Grigoriy Misilyuk
Anna VintsevskaConnected TV (CTV) is an extremely dynamic domain, driven by fierce competition among brands and advertisers. With the growing importance of AI, the domain is poised to embrace its many benefits. Moreover, the use of artificial intelligence has already become a golden standard in CTV advertising.
The key benefits of using machine learning algorithms in this sphere include improved precision and automation. For example, for many publishers, using AI is the ideal way to move away from outdated bidding logic and manual processes that simply don't fit.
But AI in CTV advertising actually enables predictive monetization and helps publishers increase their revenue. In this article, we will explore how artificial intelligence reshapes CTV advertising and redefines how auctions operate. The shift from static auctions to predictive monetization has already begun, and it's time to pay closer attention.
Why the CTV auction is more complex than the web
Auctions in traditional web advertising are rather straightforward. First, a user visits a page. As a result, a bid request is automatically sent to a few competing DSPs. The highest bidder wins the impression. Meanwhile, in CTV, this workflow looks quite different. Its inventory is time-based, content-driven, and tied to premium experiences rather than random page views.
Here are the core layers of context behind each impression in CTV:
Ad value often depends on a time slot (e.g., prime time or a late-night replay)
Type of broadcast content during which an ad is displayed
Household reach
Platform fragmentation, as different devices, operating systems, and SDK versions can make the supply side inconsistent.
As can be seen, the programmatic logic behind CTV advertising is much more complex than that behind web advertising. Unlike web actions, which depend on cookie-level identity, granular user behavior, and standardized environments, CTV is more focused on device-level IDs, limited targeting signals, and heavily controlled inventory.
The drawbacks of manual supply packaging in ctv advertising
While the CTV monetization process is rather complex, it has also largely relied on manual supply packaging. Sales teams have bundled inventory by genre, daypart, or app category. Then, this inventory has been sold as predefined packages to buyers.
This approach proves to be outdated for the highly dynamic domain of CTV advertising due to several reasons:
High risks of inventory underutilization
Slow response cycles
Lack of flexibility in pricing due to the prevalence of fixed rates
Significant operational overhead.
In short, manual packaging limits yield and increases labor costs. It’s a model built for a slower, more predictable world of old-school digital advertising. AI-driven automation offers a way out, not by replacing human decisions, but by scaling them intelligently.
AI in optimizing CTV advertising
AI can automate almost all aspects of CTV advertising. With advanced ML models and fine-tuning capabilities, both publishers and advertisers can receive efficient, tailored solutions that streamline common workflows. Here, we will focus on the most common and efficient AI use cases in CTV advertising.
Real-time slot clustering
With advanced AI algorithms, companies can analyze tens of thousands of impressions per second. In addition, such algorithms efficiently group impressions into dynamic clusters based on factors like:
Content type
Engagement level
Audience probability.
This approach introduces the notion of fluid clusters. These are ad slots that adapt to real-time contextual performance. The key benefit of AI-driven clustering is greater flexibility in the dynamic CTV domain.
For example, a comedy show watched on Saturday night might share a cluster with a lifestyle series if their engagement profiles align. With the powerful capabilities of machine learning models, the system can learn and autonomously improve the accuracy of fluid clusters.
Predicting win rate and dynamic floor pricing
In traditional auctions, static floor prices often play a critical role. The problem with such a pricing approach is that it fails to reflect actual competition levels. Meanwhile, AI enables companies to forecast the probability of winning for each impression.
Its analytical algorithms consider factors like:
Historical bidding behavior
Seasonality
DSP responsiveness.
As a result, companies can ensure dynamic floor pricing. In such cases, floors adjust automatically based on predicted demand. Meanwhile, high-value impressions rise in price while lower-value ones remain accessible.
Throttling and QPS optimization
AI helps publishers address another common CTV challenge: QPS optimization. The point is that an excessive number of bid requests can overwhelm DSPs. Meanwhile, sending too few bid requests is also not a good option, as it can result in missed buyers.
AI models enable intelligent traffic throttling. As a result, QPS can match demand capacity and response quality in real time. This avoids “bid noise” and reduces infrastructure costs without hurting revenue.
Balancing yield and cost
AI doesn’t just maximize revenue—it optimizes the yield-to-operational-cost ratio. You can train a model to understand when to hold back and when to bid more aggressively. This automated balancing can lead to significant benefits over time.
In particular, you may expect such an approach to lead to higher net margins, not just higher gross CPMs. Here comes a powerful shift from “more” to “better” — from chasing every impression to valuing the right ones.
The role of AI in balancing demand
One of the biggest challenges in CTV advertising is that DSPs vary widely in performance. Some platforms bring premium buyers but are slow to respond. Meanwhile, other DSPs bid aggressively but drop win rates when inventory floods in. It might be very challenging to balance this demand manually. Fortunately, AI algorithms are here to bring structure to that chaos.
ML models perform efficiently when analyzing bid data, latency, and response rates. Based on such analysis, these models can redistribute demand across DSPs based on efficiency. AI automatically learns which DSPs perform best for specific inventory types or time windows. For instance, one DSP might deliver stronger results during weekday news streams, while another performs best when popular TV shows are on air.
AI algorithms consider all this information to manage supply so that each DSP gets what it handles best. As a result, publishers can achieve greater workflow efficiency. CPMs become more stable, while fewer bids are wasted. The result is a self-tuning marketplace that aligns with each buyer’s real performance, not their promises. This, in turn, leads to improved performance across critical metrics like sRPM and fill rate.
