Scaling AI opportunity across the globe: Learnings from GitHub and Andela

Across the globe, developer talent is abundant. But what has been historically inequitable is the access to emerging technologies, mentorship, and enablement when those technologies are reshaping the industry. Developers in regions like Africa, South America, and Southeast Asia can build products at scale, yet access to emerging tools and learning pathways often varies by geography and employer.

Andela is a global talent marketplace built on the belief that where you live should not determine your access to opportunity. Over the past two years, GitHub and Andela have been working together to expand structured AI access across Andela’s 5.5-million-member global talent network. As of now, 3,000 Andela engineers have been trained on GitHub Copilot through Andela’s AI Academy.

Starting in 2024, Andela began rolling out structured AI training to selected developers across Africa and Latin America whose day-to-day work directly involved complex production systems. Instead of treating AI as a standalone experiment, the program integrated Copilot directly into day-to-day development processes—within IDE environments, pull request reviews, and active refactoring work—ensuring it was evaluated under real production constraints.

To understand how the approach worked in practice, we spoke with Andela developers. Below, you’ll learn how they introduced AI into active production systems and identified a model that you can apply, whether you’re experimenting independently or integrating AI tools at work.

The challenges that global developers face today

Developers in regions such as Africa, South America, and Southeast Asia face a distinct set of challenges when it comes to AI skilling and reskilling.

Many developers contend with unreliable connectivity, limited access to high‑performance compute, and the high cost of cloud tools and data—all of which are foundational to learning and practicing modern AI. Training content is often designed for well‑resourced environments, assumes constant internet access, and is rarely localized for language, context, or regional use cases. At the same time, many developers are navigating informal or contract‑based work, leaving little time or financial cushion to invest in reskilling.

Without intentional investment in affordable access, localized learning pathways, and community‑driven ecosystems, the rapid pace of AI advancement risks widening existing inequities—excluding talented developers from across the globe from fully participating in, shaping, and benefiting from the future of AI.

Learning AI inside real work

For most mid-career developers, stepping away from production responsibilities to experiment with AI tools is not realistic. Deadlines continue, systems remain live, and reputations are earned over time. This is why learning has to happen inside real work.

In many organizations, AI tooling is provisioned broadly, and teams are told to experiment. Access is assumed to be enough. But without clarity around which roles benefit most, what jobs are being targeted, and how review standards evolve, adoption can stall or fragment.

Andela took a different approach. Developers were identified based on the relevance of AI to their responsibilities, job profiles were defined, and training programs reflected the actual systems developers were accountable for maintaining.

This is because the team at Andela knows that developers are rarely starting from scratch. More often, they are working inside dense, high-stakes systems where mistakes carry consequences. For many engineers across the globe, access to structured experimentation with emerging tools has not always been guaranteed, which makes learning inside real work both necessary and consequential.

Stephen N’nouka A’ Issah, a React developer from Cameroon who works in Rwanda, assumed early on that AI tools would not perform well under that level of complexity.

I thought it might help with simple things. But I didn’t expect it to work with advanced patterns or legacy code.

Stephen N’nouka A’ Issah, React developer

That skepticism reflected experience. Many developers have seen tools demonstrate well in controlled environments and struggle once deployed in production systems.

Recognizing this reality, Andela chose not to treat AI as a separate discipline or certification exercise removed from day-to-day work. Instead, through its AI Academy, it embedded learning directly into production workflows.

Abraham Omomoh, a learning program manager at Andela, explained the philosophy clearly.

Training has to reflect what developers are actually asked to do at work, not idealized exercises.

Abraham Omomoh, learning program manager at Andela

This way, learning occurs within the same systems developers are already accountable for maintaining.

The first payoff: Faster orientation

One of the earliest recognized benefits for developers was wasn’t increased output, but faster orientation within unfamiliar systems.

Daniel Nascimento, a senior engineer in Brazil with more than 25 years of experience, described what it’s like to work on legacy code that “nobody wants to touch,” where the real risk isn’t speed so much as unintended consequences.“The first thing I ask is: what does this project actually do?” he said. “What’s the architecture? What are the weaknesses? What are the strengths?”

To make change safer, he now uses AI tools to generate unit tests before refactoring, creating clearer boundaries for what can be modified without breaking behavior.

Legacy code usually doesn’t have coverage. So I use it to build that coverage first. Then I know what I’m playing with.

Daniel Nascimento, senior engineer

Stephen described a similar pattern when onboarding to unfamiliar systems. In his experience, AI doesn’t replace understand, it compresses the time it takes to surface intent, architectural patterns, and constraints before making changes. Much of this work involves:

• Generating tests to understand behavior
• Drafting refactors to clarify control flow
• Sketching diagrams to reason about system boundaries

Even then, many suggestions still require cleanup or introduce subtle issues, reinforcing the importance of disciplined reviews.

With AI, confidence compounds

After several weeks of applying AI inside production systems, we could start to measure incremental improvements.

Developers reported:

• Faster onboarding to unfamiliar systems
• More confidence taking ownership of ambiguous work
• Less time spent on setup and more on decisions

Daniel estimated a significant productivity gain, largely driven as an outcome of working differently.

“Using GitHub Copilot, I boosted my productivity by around 50%,” he said.

But it’s not just speed. It gives me more time to connect with the business and focus on real impact.

Daniel Nascimento

He emphasized much of that gain came from reducing repetitive overhead rather than replacing core engineering judgment.

For developers who previously lacked structured exposure to AI tooling, that access translated into expanded professional skills. Certifications strengthened their credibility and AI fluency expanded the scope of work they could take on.

The AI skills gap shows up as access, not ability

This work reinforces a broader pattern: the AI skills gap is, at its core, about structured access to tools, mentorship, and practical enablement.

Developers who adapt faster typically have:

• Access to modern tools
• Space to experiment safely
• Teams aligned on how those tools should be used

Where those conditions exist, learning compounds. Where they don’t, AI impact is limited.

And this also matters for developers across the globe where increased skilling translates to better job and economic opportunities. Koffi Kelvin, an Andela engineer based in Kenya, shared, “GitHub Copilot is a portal that catapulted my professional trajectory into a literal other dimension.”

Between the workflows, security, testing and high-octane pipelines, it’s been less like a career path and more like a rocket launch.

Koffi Kelvin, Andela engineer

Expanding structured access in the Global South isn’t about catching up. Instead, it’s about ensuring that the developers shaping AI-assisted systems reflect the full diversity of global engineering talent.

Everyone benefits with access

When everyone across the globe has structured access to learning, we all benefit from it. AI upskilling is not about chasing hype or predicting the future. It is about learning how to integrate new tools into real systems without stepping away from the job. It allows developers to take on more complex work, contribute more confidently to global teams, and continue building at the edge of modern practice regardless of geography.

When that learning is structured—when access is intentional rather than incidental—it compounds. Sammy Kiogara Mati, an Andela engineer who works on GitHub, shared that “GitHub Copilot has expanded my view of what’s possible for global tech talent.”

AI does not level the playing field on its own. Structured access does.

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