Industry Insights: End-to-end visibility drives data-driven media supply chain improvements

Building on the foundational workflow optimization strategies explored in part one, this second installment of our Industry Insights roundtable delves deeper into the advanced technologies and methodologies transforming media supply chain efficiency.

Today’s content creators are increasingly leveraging intelligent automation, AI-driven metadata management and real-time analytics to not only streamline operations but also maximize revenue per asset.

Industry Insights: Media supply chain optimization requires strategic automation and cloud infrastructure

The discussion examines how organizations can consolidate fragmented vendor ecosystems, implement consistent quality standards across automated processes and harness audience insights for dynamic content personalization. As the industry moves toward more sophisticated measurement frameworks and data-driven decision making, these expert perspectives reveal how integrated platforms and standardized metadata are becoming essential for sustainable competitive advantage in an increasingly complex media landscape.

Key takeaways from this Industry Insights roundtable

• Centralized platforms consolidate: Cloud-based content supply chain platforms with unified interfaces eliminate departmental silos by connecting disparate systems through APIs and automation while providing single sources of truth.
• Automation ensures consistency: Intelligent workflow automation standardizes repeatable tasks like file validation, transcoding, and quality control, reducing human error while enabling 24/7 processing without additional headcount.
• Rich metadata drives discovery: AI-generated metadata with scene-level markers, transcripts, and semantic tags transforms static content libraries into dynamic resources, enabling faster asset retrieval and more efficient content reuse across multiple platforms.
• Real-time insights optimize performance: Operational intelligence tools that monitor key metrics like processing duration, error rates, and delivery latency enable data-driven improvements and continuous supply chain optimization.
• Personalization scales revenue: Leveraging real-time audience data and AI technologies creates hyper-personalized content experiences that reduce subscriber churn while enhancing monetization through smarter content placement and marketing spend allocation.

What strategies have proven most effective for consolidating fragmented processes across multiple departments or vendor systems?

Daniel Medina, business development, NPAW: Strategies must involve proper unification and accurate handling of data across different processes, as well as how this data is connected. Once that is in place, current technology makes it possible to draw insights that are beneficial for the business.

Aaron Kroger, director of product marketing and communications, Dalet: To consolidate fragmented processes across departments or vendor systems, organizations should begin with a centralized, accessible platform that enables unified viewing and management. Building on that, integrations can connect disparate tools, workflows, and people while having a single source of truth ensures consistency and eliminates duplication.

Lucas Bertrand, founder and CEO, Looper Insights: Centralizing merchandising data into a single platform helps unify reporting and removes redundant or conflicting workflows across teams. This eliminates silos between marketing and platform partners. The result is a more coherent strategy and faster response to market shifts.

Nav Khangura, VP, sales and business development, TMT Insights: Fragmented processes often result from siloed teams and legacy systems that were never designed to work together. The most effective way to consolidate is by connecting departments and vendors through a centralized, cloud-based content supply chain platform, with APIs and automation doing the heavy lifting. A single user interface becomes the overarching control centre, giving all stakeholders access to the same information and task status from the underlying systems.

Ian McPherson, global M&E business development, media supply chain and generative AI, Amazon Web Services:Cloud-based media orchestration is one of many highly effective strategies for consolidating fragmented processes in the media supply chain, especially because it can minimize content transfer between workstations and external vendors. By coupling cloud-based workflow orchestration with AI generated metadata, media and entertainment companies can take digital content and programming elements through a series of automated steps, from content ingest to delivery. It can help ensure that content is properly formatted and packaged to meet the unique requirements of various platforms and endpoint destinations.

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How can organizations measure and improve the end-to-end efficiency of their content lifecycle from ingest to delivery?

Daniel Medina, business development, NPAW: To achieve that level of efficiency, it’s essential to measure the performance of each component in the chain. Often, organizations lack that information — they have blind spots — which makes optimization very difficult. To optimize, you first need to measure. Market benchmark data is also crucial.

