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This case study contains information from work completed under non-disclosure agreements. Sensitive details have been modified or omitted to respect confidentiality obligations. The content represents my personal analysis and work contributions, and does not necessarily reflect the views or positions of Whatagraph.

As agencies scaled their client portfolios, performance summaries became essential. While data visualization was automated, interpretation remained time-consuming and repetitive.

INTRODUCTION

This initiative embedded AI-powered insight generation directly into the reporting workflow, transforming manual analysis into a scalable intelligence capability.

Monthly reports required account managers to review performance across multiple channels, highlight key shifts, identify issues, and recommend actions.

CONTEXT

Although the insights were often straightforward, writing them required both data digestion and structured articulation; it typically took 10–15 minutes per report.

Across 20+ clients, this created meaningful operational overhead.

IMPACT

Increase reporting efficiency across agencies and reduce time spent on manual performance summaries.

MY ROLE

I led the design of the AI summary experience, defining how pre-built prompts, tone options, and language selection would work within real reporting workflows. I shaped how AI generation integrates directly into the existing text widget by positioning it naturally alongside manual input to ensure it feels like an enhancement, not a replacement.

Working closely with the manager and engineering team, I translated the concept into a scalable, intuitive solution ready for development.

CHALLANGE

Agencies needed to deliver thoughtful, high-quality performance insights, but doing it manually for every client report was slowing them down.

OBJECTIVES

Ensure generated summaries are accurate, editable, and aligned with user intent

Reduce manual effort in performance interpretation

Integrate AI insight generation seamlessly into existing reporting workflows

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An Embedded Intelligence Layer

To deliver on these objectives, we introduced intelligence across three focused layers.

Native Workflow Integration

Integrated AI generation into the existing text widget to ensure natural adoption without introducing new system complexity.

Context-Aware Analysis

Designed the system to analyze selected tab data within the active date range, generating structured summaries with configurable focus modes (wins, issues, opportunities).

Controlled Automation

Enabled preview, editing, tone and length adjustments, language selection, and optional auto-updates tied to date changes — balancing automation with user trust.

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Rapidly design and launch a scalable AI capability, grounded in proven UX standards.

Explain the general idea of how everything can be done easily when you start from a prompt (happy path)

Show element

Show summary drawer/widget (report)

Benefits, templates based use-cases

Build an AI layer that continuously translates live data into clear, up-to-date performance narratives.

Explain what the solution is, how easy jobs to be done can be done based automated generated summary

Single element display

Report view

Benefits

AI SUMMARY INTEGRATION

AI Summary integrates directly into the existing text widget, enabling users to generate structured performance insights in seconds. With a built-in preview, teams can review results before applying them, refine prompts based on focus (wins, issues, opportunities), adjust tone, length, or language, and convert everything into fully editable text.

The summaries are tied to the report’s date range and can automatically update as periods change, removing repetitive manual rewrites. The result is faster analysis, consistent quality, and meaningful time savings at scale.

Turning performance data into clear, actionable insights — instantly.

OVERALL IMPACT

Reduced insight creation time from up to 15 minutes per report to seconds, saving teams an estimated 6+ hours monthly while increasing reporting consistency and positioning the platform as an intelligent decision-support system.

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