AI Client Reporting System

The Problem

Agencies that manage paid ads, SEO or social accounts often spend hours each month pulling numbers out of different dashboards, dropping them into a spreadsheet, and writing up what happened for the client. The work is repetitive and the write-up quality depends on who has time to do it that week.

Existing Workflow

A team member logs into each ad platform and analytics tool, exports the numbers, pastes them into a shared spreadsheet, and manually drafts commentary before formatting everything into a document to send to the client.

The Solution

A scheduled workflow pulls KPI data directly from connected platforms through their APIs, normalizes it into a consistent format, and passes it to an AI step that drafts plain-language commentary on what changed and why. The output is assembled into a formatted report and delivered automatically on a recurring schedule, with a human review step before anything goes out.

Automation Architecture

Scheduled trigger -> API data pull from ad/analytics platforms -> data normalization -> AI analysis and commentary drafting -> report template assembly -> human approval step -> email delivery.

Before vs After

Results

• Removes the manual data-pulling step from the reporting cycle.
• Produces a consistent first draft of commentary for the team to review, rather than starting from a blank page.

Limitations

The AI-drafted commentary is written to be reviewed by a human before it goes to a client — it is not designed to send without a review step. Platforms without a usable API need a different data-collection approach.

Lessons Learned

The most valuable part of this system wasn't the AI writing — it was removing the manual export/copy-paste step, which was the biggest time cost in the original workflow.