In today’s digital ecosystem, automation and AI tools are embedded in almost every aspect of the advertising workflow. The promise is clear: faster execution, better scalability and reduced human error.
But as adoption grows, so do questions about balance. Are we building smarter systems or losing our edge to them? While AI excels at pattern recognition and repetitive tasks, it still struggles with nuance, context, and emotion.
In industries like AdTech where success relies on intuition, storytelling, and strategic oversight — this matters more than ever.
We work at the intersection of automation and human insight daily. And our CEO Emin Alpan shares a candid reflection on where AI supports growth and where it risks flattening creativity and decision-making.
At Aceex every day, we use automation and AI tools to manage bidding, segmentation, traffic quality, and reporting. These tools help us save time and work faster. But sometimes I feel like we rely on them too much, and that makes our work feel less creative and even a bit average.
One big problem I notice is with creativity. AI tools can generate ad banners or videos quickly, but the results often feel flat. For example, we once ran a campaign for a mobile game and used auto-generated banners. The banners had the logo, a call-to-action, and a game image — everything looked correct. But the performance was weak.
Later, one of our designers created a banner by hand, with a better understanding of what gamers like. That version got much better results. So even though the AI saved time, the creativity made by a real person worked much better.
Another issue I’ve seen is with audience segmentation. Automated systems can build lookalike audiences or choose targets based on previous data. Usually, this works fine. But once, when we ran a campaign in Eastern Europe, the algorithm picked cheap traffic sources to hit a low CPM. The quality of the traffic was bad, and the results dropped. We had to step in and manually choose a whitelist of good websites. After that, the campaign performed better. This showed me that automated choices still need human checks, especially when entering a new market.
Also, when working with clients, automation doesn’t always understand what they really want. Some clients care not only about clicks or installs, but also about how people feel when they see the ad or where the ad appears. In those cases, we often need to work manually. For example, we may pick websites one by one or match ads to specific content. An AI system can’t fully understand these emotional or brand goals.
Of course, automation is still very useful in our daily work. It’s great for things like ad measurement or scanning traffic for fraud.
I want to give two real examples from my own work.
One time, an AI tool created an audience labeled as “high LTV users” for a mobile app campaign. But it included too many users with old devices who didn’t convert well. I checked the data myself, cleaned it up, and made a more specific audience. After that, the return on ad spend improved by 17%.
In another case, the client told us our report didn’t match what they saw from their side. Our system didn’t track cross-device conversions properly. I went into the raw logs and matched the user-agents manually. I found that many conversions were missing. After that, we updated the reporting method.
In the end, I believe automation is a great tool, but it’s not a full solution. It helps with routine tasks and gives us speed, but it can’t replace human strategy, creativity, or critical thinking. If we trust it too much, we risk falling into mediocrity. The best results come when automation does the simple work and people focus on the more complex, creative parts of the job.
So where do we draw the line between automation and human input?
Emin’s stories underline a core truth: automation is an accelerator, not a replacement. When used wisely, it can amplify results, uncover efficiencies, and free teams from routine. But when relied on blindly, it can turn strategy into standardization and creativity into templates.
With our team we believe the future lies in thoughtful hybrid models where machines handle the speed and people shape the vision. Real impact still comes from human curiosity, judgment and the courage to question what the data doesn’t show.
