How Brands Can Stay in Control as AI Reshapes Advertising

In 2026, artificial intelligence isn’t just an experimental tool for marketers — it’s become a central force reshaping how advertising is bought, created, optimized, and analyzed. From campaign ideation to measurement models, AI systems are now deeply integrated into every corner of the ad landscape. Yet while these technologies promise unprecedented automation and personalization, they also introduce new challenges for brands that want to maintain control, transparency, and strategic direction in their advertising efforts.

Artificial intelligence has long been heralded as a way to help marketers finally achieve what many have struggled to deliver under traditional approaches: personalization at scale and truly closed-loop measurement. As connected TV, retail media networks, and other emergent channels grow more performance-driven, there’s an expectation that AI will help unify data, creative, and delivery in ways that past tools never could. But realizing that potential is proving to be more complex than many vendors and platforms suggest.

At the core of this transformation are a range of AI-powered advertising solutions offered by tech giants and major ad-tech firms. Platforms such as Meta’s Advantage+, Google’s Performance Max, and Amazon’s full-funnel campaign tools aim to automate significant parts of the advertising workflow. Similarly, major holding companies — including WPP, Publicis, and Omnicom — have developed their own AI suites that promise to handle everything from planning to optimization. Even media owners like NBCUniversal and Disney now sell AI-augmented advertising products designed to streamline campaign execution.

These tools, while powerful, often function like “black boxes”: highly automated systems that yield outcomes with little insight into how decisions are made. This opacity is acceptable for some advertisers, particularly those focused solely on performance outcomes such as clicks or installs. However, many brand-focused marketers find themselves uneasy about relinquishing so much control to opaque algorithms — especially when those same systems are shaping audience targeting, creative selection, and media buys without clear accountability.

One of the key tensions in today’s AI-infused advertising ecosystem lies between automation and brand control. As platforms push toward greater simplification, there’s a risk that brands may lose sight of the strategic rationale behind their campaigns in favor of purely algorithmic optimization. Brand leaders fear scenarios where machine-led decisions, optimized for short-term metrics, might drift away from core brand values or long-term objectives.

To avoid this, marketers are being urged to differentiate hype from reality. Not all AI systems are created equal, and not every tool delivers what it promises. Emerging technologies like agentic AI — autonomous systems that can continuously optimize campaigns with minimal human intervention — are particularly hyped. Advocates argue that agentic systems can execute thousands of micro-optimizations in real time, far beyond the capacity of human teams, freeing marketers to focus on high-level strategy and experimentation. According to Upwave CEO Chris Kelly, early adopters of these systems could move faster and learn more quickly than competitors.

Yet fully autonomous systems come with their own set of caveats. Successful deployment requires that brands have robust data infrastructures, well-defined APIs, and clear measurement frameworks. Without those foundations in place, agentic AI may optimize toward the wrong objectives or fail to integrate effectively across platforms. Gartner analysts warn that seamless connectivity across disparate systems remains a significant hurdle for many organizations.

Another concern relates to identity resolution and data strategy. Even as platforms automate more tasks, brands still must decide how much they rely on platform-owned data versus investing in their own identity systems. Some marketers argue that spending on identity resolution tools — which help link disparate data points back to real consumer profiles — is a smarter long-term play than simply feeding more budget into AI-driven media buys.

Amid all this change, agencies continue to play an important role, albeit one that is evolving. With the rise of platform-specific AI tools, brands often need partners who can translate across multiple environments and offer visibility into how different systems are performing. Agencies with the ability to integrate data, creative, and strategy can help brands avoid pitfalls associated with isolated or siloed AI solutions.

Ultimately, marketers are being encouraged to approach AI with a mix of realism and strategic agency. That means understanding both the strengths and limitations of automated systems, and being willing to retain human oversight where it matters most — such as defining business objectives, shaping brand narratives, and interpreting outcomes in context. As the industry continues to experiment and standardize around new protocols and frameworks, brands that can balance automation with strategic control are likely to navigate this AI-rewritten playbook most successfully.

📌 Key takeaways:

• AI is now deeply embedded in advertising, promising personalization at scale but often operating as a “black box.”

• Platforms and agencies offer AI-driven automation, yet strategic choices about data and identity remain crucial.

• Agentic AI promises continuous optimization but requires strong infrastructure and human oversight.

• Brands that assert strategic control — balancing automation with human decision-making — are best positioned for success.