From Four-Plus Hours to Under One: A New Interview Workflow for a Strategic Planning Firm

Marv Weidner helps cities and counties build strategic plans. As CEO of Managing Results, he facilitates the conversations that shape how communities move forward: focus groups with elected officials, interviews with department heads, and public input sessions with residents.

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

The Challenge

Marv conducts intensive focus groups and interviews for community planning projects. His process was thorough but time-consuming:

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

For a typical project involving a dozen interviews, that added up to 48+ hours just processing conversations before the real planning work could start.

The Solution

The solution wasn’t a single tool. It was a workflow Marv could own and adapt.

Working together, we focused on three things:

1. Better inputs. I advised on recording hardware that could capture clearer audio in large meeting rooms. Cleaner audio made transcription 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 Marv’s existing deliverable formats

3. Skills, not just tools. We worked through how each part of the workflow functioned and how Marv could 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 moves communities forward.

Not Just Faster, but Better

The AI-assisted workflow also changed the material Marv had available for synthesis.

When consultants take notes by hand, they’re translating what people say into their own shorthand, capturing issues and themes but losing the texture of how people actually talk. The AI workflow starts from full transcripts, which means Marv is working with real language from real conversations. Not as direct quotes (he strips the attribution so nobody feels singled out), but the natural phrasing comes through in a way that handwritten notes never captured.

The result? When Marv presented findings to a repeat client, someone who’d been through this process with him multiple times before, the client couldn’t tell that anything had changed about his methods. But Marv noticed the difference: the findings had an authenticity that made stakeholders recognize their own concerns in the material. People saw themselves in the work.

The Bigger Picture

This isn’t about replacing human judgment. Marv 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, he’s spending that time on synthesis and strategy, the work his years of experience are for.

What Made This Work

Three factors made this engagement successful:

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

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

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

Engagement: June 2025 – January 2026.

If you’re working with a workflow like this, the Brief is where that conversation starts.