Aceex's Dariia Kutsopal on Ad Tech Challenges, Maintaining Stable Performance, and Tougher Privacy Regulations

by editorial on 2nd Feb 2026 in News

Dariia Kutsopal, COO of Aceex, looks at the biggest current challenges for ad tech companies, from privacy regulations to signal loss and measurement. She expands on why so many ad tech companies struggle to maintain stable performance, as well as how they can overcome this...

In the current volatile times, what are the biggest challenges for ad tech companies?

The main challenges come from constant ecosystem shifts. Privacy regulation, signal loss, and platform changes are forcing ad tech companies to continuously adapt their technology and data strategies. At the same time, competition is intensifying, margins are under pressure, and investment in AI and infrastructure is no longer optional. Maintaining trust, transparency, and performance in this environment is increasingly complex.

Each of these challenges compounds the others. Privacy regulation is no longer a distant compliance topic. It directly affects how targeting, measurement, and optimisation work on a daily basis. As identifiers become restricted or aggregated, many previously reliable optimisation signals lose precision, forcing platforms to rethink how value is inferred and priced.

Signal loss amplifies this problem. With fewer deterministic inputs, systems must rely more heavily on probabilistic logic and contextual understanding. This requires not only more sophisticated models, but also a higher tolerance for uncertainty… Something many legacy systems were not designed for.

Platform changes add another layer of instability. Updates from browsers, operating systems, or major media environments can quickly shift auction mechanics, latency patterns, or inventory availability. For AdTech companies, this means constant recalibration rather than periodic upgrades.

At the same time, competition continues to intensify. More players are optimising aggressively within narrower margins, while advertisers demand both efficiency and accountability. This puts pressure on infrastructure investments, especially in AI, data processing, and monitoring systems, which are now baseline requirements rather than differentiators.

Many ad tech companies struggle to keep their performance stable. What is the biggest problem behind this?

The biggest issue is reliance on unstable and changing inputs. Data signals, auction dynamics, and user behavior evolve faster than traditional optimisation models can adjust. RTB environments are inherently volatile, and when automation lacks sufficient controls or context, small changes can quickly lead to performance swings.

Many systems were built during periods of higher signal availability and relative market predictability. As a result, they assume a level of input stability that no longer exists. When those assumptions break, performance volatility increases.

Another contributing factor is over-automation without sufficient guardrails. Automated bidding and optimisation are powerful, but when models operate without clear boundaries, feedback delays, or anomaly detection, they can amplify short-term noise instead of correcting for it.

User behaviour has also become less predictable, especially during peak and off-peak cycles. Seasonal patterns, attention shifts across environments, and changing consumption habits introduce variability that rigid models struggle to interpret correctly. Without mechanisms to contextualise these shifts, performance instability becomes inevitable.

What can be done to help maintain more stable performance?

Stability comes from better foundations and clearer controls. Investing in first-party and contextual data, improving transparency around optimisation logic, and implementing stronger feedback and monitoring systems all help reduce volatility. Equally important is cross-team alignment, so product, engineering, and analytics respond quickly and consistently to performance changes.

First-party and contextual data provide resilience because they are less exposed to external disruptions. While they may not fully replace lost signals, they offer consistency and relevance that help models adapt more predictably.

Transparency in optimisation logic is equally critical. When teams understand how decisions are made and why performance changes, they can intervene earlier and more effectively. This reduces reaction time and prevents overcorrection.

Feedback and monitoring systems act as stabilisers. Shorter feedback loops allow platforms to detect anomalies, learn faster, and avoid cascading errors. Stability is not about eliminating volatility, but about responding to it intelligently.

Finally, operational alignment matters. Performance issues are rarely isolated to one team. When product, engineering, analytics, and business teams operate with shared priorities and clear escalation paths, the organisation can adapt as a single system rather than fragmented functions.

Do you expect the landscape to get tougher as 2026 unfolds?

Yes. Privacy requirements will continue to tighten, AI-driven competition will raise the bar, and efficiency will matter more than scale alone. The market is likely to become more selective, favouring ad tech companies that are adaptable, data-responsible, and operationally disciplined.

Privacy requirements are likely to become more fragmented and region-specific, increasing operational complexity. Ad tech companies will need flexible architectures that can accommodate different regulatory environments without rebuilding core systems each time.

AI-driven competition will also intensify. As more platforms adopt similar machine learning techniques, differentiation will come less from having AI and more from how it is applied – in data selection, feedback design, and decision governance. Poorly controlled AI will become a liability rather than an advantage.

As a result, efficiency will increasingly outweigh raw scale. Companies that can operate with leaner systems, clearer decision logic, and disciplined execution will outperform those relying solely on volume. Navigating 2026 successfully will require adaptability, not just ambition.