How I Think About This Work

Welcome to Ideas — where I'll gather my thinking about making work work.

Some of it will be about my methodology and the work I do with people and organizations. Some of it will range wider: work, ways of working, and the parts of life that happen well beyond the workplace.

All of it starts from a philosophy I've built over years (decades?) of watching things not work particularly well.

The Belief

The standard advice has always been: Move faster. Get more organized. Find a better system. Use better tools.

I don't think that's the problem.

For most of the people and organizations I work with, the problem isn't output or effort or even tools. It's that they're working extremely hard on the wrong problem — and nobody stopped long enough to figure that out before handing them a solution.

Smart, capable people don't fail because they lack discipline or the right app. They fail because the solution they're implementing was never designed for their actual problem. The complexity grew, but the diagnosis never happened. So the system — however elegant — is solving the wrong thing.

There's a meaningful difference between being disorganized and being under-infrastructured. And there's a meaningful difference between being under-infrastructured and being misdiagnosed.

Disorganized can feel like a character problem.

Under-infrastructured is an engineering problem.

Misdiagnosed is what happens when you try to solve the engineering problem before you know what you're actually building for.

That's where most people get stuck, and that's where my work — and, I increasingly think, all good work — begins.

On AI

AI is the most powerful solution-delivery mechanism most of us have ever had access to. You can hand it a problem and get a plan, a framework, a system, a strategy, in seconds.

That's remarkable, but it’s also exactly where the risk lives.

AI is only as good as the problem you give it. Feed it the wrong diagnosis and it will solve the wrong problem flawlessly — faster and more thoroughly than ever before.

The question AI can't answer for you is whether you're asking the right question in the first place. That requires judgment, pattern recognition, and someone willing to say: "wait, let's back up."

That's not a limitation of AI, it's a clarification of what human advisory work is actually for in 2026. AI handles execution, but I handle diagnosis. And the diagnosis requires something AI can't replicate: the ability to hear what you mean, notice your expression as you say it, and bring twenty years of pattern recognition to what you're not saying at all. Used together, in the right order, that's an extraordinarily powerful combination.

What I do isn’t something you can get from AI alone — not because AI isn’t useful or capable, but because diagnostic work requires someone who knows your specific situation, can see the patterns underneath it, and will tell you the truth about what they see.

That's not a prompt; it’s a practice.

The Methodology

I call the diagnostic process Plumb — after the plumb line, the instrument builders have used for centuries to establish what's actually true and vertical before construction begins. You don't build on ground you haven't tested and you don't design a solution for a problem you haven't verified.

Plumb work establishes Ground Truth: an honest picture of what's actually driving the chaos, the friction, the stall. Not the solution someone already decided on. The real thing underneath.

Once we have Ground Truth, we build the architecture. The sequence matters.

Plumb → Ground Truth → Architecture. In that order, every time.

The Framework

I organize architecture — for people and for organizations — across six elements in three phases. Not because complexity is actually this clean — it isn’t. But naming what’s broken is the first step to fixing it, and shared language makes that possible.

Phase 1: Get Clear — What's actually happening, and what matters now

• Direction: Comes first. Always. For an individual, it's an honest answer to "where am I actually trying to go right now?" For an organization, it's an explicit shared understanding of what success looks like and what's standing between here and there. Without it, every decision requires rebuilding context from scratch. With it, most decisions get easier, because the filter already exists.

• Inputs: Everything arriving before you've decided what any of it means: email, Slack, requests, feedback, other people's needs and urgencies. The problem is almost never laziness. It's that too much is coming in unexamined, with no agreed-upon place to land.

Phase 2: Get Sorted — Where things belong so they stop floating

• Filters: How decisions actually get made — what gets through and what doesn't. What's urgent versus just loud. What's yours to handle. Without filters, urgency becomes the only decision-making tool available, and everything competes for the same attention.

• Containers: Where things live so brains — individual or collective — don't have to hold them. Calendars, task systems, decision documentation, knowledge infrastructure. When things exist everywhere, nothing feels settled.

Phase 3: Get Going — How to move forward sustainably, given reality

• Rhythms: The cadence of review and action — when attention moves, when decisions get made, when things get processed and reset. The difference between people and teams who feel in control and those who don't is rarely what they're managing. It's whether there are reliable moments to reset.

• Seasons: Capacity is not constant — for people or for organizations. What works at one stage of life, or one stage of growth, breaks at the next. The systems that fail are almost always the ones designed for the wrong season, applied past their expiration date.

Where the Ideas Begin

The framework is the same whether I’m working with an individual executive, advising a fast-moving team, or exploring one of these questions on the podcast. The complexity looks different on the surface; the infrastructure problem underneath it is the same.

Ideas is where I'll dig into those problems: what's really happening, why the standard solutions so often fail, and what it takes to build something that works in the world as it actually is.