David Sugg ran global media supply chain operations at Warner Bros. Discovery and is now consulting independently. This is his first piece for Engines of Change.
MIT reported in August 2025 that 95% of corporate GenAI pilots are failing. The cause is not the quality of the models. It is that the tools are being pointed at workflows the systems were never built to read.
The media industry is about to make that number worse. In a typical media supply chain, the work that controls, integrates, and orchestrates the actual workflow lives in the heads, runbooks, and habits of the people executing it. That is the control layer this piece is about. It has been holding the supply chain together for two decades because the foundation that should have replaced it never got built. I have been calling that pattern Human Automation. Years of it are not replaced by an Agent in a day, and this piece is about why that matters now.
Years ago I was on a team replacing an asset management system that had been in place for about fifteen years. I had also been on the team that built the original, which gave me one piece of context most don’t get: I knew what the system was for the day it shipped. We started discovery the way you start most of these, asking the people who used the system every day to walk us through their work.
The conversations went like this.
“So you ingest these images. Why?”
“Because that’s my job.”
“Right, but these less obvious metadata fields. Why these specific values?”
“Because the runbook says to.”
People were surprised when that work they’d been doing often had no meaningful relevance to the then current workflows.
The people answering those questions had inherited a workflow and lost the reasons for it years ago. But, they kept the “machine” running while the organization above and around them spent its time, effort and capital on finance systems and back-office platforms and told itself, year after year, that media operations was fine or would be addressed eventually.
This exemplifies the concept of Human Automation. It demonstrates why the current industry conversation about Agents and GenAI replacing operations work is heading toward the wrong conclusion. The human layer in a media supply chain is not a deficiency, it is the layer that controls, integrates, and orchestrates the work. The Agents we are about to point at it do not know that, because the data and process logic they would need does not yet exist in any system the Agents can read.
What I mean by Human Automation
Human Automation is what happens when an organization decides, explicitly or by default, that building the technology to connect two systems costs more than putting a person in between them. A fulfillment coordinator reading an order out of a sales system and typing it into a media order system because no integration exists. A mastering PM reading a release calendar and creating work orders by hand because there is insufficient demand tooling. People are used as the integration layer itself. It is not a failure of the operators. It is a default position the industry has chosen, repeatedly, when it had other options.
The phrase collides with three adjacent terms that mean different things. Human-in-the-loop is an automated system that intentionally inserts a person at decision points. Robotic process automation (RPA) is software bots that imitate what a human does inside a user interface. What Andy calls human-enhanced is the human contribution that turns generative output into a legally protectable work. Human Automation is none of those. The other three describe specific patterns of human-machine collaboration. Human Automation describes what fills the gap when the machine was never built at all.
The human is the control layer, and does not know it
In a Human-Automated supply chain, the people doing the work are often not executing a documented process. They are absorbing variation the system was never built to handle. The absorbing is invisible to whatever tool comes next. The system reports green because the human effort kept it green.
The control layer is also not uniform. There are at least two tiers of it inside any real operation, and they fail differently when an Agent shows up.
At one tier are the people who execute. What looks like a documented process is mostly habit, with a runbook capturing the visible part. Context across the team varies. Some have built real understanding over years and know when something looks wrong. Others know the actions but not the reasoning behind them. The asset ingest conversation at the top of this piece is from someone closer to that end. They have been running this habit long enough that it looks indistinguishable from a process.
At another tier are the senior staff that carry the context the systems were never built to hold. The Director of Mastering tracking talent relationships and preferences in a leather-bound notebook. The Mastering or Localization PM who knows from one phone call this morning that a vendor is over capacity this week or where to route that job for that recipient. None of that knowledge is in a system. It is the reason the operation keeps running. It is also the reason the next person in the role takes eighteen months to be effective.
The people inside Human Automation have not been passive. They have innovated continuously inside the space they were given, building spreadsheets that hold rules that were never formalized, writing runbooks for processes nobody at the system level would document, absorbing turnover and tool changes and merger integrations. The complaint about Human Automation is not that the humans were not capable. It is that they were capable enough to make the underlying problem invisible to the organizations that should have been solving those problems.
