Media and marketing firms are ramping up AI investments. But experts warn that weak data strategies and security gaps could threaten business models. Data quality and resilience are now critical for growth.
Media and marketing companies are accelerating their use of AI to drive efficiency, scale growth, and target audiences more precisely. According to a recent Adobe study, 89 percent of businesses in the sector plan to increase their AI investments. Yet Anna Mleczko, Head of Media & Advertising Technology Practice at Future Processing, cautions that many organizations are overlooking data security and quality-two factors she says are essential for sustainable business models.
While AI enables faster content production and more targeted campaigns, Mleczko notes that many companies have not integrated these technologies into robust data strategies or security frameworks. She argues that AI is not just a tool for isolated processes but fundamentally alters how companies operate, making structural data management and security critical from the outset.
Media organizations often hold vast troves of data, including archives, user behavior, and metadata. However, these assets are frequently siloed across different systems, limiting both the effectiveness of AI and the ability to control its use. Mleczko emphasizes that the key issue is not whether to use data, but how to do so responsibly. Regulatory frameworks like the GDPR provide direction, but she says that treating user consent as part of a transparent relationship-rather than a checkbox-lays a stronger foundation for personalization.
Security, Mleczko stresses, cannot be an afterthought. She points to the "Cost of a Data Breach Report 2025," which found that the average data breach costs $4.44 million. For media companies, the stakes are even higher if outages disrupt streams or ad campaigns, leading to lost revenue and damaged trust.
Data quality is another limiting factor. Incomplete or inconsistent data can undermine AI performance, resulting in poor content recommendations or campaign outcomes. Mleczko observes that many firms deploy new AI tools without first consolidating their data, which prevents these systems from reaching their full potential. She recommends harmonizing data sources, defining standards, and assigning clear responsibilities before scaling personalization efforts.
For advertisers and publishers, AI-powered personalization is a key driver of growth and monetization. Recommendation engines analyze user behavior in real time to deliver relevant content and ads. But if these systems malfunction or are compromised, the impact on quality and trust is immediate. Security incidents can go undetected for months, with attackers sometimes remaining in systems for an average of 241 days. This can expose production environments, unpublished content, or subscriber data long before a breach is discovered. Advertisers expect brand-safe environments, and any lapse can lead to paused budgets, renegotiated contracts, and lost partnerships. Publishers risk not only revenue but also long-term trust and market share.
Operational resilience is now a business imperative. Even minor disruptions-such as unstable platforms or delayed content delivery-can drive users to competitors and prompt advertisers to reconsider their spend. Investors, insurers, and regulators are also raising their expectations for stability and risk management. Yet, as PwC analysis shows, only a small fraction of companies have fully established resilience structures, even as AI adoption accelerates. This creates a clear imbalance between technological advancement and risk mitigation.
Partnerships are also evolving. Advertisers, platforms, and tech vendors are more interconnected than ever, and the reliability of one system affects the entire value chain. Companies must now demonstrate that their systems are stable and well-controlled, making resilience a competitive differentiator. Those who can offer transparent, reliable operations are better positioned to secure long-term relationships.
Mleczko outlines several priorities for building resilience: identifying and securing critical systems, designing architectures that separate sensitive areas, establishing clear access controls, detecting anomalies early, and regularly testing incident response processes. Rapid recovery is essential, as technical failures can quickly become financial problems without effective backup strategies.
AI is a major driver of innovation in media and marketing, but its full potential depends on stable, well-managed systems. The challenge is to align technology, data strategy, and security from the ground up. As seen in recent industry moves-such as when major German publishers restricted AI crawler access-the ability to control and secure data is becoming a defining factor for business models built on AI-driven personalization.
