Olga ZharukMost AI tools still leave your team doing the heavy lifting. They generate dashboards and insights — but your AdOps team still spends hours chasing pacing issues, adjusting floors, and resolving delivery errors manually.
Manual fixes and delayed responses reduce campaign efficiency. AdOps teams can’t scale your business if they’re always playing catch up. Traditional AI works as an add-on, generating dashboards or basic alerts. These may help teams see campaign metrics, auction activity, or inventory performance, but there is still a need to interpret this data and take action.
73% of AI insights never turn into action. That’s revenue left on the table — daily. Without automation embedded directly into the platform’s foundation, nothing really scales.
For real impact, AI must live in the decision engine itself. It should continuously analyze and interpret data, alerting AdOps to issues before they escalate.
Why regular AI tools don’t always work
Behind the scenes, your team still handles most platform tasks manually — from configuring deals to troubleshooting misfiring campaigns. These time consuming workflows are often at the center of your hidden costs.
Trading setups. Optimizing trading setups typically takes 2–4 hours daily. When updates contain misconfigurations, campaigns immediately underperform, leading to lower win rates, missed higher bid opportunities, and steady revenue leakage.
Bid Request field management. A missing geo-targeting parameter or an incorrect buyer ID causes delivery failures and undermines buyer trustsl. Spending 2–4 hours daily on checking and correcting the fields is not only just labor cost but also wasted impressions that could have been monetized.
Delivery and pacing QA. Monitoring campaign delivery and pacing is another time sink that typically takes 1–3 hours per day. When pacing mismatches or overdelivery go unnoticed, budgets are wasted and KPIs are missed, making it harder to retain demand partners and grow long-term deal flow.
Documentation review. Even internal knowledge retrieval slows teams down by an additional 2–4 hours. This results in slower campaign launches, delayed onboarding for new clients, and a higher risk of setup errors.
The main difference between AI as a reporting tool and embedded AI as an operational driver is that one relies on data to generate reports and respond to questions. It operates within fixed constraints, using static rules with limited adaptability and problem-solving capabilities.
The other goes a step further, analyzing the data, applying reasoning, and identifying potential issues before they escalate. In a fast-moving programmatic environment, only the latter approach allows platforms to scale profitably and adapt to market changes.
How built-in AI agents change the game
Unlike traditional AI, which often works in isolation, AI agents operate inside your existing tech stack, improving transparency and keeping teams aligned. They fix malformed bid requests, optimize floors, reroute traffic, and adjust pacing in real time — no human delay, no costly errors.
As your traffic may grow, you don’t have to double the size of your AdOps team. Built-in AI agents allow you to expand without additional operational costs.
Since buyers value consistency, they expect reliable delivery and transparent performance from your side. By catching pacing issues early and ensuring KPIs aren’t missed, AI agents turn that reliability into stronger relationships and longer-term contracts with premium partners.
This isn’t just a technical upgrade but a business safeguard that lets you scale without inflating costs. You get a system that performs in real time and gives your platform the resilience and competitiveness.
Real-time automation without losing AdOps oversight
With embedded AI, the role of human leadership remains unchanged. AI agents aren’t aimed at replacing professional oversight. On the contrary, they enhance it by taking on repetitive operational work, allowing AdOps teams to focus on high-impact initiatives and strategic decision-making.
Instead of simply identifying patterns or highlighting trends, embedded AI acts on those insights immediately, triggering structured, automated workflows within existing systems. This ensures that market changes, customer behaviors, and revenue opportunities are addressed in real time, not lost in the lag between detection and human response.