The three ways we did not build the necessary foundation
For decades, the industry has made the same decision in three different ways: do not build the underlying technology ourselves. The three patterns have never been sequential. Human Automation persists while outsourcing continues at scale. Outsourcing persists as DTC investment ramps. GenAI and Agents are being layered on top now, while the previous two are still doing the load-bearing work. All three are active, and all three have left the foundation work unfulfilled.
The first choice was Human Automation. Major media companies spent most of the licensing era avoiding the conclusion that they needed to effectively operate as technology companies. My own analysis of technology capital allocation during this period at a major media company across a fifteen-year window found less than ten percent of the spend going to content systems. The rest went to finance and back-office. Headcount filled the gap. The phrase that justified those decisions was “we’re content creators, not a technology company.”
Underinvestment was only part of the problem. Where money did show up, often around the direct-to-consumer (DTC) rollouts of the last five or six years, the partnership between operations and technology did not. Technology organizations sat at arm’s length from operations, treated by accounting governance and capital committees as project shops that delivered something with a beginning, a middle, and an end. I clearly remember fighting battles a decade ago with finance departments who insisted IT projects had to have a discrete close-out or they could not approve or track the investments. The shift from an “IT organization” to a “Technology Delivery organization” capable of co-owning living systems with operations came late, was ill-understood, and is often still incomplete. Operations teams that had spent decades being let down by IT had no reason to trust even these new tech teams now. The phrase that justified that resistance was “we don’t have time to change.”
The pattern that produces successful technology transformations in other industries is well known. Operations and technology co-own the work. The technology is treated as a living system rather than a discrete project. Finance funds capability rather than feature delivery. None of those preconditions has held in media operations long enough or consistently enough to produce a durable foundation. Until they do, the next wave of technology will land on the same hollow ground regardless of what the wave is.
The second was outsourcing. Media companies that did not want to build internal technology had another option, and they’ve used it for decades. They handed operational complexity to specialty vendors. Technicolor, Deluxe, Vubiquity, and many others absorbed work the media companies chose not to own. It let them delay the underlying investment by paying someone else to live with the consequence. The perceived ability to hold the vendor accountable was a fallacy. A failed delivery still failed, and legal recourse was monetary rather than operational. Then, in early 2025, Technicolor itself was liquidated globally after more than a hundred and ten years in business. Thousands of jobs gone. In-flight releases scrambling for substitutes. The remaining vendors struggled to fill the gap and the media companies finally started to look at in-sourcing. The industry’s most visible escape hatch partially collapsed, and the foundation it had been propping up was suddenly responsible for bearing the load.
The third is the one we are in now. Leadership that did not invest in or partner with internal technology, and that handed operational complexity to outside partners, is either reaching for GenAI and Agents as the next way to avoid the foundational work, or pretending those tools are not the future. GenAI and Agents are clearly the future of operational automation. Decades of underinvestment, broken partnership, and outsourced capability created the gap. GenAI and Agents will not close it on their own. The same leaders are making the same bet for the third time. While investment has definitely showed up in some organizations, the partnership and the trust deficit have not caught up. The phrase that describes this is “we have to keep the plane in the air while we change the engine,” which sounds responsible until you notice it has been the justification for not changing the engine for the entire life of the airline.
What the Agents are actually inheriting
At the execution tier, the Agent inherits the runbook but none of the reasoning behind it. It reads the runbook the same way the human did. Confidently, but without the comprehension built over years of habit. The difference is the human had an accumulation of feedback from years in the operation that occasionally surfaced the gap between the runbook and reality. A rejected asset, stalled workflow or a partner complaint. The Agent does not have that accumulation of feedback to build upon. It then executes that imperfect understanding at speed, and the throughput problem shows up at scale before anyone notices.
At the senior tier, the Agent inherits nothing. The leather notebook is not in any system. The Tuesday phone call about vendor capacity is not in any system. The routing knowledge in one PM’s head for ten years is not in any system. When that PM is asked to do something else with her time because an Agent now handles routing, the routing decisions stop being good.
The Agent is also inheriting workflows that suffer from the ongoing failure of two of their three former crutches. Internal Human Automation continues to be cut. The outsourcing market has just demonstrated that even the largest specialty vendor in it can disappear. Both buffers are weaker than they have been in recent memory. That is the operational risk nobody senior is talking about.