Chris McCarthy, VP, media solutions, TMT Insights: Improving efficiency across the content lifecycle begins with deploying operational intelligence tools that provide visibility into every stage, from ingest through processing to final delivery. These tools offer a comprehensive view of both the media catalogue and workflow health, helping organizations pinpoint inefficiencies and identify where intervention is needed. By leveraging metadata to monitor key performance metrics, like ingest time, processing duration, error rates, bottlenecks, and delivery latency, teams can surface actionable insights and make data-driven improvements.

Kathleen Barrett, CEO, Backlight: Driving operational efficiency in media today requires more than intuition — it demands measurable outcomes. Organizations using Iconik by Backlight are seeing tangible results: the Philadelphia 76ers and New Jersey Devils reduced storage management costs by 34% while streamlining access to nearly 2 million assets. Orange Prestations TV tripled graphic output while managing over 730,000 assets, and NowThis cut a third of its post-production budget by consolidating fragmented workflows. These gains reflect a broader shift toward centralized platforms that deliver real-time visibility, automated handoffs, and scalable collaboration across teams.

Ivan Verbesselt, chief strategy and marketing officer, Mediagenix: Organizations should measure content lifecycle efficiency through three interconnected metrics: Effective Catalog Size (how broadly content is consumed—good personalization increases this 4x), Content Lifecycle Velocity (speed from ingest to delivery), and Engagement Conversion Rates (actual viewing conversion, not just browsing)… Instead of measuring stages in isolation, track how upstream decisions impact downstream performance and use this closed loop to fine tune end-to-end content performance.

Improvement comes from feeding personalization data upstream: use audience engagement insights to inform content strategy and acquisition decisions. The anonymized demographic clustering reveals which audiences connect with which content types—invaluable intelligence previously invisible to content strategists. Implement smart curation tools that automatically surface semantically similar content, cutting editorial effort by 50%. Automate scheduling with continuous optimization that adapts to real-time performance data, substituting underperforming content with similar assets.

Create one source of truth for content rights across geographies—fragmented title management kills efficiency. Then let the flywheel spin… The system becomes self-optimizing, with companies exposing 62% of their catalog daily versus traditional approaches. Treat personalization as a full-cycle intelligence layer, not just end-stage recommendations.

What role does workflow automation play in reducing human error and increasing throughput consistency?

Geoff Stedman, CMO, SDVI: A supply chain management platform must be able to orchestrate all the automated and manual steps of a supply chain, as well as provision the necessary resources (both software and infrastructure) to complete every job. Data collected from each step is used to inform and enforce decisions, while also providing specific guidance for manual tasks, thereby increasing accuracy and productivity. Fully automating repetitive tasks and leveraging cloud infrastructure is the only way that media operations can scale and be responsive to business demands.

Ali Hodjat, senior director of marketing, Telestream: Intelligent automation reduces the variability and inconsistency that come with manual processes. By leveraging AI capabilities for task such as captioning, quality control, and speech-to-text, automated systems ensure that media meets delivery specifications with fewer errors and reduces subjective variability. The AI and automation enable systems to process content around the clock, increasing overall throughput without adding headcount.

Lucas Bertrand, founder and CEO, Looper Insights: Automating visibility tracking and merchandising classification ensures consistency across regions, devices, and interface layouts. It reduces human error and allows for always-on auditing that manual processes simply can’t sustain. As volume scales, automation becomes the only way to maintain reliability.

Nav Khangura, VP, sales and business development, TMT Insights: Workflow automation helps standardize repeatable tasks like file validation, transcoding, rights management, and QC, removing the guesswork and human-led mistakes that come with manual handling. It’s like having a reliable assistant that never gets tired, never misses a step, and always follows the rules. When embedded into an end to end, single pane of glass, automation not only boosts throughput but also ensures consistency across every project and team.

Kathleen Barrett, CEO, Backlight: Workflow automation is essential for reducing operational risk and improving consistency across media production. By replacing manual tagging and disparate naming conventions with AI-driven, standardized metadata, organizations significantly reduce errors and accelerate asset retrieval — from hours down to seconds. This precision not only streamlines editorial processes but also safeguards content integrity by providing clear visibility into version control, approval status, and usage rights, with automated alerts that prevent costly mistakes.

How can supply chain optimization directly impact content ROI and revenue per asset?

