A couple of years ago, we published a practical guide to managing remote development teams. The core advice — structured communication, deliberate overlap hours, async documentation, clear ownership — still holds. Engineering leaders who applied those principles consistently tend to run tighter distributed teams than those who didn’t.
But the context those principles have to operate in has shifted significantly since 2023. The teams that mid-sized and large organizations are managing today are structurally more complex, move faster, and carry more invisible coordination risk than the ones that guide was written for. This post is the update.
The Remote Work Landscape Has Changed Since 2023
When we wrote that guide, the conversation around remote work was still shaped by post-pandemic norms — companies were figuring out whether distributed teams could work at all. That question has been answered.
Work location trends have remained fairly stable since 2022, reflecting the durability of the hybrid model. The back-to-office push you’ve read about in the headlines has had limited effect: the percentage of remote-capable U.S. employees working in a hybrid model has shifted only modestly, with fully on-site work remaining uncommon.
For tech specifically, the numbers are even clearer. The U.S. has the highest proportion of developers working fully remotely among the top surveyed countries, at 45%. In the tech sector, remote-capable employees are equally likely to be fully remote (47%) as hybrid (45%), with just 9% fully on-site.
What this means in practice: forcing a return to office is no longer a neutral management decision — it’s a retention risk. The top driver of job satisfaction among developers is autonomy and trust to manage their own tasks, ranking above competitive pay and solving challenging problems. Organizations that remove that autonomy without a compelling reason find themselves competing harder for the same talent pool they already struggled to access.
The question has moved on. It’s no longer “can distributed teams work?” It’s “how do we manage them well at scale, in an environment that didn’t exist three years ago?”
How Distributed Software Development Teams Are Structured in 2026
Two or three years ago, most distributed setups had a relatively simple shape: one core team, one remote group, one collaboration layer. That’s what most management frameworks were designed around.
Organizations at scale today are often running something structurally different. Rather than one remote group doing similar work in a different location, they’re combining an architecture and leadership layer across North America with a nearshore execution layer in Latin America — Argentina, Colombia, Costa Rica, Brazil — where the roles, seniority profiles, and expected autonomy levels don’t match. That mismatch isn’t a problem in itself. But it means the collaboration layer between them has to do more work than the old model required: translating context, not just syncing status.
This shift is well underway. Companies aren’t turning to LATAM as purely a cost-cutting measure — they’re doing it because they’re stuck: unable to fill roles at US rates, frustrated with overnight delays from distant offshore teams, or simply unable to find the skills they need domestically. The result is a growing number of hybrid North America + LATAM structures that require a different management approach — something we covered in depth.
AI Tooling Has Shifted Where the Bottleneck Lives
The management challenge that isn’t getting enough direct attention: AI-assisted development has accelerated individual output in ways that distributed collaboration structures haven’t caught up with yet.
The acceleration is real. According to the 2025 Stack Overflow Developer Survey, 51% of professional developers now use AI tools daily, and 59% already use AI partially for writing code. Engineers are producing more work, faster, than they were two years ago.
But here’s what the same data reveals about the other side of that equation: 75.8% of developers don’t plan to use AI for deployment and monitoring, 69.2% won’t use it for project planning, and 58.7% don’t plan to use it for committing and reviewing code. The tasks that require architectural judgment, business context, and human accountability — the exact tasks that sit on the leadership and review layer — are not being accelerated at the same rate.
The result is a structural mismatch. More code is being produced, but it still requires the same (or greater) level of human review. Two-thirds of developers say their biggest frustration with AI tools is getting outputs that are almost right but not quite — and 45% say debugging AI-generated code takes more time than debugging code written without it. For distributed teams, this creates a structural mismatch: whichever part of the team holds review authority, architectural context, or product direction becomes the rate limiter — regardless of where it sits geographically. If that layer isn’t scaled to match the increased throughput from the rest of the team, capacity goes unused.
Engineering leaders who haven’t adjusted their review processes, escalation paths, or context-sharing practices to match this new pace are leaving capacity on the table. Not because the team is slow, but because the handoff layer wasn’t built for an environment where code production outpaces code review.
