Sprinkling Fairy Dust on CTV Ads: When Artificial Intelligence Meets Artificial Results

Connected TV (CTV) advertising was hyped as the marketer’s latest shiny toy—a seamless fusion of creativity and data-driven precision, all orchestrated by the ever-mystical artificial intelligence (AI).

 The pitch? Hyper-targeted ads that not only know what you want but also when you want it, blending so smoothly into your favorite shows that you’d swear they were part of the plot. 

The reality? It’s more like a badly scripted sitcom where the punchlines fall flat and the guest stars are utterly forgettable.

The ACR Fiasco: When AI Can't Read the Room

Automatic Content Recognition (ACR) was touted as the holy grail of contextual advertising. The promise was simple: AI would read the room, detect the emotional tone of your current binge-watch, and serve up an ad that’s not just relevant but contextually flawless. Imagine watching a spine-chilling episode of The Walking Dead and getting interrupted by an ad for knitting needles instead of, say, zombie repellant. Sounds absurd? Well, that’s where ACR often lands.

Yan Liu, CEO and Co-founder at TVision, doesn’t sugarcoat it: “AI is more about efficiency at this point, especially on some tasks you typically outsource. I think it will create more spam, MFA websites, and better creative for DR ads. AI is not good at linking multiple tasks yet. So I don't think it can add tons to quality of execution or creative.” He’s right. Most ACR systems can’t quite grasp the subtleties of human emotion. They recognize the genre but not the mood shifts that dictate what type of ad should follow. So instead of a seamless transition, advertisers end up with mismatched jingles that make viewers want to change the channel faster than you can say “ROI.”

Programmatic Buying: Precision or Pricey Guesswork?

Programmatic ad buying on CTV was supposed to be the sharpshooter’s dream—AI analyzing real-time data to hit the exact target with surgical precision. In theory, sounds like a marketer’s nirvana. In reality, it’s more like throwing darts blindfolded and hoping one lands in the right sector. Shared devices, fragmented data, and inflated CPMs (cost per thousand impressions) mean that “precision targeting” often misses the mark. You’re paying top dollar to reach your ideal demographic, only to have your ads shown to someone’s grandma binge-watching Golden Girls.

David Nyurenberg, founder of Rain the Growth Agency, cuts through the nonsense: “AI has fundamentally changed how we approach CTV, allowing us to score each impression based on its likelihood of achieving the outcomes we need.” While this sounds revolutionary, it’s essentially just a fancy way of saying, “We’re making educated guesses with more data.” And let’s face it, even educated guesses can be wildly off when you’re dealing with the chaos of CTV.

Shoppable Ads: Novelty Over Functionality

Shoppable ads were pitched as the future of CTV—ads so interactive that you could buy products without ever leaving your couch. Hulu and Roku have toyed with features like QR codes and product carousels, but let’s be real: navigating a purchase with a remote is about as enjoyable as trying to text with oven mitts on. Most viewers would rather swipe on their phones or click through on their laptops. Shoppable CTV ads remain more of a novelty than a mainstream solution, leaving advertisers scratching their heads and consumers frustrated.

Take, for example, Hulu’s attempt to integrate clickable product carousels during prime-time shows. The idea was brilliant on paper—seamlessly blend commerce with entertainment, allowing viewers to instantly purchase the stylish jacket their favorite character just donned. In practice, though, the execution falls flat. Viewers are left fumbling with their remotes, trying to select tiny QR codes or navigate awkward drop-down menus while half-watching an intense drama. As Yan Liu, CEO of TVision, bluntly puts it, “AI isn’t good at linking multiple tasks yet. So I don't think it can add tons to quality of execution or creative.” The disconnect between the innovative concept and the clunky user experience is glaringly obvious, making shoppable ads feel more like a gimmick than a game-changer.

