The Coffee Walk
Last month I walked through three floors of an office I was visiting for the first time. On the first floor, a gleaming espresso machine sat untouched, a laminated sign taped above it: \"OUT OF ORDER – TICKET SUBMITTED.\" The sign had yellowed slightly. On the second floor, someone had wedged a French press between a microwave and a pile of shipping boxes, next to a hand-labeled bag of beans. On the third floor, there was a person, not a machine, making coffee, chatting with people in line, clearly known and clearly appreciated.
Three floors. Three coordination styles. One building.
Coffee is one of the easiest places to see how an organization actually works, not because coffee matters in some grand way, but because it's small, frequent, and shared, and it lives right at the boundary between formal operations and informal norms. It creates a daily moment where people coordinate without a meeting, maintain a commons without a committee, and express care without a slide deck.
That's the through-line: coffee exposes how an organization treats shared infrastructure, including who owns it, what \"good\" looks like, how friction gets resolved, whether defaults are trusted, and whether the commons quietly degrades or gets maintained with pride. The same underlying variable shows up later in every attempt to roll out a shared platform, change a workflow, or introduce a new capability that only works if people adopt it together.
And that's why it's a useful proxy for AI adoption. When I say \"AI success\" in this essay, I don't mean universal usage or a spike in tool installs; I mean the ability to make AI a maintained, routine capability that reliably improves real workflows, creates spillover benefits for teams, and keeps getting better instead of quietly decaying. In practice, the model is the espresso machine: impressive, capable, and ultimately secondary if the ownership, norms, support, escalation paths, and day-two maintenance aren't there.
Coffee is a low-stakes rehearsal for those dynamics.
The coffee walk
If you want to understand how a company works, take a coffee walk. Treat it as anthropology: a random walk through a system that exists in almost every office and is rarely discussed with precision. The goal is not to fix anything. The goal is to notice what is true.
Start with questions that sound trivial until you watch how quickly they turn into a map of the organization. Let the walk guide you in a natural sequence. Notice where coffee lives and how visible it is (one place, many places, hidden places, executive places) and then pay attention to who makes it and who maintains it. “Everyone” and “a named owner” are very different worlds.
Listen for what people mean when they say it's \"working.\" That definition tells you what's valued here: speed, quality, availability, ritual, status. It also tells you what people are willing to tolerate, and what they quietly route around.
Then watch what happens when it breaks, and whether the fix travels through a ticketing system, a Slack thread, a shrug, or a hero who quietly makes it all work. Pay attention to the norms and how they're enforced (signs, vibes, silent expectations), and to the behaviors people don't advertise: hoarding pods, labeling milk, hiding mugs, bringing a personal setup, or keeping a backup stash because they don't trust the commons.
Finally, notice the economics and the entitlement model. Is coffee treated as a shared baseline, a subsidized perk, a BYO situation, or something that varies by floor? In most organizations, that answer isn't really about coffee. It's about how they think access and equity work in practice.
Coffee surfaces the shape of ownership and the texture of coordination. It shows where the organization expects self-service, where it expects service delivery, and how it handles shared infrastructure when nobody is explicitly in charge. If you can learn to see culture in coffee, you can learn to see it in other shared systems too: the internal platforms you want teams to adopt, the workflow changes you want to land, and the operating rhythms you assume will hold.
Four archetypes, and what to do with each
Most coffee cultures cluster around patterns. In practice, most companies are blends: one dominant shape and a few competing subcultures. None are good or bad. Each has strengths and predictable failure modes.
Self-serve standardization. A central station, reliable defaults, optimized for throughput, with someone who clearly owns the experience and the repair loop, and with people who largely trust the default.
This kind of organization tends to do well with AI platforms that have strong guardrails and strong defaults. Adoption becomes routine when the happy path is genuinely happy, but the risk is that edge cases get ignored and innovators route around the platform if it can't flex. The move here is to make the default genuinely lovable and then protect it like infrastructure. Great templates, safe guardrails, clear ownership, fast repairs, and a steady release cadence will beat a big launch followed by drift, and you win by making it boring in the best way.
Artisan autonomy. Many micro-stations, personal preferences, local hacks, and people bringing their own gear, which produces pockets of excellence fast and often creates genuine pride in craft.