Common Pitfalls When Managing Nearshore Teams (and the Metrics That Actually Work)
The most consistent failure mode in distributed teams at scale isn’t poor tooling or cultural misalignment. It’s measuring the wrong things and managing symptoms rather than structure.
- Pitfall 1: Managing activity instead of outcomes. Monitoring hours logged, Slack response times, or ticket volume tells you how busy a team appears — not whether it’s moving in the right direction. This is especially damaging in multi-layer structures where the LATAM execution team may be highly active but working on the wrong priorities because context from the North American layer didn’t transfer clearly. Our post on the hidden ROI of self-managing teams covers the business case for shifting away from this approach in detail.
- Pitfall 2: Treating all layers of the team identically. A senior architect operating in EST and a mid-level developer in Buenos Aires have different communication needs, different decision-making authority, and different relationships to the product context. A single standup format and a single escalation path won’t serve both well.
- Pitfall 3: Skipping cultural fluency in favor of process. Process can compensate for a lot, but not for the accumulated friction of misread signals, unstated expectations, and collaboration norms that were never made explicit. Our guide to cultural intelligence in distributed teams goes deeper on this.
What actually works — metrics that matter:
- Cycle time per feature, not hours logged. How long does it take for a defined piece of work to move from kick-off to review-ready? This surfaces handoff friction faster than any activity metric.
- Context transfer quality. After a sprint planning session, can engineers articulate the business reasoning behind their top priorities? If not, context isn’t moving.
- Escalation frequency and resolution speed. How often is the team blocked, and how long does it take to get unblocked? High frequency with slow resolution points to a structural problem in how the layers connect, not a performance problem.
- Segmented retention metrics. Company-wide turnover numbers hide the real story. When attrition spikes in one part of the team but stays flat in another, the root cause is usually structural — how work is distributed, how growth paths are communicated, or how visible each segment is to leadership.
Autonomy at Scale Requires Deliberate Infrastructure
One consistent pattern in distributed teams that perform well at 50, 100, or 200+ engineers: the engineers contributing most effectively aren’t waiting for instructions. They understand the problem space well enough to make sound decisions independently.
What makes that possible isn’t talent alone — it’s documented architecture decisions, accessible business context, clear escalation paths, and a culture where asking a clarifying question is faster and safer than guessing. That infrastructure requires ongoing investment. Teams that maintain it scale more cleanly. Teams that deprioritize it accumulate invisible friction that compounds as headcount grows.
This is especially relevant for organizations running hybrid North America + LATAM structures, where context often lives in one part of the team and execution capacity lives in another. If you’re evaluating what that structure looks like in practice, our guide to hiring and managing remote developers in Latin America is a useful companion to this post.
What This Changes About How You Build the Team
The management challenges above aren’t solved by process alone. They’re shaped by who is on the team, how they were onboarded, and whether they have genuine experience operating in distributed, remote-first environments under North American delivery standards.
Engineers who know how context travels in async environments, how to flag blockers early, and how to collaborate across time zones without hand-holding require significantly less management overhead — and create less coordination drag for the rest of the team.
That’s why DevEngine’s sourcing and evaluation process for both Canada and LATAM placements treats communication skills and distributed team experience as core technical criteria, not optional soft skills. At the scale mid-sized and enterprise organizations are operating at, the compounding effect of getting that wrong is significant.
The Foundation Hasn’t Changed — The Environment Has
The 2023 guide to managing remote development teams remains a solid starting point. Structured rituals, cultural intelligence, clear expectations — those are still the foundation.
What’s changed is the complexity of the structures those practices have to support, the pace at which distributed teams are now expected to deliver, and the new variables that AI-assisted work introduces at the team level. Engineering leaders who account for that shift — rather than running the same playbook unchanged — tend to get more out of their distributed teams, at scale.
Building or scaling a distributed engineering team across Canada and Latin America? Let’s talk through the structure →
