Rebecca Avery is a Senior Streaming Operations Executive, SME of the SVTA Metadata Working Group, and writes about the operational realities of streaming media at integrationtherapy.substack.com.
In February of 2023, I was visiting my employer’s corporate headquarters, having the same conversations with the corporate P&L owners that operators in media companies across the globe were having at the same time. How can AI save us money? When can we have a completely automated supply chain? They were the wrong questions at the wrong time, and the industry has spent a meaningful amount of time getting situated since.
The pressure has only intensified. The era of celebrating monthly active user (MAU) growth at any cost is over. Boards and Wall Street are asking when each platform turns profitable, what the unit economics actually look like, and how the cost structure bends from here. Streaming is being forced to mature, fast, against a clock the industry leaders did not get to set. AI was supposed to be part of the answer to that pressure. Three years in, it has not been.
The conversations I am part of with networks and studios are still circling problems we were trying to solve a decade ago. Dirty metadata. Rights data trapped in spreadsheets and email threads. Library content under-leveraged because nobody is sure what the company owns. Cross-functional gaps between content operations, programming, acquisitions, and marketing that keep titles stuck in the same places they were stuck five years ago. The supply chain is still mostly held together by people doing heroic manual work to make systems talk to each other.
For a while it looked like AI might be a shortcut to those problems. What it has turned out to be in the short term is a monkey wrench in the conversation. There are three reasons for that. Different companies use AI in different ways for different goals, so there is no universal playbook. AI is not a bolt-on to existing workflows. And streaming operations are particularly complex, which means there is a lot to break down before anyone can figure out how to apply this technology to create meaningful value.
The work in front of the industry is not throwing AI into existing operations to cut costs or shortcut existing problems. It is rethinking systems from zero. Disney established the Office of Technology Enablement in late 2024, a hundred-person organization that reports to the Co-Chairman of Disney Entertainment and coordinates AI and emerging tech across film, television, theme parks, and streaming. Sony’s AI division, founded in 2020, operates as a strategic R&D unit reporting to the Group CTO and CEO, with cross-functional work across Pictures, Music, Gaming, Electronics, and Semiconductors.
Both models share a structural decision worth noticing. The AI organization is centralized for coordination and embedded for execution. It does not own the AI projects of every business unit. It makes sure those projects fit a coherent enterprise strategy. That requires examining the supply chain as it works today, examining where AI capabilities are today and where they will be in eighteen months, and building the connective tissue between those two pictures.
MIT NANDA’s July 2025 study of three hundred enterprise AI deployments found that ninety-five percent delivered no measurable P&L impact. Sinch reports that seventy-four percent of enterprises have already rolled back a live AI agent. Gartner projects that sixty percent of AI projects will be abandoned through 2026 because the underlying data is not ready, and that forty percent of agentic AI projects will be cancelled by the end of 2027.
There are five pressures driving this in media, and they compound on each other.
The governance conversation
Before any of the technology conversations can produce a result, somebody has to answer the ownership questions. Who owns the data? What can be sent to a model? What cannot? Who decides? What gets logged? What gets audited? What happens to a model that has been trained on the company’s catalog when the contract with a vendor ends? What rights apply to the outputs?
These questions stall initiatives because the answers are not technical. They are operational, legal, and often deeply political. The COO, the CTO, the General Counsel, and Finance have to agree about a category of risk they have never previously had to govern together, and that alignment takes time the profitability clock does not allow for. So a lot of companies are short-circuiting the conversation and discovering the cost later.
Underneath the governance conversation sits a principle I keep finding myself reaching for. Metadata is the quantifiable, trackable representation of content. When a company cannot manage its metadata to the specifications of its business, it leaves a lot of power on the table, often outsourcing it to vendors who promise they can fix today’s uncomfortable problems quickly. Over time, content gets harder to merchandise, harder to localize, harder to monetize, harder to recommend, harder to defend in a rights dispute, and harder to mine for downstream revenue. Every operational shortcut taken at the metadata layer becomes a tax the business pays every quarter until somebody pays the cost of fixing it by somehow buying back the flexibility of their own data at a premium cost.
That principle is where a lot of AI vendor relationships start to wobble. The vendor takes more control of the customer’s metadata than the customer realized they were giving up. The data model gets shaped by the vendor’s product architecture instead of the customer’s business.
The drift that follows is the part worth understanding. Vendors optimize for the average client. A serious operator running an enterprise global catalog is not the average client. The vendor’s roadmap will drift toward whatever is easiest for them and useful for everyone else, and eighteen months later the customer is trying to negotiate back access to data they used to own. I have watched companies hand over the keys to their own metadata for a short-term solution, then spend the next year and a half trying to claw back control. The vendor by then has a roadmap. The customer by then has a dependency.
