AI has stopped getting things wrong, but it has become exceptional at producing work that is merely acceptable, and companies are starting to mistake that for progress. As organizations race to automate, leaders must decide which human capabilities are too valuable to optimize away.

Building an AI engineering team isn't just a headcount decision. Companies also need to account for recruiting time, compensation, onboarding, specialized skills, infrastructure, and the cost of keeping an AI initiative running after it reaches production. That challenge is becoming more important as demand grows for roles that were uncommon or far less defined only a few years ago. LinkedIn ranked Artificial Intelligence Engineer as the fastest-growing job in the U.S. in its 2025 Jobs on the Rise report. For engineering leaders, the challenge is deciding which AI skills the team needs, where to find them, and which roles need to become permanent headcount.

AI adoption has moved well beyond experimentation. According to McKinsey's 2025 State of AI survey, 88% of respondents say their organizations regularly use AI in at least one business function.

Healthtech leaders are caught between two forces that don't naturally pull in the same direction. Clinical and operational teams want AI-powered tools now, from clinical documentation assistants to triage support to administrative automation. Compliance and security teams need every one of those tools to respect HIPAA's requirements around protected health information (PHI), with no exceptions for "it's just a pilot."

Every engineering leader trying to hire for agentic AI right now is running into the same wall: the talent pool is small, the demand is enormous, and US-based compensation for senior AI engineers has climbed to a point where headcount plans get rewritten mid-quarter. That pressure is pushing more companies to look outside their own borders for this specific skill set, and Latin America has quietly become one of the strongest answers.