The broadcast and streaming industry has invested heavily in AI-powered tools over the past three years — automated quality monitoring, content-aware encoding, predictive CDN routing, real-time caption generation. But for many operations teams, the unglamorous reality is that their biggest efficiency losses have nothing to do with the sophistication of their AI models. They are losing hours every week to manual handoffs between systems, inconsistent metadata pipelines, and approval workflows that were designed for a different era of content volume.
Sam Cooper is the founder of Flowpast, a consultancy that helps organisations implement AI-driven workflow automation across their operations. Rather than building bespoke AI models, Flowpast focuses on the automation layer — connecting existing tools, eliminating manual process steps, and building the operational infrastructure that allows AI capabilities to deliver value at scale. We spoke with him about why workflow automation is becoming a priority for media and technology organisations, where the biggest efficiency gains are hiding, and what streaming operations teams should consider before investing in another AI tool.
The workflow problem in media operations
T-21: You work with organisations across several industries. What patterns do you see when companies come to you for help with automation?
Sam Cooper: Almost universally, the organisations we work with have already invested in capable tools. They have good encoding platforms, decent monitoring systems, functional content management. The problem is rarely that they lack technology — it is that the technology does not talk to itself. A file lands in an ingest system, someone manually checks the metadata, emails a team to confirm the delivery specification, waits for approval, then triggers the transcode job. Each individual step takes five minutes. But when you chain twenty of those steps together across a content pipeline that handles hundreds of assets per day, you have built a system that runs on human memory and email threads. That is where things break.
T-21: Is this specific to media and streaming, or is it a broader problem?
Sam Cooper: It is universal across data-intensive industries, but media operations have some unique characteristics that make the problem particularly acute. Content pipelines are time-sensitive — a live event has a hard deadline that does not move. The number of output formats and delivery specifications has exploded — a single piece of content might need to be transcoded into fifteen different profiles for different platforms and territories. And the volume has increased dramatically while team sizes have stayed flat or shrunk. When you combine time pressure, format complexity, and volume growth with manual handoff processes, the result is predictable: errors, bottlenecks, and teams that spend their time firefighting instead of improving their systems.
Automation before optimisation
T-21: You have a phrase you use with clients — “automate before you optimise.” What does that mean in practice?
Sam Cooper: It means that most organisations try to make individual steps faster when they should be eliminating steps entirely. I will give you a concrete example. A streaming platform we worked with had invested in an AI-powered quality assessment tool that could analyse transcoded output and flag artefacts in near real-time. Impressive technology. But the output of that tool was a report that was emailed to a QC team, who would manually review it, log the findings in a spreadsheet, and then send a re-transcode request through a ticketing system. The AI tool was doing its job in seconds. The human workflow around it was adding hours of latency.
As an AI workflow consultant, what we did was not replace the QC tool or build a better model. We automated the surrounding process — the quality assessment output now triggers conditional logic that either auto-approves clean assets, routes flagged assets directly into a re-transcode queue with the correct parameters, or escalates genuinely ambiguous cases to a human reviewer with all the relevant context pre-assembled. The AI model did not change. The workflow around it transformed the actual operational impact.
T-21: That sounds straightforward. Why do organisations struggle to do this on their own?
Sam Cooper: Two reasons. First, workflow automation sits in an organisational gap. The engineering team builds and maintains the tools. The operations team runs the processes. Nobody owns the connective tissue between them. When we come in, we are often the first people who have mapped the entire end-to-end process from ingest to delivery and asked the question: where does a human touch this, and does that touch add judgment or just add latency?
Second, there is a cultural bias towards building new capabilities rather than connecting existing ones. It is more exciting to pitch a board on an AI-powered content recommendation engine than to explain that you automated forty-seven manual steps in your content supply chain. But the forty-seven manual steps are costing you more money and causing more operational risk than the absence of a recommendation engine ever will.
The economics of workflow automation
T-21: How do you quantify the return on automation investment for clients?
Sam Cooper: We measure three things. First, time recovered — how many hours per week were spent on manual process steps that are now automated. This is the easiest to measure and the most immediately visible. Second, error reduction — how many re-transcodes, missed deliveries, or specification errors were caused by manual process failures. Every error in a content pipeline has a cost, whether that is a re-processing charge, a late delivery penalty, or a customer experience impact. Third, and this is the one that takes longer to materialise, throughput capacity — how much additional volume can the existing team handle without adding headcount. That third metric is where the long-term economics become compelling. If your operations team can handle thirty percent more content volume without hiring, the cost avoidance over two or three years dwarfs the automation investment.
T-21: Are there areas within streaming and broadcast operations where you see particularly high automation potential that organisations are not yet addressing?
Sam Cooper: Metadata management is the big one. The amount of manual metadata entry, validation, and correction happening across the industry is staggering. Every content asset needs technical metadata, descriptive metadata, rights metadata, localisation metadata, and platform-specific metadata. Much of this information already exists somewhere in the supply chain but is being manually re-entered or copy-pasted between systems. Automating metadata propagation and validation across the content lifecycle is probably the single highest-return automation project most media organisations could undertake today.
The other area is compliance and regulatory reporting. As content regulation becomes more complex across different territories, the reporting burden on operations teams is increasing. Automating the assembly of compliance documentation from existing system data is a significant opportunity that most organisations have not yet addressed systematically.
Advice for operations teams
T-21: For streaming or broadcast operations teams reading this who want to start their automation journey, where should they begin?
Sam Cooper: Map your processes before you buy any tools. Literally draw the flow of a content asset from the moment it arrives to the moment it reaches the end consumer. Mark every point where a human intervenes. Then ask yourself at each of those intervention points: is this person adding judgment, or are they acting as a manual integration layer between two systems? If the answer is the latter, that is your automation candidate.
Start with one workflow. Do not try to automate everything at once. Pick the process that fails most visibly or most frequently, automate it well, measure the impact, and use that evidence to build organisational confidence for the next project. Automation adoption in operations teams is as much a change management challenge as it is a technical one. Showing your team that automation makes their work better rather than replacing their jobs is essential for long-term success.
T-21: Sam, thank you for your time.
Sam Cooper: Thank you. The media and streaming industry is at an inflection point where the competitive advantage shifts from having the best individual tools to having the best-connected operational systems. The organisations that figure that out early will be significantly more efficient than their competitors within two or three years.