Is Ad-Tech Still Fighting the Wrong Battle?
Or how AI is rapidly changing the game
Our industry has long been obsessed with efficiency—automating parts of the advertising process to deliver results faster. Reading about NBCU’s recent platform in Ad Exchanger seems a step in that direction. But here’s the real question: are we still fighting the wrong battle?
The real issue lies in efficiency and in rethinking the workflows entirely. AI can potentially simplify ad-tech operations to a degree that’s barely being touched. What if the manual tasks that dominate ad tech—data entry, quality assurance, campaign management—could be almost eliminated? AI isn’t just a faster tool; it’s a fundamental reimagining of how these workflows are structured. And yet, many platforms, seem content to make incremental improvements rather than embrace the revolution AI offers.
The Problem With Current Workflows
From creative review and ad delivery to data quality checks and campaign reporting, countless employees across the industry spend their days entering, reviewing, and validating information. This manual labor isn't just tedious— error-prone, time-consuming, and expensive—the sheer volume of human touchpoints in something as basic as ad delivery is staggering.
Data entry roles are rampant across the industry. Think about all the tasks that require humans to input or review data: audience segmentation, campaign performance tracking, ad trafficking, creative reviews, budget allocations, quality checks, and more. These processes involve hundreds of people across multiple teams, departments, and external partners. And it’s not just large enterprises; smaller agencies and media buyers deal with the same inefficiencies.
A Step Forward or More of the Same?
Reading about new platforms like NBCU’s, which aims to centralize creative ad delivery and integrate quality assurance checks, it seems they can address some of the industry’s inefficiencies. Reducing the back-and-forth that typically plagues CTV ad delivery by consolidating creative delivery is undoubtedly good.
But in many ways, it’s still more of the same.
The platform doesn’t fundamentally rethink how workflows could be rebuilt from the ground up with AI. Instead, it focuses on streamlining existing processes, using automation to make things slightly faster or easier.
While this is useful, it doesn’t address the core issue: manual workflows shouldn’t just be streamlined but eliminated.
How can we make things better?
At the agency level, media buyers and planners manually input targeting parameters, adjust bids, and track performance across multiple platforms. In-house marketing teams do the same on the brand side and let’s not forget the teams involved in trafficking ads to ensure that they run correctly across different platforms and placements.
And then there are the quality checks. Whether it’s ensuring that creative content meets guidelines or that campaigns are tracking correctly, the number of manual touchpoints involved in running a successful ad campaign is enormous. And every one of these touchpoints is a potential point of failure.
AI can eliminate many of these tasks, reducing the number of people involved and the likelihood of human error.
Creative Review and Approval: AI can automatically analyze creative content for quality, brand safety, and adherence to guidelines. Using machine learning models trained on vast datasets, AI can flag issues faster and more consistently than any human review team. Will it make mistakes? Sure… but fewer than humans.
Ad Trafficking: Setting up, targeting, and launching campaigns involves manual data entry across multiple platforms. AI can automate this by pulling in data from various sources, segmenting audiences in real-time, and adjusting bids or targeting based on performance—all without human intervention.
Campaign Reporting and Optimization: We spend countless hours pulling reports, analyzing performance metrics, and adjusting strategies based on those insights. AI can automate reporting, continuously analyze performance, and optimize campaigns on the fly, eliminating the need for manual adjustments.
Budgeting and Forecasting: Allocating ad budgets across platforms and campaigns is typically manual, with teams adjusting numbers based on projected performance. AI can use predictive analytics to distribute budgets the most efficiently, even making adjustments based on real-time performance.
Data Quality Assurance: Many companies rely on manual quality assurance processes to ensure accurate and up-to-date data. AI can automate this by detecting anomalies, identifying potential errors, and cleaning data in real-time without human oversight.
So, are we fighting the wrong battle?
The future of the industry won’t be about incremental improvements. It will be about a complete overhaul of how things are done. AI has the potential to automate nearly every aspect of the ad delivery and measurement process. Platforms like NBCU’s, which focus on making existing workflows more efficient, are helpful but ultimately limited in scope.
The real revolution will come when we fully embrace AI as a tool for automation and as the foundation for how advertising is delivered, measured, and optimized. AI can do more than streamline—it can completely eliminate manual data entry jobs and transform the way campaigns are run, freeing up human talent for more strategic and creative work.
The future of advertising isn’t just automated. It’s optimized, predictive, and dynamic. AI is the key to unlocking that future. The question is whether we are ready to embrace the coming revolution.