Transparency and explainability of AI decisions
Despite its evident benefits, full-scale AI implementation still remains a risky affair. Or, at least, many publishers view it like this. The point is that artificial intelligence is associated with the so-called black-box effect. When an algorithm sets floors, allocates inventory, or throttles traffic, publishers want to know why.
The best way to provide publishers with such control and visibility is by implementing explainable AI (XAI). Such a system can provide human-readable rationales for the decisions made by a machine learning model. For instance, a publisher may see messages like these:
Floor raised by 12% due to consistent overbidding from DSP A.
Cluster reassigned to lifestyle category based on 78% content similarity.
QPS reduced by 15% due to latency increase on DSP C.
This level of transparency builds confidence and accountability. It also enables manual overrides, which may be really important in high-value CTV environments where security is paramount.
The human in the AI loop
Do not expect AI automation to replace human judgment. View artificial intelligence merely as a way to amplify it. Here are some critical areas where human involvement remains irreplaceable:
Setting strategic goals and constraints for an AI system
Auditing model outputs
Understanding the context is necessary for decision-making and challenging to convey to the ML model.
Suppose an algorithm observes a sudden CPM spike and automatically raises floors. A human operator will likely recognize that it is tied to a temporary event, such as a live sports final. He or she will not make any dramatic responses to such a spike and will hold pricing steady to avoid alienating buyers.
The same judgment applies when evaluating sRPM metrics. While AI can detect shifts in efficiency across campaigns, only humans can interpret whether those fluctuations stem from genuine audience value or from external, short-term factors.
Therefore, it is important to combine AI capabilities with human-specific knowledge to create an efficient feedback loop. This will form the foundation of adaptive monetization and help organizations build a system that learns, adjusts, and never stops optimizing.
The value of predictive monetization
One of the most critical trends tied to the expansion of AI in CTV advertising is predictive monetization. Here, it's about pursuing strategic transformation rather than a minor technical trend. Historically, CTV yield management resembled weather forecasting.
Teams analyzed data, reacted to fluctuations, and made manual adjustments. Meanwhile, AI helps organizations adopt more proactive, less cautious approaches to advertising campaigns. ML algorithms can forecast value before impressions occur by considering factors like:
Historical performance
Audience patterns
Factors like time of day or show popularity.
As a result, predictive monetization introduces the following changes to CTV advertising:
Campaign pacing becomes proactive, not reactive
Revenue prediction grows more accurate, which ensures more efficient business planning
Operational overhead drops as repetitive manual adjustments disappear.
We at TeqBlaze believe that an ideal CTV advertising system, enhanced with predictive monetization, is a self-optimizing ecosystem where every new data point refines the next decision. Such an architecture impacts content strategy, ad frequency, and creative selection.
Common challenges in adopting AI for CTV
While the implementation of AI brings many more promises than many advertisers and publishers may expect, integrating it into a CTV advertising platform is not a walk in the park. These are the most common challenges you should consider:
Data fragmentation is associated with the fact that CTV data often comes from multiple SDKs, devices, and vendors. You may need to put some effort into consolidating it into a single set.
Model bias that may lead to bad decisions or misinterpretations.
Compute the cost associated with real-time inference at the CTV scale.
Organizational readiness to adapt data-driven workflows.
The best approach is incremental adoption. We suggest you start with narrow, measurable use cases (e.g., floor-pricing optimization or QPS control), validate the results, and scale gradually.
The broader business value
While addressing the challenges of implementing AI in CTV advertising, keep long-term success in mind. AI is not here to immediately optimize your revenue. It helps you transform the entire business logic, which will pay off in the long run.
By adopting AI in CTV advertising, you aim at the following:
More intelligent forecasting and greater visibility into ad revenue streams
Ideal conditions for extensive experimentation
Sustainable scaling of your infrastructure
Perhaps most importantly, AI levels the playing field. Smaller publishers can now compete with large networks by using intelligent algorithms rather than massive sales teams.
The future: Adaptive, ethical, and interconnected CTV advertising
It may be challenging to forecast CTV monetization with precision. However, given current trends, we may expect it to evolve toward adaptive ecosystems. These are environments where AI not only reacts to data but also collaborates across the entire value chain.
Some properties of such a system may include:
SSPs that share anonymized data with DSPs in real time for joint prediction models.
AI systems that coordinate frequency caps across devices to improve the viewer experience.
Pricing algorithms that learn from creative performance metrics, not just bids.
Meanwhile, it is important to establish a clear line beyond which AI implementation in CTV should never go. Here it goes about ethical and privacy considerations and, probably, new standards and rules governing how CTV advertising campaigns can use ML models. Overall, the next generation of CTV AI won’t just be powerful—it will need to be responsible.
Conclusions
AI is not a passing trend in CTV—it’s a new strategic layer of control. It transforms monetization from a reactive exercise into a predictive, continuous-learning, adaptive system. As long as CTV advertising differs from other ways of digital advertising, dynamic AI algorithms bring great value to this domain. By implementing machine learning models for CTV, publishers can enhance revenue while making their system more stable, transparent, and flexible.
As static auctions give way to predictive monetization, those who harness AI wisely won’t just optimize their ad operations—they’ll redefine what CTV can be. The key concern is doing things the right way.
TeqBlaze, a company with significant adtech expertise and a deep knowledge of modern AI capabilities, is ready to help you with this task. Contact us to discuss the opportunity of enhancing your CTV advertising and predictive monetization with tailored ML models.
