The rise of AI-generated video has sparked new ambitions among media publishers and retail marketers, promising to supercharge content creation, reach new audiences and deliver video at the pace required by today’s digital economy.
For brands, the message is clear: Without video, you’re invisible. For publishers, video enables both deeper audience engagement and the creation of valuable instream ad inventory.
However, beneath the excitement lies a critical operational challenge: The true yield—or usable output—of AI video is far lower than many expect. It’s a reality with implications that extend beyond editorial and marketing, directly impacting ad and revenue operations.
Demystifying AI Video Yield: Data and Workflow Realities
Yield, as it relates to AI video, describes the percentage of generated videos that are immediately usable—meeting the standards for accuracy, brand consistency, compliance and creative impact.
Despite bold marketing claims, the data is sobering. Take Kalshi’s AI-generated NBA Finals TV commercial: Nearly 400 generations produced just 15 usable clips, a yield of about 4%-5%. In practical terms, that’s about 20-27 attempts for each asset suitable for broadcast.
This is not an isolated case. Whether in article-to-video automation, video cutdowns, UGC-style social clips or narrated shorts, brands and creators report similar experiences—10 to over 40 prompts per final usable video is the norm, not the exception.
Even for reasonably straightforward, visually consistent videos, creators using tools such as Invideo AI often require 5–20 takes to reach a “good enough” output, with substantial human touch required for selection, further edits and postproduction effort.
These low yields stem largely from high rates of “hallucinations”—AI outputs that contain errors or deviate from intended prompts. Studies and real-world case analyses indicate that hallucination rates can range from 10% to 29% per video generation attempt, making manual oversight and repeated iterations essential.
The Incentive Paradox: Tokenized AI Models and the Real Cost of Creation
An often-overlooked aspect—the economic logic of tokenized AI video platforms—has direct bearing on both content teams and ad/revenue operations. Many generative tools operate on a pay-per-prompt or token model, charging for each new attempt regardless of its quality or completeness.
This structure subtly encourages ongoing iteration, as each new prompt or regeneration means increased platform revenue but also greater expense and time investment for publishers.
For an ad ops professional, this means actual costs per usable video asset may escalate far beyond headline rates: The ad inventory investment only pays off when the asset is finished, compliant and ready to go live.
Without robust workflow controls, tokenized models may quietly reward process “churn” over finality, potentially undermining both production efficiency and content margins.
Why This Matters for Ad and Revenue Operations Professionals
For those overseeing programmatic and direct ad yield, the implications are two-fold:
- Inventory creation cost: With each formative asset requiring dozens of paid attempts and human touch, the cost per ready video must be carefully modeled to factor not just the AI vendor’s fees but also internal resource allocations for curation and QC.
- Ad revenue uplift: On the positive side, every high-quality video published unlocks new instream (and accompanying video) inventory. Video CPMs outpace display by 5x-10x. Quality video units increase engagement, fill rates and overall yield from both programmatic and direct ad sales. Thus, even at low yield rates, scaling compliant video can drive measurable revenue uplift and margin expansion for publishers.
Effectively, ad and revenue leads need to align with content and tech teams to:
- Benchmark true cost per usable video considering typical yield rates
- Design workflows or partner with orchestration-focused platforms to maximize yield and minimize run-up expenses from iterative prompting
- Strategically quantify and track revenue delta from new instream inventory post-AI workflow adoption.
Towards High-Yield Video: Orchestration and Quality Control
What distinguishes successful operators is not just their adoption of AI point solutions, but also their willingness to implement workflow orchestration. Orchestrated platforms like Aeon enhance yield by layering multiple AI models, embedding brand guardrails, automating curation and streamlining compliance checks.
These systems reduce both wasted effort and unnecessary token spending, allowing ad and revenue teams to better forecast costs and ramp up inventory with fewer brand or compliance risks.
Bridging AI Promise With Revenue Reality
For media publishers—and specifically for ad and revenue operations—AI video is not a magic bullet. But it is a formidable engine for incremental value when managed astutely.
Yield is low by default, iteration is inevitable and the economics of tokenized models demand scrutiny. Yet, by marrying disciplined workflow orchestration with market-driven inventory expansion, publishers can transform the bottleneck of low-yield AI into a lever for new revenue, scaling high-margin instream units while insulating themselves from cost overruns and quality pitfalls.