Agentic advertising is quickly becoming a reality.
As standards and technical frameworks mature, marketers are building AI agent-powered workflows to automate some of the most resource-intensive parts of campaign planning, execution and measurement. The result is less time spent manually pulling data and reports, more time acting on insights, and ultimately, better business outcomes.
At DV, for example, we recently worked with Eberjey, a premium sleepwear and intimates brand, to build an AI-powered workflow using DV Neura™, our AI-powered intelligence engine. The project demonstrates how AI agents can automate routine campaign analysis and help marketers identify optimization opportunities faster.
Here's how it all came together.
The Challenge: Too Much Manual Reporting
Like many fast-growing ecommerce brands, Eberjey relies on paid media to drive customer acquisition and revenue. But determining whether campaigns were on pace to meet business goals required the growth team to manually compare multiple reporting systems each day.
The process was time-consuming, slowed optimization decisions and made it harder to identify issues before they impacted performance.
To solve this, the team wanted an automated way to answer one simple question: are our campaigns on track, or do we need to make changes today?
The Solution: An Automated AI Workflow
To address the challenge, Eberjey created an automated workflow using Claude Code, Anthropic’s command-line interface (CLI), to access DV Rockerbox™ attribution data and other systems in natural language.
A CLI is a developer tool that allows software and AI systems to securely access data through text-based commands instead of requiring users to manually log into dashboards or export reports. Think of it as a secure bridge between AI and business systems, allowing the AI to retrieve the data it needs automatically rather than relying on someone to gather it by hand.
Each day, the workflow automatically gathers ad performance data, DV Rockerbox attribution data and Eberjey's internal forecasts and business goals. Claude then analyzes that information to determine whether campaigns are pacing toward their targets and produces a prioritized summary of where marketers should focus.
If a campaign falls more than 10% above or below its expected performance, the workflow automatically flags it. For example, if a campaign is expected to generate 100 attributed conversions by a certain point in the month but is projected to deliver only 85, the system identifies it as roughly 15% behind goal. The same logic also applies to metrics such as return on ad spend (ROAS), cost per acquisition (CPA), and campaign spend.
By automating that analysis, the workflow eliminates the need to manually compare reports each morning. Instead, the team starts the day with a prioritized view of campaign performance, allowing marketers to quickly identify issues and focus on optimization rather than reporting.
Making Attribution AI-Ready
This workflow demonstrates how measurement platforms can move beyond traditional reporting. Rather than serving solely as a dashboard, CLI allows trusted attribution data to flow directly into the AI workflow, where Claude can analyze it alongside media performance data and business goals to identify potential issues and surface optimization opportunities.
Instead of treating attribution as something marketers review after the fact, the workflow turns it into an active input for AI-powered decision-making. That gives marketers faster, more actionable insights while freeing them to spend less time on reporting and more time optimizing campaign performance.
Business Impact
We’re already seeing instances of improved campaign optimization by automating analysis and accelerating decision-making. These include:
- Automated campaign monitoring: Continuously tracks campaign pacing against business goals without requiring daily manual reporting.
- Earlier issue detection: Flags campaigns that fall outside predefined performance thresholds, allowing teams to optimize before goals are missed.
- Faster decision-making: Consolidates attribution, media performance, and business goals into a single AI-generated summary each day.
- Greater operational efficiency: Reduces time spent comparing reports across platforms, freeing marketers to focus on campaign optimization.
By automating routine analysis, the workflow helps turn performance data into faster, more informed marketing decisions.
Looking Ahead
Eberjey is among the forward-thinking brands exploring how AI agents can streamline day-to-day marketing. Powered by our AI engine, this workflow shows how trusted attribution and campaign data can be combined with AI to automate routine analysis, allowing marketers to spend more time optimizing campaigns and less time gathering information.
As agentic AI adoption grows, examples like this offer a glimpse of what's possible. Rather than treating measurement as a reporting function, marketers can use trusted data to power AI-driven analysis, faster decisions and better campaign outcomes. We look forward to helping more brands and agencies put these capabilities into practice with DV Neura.
DV Neura is embedded across the DV Media AdVantage Platform (DV MAP™), a full-spectrum media effectiveness platform that combines media verification, AI-powered optimization through DV Scibids AI™ and campaign outcomes measurement with DV Rockerbox to improve media effectiveness and return on ad spend. Together, these capabilities help advertisers drive stronger business outcomes across channels, devices and formats.
Next Steps
Contact a DV representative.