There are vendors solving for this structurally. Twelve Labs is one of the more interesting examples. Their public positioning is that the entire intelligence stack deploys where the customer wants it to, with SOC 2 Type II certification and encrypted data handling as the baseline. That is structurally different from the standard AI vendor posture, which is “send us your data and trust us.” It is proof that the problem is solvable, and the vendors who have chosen not to solve it have made that choice for their own reasons.
Vendor instability makes the math harder
The AI vendors pitching media operators in 2023 are not pitching the same product in 2026. Many have shifted their model focus. Some have changed their pricing structure entirely. Others have exited features they were demonstrating two years ago. Some have been acquired. A few have stopped existing.
This is the consequence of technology lifecycles compressing. What was state of the art twelve months ago is two model generations behind today. For a vendor competing in a market that resets every six months, holding the same product strategy for three years is structurally impossible. They have to pivot. From their seat that is rational.
From the buyer’s seat, there is a lot of risk. A media company evaluating an AI deployment is not making a six-month decision. They are signing a contract, integrating the vendor over six to nine months, training their team, restructuring workflows, and hoping the vendor is still pointed in the same direction eighteen months later. When the vendor pivots, the company is left with a workflow built around a product that no longer exists in the form they bought. The renegotiations that follow tend to have the same flat quality in the room. Everybody knew the risk going in, and nobody knew what to do about it. The company has already burned eighteen months of runway and still does not have the margin improvement they bought the vendor to deliver.
Token economics break the pilot-to-scale math
Many AI services are still priced in tokens, which makes spending predictable for a single user typing into a chatbot and wildly unpredictable for a content supply chain running thousands of titles through enrichment, localization, or quality control. The same task can cost dramatically different amounts depending on the model version, the prompt construction, and how the supply chain is calling the AI underneath.
CFOs trying to get to a per-unit number for forecasting tend to walk out of those meetings without one. They need to know whether AI is going to be a margin lever or a margin liability, because the answer determines whether the platform makes its profitability target. The pricing models the AI industry has adopted are not yet compatible with the volume economics of a content supply chain. Until they are, deployments at meaningful scale will keep running into the same wall.
The data is the foundation, and the foundation is not ready
AI cannot perform on dirty data. Most catalog data in the industry is dirty in ways that took fifteen years to accumulate, and the people who built the workarounds are still the ones holding it together.
The picture rhymes across most of the companies I have worked with. Inconsistent asset IDs across systems. Metadata schemas that were standardized at the team level but never at the company level. Rights data trapped in formats no system can parse. Title-level information that was correct when it was entered but has drifted as platforms, regions, and licensing windows have evolved. Workflow handoffs that produce different versions of the same record in different systems. AI deployed against this substrate produces confident outputs that are confidently wrong, which is worse than no output at all.
The work to fix metadata debt competes for budget with everything else the company is being asked to deliver on the same profitability deadline, and many companies are choosing the visible work over the foundational work. That choice has a compounding cost. AI, metadata, and technology become a flywheel of institutional debt that continues to cycle, and the cost shows up in every pilot that refuses to scale. MIT NANDA found that the ninety-five percent failure rate is concentrated in the scaling phase, not in development. Pilots demonstrate technical feasibility. Production requires operational discipline that a lot of companies are still building.
The harder pattern underneath is that most pilots were designed to demonstrate technology, not to produce business outcomes. The pilots that survive scaling started with the operational question first and the AI tooling second. That sequence matters. A pilot that asks “can AI do this thing” produces a yes-or-no answer about the technology. A pilot that asks “can this workflow run reliably at scale, and does AI help us get there” produces a deployment that survives contact with the production environment. A lot of the industry is still running the first kind.
The part the technology conversation keeps trying to skip past
AI does not change people. Entire systems still need to be designed, built, and held together by the humans inside them. The principles of good operations still apply. The planned workflows shift somewhat because the technology choices shift somewhat. Vendor selection looks different. Standards evolve. Business goals can actually expand when all of the pieces come together inside a thoughtful plan. None of that changes the underlying truth that the companies who will do the best with AI over the next five years are the ones who understand that their people are going to be people, whether AI is in the room or not.
Teams adopt what works for them. They work around what is imposed on them without their input. The operational rhythms of a content supply chain are held together by humans making judgment calls about edge cases the system did not anticipate. That work does not go away when AI shows up. It becomes more important, because the cost of letting AI make a confidently wrong decision at scale is higher than the cost of letting a person make a slower right decision at human pace.
The industry is being forced to mature, fast, on a clock it did not set. AI is the technology a lot of leaders hoped would let them skip the painful step of getting the operational foundation right. It has turned out to be the technology that proves the step cannot be skipped. The companies pretending otherwise are losing time they do not have. The companies accepting it are getting somewhere.
The supply chain is the strategy. The people running it are the operation. AI did not change either of those things. It only made the cost of pretending otherwise impossible to hide.