The scaling challenge is predictable: duplication everywhere, little shared memory, constant reinvention of the same wheel. AI adoption follows the same shape: impressive local workflows, uneven quality, and a hard time turning \"what worked for me\" into \"what works for us.\" The move here is not to centralize everything first, but to connect it so local excellence can travel. Interoperability, shared components, and lightweight standards will do more than heavy process, as long as you also make sharing easy and rewarding without turning it into a compliance program.
Perk signaling. High-end machines, premium beans, and \"experience\" as message, in a culture that can move fast on procurement and optics and often has leadership attention.
The trap is confusing investment with outcomes, and AI adoption can drift into a portfolio of tools without durable workflows, simply because it's easy to buy capability and surprisingly hard to embed it. The move here is to treat AI like any other shiny thing that becomes shelfware unless it is anchored in real work. Define \"done\" in workflow terms, measure outcomes, and make integration unavoidable so it becomes part of how work happens rather than a collection of tools people demo.
Concierge service. Coffee as a service relationship, with a person or small team delivering it with pride, with people who know them, and with an experience that creates community because the commons is cared for in a visible, human way.
This archetype maps cleanly to what most organizations actually need from AI: enablement wrapped around capability, with ownership and support made explicit rather than assumed. The move here is to lean into that service model through office hours, coaching, champions, playbooks, and a trusted steward who owns the experience end-to-end. The cultural move is not \"make everyone an AI person,\" but \"care for the commons,\" so the organization gets shared capability without requiring everyone to become an expert.
An example: service as a cultural primitive
At PwC, coffee isn't only a machine in a kitchen. In at least one office, it has a person at the center of it: someone who is liked, trusted, and appreciated. People greet them by name. Regulars have their usual. Newcomers get gently pulled into the line and the rhythm of the place. When the office moved, that coffee service moved too, seamlessly. The system recognized what mattered and treated it as real infrastructure. The heart of the experience wasn't a device. It was a human service delivered well.
That's a signal.
It suggests a service ethos that is relational, not transactional. It suggests that invisible work gets noticed. It suggests that \"experience\" is a maintained commitment, not a one-time purchase.
AI succeeds in cultures like this when it ships the same way, wrapped in enablement, owned by someone people trust, and maintained as a commitment rather than a launch. Concierge coffee cultures don't require every individual to become a coffee expert. They require the commons to be cared for. AI works the same way.
The coffee halo
Not everyone drinks coffee. The culture still reaches them.
Coffee creates a halo: a shared ritual that generates benefits beyond the direct consumers. It creates a place where people cross paths. It creates a moment where newcomers learn norms without a formal introduction. It creates small chances for help, context, and belonging. It changes the pace of a day. It creates a shared reference point.
This is a helpful correction for AI strategy, because it reframes what success looks like. The goal is not universal usage. The goal is distributed capability with spillover benefits. A small number of power users can raise the quality of meetings, drafts, onboarding, and handoffs. A well-designed assistant can reduce repeated questions even for people who never open the tool. A team can benefit from one person who can summarize, structure, and synthesize quickly.
The halo is the outcome. Usage is an input.
Coffee as a leading indicator
Coffee also helps you notice change. When norms shift, coffee patterns often shift early: hybrid work changes where people gather, cost pressure changes what gets maintained, office redesign changes how teams collide, and leadership changes alter what is valued and what is tolerated. A small ritual becomes a symbol quickly when expectations are under negotiation, and coffee is often where that negotiation becomes visible.
Coffee is a sensor. It reveals the delta between the culture you describe and the culture you live. It does it without requiring a formal cultural analysis, because it's culture at human scale: ordinary, repeated, and emotionally charged in just the right amount.
The point
Coffee is not important because it is coffee.
It's important because it's a shared system people touch every day. It sits in the commons. It collects norms. It exposes ownership. It generates a halo. And it changes early when the culture changes.
If you can learn to see culture in coffee, you can learn to see it in AI adoption. The same dynamics show up, with higher stakes and less patience. Three floors in one building can hold three different operating systems at the same time, and you'll see the same thing with AI: one team thriving on a default, another building personal stacks, another waiting on a ticket that never quite closes.
A coffee walk is practice. It's also a reminder: durable change rarely starts with policy. It starts with the routines people keep, the commons they maintain, and the service they deliver to each other without being asked.