Andy framed the creative-side resistance around four questions about who benefits, who is retained, who decides, who owns. The operational parallel is similar: who carries the context, who notices when the Agent is wrong, and who is accountable when the green status stops being green.
The teams being asked to live through this transition are legitimately concerned. The fear of job loss is real. The distrust of the technology is earned, because the same leadership now pointing at Agents has been promising better tools for decades and not always delivering them. The commonly held instinct that the data has to be perfect before the tools are useful is technically wrong but emotionally honest, because the price of the tools being wrong has historically been paid by the operator, not by the organization that deployed them. Treating that resistance as irrational misunderstands the problem.
Step back, then tool it
The way out of this is not faster automation. It is the work the industry has historically declined to do. Step back and define what the process should be, not what it has accidentally become. Then build the foundational technology that lets demand, task, and state live in systems rather than in people. That foundation is concrete: demand management systems that make incoming work legible to automation, task management that holds in-flight state outside someone’s head, process orchestration that runs without the person who understands it in the room, and operational telemetry that measures what is actually happening rather than what the dashboard claims. Build the Agents on that foundation, where they can actually be useful, rather than on top of a fifteen-year-old runbook and systems nobody can defend.
The most credible Agent deployments I have seen recently look less like one Agent doing one job and more like what technologists have started calling an Agentic mesh: specialist Agents, loosely coupled but tightly integrated, orchestrated into a workflow. The pattern works for the same reason microservices worked. Smaller units fail in isolation, can be tested and replaced without rebuilding the whole, and surface their foundation requirements explicitly rather than hiding them inside one large black box. The mesh approach requires more from that foundational tech and process, not less. Each specialist Agent needs a system-legible view of demand, task, and state to do its job. The mesh does not solve the problem the runbook created. It makes the problem easier to see.
There is a rule I have repeated for years, and it gets sharper in this moment: you should never tool a bad process. Agents do not make that rule less true. They make it more expensive to ignore.
Start with the diagnostic
The diagnostic is clear and approachable. Inventory where your in-flight work state actually lives. Not where you wish it lived, not what the dashboard reports. Where would the next person taking the role have to look? If the honest answer involves a spreadsheet, an email thread, or a senior PM’s memory, that is the foundation gap, and no Agent will close it for you. For vendors and investors, the same question applied to the companies you work with. Their answers will tell you more about their readiness than any AI roadmap they share.
The wrong answer in 2026
Andy’s Human Layer series asks whether the person inside the work survives the transformation. Whose decisions are in the output. Whose face is in the frame.
The operational Human Layer asks a quieter version of the same question. Whose judgment was holding the process together. Whose context was the reason the dashboard reported green last quarter. Whose absence will be revealed by the next bad status report.
Both questions have the same wrong answer in 2026, and the industry is converging on it at speed. Pretend the human layer was not there. Pretend the runbook was the process and the leather notebook was inefficiency. Pretend the variation humans absorbed every day for decades was somehow being absorbed by the systems all along.
It wasn’t. It isn’t now. The Agents are arriving to try to fill a gap that should have been closed by foundational work the industry chose not to do. The people who held that gap closed for decades are the people the industry is calling inefficient and trying to move past. When the Agent is wrong, no one will be left to say why. When it needs direction, no one will be left to give it.
Years of Human Automation in the media supply chain are not replaced by an Agent in a day. They are replaced by the work the industry chose to gloss over, ignore, or not fund. Fix that and the Agent is the part that comes after.
Sources and further reading:
Andy Beach, Human-Enhanced is the New Automation, Identity as Infrastructure, Observability as Default.
MIT NANDA, “The GenAI Divide: State of AI in Business 2025,” reported by Healthcare IT News. Finding: 95% of enterprise GenAI pilots fail to deliver measurable ROI. Methodology: 150 leader interviews, 350 employee survey responses, 300 public deployment analyses.
Lisanne Bainbridge, “Ironies of Automation,” Automatica, 1983.
Bloomberg, “The Collapse of Technicolor.”
Variety, “Technicolor Bankruptcy: How The Mill’s U.S. Team Launched ARC Creative.”
McKinsey QuantumBlack, “How We Enabled Agents at Scale in the Enterprise with the Agentic AI Mesh.” For the multi-agent / agentic mesh pattern referenced in the “Step back, then tool it” section.
