On a factory floor, you knew whether you'd had a good day the parts moved down the line or they didn't. Knowledge work took that away and left a constant, low-grade question in its place: what am I supposed to be doing, and how would I know if I'd done enough? The systems we work in aren't neutral. They carry judgment. "I got 500 unread emails" and "I have 500 emails" are the same inbox one's a verdict, one's an observation. And the judgment is the part that's actually hurtin

The best AI integrations do one small thing reliably and get out of your way. The worst find twelve new ways to interrupt you before you've had your morning coffee. But there's a third category worth paying attention to: AI that lets a well-meaning person — a team lead, a business analyst, even a CEO — execute plain-English commands directly against your live production data. No developer. No IT ticket. No sandbox. That's process debt in a new form. And it moves faster than t

There's a difference between a rollout and a seep. A rollout has a project plan, a go-live date, and a post-mortem where everyone agrees it went better than expected while privately knowing it didn't. AI didn't do any of that. It just showed up — in your search engine, in your SaaS platforms, in your teammates' emails — while you were handling something else. That's what makes this adoption moment genuinely strange. And why the usual change management playbook doesn't quite

Most SaaS tools present as the easiest solution to all your problems. For a single user, that might even be true. But get a team involved — without a clear process and the right people at the table — and you've just handed everyone a bucket of 300,000 Legos and walked away. In this episode, Toby and Chris break down the requirements trap, the hidden cost of anchoring to your old system, and why pushing people into the tool before defining the process is sometimes exactly the

“Hey, what’s up Toby? We made it another week.” And if your week included insane weather, unhinged headlines, and a football game that made you question your life choices… welcome to the club. But here’s the thing. When the news gets loud, it’s easy to get pulled into the politics of it all. This week, I want to do something different. I want to look underneath the argument and ask the question we ask on this podcast every week: What process produced the outcome we’re starin

Buying is easy to present; trying is how you ship real outcomes. This piece breaks down “try vs. buy,” how to spot and grow triers, why tiny experiments beat shiny decks, and the simple fixes (think box-fan energy) that slash process debt and compound results—especially when 2% of your org works on the business, not just in it.

A sore knee sent us spiraling into worst-case thinking—then a PT said, “You’re just tight.” Sound familiar at work? When handoffs creak or reports slip, we rush to replace systems instead of rehabbing the flow. In this episode we unpack the culture of buy vs. try: why shiny tools get credit, how soft-tissue fixes (handoffs, cycle time, rework) deliver real speed, and a five-step play to run “try” like a product launch. Stretch the work before you swap the joint.

SaaS-fished is what happens when glossy demos and executive FOMO lure teams into buying tools that can’t fix broken workflows. Skip the slideshow, get a sandbox, run a paid short trial, and design the process first—then pick the software that actually fits.

Layoffs and reorganizations don’t just remove people, they remove the living memory of how “good” gets delivered. To keep outcomes predictable amid uncertainty, design processes that are discoverable and transferable, anchor rituals to proof, and make precision explicit. If your system only works when a specific person is in the room, you don’t have a process, you have a liability.

Silos chase speed; systems chase balance. When you optimize one piece without seeing the whole, you build hidden process debt, burning people, trust, and time along the way. The Fabletics fiasco proves it: great marketing can’t fix a broken system.