Geoff Stedman, CMO, SDVI: Optimizing the content supply chain directly results in lower costs (per asset), and better utilization of processing infrastructure needed to perform the work required for each asset. Automated media supply chains also tend to support higher content throughput and greater content capacity, which directly translates into faster time to market for content licensing deals.

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Aaron Kroger, director of product marketing and communications, Dalet: Optimizing the media supply chain can significantly boost content ROI and revenue per asset by enabling broader distribution and increased reuse of existing content. It also provides granular tracking of asset performance, helping to clearly demonstrate return on investment. Additionally, it ensures efficient access and delivery across the entire content library, regardless of storage tiering.

Lucas Bertrand, founder and CEO, Looper Insights: Knowing which placements yield the highest returns enables smarter allocation of marketing spend and real estate. Optimization isn’t just about reducing costs, it’s about using visibility data to drive more value from each content asset. Better placement means stronger performance and more revenue per title.

How do you maintain consistent quality standards while scaling up automated processes?

Daniel Medina, business development, NPAW: Automation is part of the natural evolution of any solution. To maintain quality standards, it’s important to have measurement tools that allow us to verify whether thresholds are still being met after changes are made. And if they’re not, to be able to correct accordingly.

Lucas Bertrand, founder and CEO, Looper Insights: The key is balancing automation with clear definitions for what’s being measured, especially across different devices and user interfaces. Automated capture must follow structured logic that mirrors how real users experience content placement. Consistency is achieved not just by the tech, but by aligning measurement criteria across platforms from the start.

How do you leverage operational data and analytics to continuously improve supply chain performance?

Lucas Bertrand, founder and CEO, Looper Insights: Real-time analytics reveal what’s working by device, region, or content category, creating a loop of constant optimization. By comparing visibility, placement type, and performance trends, teams can fine-tune strategies with each release. It’s about making smarter choices, faster, with every data cycle.

Geoff Stedman, CMO, SDVI: The data that can be collected by a supply chain management platform provides a rich source of information for establishing a baseline of unit costs, time, delays, and more from every step in a supply chain. From this baseline, identify where the biggest costs and delays are, and work to eliminate them first. Use a data visualization tool (or one built into the platform) to surface issues that might otherwise be unseen from the raw data. And treat a supply chain as a living thing, embracing a process of continuous improvement, not just build once and then leave alone.

What metrics and KPIs are most valuable for measuring and driving ongoing optimization efforts?

Daniel Medina, business development, NPAW: That depends on the nature of the solution. It’s important that the KPIs are tailored to the specific solution. Standard KPIs are often used, but they’re not always informative enough. It’s crucial to choose the indicators that are appropriate for each case.

Lucas Bertrand, founder and CEO, Looper Insights: Metrics that connect content placement visibility with expected or real-world impact are the most actionable. Dollar-based values or projected impressions, like those used in $MPV and pMPV models, help quantify the business outcome of each promotional slot. These KPIs move teams beyond vanity metrics and toward measurable performance.

How can real-time audience insights drive dynamic content delivery and personalization at scale?

Ian McPherson, global M&E business development, media supply chain and generative AI, Amazon Web Services:Audiences generate a wealth of real-time and historical data that can be used to personalize content delivery beyond simple genres, from their browsing behavior and watch history through to their content preferences and ad interactions. By employing AI technologies like Amazon Personalize, Amazon Bedrock and Amazon SageMaker, media and entertainment companies can harness audience data to create hyper-personalized experiences and develop custom recommendation models. Identifying the trends, topics, actors and settings a viewer cares about becomes much more straightforward to create a more engaging user experience, reduce subscriber churn and enhance monetization.

Kathleen Barrett, CEO, Backlight: Real-time audience insights empower media organizations to deliver dynamic, personalized content at scale by continuously tracking viewer behavior and preferences. When integrated with AI-driven metadata and centralized asset management, these insights enable teams to rapidly find and repurpose the most relevant content across multiple channels and formats. This not only enhances viewer engagement through timely and tailored experiences but also drives operational efficiency by reducing manual effort and speeding up workflows.

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What role does intelligent metadata management play in optimizing downstream processes and decision-making?