Moreover, the user behavior simply isn’t aligning with these experimental ad formats. While the promise is to turn passive viewers into active shoppers, the reality is that most people prefer the convenience of using their smartphones or laptops for purchases. Andrew King, GM and Product Lead at TripleLift, observes, “We’re already witnessing applications—smarter ad placements within content, more relevant programming schedules, enhanced insights atop campaign reports, even upscaled creative assets.” Yet, even with these advancements, the fundamental issue remains: the CTV interface isn’t conducive to seamless shopping. Advertisers are left investing in a feature that, despite its high hopes, hasn’t convinced the average viewer to ditch their preferred shopping devices. Until the user experience is dramatically improved, shoppable CTV ads are likely to remain a niche curiosity rather than the next big thing in advertising.

AI-Powered Brand Placement: The Awkward Cameo No One Asked For

AI-powered brand placement was sold as a groundbreaking tool that would seamlessly insert brands directly into the content you love—blending logos, products, and billboards into the very scenes of your favorite shows without the need for traditional ad breaks. The vision? A fully integrated brand experience where ads would feel as natural as the storyline itself. Take, for instance, Rembrand’s Generative Fusion™ technology, which aimed to embed branded elements into post-production, letting characters casually sip from a strategically placed soda can or walk by a logo-embellished billboard, supposedly without pulling the viewer out of the narrative. Sounds futuristic, right? Well, not quite.

In reality, these placements often stick out like a bad CGI effect from a B-list movie. Instead of enhancing the content, these awkward insertions end up drawing attention to themselves, breaking immersion rather than adding to it. You might be watching a dramatic scene, but when an out-of-place product appears, it’s like getting hit over the head with a brand. Suddenly, the emotional moment between two characters is hijacked by a poorly rendered energy drink can that feels jarringly forced. Instead of seamless integration, these placements often feel like a desperate attempt to gain visibility, which ironically does more harm than good by reminding the viewer of the artificiality of the experience.

Jason Fairchild, CEO of TvScientific, hits the nail on the head: “AI is being used like magic fairy dust by most companies.” But he’s optimistic about true AI innovations: “Real AI is magic and will be transformative to most business categories.” TvScientific focuses on two main areas:

1. Campaign Optimization: Using AI to drive advertiser-declared outcomes like ROAS, CPA, and CPI by automating campaign adjustments based on a vast array of data points.
2. Creative Optimization: Building and optimizing TV ad creatives at the element level, determining which ad variations perform best with specific audience segments.

Fairchild believes these areas will unlock AI’s true potential, allowing for more precise and effective advertising strategies that go beyond the superficial buzzwords. However, until AI can master the art of narrative integration—understanding the tone of a scene, the motivations of characters, and how to place a brand within that environment without breaking the fourth wall—AI-powered brand placement is going to remain an awkward gimmick, not the seamless advertising revolution we were promised.

Who’s Actually Delivering? A Few Shining Stars

Amidst the sea of overhyped AI tools, a few companies are actually making meaningful strides:

• Origin’s Slingshot: Using AI to optimize ad delivery timing, Slingshot boosts viewer retention and ad effectiveness by aligning ads more closely with how people actually watch CTV.
• KERV Interactive: Pioneering shoppable CTV ads, KERV adds interactive elements that allow viewers to explore products in real-time, though the remote-based interface remains a hurdle.
• Vizio’s Inscape: Leveraging real-time viewing data, Inscape offers granular insights that help advertisers optimize placements based on actual viewer behavior, rather than guessing from broad demographics.
• The Trade Desk’s Koa: Analyzing millions of data points, Koa enhances campaign effectiveness and audience reach across multiple devices, providing a more accurate targeting mechanism.
• Comcast’s FreeWheel: Integrating AI into programmatic buying, FreeWheel optimizes ad placements by finding premium inventory that aligns with real-time viewership trends.

