We tend to assume that if the people who will live with an architecture standard help create it, adoption takes care of itself. Invite the teams, let them design the process, give everyone a voice, and the standards will be theirs.

Wouter Lagerweij tells the story of an organisation where that assumption did not hold. A large company grown through mergers and takeovers, where "no one can tell us what to do" had quietly turned into "no one is actually telling anyone what they're doing." The ask was sensible: standardisation and a common architecture, coming from the teams. Technical leads self-selected into the sessions, ADRs were written, debated and ratified, and everyone had an opinion. Half a year later, the standards were parked on a website and practically nobody was using them. Not even the teams whose own members had written them.

The moment it became undeniable was a sprint planning session. On the backlog: a technical improvement implementing one of those standards. There was a document, example code, a production service already using it, and the author, a former teammate, explicitly offering to help. The team's answer: "Yeah, we don't know. We're gonna postpone it to the next board."

This conversation, with Suzanne Lagerweij joining in, explores why doing all the right things still didn't produce the change. We dig into how a process built so that everyone has their say can end with nobody feeling they own the outcome, why consent is not consensus, why the technical lead is not always the person who moves a team, and why Wouter believes the missing ingredient was not more process but people whose only job is to keep the energy on one change until it lands.

Key Discussion Points

* [00:02] Standardisation From the Teams: A merged organisation where autonomy has become "nobody tells anyone anything" asks for a common architecture built by the teams themselves

* [00:05] Parked on a Website: After half a year of ratifying ADRs, the standards sit unused, even by their authors, and the process gets seen as a "distributed ivory tower"

* [00:07] "Postpone It to the Next Board": Documentation, example code, a production service and a willing author, and the team still defers the work

* [00:08] Pulling Every Lever: Easier adoption, wider representation and explicit leadership permission fall flat, while defect-only sprints actually deliver

* [00:13] Nobody Owned It: Not even the people in the room felt the standards were theirs, and Suzanne asks whether technical leads were the teams' informal leaders

* [00:16] Too Much Room to Object: A process designed so everyone has their say, design by committee, and the difference between consent and consensus

* [00:22] One Change, Real Slack: What Wouter would do differently, from landing a single standard to sending in people whose only job is to raise the energy

* [00:24] More Emotional Than Cognitive: Connecting the "because" to what teams care about, and why a dashboard is not the change

**Guests:** Wouter Lagerweij, Suzanne Lagerweij

**Hosts:** Kenny Schwegler

Most organisations have a community of practice that nobody actually wants to attend. It sits in the calendar every two weeks, a handful of people talk, and the rest stay silent. Suzanne Lagerweij has a simple explanation for why: people don't feel like they matter there. Why would anyone go the extra mile if they go unnoticed?

Suzanne brings a background in psychology and social work, including working with troubled teenagers in group homes. She sees clear parallels with software teams: stubborn people you can't order around, where nothing moves until you understand what they need.

At her current client, AI adoption consisted of exactly one thing: an Excel sheet where a few early adopters logged their experiments. There was no direction and no support, and nobody was listening. So Suzanne, brought in to coach a very difficult team, organised an internal AI conference that nobody had asked for. Management said yes, but mostly worried about the hours lost from the roadmap. About 100 people came to the first edition and 150 to the second.

Alongside the conference she started a biweekly AI café: a demo, a conversation and a freshly baked cake. She refused to make it hybrid, because "if you want to have an actual community that works, people have to be together."

Her own team went from being the problem team to being the department's AI frontrunners. Not everything moved, though. Management loved the energy, right up to the point where they realised they were part of the problem. Suzanne proposed a questionnaire to understand where developers were on the learning curve. It came back longer and mandatory, with everyone summoned to a meeting to fill it in. "Well, that's not how it works."

This conversation explores what it takes to build a community people actually want to be part of. That means making invisible work visible to the people who can support it, and recognising people as individuals. It also means finding successors before you leave, and being honest about why you're doing it when it isn't your job. Wouter Lagerweij joins in to connect the mandatory survey to Goodhart's law, with token spend as the new lines of code. As Suzanne puts it: if you don't, then who does?

