutions in one shot)The on-prem vs. API debate: Why most broadcasters refuse to send their broadcast data to ChatGPT APIs (data sovereignty, latency, regulatory constraints)Small Language Models (SLMs): The unglamorous secret weapon for broadcast-specific AI that doesn't need trillion-parameter models๐Ÿ“Š Why This Matters Right Now: We're at an inflection point. Generative AI got all the headlines in 2023-2024. But 2025 is when the predictive and operational AI revolution actually landsโ€”and broadcast is one of the industries where it can deliver immediate, measurable ROI if done right. Amira Labs represents the breed of startup that actually understands broadcast constraints (scale, 24/7 operations, compliance, international complexity) versus just bolting AI onto existing architectures. ๐ŸŽฏ For Broadcast Decision-Makers:If you're evaluating AI vendors for facility centralization, compliance automation, metadata enrichment, or international distribution, this episode asks the right questions: Do they understand your workflows? Are they partnering with your ops teams? Can they actually access and move your data? What's their on-prem strategy? Amria's website: https://amiralabs.com/LinkedIn's:Stefan Cardenas: https://www.linkedin.com/in/stefan-cardenas-2b5b0237/Kyle Suess: https://www.linkedin.com/in/kyle-suess/Adi Itzhaki: https://www.linkedin.com/in/adiitzhaki/ #BroadcastAI #AIAgents #OperationalIntegrity #SLMs #MediaTech #AmiralLabs #BroadcastEngineering #DataGovernance #ThinkingModels #AI #Broadcasting #StartupLife #TechImplementation #ContentUnderstanding #ComplianceAutomation #CloudNative #OnPremAI #InternationalDistribution More insights at Ancast.co.uk | Part of the ongoing broadcast transformation series

    21 min

  8. 11/25/2025

    Supervised Learning for FAST Channels: AI to Real Revenue

    FAST channels are growing at fifty-three percent year-on-year. ๐Ÿ“ˆ But here's the problem: they're running web-style business models on nineteen-nineties operations. And it's costing you millions. While everyone's obsessing over ChatGPT in the newsroom, the real opportunity in broadcast is sitting right in front of youโ€”unseen and untapped. ๐ŸŽฏ WHAT YOU'LL DISCOVER: ๐Ÿ’ธ The revenue gap: Why FAST channels with CPM-based advertising can't compete when they're scheduling seven days in advance ๐Ÿค– Why ChatGPT won't help: The difference between generative AI and the predictive data science that actually drives broadcast revenue ๐Ÿ“Š What nowcasting actually is: Using live signals (Google Trends, weather, social sentiment, BARB ratings, streaming logs) to predict audience engagement in real-time โฑ๏ธ How it works in practice: The three-month proof of concept that could unlock 3-5% annual revenue improvement ๐Ÿ’น The numbers that matter: Reducing prediction error from 20% down to 10% (MAPE), translating to better content placement, higher CPM retention, fewer advertiser disappointments ๐Ÿš€ Why this is happening now: The technology is mature. The data exists. The business case is clear. Only question: first-mover or follower? ๐ŸŽ™๏ธ BEN & RAIANA EXPLORE:โœ“ Why FAST is the perfect entry point (not traditional linear TV)โœ“ The partnership approach: broadcast expertise plus data scienceโœ“ How editorial control stays with YOUR teamโœ“ Success metrics: confidence scoring, override processes, model healthโœ“ The three-month structure: clear deliverables, clear exit criteriaโœ“ Why first-movers always win broadcast transformation The future of broadcast scheduling isn't about creativityโ€”it's about data. Organizations that move first will capture millions in recovered revenue before competitors even realize the opportunity exists. This is the conversation about AI in broadcast that actually matters. If you're running FAST channels and this resonates, let's talk. Reach out on LinkedIn or ancast.co.uk #FASTChannels #BroadcastAI #Nowcasting #RealTimeOptimization #DataScience #AdTech

    15 min
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๐ŸŽ™๏ธ Reinventing Broadcast: AI, Content, and the Future of Media The media industry is evolving fastโ€”AI, automation, and digital transformation are reshaping broadcasting and content creation. Join Ben, a broadcast consultant & AI strategist, as he explores: โœ… AIโ€™s impact on media & content โœ… Expert insights & consulting case studies โœ… Practical strategies for staying ahead With a mix of AI-driven conversations, deep dives, and guest insights, this series is a must-listen for media professionals. ๐ŸŽง Subscribe now & explore more at Ancast.tv

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