Automation has become this industry’s comfort word. Leaders say it and imagine cost savings, cleaner workflows, maybe even smaller teams. But automation applied to a broken process only accelerates disorder. If you haven’t stabilized the foundation, you’ve simply built a faster way to make mistakes.
Efficiency depends on structure, not software. Real progress comes from clear ownership, governed metadata, and shared understanding of what “done” means at each step. Automation belongs on top of that foundation, not underneath it.
What companies are really chasing when they say “efficiency”
When corporate strategy pivots toward efficiency, it’s usually because pressure is coming from the top. Budgets tighten, expectations rise, and people start reaching for silver bullets. Automation looks like an easy fix, something that can be purchased rather than built through discipline.
What they’re usually chasing is one of four things:
- Time to value – How long it takes to get from greenlight to monetization.
- Cost per title – The total labor, tech, and vendor friction required to deliver content.
- Error rate – How often poor metadata or rights mistakes cause rework or penalties.
- Reliability – How consistently work moves through each department.
These are coordination problems first, design problems second, and automation problems last. Reversing that order leads to expensive lessons.
Automation amplifies whatever system it touches.
Where automation helps and where it doesn’t
Success depends on knowing what deserves automation and what still needs human judgment. Automate the rules. Let people handle the choices that carry risk or context.
Smart uses
- High-volume, rules-based work: spec validation, caption checks, ad-break placement.
- System synchronization: pushing verified metadata from CMS to storefronts.
- Exception surfacing: highlighting anomalies that need review.
Poor uses
- Automating subjective decisions: ambiguous rights or editorial calls.
- Extending broken approval chains: bots that reinforce unclear ownership.
- Masking structural issues: using RPA to compensate for missing integrations.
A process that cannot be diagrammed clearly is not ready for automation. That reflects a leadership gap that must be addressed before tools are added.
The efficiency stack, in sequence
Every durable operation rests on a clear order of priorities. Start with agreement on goals, then build supporting systems.
- Strategy – Define how each title contributes to revenue. Automation should serve that goal.
- Operating model – Assign single ownership per phase. Avoid shared accountability.
- Data and metadata – Establish a verified source of truth and enforce it.
- Workflow – Document the standard path and the exception path. Track rework and latency.
- Automation – Apply only after the prior layers are reliable.
When these layers are stable, operations gain natural rhythm. Skip steps and you end up managing tools instead of outcomes.
A quick sniff test before automating
Before implementing any automation, ask the fundamental questions: What will improve? How will we measure it? Who will take responsibility for success?
- Can your team illustrate the workflow in a single page?
- Is there one verified source for rights and metadata?
- Who approves changes to technical specifications?
- What measurable result defines improvement?
If the answers are uncertain, automation will multiply that uncertainty. Clarity must come first.
The common failure pattern (FAST/AVOD example)
This story repeats across the industry. A team under deadline pressure deploys auto-ingest and auto-QC because it seems efficient and progressive. The existing data is inconsistent, but no one slows down to verify it.
- Operations activates automation tools.
- Metadata remains fragmented, with rights still tracked in spreadsheets.
- Content is published with incorrect windows and ad-break errors.
- Finance and partners must reverse the damage.
- Management concludes that automation failed.
In truth, the automation performed exactly as configured. The foundation was what failed.
What real efficiency looks like
True efficiency feels stable and predictable. Teams understand their responsibilities, data moves cleanly, and reports align with results. Stability replaces noise.
- Rights-first logic – Contracts structured into machine-readable terms.
- Metadata discipline – Defined fields validated at ingest.
- Exception dashboards – Visuals that show blockers, not vanity totals.
- Consistent feedback loops – Operations, Finance, and Ad Ops reconciling weekly.
- Selective automation – Beginning with one or two well-governed steps and expanding from there.
Efficiency shows up as rhythm, not speed.
Iteration Equals Success
Attempting to automate everything at once overwhelms both teams and budgets. Progress depends on sequencing. Build in stages, release gradually, and let actual use guide the next improvement.
Media operations thrive on agile principles for a reason. Small releases create visible wins. Each phase builds trust between the people who design the system and those who rely on it every day.
This approach earns participation, collects useful feedback, and avoids waste. The result is better adoption, stronger data discipline, and fewer abandoned tools. Iteration builds maturity through momentum rather than force.
The executive takeaway
Automation is a force multiplier. The essential work happens before it: clarifying goals, ownership, and data. When those elements are aligned, automation accelerates growth rather than confusion.
Follow the right sequence and your systems reinforce performance. Ignore it and the same tools will reinforce disorder.
Rebecca Avery is the founder of Integration Therapy, partnering with streaming-media leaders to contain revenue and accelerate growth by aligning people, systems, and strategy
