Talent is everywhere; structured AI access is not

The uneven distribution of AI opportunity has little to do with raw ability. Developers across Africa, South America, and Southeast Asia routinely build and maintain complex, production-grade systems. What often separates them from peers in better-resourced regions is access: to modern tooling, to mentorship, and to structured learning pathways that keep pace with how the industry is changing.

That gap is what GitHub and Andela have been working to close. Over the past two years, the two companies have collaborated to bring structured AI training to Andela’s 5.5-million-member global talent network. So far, 3,000 Andela engineers have completed training on GitHub Copilot through Andela’s AI Academy, with the first rollouts beginning in 2024 across Africa and Latin America.

Learning inside production constraints

Andela’s approach differs from the common pattern of broadly provisioning AI tools and hoping for adoption. Instead, developers were selected based on how directly AI related to their responsibilities. Job profiles were defined, and training was built around the actual systems those engineers were accountable for. Copilot was integrated into daily workflows—IDEs, pull request reviews, refactoring sessions—so it was evaluated under the same pressures as any other production tool.

This matters because most mid-career developers cannot step away from live systems to experiment with new technology. Deadlines hold, services stay up, and mistakes have consequences. Learning, therefore, has to happen where the work happens.

That reality informed Andela’s design, according to Abraham Omomoh, a learning program manager at Andela.

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

Early skepticism was common. Stephen N’nouka A’ Issah, a React developer from Cameroon working in Rwanda, assumed the tooling would not hold up under real-world complexity.

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

First payoff: faster orientation

The earliest measurable benefit was not increased output but compressed onboarding time. Developers found that AI helped them understand unfamiliar codebases faster—surfacing intent, architecture, and constraints before making changes.

Daniel Nascimento, a senior engineer in Brazil with more than 25 years of experience, describes legacy code as something “nobody wants to touch,” where the primary risk is unintended side effects rather than speed. His first question is always: What does this project actually do? What are its architectural weaknesses and strengths?

To make refactoring safer, he uses GitHub Copilot to generate unit tests first, establishing a behavioral baseline before modifying anything.

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

This pattern of using AI to understand behavior before changing it showed up consistently, particularly in tasks like:

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

Engineers also noted that suggestions frequently still required cleanup or introduced subtle issues, reinforcing the necessity of disciplined human review.

Confidence compounds with use

After several weeks of working with Copilot inside production systems, developers reported incremental gains: faster onboarding, greater willingness to take on ambiguous work, and less time on setup in favor of decision-making.

For Nascimento, the efficiency gains were substantial and came from reducing repetitive overhead rather than outsourcing judgment.

Using GitHub Copilot, I boosted my productivity by around 50%. But it’s not just speed. It gives me more time to connect with the business and focus on real impact.

That expanded fluency also translated into professional credibility. Certifications strengthened engineers’ standing, and AI proficiency broadened the scope of contracts they could pursue.

What the skills gap really is

The outcomes point to a broader conclusion: the AI skills gap is less a matter of capability than of structured access. Developers who adapt quickly tend to have modern tools, safe spaces to experiment, and teams aligned on how those tools should be applied. Where those conditions exist, learning compounds. Where they do not, the impact of AI is muted.

The individual effects can be dramatic, as Koffi Kelvin, an Andela engineer in Kenya, described.

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.

The goal of expanded access in the Global South is not a matter of catching up. As Sammy Kiogara Mati, an Andela engineer who works on GitHub, put it:

GitHub Copilot has expanded my view of what’s possible for global tech talent.

AI alone does not balance opportunity. Intentional, structured access does—and it ensures that the developers shaping AI-assisted systems reflect the full diversity of global engineering talent.