AI-generated video is moving from experimental trials into mainstream production workflows at Netflix, Lionsgate, and other major studios. The market for AI video generation in over-the-top (OTT) streaming platforms is expected to expand at a 26.62% compound annual growth rate through 2031, according to research from Mordor Intelligence. This acceleration reflects a fundamental shift in how streaming services, broadcasters, and production houses approach content creation, localization, and promotion.

What's Driving the Explosive Growth in AI Video for Streaming?

The expansion of AI video generation in the OTT market stems from several converging pressures. Streaming platforms need more content faster, production budgets are tightening, and international distribution requires expensive localization work. AI-generated video addresses all three challenges simultaneously. Netflix demonstrated this in 2026 when it used generative AI to produce 17 minutes of documentary footage at half the cost and twice the speed of conventional methods. This isn't speculative future technology; it's already reshaping how major studios allocate production resources.

The market is moving through distinct use cases that are proving commercially viable:

• Faster Content Turnaround: AI video generation enables studios to refresh catalogs, create seasonal content, and extend existing intellectual property without extending production timelines, making it practical to test narrower formats before committing larger budgets.
• Scalable Localization and Dubbing: South Korea's K-FAST initiative localized more than 1,200 titles totaling 1,400 hours into English, Spanish, and Portuguese during 2025 and 2026, reaching 100 million cumulative views in 22 countries through 20 AI-dubbed channels within five months.
• Promotional Content Creation: Lionsgate took an equity stake in a generative AI video platform in June 2026 after working with it since 2024, expecting savings of tens of millions of dollars per year by automating trailer and promotional content generation.
• Short-Form Content for Social Distribution: Netflix introduced a Clips feature in 2026 and pursued publisher agreements for video content ranging from 3 to 20 minutes, recognizing that streaming libraries and social video outlets require different editing styles and viewing lengths.

Which AI Video Capabilities Are Seeing the Most Adoption?

Text-to-video generation is the dominant segment, accounting for 41.63% of the market in 2025 and projected to grow at a 27.48% compound annual growth rate through 2031. This reflects the practical appeal of converting written scripts, prompts, or descriptions directly into video assets without requiring filmed footage. Film studios and production houses held 32.65% of revenue in 2025, while streaming platforms themselves are projected to grow at a faster 27.71% compound annual growth rate, indicating a shift toward in-house AI video production capabilities.

Geographic adoption patterns reveal important market dynamics. North America held 41.56% of the market in 2025, but Asia-Pacific is projected to grow at a 26.93% compound annual growth rate, suggesting that international streaming expansion and localization demand are driving adoption in emerging markets. This geographic divergence reflects different content production challenges; North American studios focus on faster turnaround and cost reduction, while Asia-Pacific platforms prioritize localization and regional content adaptation.

How to Evaluate AI Video Tools for Your Production Workflow

For content creators and production professionals considering AI video generation, understanding the practical trade-offs is essential. A new educational course from AI Academy by SmarterX, focused on Topaz Video AI, highlights the key decision points:

• Processing Method: Unlike cloud-based enhancement platforms, Topaz Video AI processes footage locally on your own computer, keeping your video private while using specialized AI models to upscale, denoise, sharpen, stabilize, and restore footage.
• Model Selection: Different AI models solve different video problems; understanding which model addresses your specific footage issues is critical to achieving dramatic improvements rather than marginal gains.
• Hardware and Rendering Requirements: AI video enhancement requires significant computing power and time; evaluating whether your hardware can handle render times and whether the subscription pricing fits your workflow is essential before adoption.
• Output Quality for Commercial Use: AI-generated video often carries an artificial appearance that limits commercial viability; polishing generated video to produce more realistic, client-ready results requires additional refinement steps and expertise.

What Obstacles Could Slow This Market's Growth?

Despite the momentum, significant headwinds remain. Intellectual property, copyright, and talent likeness risks represent the most immediate constraint on widespread adoption. New York enacted the AI Transparency Law in December 2025, which took effect on June 9, 2026, requiring disclosure of synthetic performers in commercial advertising and providing civil penalties of up to $5,000 for violations. This regulatory environment is likely to expand as other states and countries establish their own rules around AI-generated content.

Additional technical and operational challenges include inconsistent output quality and enterprise-grade brand-safety concerns, high compute and storage costs for long-form, high-resolution video generation, and limited interoperability between AI video tools and existing OTT production workflows. Legacy broadcasters in particular face integration challenges when adopting new AI video platforms alongside decades-old production infrastructure.

The market's trajectory suggests that AI video generation will become a standard component of streaming production by 2031, but adoption will follow a segmented path. Studios with large budgets and established workflows will integrate AI tools for specific, high-volume tasks like localization and promotional content. Smaller production houses and independent creators will rely on specialized platforms that offer simpler interfaces and lower upfront costs. The competitive landscape will increasingly combine specialized video-model providers with large technology platforms that can bundle generation tools with cloud, consumer, and enterprise products, creating an ecosystem where AI video generation is embedded rather than standalone.