From 4 Hours to 1: How AI Transformed Municipal Planning Interviews

Municipal planning consultants spend much of their time listening. Community focus groups, stakeholder interviews, public input sessions—the work of understanding what a city needs starts with hearing what residents have to say.

But between the listening and the planning lies a bottleneck: processing all those conversations into actionable insights. For one consulting firm, this meant 4+ hours of work for every single interview.

The Challenge

The consultants conduct intensive focus groups and interviews for community planning projects. Their process was thorough but time-consuming:

• Hand-transcribing meeting recordings
• Typing up detailed notes
• Manually synthesizing findings
• Formatting outputs for presentations and comprehensive plans

For a typical project involving a dozen interviews, this added up to 48+ hours just processing conversations—before any actual planning work could begin.

The Solution

The solution wasn’t a single tool—it was a workflow the consultant could own and adapt.

Working together, we focused on three things:

1. Better inputs. I advised on hardware choices—specifically, quality speakers that capture clear audio even in large meeting rooms. Transcription quality depends entirely on audio quality, and this simple upgrade made everything downstream more reliable.

2. A three-part AI workflow. Using Gemini Gems (Google’s customizable AI assistants), we built a process that:

• Processes transcripts automatically from the audio recordings
• Extracts key themes, concerns, and insights
• Generates structured outputs matching their existing deliverable formats

3. Skills, not just tools. The goal was capability transfer. After a few sessions working together, the consultant understood not just how to use the workflow, but why each piece worked—and how to adapt it when project needs changed.

The Results

Across a typical project with 12 interviews, that’s 36+ hours recovered—time that now goes into the strategic planning work that actually moves communities forward.

But the numbers only tell part of the story. The consultant described feeling “blown away” by the transformation. For the first time, the tedious work of processing interviews wasn’t hanging over every project.

The Bigger Picture

This isn’t about replacing human judgment. The consultant still reviews every output, catches nuances the AI misses, and applies decades of professional experience to the final analysis.

What changed is where that expertise gets applied. Instead of spending hours on transcription and formatting, they’re spending that time on synthesis and strategy—the work that actually requires their years of experience.

What Made This Work

Three factors made this engagement successful:

1. Starting with the actual work. We didn’t begin with “what AI tools should you use?” We started with “walk me through how a project actually unfolds.” The solution emerged from understanding the real workflow.

2. Building on familiar tools. By using Gemini within the Google Workspace they already knew, adoption was natural. No new logins to remember, no new interfaces to learn.

3. Transferring capability, not just delivering a solution. The consultant can now apply this approach to other repetitive knowledge work in their practice. The specific workflow we built was just the first application.

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This is what a genuine leap looks like. Not incremental improvement, but a fundamental shift in how the work gets done—with skills the client owns forever.