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5 ways to collaborate with our agentic advisors

By Ashutosh Kumar Mishra·Mar 25, 2026·2 min read

Summary

Marketing AI tools like Ads Advisor and Analytics Advisor go beyond chat interfaces to become agentic collaborators that connect data to decision-making. They enable users to converse in natural language, ask follow-up questions for deeper insights, and recall previous conversations for tailored guidance. Analytics Advisor proactively identifies hidden trends, such as unexpected traffic spikes or funnel drop-offs, and calculates metrics on the fly.

By asking open questions and investigating causes, users can uncover insights like channel performance and conversion impacts, ultimately bridging the gap between 'what happened' and 'what to do next.'

Analyst Note

The article's framing of 'agentic collaborators' rather than simple chatbots signals a maturation in AI applications for ad tech. What's notable here is the shift from passive reporting to proactive insight generation, particularly the ability to surface unexpected trends without being prompted. For UA and monetization teams operating under privacy constraints—where granular third-party data is eroding—this capability addresses a critical pain point: extracting maximum value from first-party data without requiring advanced analytical skills.

The practical impact lies in reducing the time between data observation and action; natural language querying eliminates the need for SQL or analyst handoffs, enabling faster optimization cycles. However, the competitive angle is subtle: by building memory and context into these tools, vendors create stickiness—the more a team uses the advisor, the more tailored and valuable the recommendations become, potentially making it harder to switch platforms. This aligns with broader industry trends toward autonomous marketing systems, but the article wisely positions the tools as collaborators, not replacements, acknowledging that strategic oversight remains human.

The key implication for ad ops professionals is that integrating such agents could democratize data access across roles, but only if teams invest in the initial training and trust-building to effectively leverage the 'agentic' nature of these tools.

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