Key Discussion Points

• [00:01] From Group Homes to Lifeless Chapters: Suzanne's background in social work, and why communities of practice become just another meeting when people feel unnoticed
• [03:45] The Spreadsheet Was the Strategy: AI adoption amounts to an Excel sheet of experiments, so Suzanne organises an internal conference nobody budgeted for
• [05:00] A Stage, Not a Finished Product: Convincing reluctant speakers that a session is a story, and bringing management in to see what's really happening
• [07:00] Cake, Demos, and No Hybrid: The biweekly AI café that turned a problem team into frontrunners, and why Suzanne insisted people be in the room
• [10:00] Management as Part of the Problem: Managers who liked the movement, but not what it revealed about them
• [13:30] If You Don't, Who Does?: Why communities collapse when the organiser leaves, and the effort of finding people to carry it forward
• [15:00] Making People Change Makers: Small steps, visible value, and Wouter on appreciation and recognition as the basics that make people feel important
• [19:00] The Mandatory Questionnaire: Measuring productivity versus understanding the learning curve, and why enforced participation breaks the instrument

Guest: Suzanne Lagerweij (with Wouter Lagerweij)

Host: Kenny Schwegler

We spend a lot of design effort on how data gets into a system — the forms, the validation, the transactional database quietly recording everything in neat rows. We spend far less on how it comes back out. And when the person who needs a weekly report is a loss prevention officer rather than a software engineer, "just use the API" turns out to be no answer at all.

In this episode, Andrea Magnorsky and Kenny Schwegler are joined by Kat Hempstalk, a data and AI professional who has been doing machine learning "since before it was cool." She brings what she calls "a bit of a tragic tale" from her time as head of data and AI at a company handling retail crime reports. Before she arrived, the company had solved the export problem by hiring a graduate to write Python and SQL scripts and email spreadsheets to customers by hand. It worked — until that person went on holiday and literally handed their laptop to a colleague so the reports could keep going out.

An engineer eventually received that laptop, thought "this is a bit scary," and added a little automation: a Windows VM, scripts synced through GitHub, scheduled tasks, files dropped into blob storage. The manual button-pressing stopped. The underlying problem just got faster to reproduce. Within a few years, three or four people spent their days copy-pasting scripts into 200 bespoke customer reports — reports that turned out to be five or six shapes wearing different hats. Collapsing them was the easy part. Telling customers their format was about to change, when the customer success team said "No, I'm out — I don't wanna give the customer bad news," was not.

This conversation is about where data engineering and software engineering stop talking to each other, and what leaks through the gap. We dig into invisible contracts (a column heading is an API), why the order you migrate customers in is an architectural decision that cost this team a year, what embedding data people into software teams actually requires, and Kat's discovery that most of the customers fighting to keep their scheduled email had never once opened it.

Key Discussion Points

• [00:01] A Bit of a Tragic Tale: Kat introduces the retail crime platform — reports go in easily, and getting them back out was never designed
• [00:03] Your Users Know Excel, Not APIs: Why loss prevention experts don't want a programmatic endpoint, they want a spreadsheet
• [00:05] The Laptop Handover: The graduate goes on holiday and passes the laptop along so the reports keep flowing
• [00:06] "A Bit of Automation" Becomes Load-Bearing: A Windows VM and scheduled tasks make the problem faster to reproduce, not smaller
• [00:07] Three People Copy-Pasting Scripts: 200 custom reports, an unhappy analyst team, and customers waiting months to add a column
• [00:11] The Query That Came Back Too Fast: Moving to a data warehouse, and the insights analyst who assumed the speed meant they'd made a mistake
• [00:13] Two Hundred Reports That Were Really Five: Consolidating into templates — and why that's a conversation problem, not a technical one
• [00:19] The Emails Nobody Opened: Customers insisting on scheduled reports they'd never clicked, because they wanted reassurance, not data
• [00:24] "No, I'm Out": Customer success refuses to deliver bad news, so the data team has to own the conversation
• [00:26] The Order You Migrate People In: Starting with friendly customers built momentum, but the resistant ones set the pace for everyone
• [00:29] A Column Heading Is a Contract: Customer-defined attributes reordering spreadsheets, and moving to nested JSON so nothing downstream breaks
• [00:34] Clear Fences Make Good Neighbours: Andrea on the boundary between data and software teams, and Kat on embedding people across it
• [00:39] What Goes With the Person, What Stays: The handover conversation they skipped, and the systems that broke silently as a result
• [00:42] Collaboration in the Agentic Era: Why the people best at sharing context and contracts are also the people getting the most out of coding agents

Guest: Kat Hempstalk

Hosts: Andrea Magnorsky, Kenny Schwegler