From 4 to 1: An End-to-End Technology Overhaul for a Strategic Planning Firm

Marv Weidner has spent decades helping 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, 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 any actual planning work could begin.

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 hardware, starting with 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 Marv’s existing deliverable formats

3. Skills, not just tools. The goal was capability transfer. After a few sessions working together, Marv 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. Marv described feeling “blown away” by the transformation. For the first time, the tedious work of processing interviews wasn’t hanging over every project.

Not Just Faster, but Better

Here’s what surprised everyone, including me: the AI-assisted workflow didn’t just replicate the old process faster. It actually improved the final deliverable.

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.

That’s not something he could have achieved with the old approach, no matter how many hours he spent on it.

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 that actually requires his 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 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.

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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.