The trend: New tools are reshaping how marketing teams access analytics. Adverity’s Data Conversations, for example, lets users ask plain-language questions and instantly get insights, CEO Alexander Igelsböck said in an EMARKETER interview.

He calls this a fix for the “curiosity bottleneck,” where marketers avoid asking questions because insights are too slow or costly. Routine tasks like comparing campaign segments or analyzing demographic response no longer require custom reports.

Similarly, Nexxen’s new Discovery platform uses AI and first-party data to accelerate signal translation and unlock faster insights.

Why it matters: Conversational analytics removes inefficiencies by providing real-time access to data, reducing dependence on engineers, and empowering teams to explore, test, and optimize—efforts often sidelined by limited resources.

Conversational analytics helps eliminate inefficiencies by giving teams real-time access to performance data without specialized tools or training. This reduces reliance on engineers for routine tasks, encourages more exploratory analysis, and frees teams to test hypotheses and uncover optimization opportunities—efforts that were often deprioritized due to resource constraints.

This comes at a time when investment in AI and analytics is surging across industries. According to senior agency professionals worldwide, 75.6% are prioritizing investment in AI tools, with reporting/analytics (29.3%) ranking as well. And it’s not just agencies: CX leaders are significantly increasing investment in AI capabilities (38%) and analytics/reporting (33%), highlighting a growing emphasis on tools that accelerate insight delivery and decision-making.

Our take: This development solves a longstanding challenge in marketing: The gap between the promise of data-driven decision-making and the complexity of the tools required to deliver on it. Specialized knowledge created bottlenecks that slowed insight delivery and limited team-wide access.

What makes tools like Data Conversations especially compelling is how they pave the way for more autonomous workflows. Rather than relying on analysts, marketers can use natural language to generate summaries and explore campaign performance directly. This self-service access could evolve into more proactive features—where AI surfaces optimization opportunities automatically. In this sense, conversational analytics isn’t just a time-saver; it becomes a foundation for more scalable, insight-driven marketing.

The timing couldn’t be better. With budgets under pressure, efficiency is critical. Teams that can quickly spot underperformance, refine targeting, and reallocate spend based on live data will have a clear edge.

That said, success depends on smart implementation. Organizations must set up strong governance and train teams to ask the right questions and interpret AI-generated insights wisely.

Ultimately, the tech works best when it amplifies human creativity. Marketers who use it to test ideas and explore new hypotheses will benefit most—those who treat it as just another reporting tool risk missing its full potential.