Management by Walking Around, Without the Walking
In January my calendar was wall to wall. Next week: five meetings.
If you looked at my calendar in January, it was wall-to-wall. If you look at it today — next week I have maybe five meetings, and they’re all less than an hour. There’s a ton of empty time, which allows you to think, grow, and spend time on the business rather than in it.
Same company. Same job. Here’s how that happened.
The scheduling fix that didn’t work.
It started with a podcast, Reuven, my co-host on The Signal, shared with me about eighteen months ago. The theory: push all your meeting invites to the end of the day, and you create a morning where you have the most energy and creativity; you spend the first five or six hours just working on the business. I remember listening and thinking, that sounds awesome. So I went out and told everyone, “You want to book a meeting with me?” It’s from four to six.
It didn’t matter. The day got filled up anyway. It’s the common things, the one-on-ones, people needing information, wanting a decision made as they move through the day. The calendar was never the problem.
The decision that did
Last October, I made a real, conscious decision: to understand what’s actually operating this business.
If you’re a lender, you know that happens on the front lines. Think about a decision an underwriter makes, declining a deal. Sure, there’s policy. But much of that is inherent knowledge, and unfortunately, that knowledge dies with the decision. It doesn’t get captured anywhere. Then the next decision gets made.
So we made a real conscious effort to capture that information across the company so that it could become the knowledge base. When the AI agents we eventually built had that knowledge stack, they could actually answer the questions.
Because think about the first time you engaged with ChatGPT. You asked it to write an email; it was half right, you gave it more context, it came back, you iterated three or four times, boom, magic output. And then you started over the next time. If you don’t record it and let the system understand how you work, you’re starting over every single time.
Becoming AI-first — the unglamorous part
About seven months ago, we decided we would be a true AI-first business. It started with this: at the end of every week, I asked every leader in my company to tell me what they did. What worked, what didn’t work — I even asked how they were using AI. About seven core questions. And I gave them a prompt, so our connected tools drafted the answer for them, and they added to it.
This was not about micromanaging. I probably read one in three. It was about understanding how the company operated.
All of it went into a Notion database. Coupled with the connected tools we use every day — Granola transcripts, our emails, HubSpot, and our coding tools everything gets turned into context, and the agents can access it. That became the foundation. We started to see the compounding effect by about month three. By month six, I really think we’re an AI-first company now.
What the mornings look like now
I wake up at 6:30 every single day, make an espresso, load up my phone, and there’s this report diarizing the entire business. Not just what’s happening in my world, but my team’s world: delivery, product, finance, sales pipeline, support tickets. I get it two ways: either as a report or as an artifact with fancy graphs, charts, and outputs that I can glance at throughout the day. I don’t do a single thing to produce it.
I think back to when I worked in an office. The way a manager understood the business was the tour of the cubicles — pop your head in, “what are you working on?” — gathering information for the weekly report to their boss. This is management by walking around, without walking around.
The other big leverage point: what should I actually be working on? We’ve all read the books: Eat That Frog, tackle the biggest piece first, tackle the low-hanging fruit first. Every one of those theories required you to spend an hour, the night before or on Sunday, writing down everything you had to get done. You don’t have to do that anymore. The system creates the list for the week. You choose which one to tackle. That alone is a couple of hours a week.
Three receipts
The proposal. We were on a discovery call with a customer a couple of days ago. The call ended, and the draft proposal existed within a couple of minutes — follow-up email drafted too. Because it understands our business, there was very little refinement—a couple of tweaks — not the tweaks of six months ago.
The NDA agent. I took every NDA we’ve ever received — the redlined versions and the green-checkmark versions — and had it understand them. Now every NDA that comes in gets flagged and redlined first. There’s still a cursory read before we sign anything, but not the thirty minutes it would have taken someone on my team.
The steerco. About a week and a half ago, a product update call with a customer. The two teams agreed they needed an output and that, with both teams contributing context, it would take about a week to a week and a half. I was sitting on the sidelines. I opened Claude in real time, copied the live transcript — the call wasn’t even done — and asked it to build the output. Before the call ended, I had the whole matrix of everything both teams had to execute, because we had all the context from the months we’d been working together. Probably ten people were going to contribute to that. Instead, two people from my team reviewed it, and two or three from theirs.
It didn’t all work
Last October, we hired our first AI engineer, and my whole thought was: how do I get leverage from him building internal agents? I spent the first few weeks screen-recording what I was doing and shipping it over so he could analyze which of those jobs could actually become agents. The output was about 40% accuracy. We weren’t getting lift.
Eight months later, a screen-recorder skill comes out of Claude. Last week I recorded myself gathering board material, and it handed back the repeatable task and the option to build the skill. I ran it the night of our board meeting, and the output was incredible.
Apparently that’s everything in AI now: if you can think about it, just wait. Someone will release it.
The human side is the hard part.
AI is still scary for a lot of people. There’s a large unknown: everyone logged into ChatGPT at some point and asked it to write an email, and then they saw the transformation: “holy cow, it can do 80% of my job.” You can’t force this on people. You have to bring them along.
We worked top-down. We didn’t hand out licenses to everyone on day one; the management team got them first. At the beginning, I did show-and-tells on my own weekly management call: here’s how I’m using it. Then it evolved into a weekly rotation in which someone would demo what they built using AI.
Most people have a bit of competitiveness in them. I go back to my coaching days: Anson Dorrance, the UNC and US women’s national team coach, always said people thrive in competitive environments; you have to put them up against their peers, make it transparent. And honestly, those demos got better and better every week. After a couple of rounds, I stopped running it. I didn’t need to anymore.
One thing I still do is ask a question when I feel like we did something manually. Could AI have done that? Did we ask AI to do that? Not as a gotcha, but because I wanted people to understand it’s not scary, and it makes the job easier. The message to my team is simple: I want you on high-value work. I don’t want you running nine-to-five and then thinking you have to work three more hours at night. I know I can get 10x the productivity out of you in less time, and maybe one day we even get to that work-life balance everyone dreams of in startups.
The point
Capture the knowledge before it dies with the decision. Connect the tools. Let the system learn how the business actually operates. It took us seven months, and the compounding effect is the whole game.
The empty calendar isn’t the goal. It’s the proof.