Ali Hodjat, senior director of marketing, Telestream: Metadata becomes a critical enabler when it’s generated in real-time at ingest and enriched intelligently and systematically throughout the workflow. Structured metadata, such as transcripts, summaries, and timecodes, supports discovery, retrieval, and automated routing, making it easier for downstream teams to find and work with the right content. It also aids compliance, localization, and personalization tasks by providing context that can be acted on programmatically.

Nav Khangura, VP, sales and business development, TMT Insights: coIntelligent metadata is the engine behind an optimized content supply chain. It powers everything from automated routing and rights enforcement to localization and performance analytics. When stored in the cloud and surfaced through a centralized user interface, metadata gives operations teams real-time insight into asset status and readiness, turning raw content into structured, actionable information. Think of it as giving your content GPS; it knows where it is, where it’s going, and what needs to happen next.

How do you establish consistent metadata standards across multiple content sources, vendors, and distribution platforms?

Daniel Medina, business development, NPAW: There are interesting standardization initiatives, such as TM Forum. However, whether standards are followed or not, the most important thing is that data remains consistent across different sources and components. This ensures a unified data semantics.

Aaron Kroger, director of product marketing and communications, Dalet: To establish consistent metadata standards across diverse content sources, vendors, and distribution platforms, it’s essential to centralize metadata management through a single source of truth — typically a media asset management (MAM) system. Metadata should be required and validated upon import to ensure consistency and accuracy. From there, tailored subsets and delivery-specific standards can be generated to meet the needs of each platform.

Ian McPherson, global M&E business development, media supply chain and generative AI, Amazon Web Services:While there are various paths to establishing consistent metadata standards across sources, vendors and distribution platforms, generative and agentic AI are emerging as valuable tools. AWS has developed guidance for a Media Lake on AWS, for aggregating and normalizing various types of metadata and vector embeddings. With these technologies, media and entertainment companies can tap into semantic search and video understanding to extract incredibly rich metadata on each shot and scene that can be carried through content as it moves across the media supply chain.

Ivan Verbesselt, chief strategy and marketing officer, Mediagenix: Poor metadata quality creates a cascading failure across every downstream process—from content discovery to personalization effectiveness. Here’s why this matters: recommendation engines are only as intelligent as the metadata feeding them. Clean, consistent metadata enables accurate audience-content matching, which generates reliable engagement data that can be fed back upstream to inform content strategy decisions. The flywheel effect amplifies these gains—better metadata quality improves personalization accuracy, which produces more reliable audience insights, which enables smarter content curation and acquisition decisions. Without this foundation, even the most sophisticated AI-driven personalization becomes ineffective. Quality metadata transforms from operational overhead into strategic intelligence that optimizes every content lifecycle decision.

How does rich, structured metadata enable more efficient content discovery and reuse?

Ali Hodjat, senior director of marketing, Telestream: Rich metadata enhances searchability, allowing teams to locate, assess, and reuse content without having to manually review hours of footage. Transcripts, keywords, and scene-level markers make it possible to find specific segments quickly, enabling faster turnaround for edits, promos, or re-broadcast. In archive scenarios, searchable metadata helps dynamically resurface valuable assets that might otherwise remain in cold storage, unused and unmonetized.

Ian McPherson, global M&E business development, media supply chain and generative AI, Amazon Web Services:When content is organized and tagged with the right metadata, it becomes more discoverable during both the production and consumption phases of the lifecycle. Semantic search and video understanding powered by generative and agentic AI tools in the cloud are unlocking new opportunities in this respect. It’s now possible to use AI-powered agents to extract metadata such as technical specifications, scene summaries and chapter and shot-level details from content. That level of detail can make content easier to find and repurpose.

Kathleen Barrett, CEO, Backlight: Automatically tagging assets with faces, objects, keywords, and scenes turns static content libraries into dynamic, reusable resources. This rich metadata enables teams to quickly locate exactly what they need for repurposing — across formats, channels, and campaigns — without time-consuming manual searches. The result is faster turnaround times on cutdowns, trailers, social edits, and more, as every asset becomes instantly accessible for creative reuse across multiple projects and platforms.