The Real Magicians: Jason Fairchild and Jason Fairchild of TvScientific

Jason Fairchild, CEO of TvScientific, points out the real magic lies in AI’s ability to automate and optimize vitally important components of advertising. “AI is being used like magic fairy dust by most companies,” Fairchild notes. But he’s optimistic about true AI innovations: “Real AI is magic and will be transformative to most business categories.” TvScientific focuses on two main areas:

Campaign Optimization: Using AI to drive advertiser-declared outcomes like ROAS, CPA, and CPI by automating campaign adjustments based on a vast array of data points. This is a great use of ML/AI technology because machines can handle a LOT more variables than humans can, and there are a LOT of variables that go into driving/optimizing sales from TV ads.

Creative Optimization: Building and optimizing TV ad creatives at the element level to achieve advertiser-declared outcomes. So, for a certain audience targeting package, does the ad with the male actor with sunglasses and a red shirt perform better than a male actor in a green shirt with no sunglasses? Fairchild sees this element-level creative optimization as the killer app of AI in TV ads. As AI continues to evolve, he believes it will take over 100% of video ad creative development, unlocking unprecedented levels of

Andrew King: Cutting Through the Noise

Andrew King, GM and Product Lead at TripleLift, shares a similar skepticism but with a glimmer of hope: “AI in CTV is indeed real, but cutting through the noise is a challenge because it’s still very early.” King highlights that much of what’s being marketed as AI is merely a rebrand of existing machine learning capabilities. The next wave, particularly generative models and large language models (LLMs), are still in the experimental phase, but early results are promising, albeit subtle. “We’re already witnessing applications—smarter ad placements within content, more relevant programming schedules, enhanced insights atop campaign reports, even upscaled creative assets,” he says. These early wins hint at a future where AI can truly enhance CTV advertising, but for now, it’s more of a cautious dance around the potential rather than a full-on revolution.

The Future: Efficiency Over Revolution (For Now)

Yan Liu sums it up best: “AI is more about efficiency at this point, especially on some tasks you typically outsource.” AI in CTV is great for automating repetitive, data-heavy tasks, but it’s not yet the creative powerhouse it was touted to be. As Liu puts it, “AI will create more spam, MFA websites, and better creative for DR ads. AI is not good at linking multiple tasks yet. So I don't think it can add tons to quality of execution or creative.”

Meanwhile, Jason Fairchild of TvScientific argues that real AI can revolutionize CTV by optimizing campaigns and creatives in ways humans simply can’t manage. “We think about AI/ML in terms of automating vitally important components of our business, which is leveraging TV advertising to drive actual business outcomes for advertisers,” he explains. His company has developed patented technology that optimizes campaigns and creatives to achieve advertiser-declared outcomes, proving that AI can indeed be transformative when applied correctly.

Final Thoughts: The Emperor’s New Algorithms

So, where does that leave us? AI in CTV is still wearing the emperor’s new clothes—glamorous on the surface but lacking real substance underneath. While companies like Origin, KERV Interactive, Vizio’s Inscape, The Trade Desk, and FreeWheel are making genuine progress, the majority of AI applications in CTV remain more smoke and mirrors than actual game-changers. The real magic, as Jason Fairchild suggests, lies in AI’s ability to handle vast data and optimize campaigns beyond human capacity, but this potential is yet to be fully realized.

For marketers, the advice is clear: approach AI in CTV with a healthy dose of skepticism. Don’t buy into the hype without seeing real results. Focus on leveraging AI where it truly adds value—efficiency, data analytics, and strategic optimization—while keeping your expectations grounded. Until AI can seamlessly blend into the creative process and deliver on its grand promises, it’s best to view it as a powerful tool rather than the wizard behind the curtain.

So, until AI in CTV can truly read the room and craft ad experiences that feel as natural as a plot twist, it remains more of an overhyped feature than the transformative force it was sold to be. Until then, marketers would do well to focus on the tangible benefits and let the AI-driven fairy dust settle before placing their bets on the next big thing.

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