A new industry survey conducted by Dan Rayburn and Amagi reveals a telling disconnect: while AI dominates conference panels and C-suite discussions, 68% of media and entertainment executives say they’re not using AI in their video workflows.
Only 18% are using it today, and just 14% say they plan to. That’s a slow start for a sector that’s under pressure to reduce costs, improve margins, and rethink long-standing operating models.
AI’s Role in a More Disciplined Industry
Over the past few years, the story across media and entertainment has been one of restraint. Layoffs have continued, but at a slower pace. Budgets have tightened. Growth has been replaced by profitability as the new North Star.
Against that backdrop, it’s no surprise that “operational efficiency” ranks as a top KPI (24%) for companies exploring AI. The intent is there, but in many cases, the execution isn’t.
And that’s a missed opportunity.
For the Few Using AI, Here’s Where It’s Creating Value
Among the 32% of respondents already using (or actively planning to use) AI in their workflows, the most common applications include:
- News summarization & clipping (39%)
- Content scheduling and planning (33%)
- Personalization (29%)
- Promo creation and versioning (23%)
- Ad targeting and monetization (22%)
- Viewer insights and analytics (22%)
These aren’t futuristic use cases. They’re real, immediate operational gains—automating manual tasks, streamlining planning, and helping teams get more done with fewer resources.
Which raises the question: why are so many still sitting on the sidelines?
The “Unclear Use Case” Dilemma
According to the survey, the biggest barrier to adoption is “unclear use cases” (47%). That suggests many teams still aren’t sure where AI fits into their day-to-day workflows—or what kind of return they can expect.
To help close that gap, here are five clear, underutilized use cases that go beyond the basics:
1. Greenlighting and Development Insights
Use AI to analyze performance data across regions, genres, formats, and viewer cohorts. This helps creative and programming teams make better-informed decisions about what to develop, where to invest, and when to pass.
2. Content Versioning at Scale
Automatically generate and adapt creative assets for different platforms, regions, and formats—reducing time-to-market and production costs without compromising quality.
3. Churn Prediction and Engagement Modeling
AI can flag behavioral patterns that signal churn risk—then trigger targeted engagement campaigns designed to retain those users before they leave.
4. Ad Inventory Optimization
Use AI to forecast demand, identify underperforming inventory, and dynamically reprice or reallocate placements to improve fill rates and revenue.
5. Localization and Accessibility
AI-driven captioning, dubbing, and descriptive audio tools can reduce the cost and turnaround time of reaching new audiences, especially in non-English-speaking markets.
Each of these use cases contributes to the same underlying business objective: doing more with fewer resources while improving outcomes.
Measuring Optimism
The tone of the survey is cautious. 59% describe themselves as “cautiously optimistic” about AI’s future in media, while only 10% are “very optimistic.” That’s not surprising given the high stakes. But the industry isn’t lacking in conviction—it’s lacking in clarity and comfort with deployment.
That’s also reflected in what respondents say they want from vendors: clear ROI (41%), seamless integration (28%), and custom use case alignment (20%) top the list.
The Take
Executives don’t need to make AI the centerpiece of their operations overnight. However, in a market that’s slowly stabilizing and becoming more efficiency-driven, having a clear AI roadmap is increasingly important.
Not everything needs to be automated. But the things that can be? They should be.
