Agentic AI has quickly become one of the most talked-about narratives in advertising, yet much of the market still struggles to distinguish genuine innovation from repackaged automation. For performance marketers operating under increasing pressure to prove ROI, the distinction is becoming critical.
In this AIFN conversation, Jason Fairchild, CEO and Co-Founder of tvScientific, explains why revenue-linked intelligence, rather than workflow efficiency, is the true test of AI maturity in advertising. Drawing on decades of experience spanning paid search, programmatic media, and Connected TV, Fairchild outlines how continuously learning, product-level AI systems are reshaping performance marketing, and why generalized automation approaches are likely to fall short.
AIFN: Jason, “agentic AI” has become a dominant narrative in advertising. From your perspective, what is genuinely new — and what is simply automation rebranded?
Advertising has used automation for years to help with anything from bid optimization to budget allocation. So in general, automation itself is not particularly novel. What is new is the AI-based systems that are built around revenue-driving outcomes and continuously learn at the product level.
They optimize media inputs, yes, but they also adapt based on real business signals like customer acquisition cost, lifetime value, and incrementality. And this is different for every advertiser, and every product for a given advertiser.
The simple test is whether the system improves revenue and ties decisions to measurable financial impact. If it doesn’t, it is more narrative than true innovation.
You’ve argued that agentic advertising needs to materially move revenue at the product level, in order to truly be effective. Why is revenue the only metric that matters in this debate?
Revenue is what keeps the lights on. If you are Delta Airlines or State Farm, for example, you report KPIs to the board and the stock market anchored in revenue, not reach and frequency. Same with just about every single company out there. You can improve lots of marketing metrics without actually improving the business. Campaigns can look great in a dashboard but do nothing meaningful for growth.
If agentic advertising is to be truly revolutionary, it should do more than make media buying more “efficient”. It should drive real financial outcomes. This includes lower acquisition costs, increased lifetime value, and incremental sales that wouldn’t have happened otherwise. The CFO should be chomping at the bit to explain how agentic AI has materially improved the financial performance of the company.
Why should performance marketers evaluate agentic AI claims through the lens of CAC, LTV, and incrementality rather than workflow efficiency?
Many AI tools focus on workflow efficiency, but AI can do so much more than that. The key question is whether the system is finding customers you would not have acquired otherwise or acquiring them more profitably. There’s a massive amount of hype out there; in order to cut through that hype and evaluate whether an agentic AI solution is delivering real value, performance marketers need to ignore the slick powerpoints and sales pitches and look at the math.
Ask what the system did to customer acquisition cost, lifetime value, and incremental revenue. If it is not reacting to real conversion and value data, it is probably optimizing in a vacuum. Ultimately, performance marketing is about sustainable growth and ROAS. If AI cannot demonstrate lower CAC, higher LTV, or measurable incrementality, it is just another productivity tool. That’s cute and all, but it’s not revolutionary.
In CTV specifically, where measurement has historically lagged behind digital, what does real product-level intelligence look like?
CTV has historically been measured like traditional TV, with awareness KPIs like impressions, reach, and frequency, but that doesn’t tell you if your marketing actually drove revenue. Product-level intelligence means tying every media decision back to measurable business outcomes. Did an ad campaign lead to incremental sales? This is in turn tied to understanding which creative, which audience, and which placement drives incremental customers and at what cost. The system should learn continuously, adapting campaigns based on real conversion and value data. This enables advertisers to see the true ROI of their spend on CTV, optimize toward profitability, and make decisions that directly impact the bottom line.
Why do you believe one “universal AI brain” approach fails in performance advertising environments?
One universal AI brain assumes that one model can optimize every product, every offer, and every audience the same way. In performance advertising, that doesn’t work because every business and every campaign has different economics, different customer behaviors, and different objectives. An ad campaign for Crest toothpaste, for example, is very different from an ad campaign for a casual video game company, which is totally different from an ad campaign for Delta Airlines. You just can’t apply the same “Agent” — the same algorithm — to all three.
What matters is intelligence that is specific to the product or offer and continuously adapts based on real revenue outcomes related to that product. That is how you drive incremental growth. A one-size-fits-all AI can make general improvements, but it cannot optimize the levers that actually move the business. Performance advertising is too granular and too tied to financial outcomes for a universal approach to be truly effective.
How does hyper-customized, continuously learning AI built around a specific offer outperform generalized automation?
Hyper-customized AI that learns continuously at the product or offer level outperforms generalized automation because it allows a marketer to configure it to adapt to the actual drivers of the business. It is not simply following rules or optimizing proxies, but is responding to what truly moves revenue. Every product, audience, creative and campaign is different. By focusing on the specific offer and measuring real outcomes, the system can make smarter decisions about who to reach, when to reach them, and how much to invest.
What are the biggest misconceptions advertisers have right now about AI-driven optimization?
One of the biggest misconceptions is that AI-driven optimization automatically equals better business results. And AI may do a lot to improve efficiency by reducing the number of humans that need to be involved in an ad campaign. This is good for efficiency, but that’s just one side of the equation. The real potential of AI is not just reducing cost by replacing humans; the real potential is driving more efficient outcomes tied to core business KPIs (e.g. revenue), and doing so in an ever-improving manner through self-learning AI agents.
How should executives distinguish between AI that improves reporting and AI that improves unit economics?
Executives should start by looking at outcomes, not dashboards. Dashboards are just a way of seeing and measuring outcomes tied to ad campaigns. Yes, improving dashboards is helpful because clarity is always helpful, but that is a totally different thing than improving the machine that drives underlying business results. AI that improves unit economics directly impacts customer acquisition cost, lifetime value, or incremental revenue. You should be able to see clear evidence that the system is acquiring customers more profitably or generating sales that wouldn’t have happened otherwise. The test is simple: ask how the AI’s decisions tie to measurable financial outcomes. If it cannot show that, it is not a growth engine.
If you were advising a CMO evaluating AI platforms today, what three questions should they ask to separate substance from hype?
If I were advising a CMO, I would focus on three things:
First, how does this system impact unit economics? Can it show measurable improvements in customer acquisition cost, lifetime value, or incremental revenue?
Second, how does it learn? Is it optimizing toward measurable business outcomes, or just proxy metrics like clicks and impressions? Related, how transparent is the system in terms of sharing what is driving its decisions, where the ads are being placed, etc?
Third, how specific is it to your product or offer? One-size-fits-all models can help with efficiency, but real performance gains come from AI that adapts to the nuances of your business and continuously improves based on actual results.
Looking ahead three to five years, what will define the winners in AI-driven advertising infrastructure?
The winners will be the companies that build and adopt AI systems that significantly improve business outcomes for advertisers. Companies that focus on generalized automation or input metrics vs impact metrics will fall behind. Advertisers are going to demand clarity on ROI, and agentic advertising is in a position to provide that. In short, the winners will be those who make AI accountable to the same thing every CFO and CEO cares about: profitable growth.
