Enterprises are rethinking how they source AI talent as hiring challenges intensify, with a growing number turning to nearshore teams in Latin America (LATAM) to close experience gaps and accelerate delivery.
A report from CI&T finds more than three-quarters (76%) of technology leaders plan to increase nearshore hiring over the next two years, signaling a shift away from traditional offshore models built primarily on cost efficiency.
The change reflects a broader recalibration of priorities as AI moves from experimentation to execution. While offshore models once optimized for lower labor costs, AI development is placing a premium on speed, alignment and real-time collaboration.
Ninety percent of respondents said they have high confidence in LATAM AI talent, and 60% reported already shifting spending toward the region. The core issue is not just a shortage of talent, but a shortage of applied experience.
“The toughest AI roles to fill domestically aren’t about job titles, they’re about real, applied experience,” says Young Pham, chief strategy officer at CI&T. “There is a shortage of data engineering talent and data scientists who can support AI at scale.”
Pham notes the deeper gap is in professionals who have applied AI in real business contexts.
That gap is slowing domestic adoption, where many organizations remain focused on pilots rather than scaled deployment. Nearshore teams are helping fill that void by bringing experience in operationalizing AI across workflows, not just building isolated models or features.
Speed and Collaboration Reshape the Value Equation
The shift to nearshoring is also being driven by changes in how software—and increasingly AI—is developed. Traditional offshore models often rely on asynchronous work across distant time zones, which can slow feedback loops and delay execution.
Michael Morris, global head of platform and talent, Randstad Digital, explains one of the largest factors driving the rise of AI nearshoring in LATAM is operational alignment with domestic teams.
“As most LATAM countries adhere to eastern or central time zones, it enables LATAM workers to work synchronously with domestic workers,” he says.
In the world of AI, there are much faster iterations between specification and feedback – and that makes the feedback loop critical.
“Being aligned on time zones is crucial because oftentimes there are multiple iterations per day, versus having to wait for teams that are co-located in different time zones,” Morris says.
Although offshore models may still offer lower upfront costs, enterprises are increasingly factoring in the impact of slower delivery and weaker integration. Nearshore teams, particularly in LATAM, are seen as better aligned with agile development models that depend on continuous collaboration.
Operational Models Evolving
Real-time collaboration is also changing how teams operate. The software development lifecycle itself is being reshaped to accommodate faster iteration and tighter integration between distributed teams.
“The underlying tenets of the software development lifecycle stay relatively the same, but the fact that real-time collaboration is a new capability changes how it’s actually executed,” Pham says.
Testing and deployment are becoming more frequent, often shifting from end-of-sprint milestones to daily activities. At the same time, productivity metrics are evolving, with organizations focusing less on output per sprint and more on overall outcomes driven by AI-augmented workflows.
This shift requires companies to rethink not just where they source talent, but how they structure teams and manage delivery across global operations.
LATAM Emerges as a Strategic Talent Hub
Within the nearshoring landscape, LATAM is gaining traction as a primary destination for AI talent. Brazil is emerging as a key hub due to its large workforce and growing investment in technology and education.
“We are seeing companies in the market investing more deeply in AI, certainly ahead of the U.S. in terms of fully embedded AI initiatives rather than just experimentation,” Pham says.
Government support and continued investment in research and development are also contributing to the region’s growth, helping build a pipeline of talent with experience in deploying AI at scale.
Morris notes Brazil, Mexico, Argentina, and Colombia continue to emerge as leading AI and enterprise technology hubs in Latin America.
Recent data from Randstad Digital revealed shortages of skilled tech talent in the U.S. remain an ongoing challenge for 72% of organizations.
“As companies look to scale AI and digital transformation initiatives, these LATAM markets are becoming critical extensions of the global technology workforce,” Morris says.
These markets are emerging as strong talent hubs, as many remote IT workers in LATAM have strong technical skills, are fluent in English, and can showcase experience with U.S. business practices.
According to Statista, there are 500,000 software developers in Brazil, 220,000 in Mexico, 115,000 in Argentina, and 62,000 in Colombia available for work.
“From time zone alignment and growing expertise in AI, cloud, and enterprise technologies, the LATAM region is well positioned to support the evolving needs of U.S. organizations,” Morris says.
Regulatory, Operational Complexity Rise
As demand for AI talent grows, the risk profile is also changing. Talent churn, once a primary concern in offshore markets, is less of an issue in LATAM, but new pressures are emerging around compensation and governance.
“What we do currently see and are keeping an eye on is wage inflation,” Pham says. “As demand for AI talent accelerates, compensation is rising, particularly for roles tied to implementing and operationalizing AI.”
At the same time, organizations must address governance challenges tied to scaling AI across distributed teams, including compliance, data protection and responsible use.
Morris cautions organizations must also prepare for increased regulatory and operational complexity as they scale LATAM nearshore teams.
“AI teams often operate with sensitive data, increasing the importance of standardizing governance and security frameworks across global teams,” he says.
To mitigate these risks, tech organizations must move beyond transactional hiring models, investing in workforce planning, career development, and standardized governance practices.
“As a result, LATAM nearshore AI teams can scale effectively over the long term,” Morris says.
