My approach to AI, problem-solving, and why the most useful thing I've ever done before implementing technology was to just go talk to people. These aren't theories — they're patterns I've tested across 15 years and five domains, and I genuinely love finding where they apply next.
This isn't a framework I invented and named. It's what I noticed I was already doing, after 15 years of doing it.
Process mapping is the practice of breaking a workflow into its individual steps so you can see exactly what's happening, who's doing it, and where the real bottlenecks are. It's not bureaucratic. It's the foundation that separates successful AI adoption from expensive failures.
Elimination beats simplification. Simplification beats automation. The order matters. Automating an unnecessary step just makes you do something unnecessary faster. Automating a broken process just makes it fail faster.
I use process mapping with every client before any technology recommendation. It's how I found that Sauk County's environmental health bottleneck wasn't intake — it was that staff were spending most of their day on information lookup tasks that required no specialized judgment. That insight led to the AI implementation. The implementation didn't lead to the insight.
At Sauk County, I mapped their environmental health workflows before recommending anything. What I found: staff were spending most of their day on information lookup tasks that required no specialized judgment — pulling policies, cross-referencing regulations, formatting standard responses. That was the AI opportunity. The implementation followed the insight, not the other way around.
At Peapod, I mapped how the demand model outputs were actually being used. Nobody trusted the conclusions. So I ran PCA — the first validation the model had ever received. Speed wasn't the problem. Credibility was.
The question is always the same: does this step require human judgment or human connection? If the answer is no — that's your opportunity zone.
Process mapping tells you whether to reach for AI. MAP is how I get teams to actually use it well once they do. I lead trainings on it in our AI Community of Practice and at AOHC, and it's the same method behind the tools I ship.
A 2024 RAND study found that 80% of AI projects fail. The reason is almost never the technology. It's that no one defined the problem first. MAP starts there, which is why the AI tool I built for Sauk County's inspectors cut the time to answer a routine food-code question from 30 minutes to 5, with 83% of them reporting a lighter workload.
AI predicts the next word. That's genuinely powerful for synthesizing information, and it's also the limit. A model doesn't know your values or understand what you're actually trying to do. Ask it to write a Facebook post cold and it will, but it will miss everything that made the post worth writing. It gets useful the moment you pair it with your own judgment about what matters.
The way I think about the human role is a creative director. The skill that lasts isn't doing every task yourself. It's being able to define the goal, communicate it clearly, and say what a good result looks like along the way. Communication and persuasion get more important in an AI era, not less.
This is also how I think about it as a parent. In our house, AI is a set of intentional guardrails, not a blanket yes or no. At my kids' age, learning to read and spending time in the physical world come first: cooking, making things, being outside before being on a screen. Those are our rules for right now, not a prescription for everyone. The through-line is the same one I bring to work. Know what the tool is for, and know what it can't replace.
Not as a side project. As part of the work. The pattern is the same one I bring to product development: find where people are isolated, confused, or stuck — and create the thing that brings them together.
I grew a 2,000-person geospatial audience under the Tabulae Spatial brand — built around my own ideas and voice. I hosted Mappy Hours, a recurring community gathering for the geospatial world. I co-founded the Bulgarian English Speech Tournament Foundation during my Fulbright year — grew it from 6 schools to 35+ and 1,000+ students.
At F&T Labs I run a monthly AI for Public Health Community of Practice — peer learning for health professionals navigating AI, with guest speakers and real questions. I've led multiple storytelling with data trainings for clients. I've spoken at national conferences three times as a keynote.
The through-line: I find where people need a gathering point and I build it. The medium changes — a newsletter, a community call, a training, a keynote. The instinct doesn't.
Most AI failures aren't technology failures. They're process failures. Organizations jump to solutions before they've understood their problems. They automate broken workflows and wonder why it didn't help. My job is to slow that down — map the process, find what's actually broken, then figure out where AI fits.
AI also doesn't fix bad data. It amplifies it. Models are confident by default — they'll give you an answer whether or not the input deserves one. I've spent 15 years asking the same question across every role: can the data actually support the conclusion being drawn from it? That instinct doesn't get easier to apply when AI is involved. It gets more necessary.
I'm not interested in AI for AI's sake. I'm interested in where it actually fits — and where organizations aren't ready for it yet.
I'd rather tell you where I'm still growing than pretend I'm finished. I'm a listener by instinct, and my blind spot is the flip side of that. I sometimes assume a shared understanding I haven't actually said out loud.
So the discipline I'm building is making the implicit explicit early: naming the goal, the scope, and the plan plainly, even when I think everyone in the room already knows it.
If this is how you'd want someone approaching problems on your team, I'd love to talk about where I could help.
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