Artificial intelligence is completely reshaping how we work — and in the world of nearshoring, the shift is happening faster than ever. At this point, it's not about whether AI will impact our distributed teams, but how we can actually use it to boost productivity, empower talent, and unlock new opportunities.
How Does AI Affect the Productivity of Distributed Teams?
AI has become the ultimate “copilot” for development teams, helping automate repetitive tasks, spot errors faster, and free up valuable time so people can focus on what really matters: higher-value, strategic work. Tools like GitHub Copilot, ChatGPT, and AI-powered code review engines are making distributed teams more agile, more collaborative, and way more efficient.
Especially in nearshore environments, where juggling time zones and different work cultures is the norm, AI makes a real difference:
Speeds up delivery times.
Automating tasks that used to drain hours.
Improves technical and product communication.
Auto-generating documentation and smart ticket summaries.
Builds team autonomy.
Offering real-time suggestions so folks aren’t stuck waiting for approvals.
Accelerates onboarding.
Helping new team members ramp up faster with code assistants and contextual support.
Keeps knowledge flowing.
Organizing key insights from past projects to avoid losing critical know-how.
AI doesn’t just speed up the workflow, it reshapes the dynamics of distributed teams, leading to smoother collaboration, stronger empowerment, and faster adaptation when things scale or shift.
Which Tasks Are Being Automated and Which Still Need Human Critical Thinking?
Not every task is created equal, and recognizing the split between automation and critical thinking is key to building strong, future-proof teams.
Tasks getting automated:
- Cranking out repetitive code.
- Analyzing bugs and suggesting fixes.
- Spinning up basic unit tests.
- Drafting initial technical documentation.
- Summarizing meetings and tickets.
Tasks that still need human brainpower:
- Designing smart architectural strategies.
- Making important business decisions.
- Tackling complex problem-solving.
- Creating meaningful user experiences (UX/UI).
- Leading teams and nurturing culture.
Sure, AI is getting better at the “doing,” but judgment, creativity, and leadership? IMHO, those are still 100% human.
The Opportunity: Training Talent for a “Copilot-First” World
This is a huge moment for nearshore teams: training talent to work with AI, not around it. It’s not just about knowing how to code or design anymore. It’s about learning how to truly partner with AI to deliver smarter and stronger results.
That means:
- Learning the art of prompt engineering.
- Sharpening skills in critically evaluating AI outputs.
- Building a growth mindset that embraces tech-powered evolution.
At Bowery, we believe the future isn’t about “man vs machine” — it’s about man + machine, working side-by-side to build faster, smarter, and more human solutions.
The talent that learns to thrive in a “copilot-first” world today? They’re going to lead the next big wave of innovation.
The Challenges of Young Talent Facing AI
With all these AI tools popping up, there’s a new challenge too: what happens when junior devs lean a little too hard on AI? It’s easy to think these tools are magic shortcuts that solve everything — but real growth still demands deep learning, critical thinking, and curiosity.
It’s nowadays common to see that junior developers often depend heavily on AI-generated code, which can hinder their ability to deeply understand fundamental programming concepts. This overreliance sometimes leads to struggles in debugging, problem-solving, and adapting code to real-world scenarios. Without a solid technical foundation, juniors risk becoming overly reliant on tools rather than building the true craftsmanship their careers need.
At Bowery, we’re big believers in building “copilot literacy”: knowing how to use AI as a support system without losing your own ability to evaluate, interpret, and improve every single output. Learning how to question and challenge what AI gives you is going to be a core skill for every future-ready professional.
Because at the end of the day, AI is a lever — not a replacement for real knowledge. The juniors who thrive? They’ll be the ones who learn to think beyond the suggestions.
Resistance to Change in Companies with Small Tech Teams
Now, not everyone is jumping on the AI train right away — especially in companies where tech teams are smaller and already stretched thin. Common roadblocks we see:
- Fear of losing control over workflows.
- Feeling unsure if current skills can keep up.
- Not enough time to stop and learn something new.
- Technical debt from older legacy systems.
- An emotional connection to doing things “the old-school way.”
According to Why People Resist New Technologies from Harvard Business Review, these hesitations usually have more to do with human emotions than with the technology itself. That’s why it’s so important to lead change with empathy — by showing small wins fast, offering bite-sized training opportunities, and making "human + machine" feel like an exciting evolution, not a threat.
