Generative AI, particularly through advances in large language models (LLMs), voice interfacing, and video processing, has evolved from a lab curiosity to a core financial lever, creating an AI Cost Compression Tsunami. New AI solutions now enable content owners and distributors to expand reach and add value, not just on social media, but crucially on the large TV screen, by fundamentally rewriting the economics of key workflows and features. At this year's @IBC, we explored use cases such as voice-to-text, dubbing, video and audio analysis, metadata extraction and enrichment, content repackaging, recommendation engines, and multiscreen interface personalization. The key takeaway: these technologies are not just about finding something to watch or adding subtitles and languages, but about creating rich, relevant, and authentic content, including conversational interactions, that transform how viewers experience content and how service providers build engagement and monetize over time.
The Attention Economy: AI’s Role in a Crowded Market
The battle for viewer attention and engagement has never been fiercer. Broadcasters are betting their futures on “X streaming strategies,” building smarter, personalized AVOD/BVOD OTT platforms to complement and eventually replace linear offerings; these enhance engagement through interactivity, data-driven recommendations, relevant advertising, and better surfacing of content. Meanwhile, Smart TV and OS vendors are aggressively monetizing TV Home Screens and FAST channels, borrowing from @YouTube’s revenue-sharing playbook. Telco aggregators face pressure from broadcasters demanding smarter ad monetization, from price-sensitive subscribers ditching set-top boxes, and from TV manufacturers trying to dominate the “HDMI1” TV Home Screen. Amid this, AI-powered content enrichment, management, discovery, and engagement have become not just competitive advantages, but survival tools; revenue snapping is a real scenario.
The true business battle now lies beyond attention, it's about retaining AI-driven feature value. Instant commoditization is the biggest risk; today’s “innovative” AI features (instant clipping, AI voice-overs) can become tomorrow’s baseline within months. Your AI investment doesn’t buy a feature; it buys a 90-day monopoly on efficiency. With @Roku holding 37% of the U.S. CTV market (Q2 2025, @Amra & Elma LLC) and @Android TV over 24% of global smart TV shipments (@Amra & Elma LLC, @Omdia), the pace of AI adoption and market prevalence by such platforms mean features spread fast. To stay relevant, companies need a broader strategy that looks holistically at engagement and monetization drivers, not just quick wins. For product leaders, experimenting with AI is essential, but deploying it fast and right is critical; they must answer: Can AI create scalable efficiencies? Will it boost key KPIs? And most importantly, can it prove ROI quickly?
From Safe Bets to Bold Moves: Innovation in AI and UX
While many @IBC exhibitors admitted that clients still prefer to emulate @Netflix’s proven UX and recommendation model, innovators are taking bolder steps, using AI for smarter content scheduling and metadata enrichment. @Mediagenix, the category leader in content scheduling, is using recommendation engine insights to optimize broadcast schedules, a data-driven leap from traditional programming heuristics that’s already paying off. @ThinkAnalytics, with half a billion users (the industry’s largest footprint), has long integrated AI into its stack; its continuous learning based recommendations leverage diverse metadata sources to drive meaningful engagement backed by deep analytics.
Yet metadata remains an ROI black box. Failing to measure metadata efficiency is organizational negligence; AI metadata’s value lies in verifiable efficiency, not just new tags. AI slashes manual tagging time and introduces new, relevant categories. Providers like @Digital Nirvana report up to a 50% reduction in edit time and one-third lower archive costs. @Gracenote, the metadata category leader, is also embedding generative AI, using the MCP protocol, to automate and scale metadata enrichment. But the key question remains: if metadata doesn’t drive measurable lift in discoverability or ad yield, it’s a failed investment. If your AI initiative can’t demonstrate impact, it’s expensive digital window dressing.
Startups such as @Moments Lab (Europe), having raised over $37M and indexed 200,000 hours for @Banijay Entertainment, showcase the next generation of generative AI for metadata, indexing, and content packaging. Similarly, @Twelve Labs, with $107M raised to date, extracts highly actionable insights from video via agentic AI. In live production, @BackLight demonstrated AI-driven clipping and reframing tools, showing how AI is reshaping how content is tailored and monetized.
The Localization Revolution
Content localization, long powered by AI subtitling tools from @AI Media and @CaptionHub, now faces disruption from the Gen AI Dubbing Tsunami. New players such as @Dubhub, @Deepdub ($20M Series A), @Lingopal ($14M Series A), @Dubformer ($3.6M seed), and @Video.Taxi or @ElevenLabs ($281M raised and valued at $3.3B) are redefining dubbing economics. Their AI-driven transcription and translation tools produce culturally adapted versions in 25+ languages (80+ in lab settings) at a fraction of traditional costs, as low as $1–$10 per minute, compared to up to $300 per minute for manual dubbing. This 90%+ cost compression transforms entire archives into global assets, or liabilities if left unused. The human role is impacted, evolving into the Editor-as-Auditor: verifying AI output for quality, compliance, and nuance. Audio specialists like @AudioShake and @Gaudio Lab also leverage AI to enhance sound quality, further boosting value across the chain.
AI at the UX Frontier
The user interface frontier saw forward-looking demos from @3SS, with voice-controlled navigation, and @Accedo, whose Compose solution uses generative AI to simplify interaction and personalization. For now, most players apply AI to personalize home screens and surface local content more effectively, small steps that still deliver fast ROI and measurable NPS gains for service providers.
Real-World AI in Media Production and Distribution
Across @IBC, companies showcased AI embedded in real workflows, not just as a buzzword. AI now underpins critical automation that accelerates and scales production and distribution while de-risking cost-heavy processes. Still, some vendors limit AI features to premium tiers, a cautious approach to test value and manage costs, but the direction is clear: AI has moved from experimentation to everyday enabler status.
Charting the Path Forward: From Experimentation to ROI
@IBC conversations revealed a consistent message: adoption of AI in media remains early-stage and iterative. Many vendors are shipping new AI features in late 2025 and early 2026 releases, signaling momentum across the ecosystem. Our take: established vendors are using AI to sustain and upsell, while startups bring fresh capabilities and value. For media organizations, AI is already delivering measurable efficiency, replacing manual workflows and outdated interfaces like static EPGs and bland content discovery. The challenge for content and distribution players is to stop copying a 2018 UX model and instead focus on finally becoming the personalized, conversational content interaction provider that consumers have been craving for since the “500-channels-with-nothing-to-watch” offerings came to market. More than ever, the field is open to define use cases linked to clear business value and rigorously measure ROI. Not playing right and fast most likely means seeing your job being axed at some point.
In a Nutshell
@IBC 2025 confirmed AI’s shift from hype to mainstream, though the “paint isn’t dry yet, and more coatings will be needed.” Generative and agentic AI models now power applications from metadata enrichment to automated packaging and advanced content surfacing. This transformation is systemic and accelerating; the next phase will bring richer engagement and monetization use cases. The “AI paint” covers the surface well, it is not dry yet, and a second or third coating will be needed. Yet playing a safe “wait and see” bet today guarantees you’re a commodity product by 2027, definitely not a roadmap to sustained success for anyone or any organization.