Before AI, advertising was like an unpredictable road trip—full of detours, inefficiencies and wasted resources. Imagine navigating cross-country in the pre-GPS era. You have a paper map, but it’s outdated. You rely on road signs, make guesses and occasionally stop for directions.
Sometimes, you get lost, burn unnecessary fuel and take longer than expected to reach your destination. Now, enter AI—a GPS for digital marketing, instantly analyzing data, optimizing routes and ensuring brands reach their goals faster.
This transformation has been particularly dramatic in the digital advertising landscape, where vast amounts of user data, complex targeting parameters and real-time bidding environments create a perfect storm of complexity that human marketers alone cannot navigate efficiently. AI systems can process millions of data points simultaneously, identify patterns invisible to the human eye, and make split-second decisions that optimize campaign performance.
But here’s the catch: even the most advanced GPS needs a driver. A concerning trend is emerging—companies, eager to cut costs, are deploying AI without proper supervision. The result? Misaligned strategies, inefficiencies and hidden expenses that can quickly outweigh the initial savings.
We’ve observed numerous instances where unsupervised AI systems gradually drift away from business objectives, optimizing metrics that don’t translate to real business value. For example, an AI might fixate on maximizing click-through rates while ignoring the quality of leads being generated, ultimately driving traffic that never converts to revenue. Without human oversight to course-correct early, these AI systems can operate in counterproductive loops for months before the damage becomes apparent.
The tech industry is now shifting its focus toward making AI itself more cost-efficient. Baseten, for example, has demonstrated this through its partnership with DeepSeek, delivering top-tier AI performance at a fraction of the usual cost. Investors have taken notice of it, injecting $75 million into Baseten and driving its valuation to $825 million.
Yet, cost savings alone aren’t the full story. The right approach to supervised-AI ensures that the adtech doesn’t optimize in isolation but rather works in tandem with human expertise, ensuring brands don’t just move faster but move smarter.
The Hidden Cost of Unsupervised AI
The rush toward full automation has proven to be an expensive lesson for even the biggest players. Amazon and IBM, for instance, learned firsthand that AI without human oversight leads to inefficiencies, not savings. Maintaining a fully automated system isn’t just about deploying models—it demands constant retraining, infrastructure upgrades and relentless debugging.
Without human supervision to detect and correct bias, the tool perpetuates and amplifies existing inequalities. IBM discovered that their Watson AI healthcare recommendations sometimes offered unsafe treatment suggestions when operating without medical professional oversight. These cautionary tales highlight how AI systems, left to operate autonomously, can veer dangerously off-course.
In digital advertising without human intervention, AI often misinterprets user signals, leading to suboptimal bidding strategies and wasted ad spending.
In digital advertising without human intervention, AI often misinterprets user signals, leading to suboptimal bidding strategies and wasted ad spending. In fact, our latest report highlights a 2.3% decline in global app installs, signaling a saturated app ecosystem where growth depends on smarter, more strategic engagement rather than brute-force automation.
The saturation of app markets means that the era of easy growth through broad targeting and high-volume user acquisition is effectively over. Today’s marketplace demands precision and nuance — qualities that AI alone struggles to provide without human guidance. Consider the subtle cultural contexts that influence user behavior in different regions, or the seasonal variations in purchasing patterns that may not be adequately represented in historical data. These are areas where human judgment remains irreplaceable.
Making AI Proactive and Prompt to Maximize ROAS
One of the biggest challenges in performance advertising is measuring Return on Ad Spend (ROAS) accurately and swiftly. AI seems to be a game-changer here, but it’s not infallible. It can analyze vast datasets at lightning speed, yet it struggles with nuance — especially in retargeting. Traditional AI models chase the highest predicted ROI, often overlooking niche user segments that hold untapped revenue potential.
That’s where human ingenuity makes all the difference.
Multi-level machine-learning infrastructure bridges this gap, combining AI-driven pattern recognition with expert analysis to unlock deeper insights into user monetization behaviors. Think of AI as an ‘Iron Man’ suit—it still needs a strategist inside to make the right calls. AI might identify churned users for retargeting, but human oversight fine-tunes the approach, ensuring no high-value cohort slips through the cracks.
The impact is undeniably faster, smarter ROAS optimization. While traditional AI models take weeks to deliver meaningful performance insights, this approach provides actionable ROAS data within just seven days, helping advertisers react swiftly, refine strategies and drive sustained profitability.
Supervising AI into the Future of Marketing
AI in marketing isn’t just about machines talking to machines. True innovation happens when AI and human expertise work together to deliver transparency, trust and tangible performance improvements. The companies that embrace supervised AI will lead the next era of digital advertising—where technology amplifies human creativity rather than replaces it.
We challenge the status quo. We believe in AI, but we believe even more in the power of human intelligence to shape its future.
