How Loop built an AI agent system that took the mechanical work of monthly media reporting off a 200-person agency's team — so their time went to analysis, recommendations and client strategy instead.
Our client is a full-service, 200-person agency that has served clients in higher education, healthcare, travel and other industries for more than 40 years. Like many agencies pulling together monthly media reports, much of their time went to tedious, repetitive tasks that left little space for the critical thinking and insight creation clients value.
“We spend so much time trying to get the numbers straight and identify what's changed. The clock is always ticking — it's a race to the end of the month.”
— VP of Media
So the agency partnered with Loop to build an AI agent system that does the mechanical work of monthly reporting — aggregating the data, verifying every number, and drafting the report in the agency's own template and voice — while the team's judgment stays in charge of every step.
Anyone who has worked at an agency knows that monthly media performance reports are among the most stubborn time sinks in agency life. Data lives across a dozen ad platforms, analytics tools and vendor dashboards; assembling it into a client-ready story takes so long that by the time the report ships, the activity and outcomes it describes are old news.
This media team knew the pattern well. They were spending three weeks of every month putting reports together that didn't reflect their knowledge or expertise, only to start the whole process over again. They knew the real cost wasn't the hours; it was the strategic thinking those hours crowded out.
The goal of the engagement was direct: train AI to do the mechanical work of reporting so the team could use their expertise and judgment to help their clients succeed.
Loop started with discovery: four collaborative workshops with the agency's Media, Analytics and Account teams mapped the entire media workflow and pinpointed exactly where the time went. From there, the pilot went deep on a single flagship higher-education account.
A data-readiness check confirms that data from every campaign and channel is present before anyone drafts a word.
Every metric is computed by executed code — never estimated by the AI — cross-checked against account totals and governed by a growing set of data guardrails. Anything that can't be verified is quarantined and kept out of the report.
The AI drafts insights in the agency's voice, codified from their real narratives into principles the AI follows.
Trained on the agency's brand system, the AI delivers a fully editable PowerPoint in their own template.
The team reviews, edits and approves everything. Nothing ships without human oversight and judgment.
The real test came quickly: a live monthly cycle, with real client data and a real deadline. The question wasn't whether the AI could produce something impressive in a demo — it was whether one strategist could take the report from raw data to client-ready, alone.
In that first live cycle, the agency's VP of Media ran the entire report without the traditional back-and-forth with the media team or channel leads. When something looked off, she fixed it conversationally with the AI and re-ran the analysis — no ticket, no queue. When one vendor's data wasn't available yet, the system flagged it, completed everything else, and slotted the numbers in when they arrived. AI was a productive partner in the process, not the owner of the process or the output.
“The report was completely generated [by the AI] — the nuts-and-bolts work that would have taken the team at least two weeks. It probably saved us 30 to 40 hours a month.”
— VP of Media
“I cross-checked the data, and I didn't find any errors. And in terms of the way it wrote — it captured our tone. It hit the right chord.”
— VP of Media
The quality bar mattered as much as the hours. The system surfaced metrics the team hadn't typically reported on — several are being adopted as ongoing KPIs — and every piece of reviewer feedback is written back into the system as a permanent rule, so the output improves with each cycle instead of resetting.
The downstream effect is the one their clients will feel: a report that arrives earlier in the month, with more actionable insights and the time to do something with them.
The agency is now scaling the system: onboarding additional client accounts from the documented playbook, extending it to quarterly business reviews, and standing up an internal operating model so the agency owns the system's evolution. In the words of one senior leader, the pilot left them “light years ahead of where we were before.”
“I'm almost viewing this as three separate agents — pulling the data, analyzing it, and presenting it. It's relatively generalizable. I think we can extend it to other clients fairly easily.”
— Analytics Lead
Loop helps marketing agencies get real value from AI — not by handing them generic tools, but by encoding each agency's own standards, values and voice into AI systems their teams run and own. The result is better work that ships faster, so agencies can focus on what matters most: helping their clients succeed.
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