Belgica Reyes didn’t plan to become an AI data annotator. She’d worked in administration, customer service, and digital operations, and when she noticed AI creating jobs that hadn’t existed before, she enrolled in Miami Dade College’s associate degree in applied AI to understand how the systems worked. She now helps train the algorithms businesses depend on.
“I realized I was ready,” she says, “when I could understand what the AI system was trying to do, not just follow instructions.”
Her path is illustrative of the gap that the AI workforce conversation skips over. The jobs AI is creating right now are, by-and-large, technician-level like data annotators, AI-enabled supply chain coordinators, and machine learning specialist staff working alongside automated systems. These roles don’t require a computer science degree. In fact, the shift is already visible in federal workforce policy: the U.S. Department of Labor recently added roles such as AI data annotator (like Belgica), prompt engineer, and machine learning data curator to the O*NET system as eligible apprenticeship occupations.
The emerging AI workforce is actually a vibrant tapestry of trades and occupations, spanning healthcare, logistics, manufacturing, agriculture, and retail, each with its own version of AI integration and its own skill demands. What’s missing is a trained middle tier of workers who understand AI well enough to work with it, manage it, and apply it in their specific field.
That missing middle is the mandate community colleges are uniquely built to answer, and which becomes more critical by the day.
Community colleges already educate more than 40% of the American workforce. They are deeply embedded in their regions, accessible to working adults, and structured around applied learning by partnering with local companies. No other type of institution is better positioned to train the people who will staff the AI economy at scale. The question is, does the policy and industry infrastructure exist to support them in doing it?
The National Applied AI Consortium (NAAIC) was built to provide exactly that. NAAIC is a collaboration led by Miami Dade College with Houston City College and the Maricopa Community Colleges, founded in 2024 with NSF funding. Its purpose is practical and straightforward: to support community colleges in preparing their students for AI roles for which employers are already hiring. Its early progress illuminates what it actually takes to scale this level of workforce training. The “train-the-trainer” model works. Combining faculty training, industry alignment, and shared curriculum, colleges can establish applied AI programs quickly and enroll working adults at scale. But NAAIC’s work with industry partners has also exposed where the existing infrastructure cannot meet the moment.
The first constraint is faculty capacity. Here, student demand isn’t the issue; the challenge is preparing enough instructors to teach in a field where tools, standards, and expectations are constantly evolving. Short-term training is not sufficient. Faculty need ongoing, hands-on exposure to real-world AI applications, along with time and support to continuously update courses. Without sustained investment in faculty development, the efforts to scale these programs will stall.
We also confront the matter of the speed necessary for effective adoption. Often, state program approval and funding processes move on timelines that lag behind the pace of change in AI’s applications to the modern-day workforce, risking the curricula becoming outdated in the meantime. Community colleges are designed to be responsive to labor markets, but policy frameworks have not fully caught up to streamlining how programs are approved, updated, and connected to degrees and the labor demand.
Industry engagement is the third piece of this complex puzzle. While many companies have already stepped forward to support curriculum development and training with NAAIC—including Intel, Microsoft, Google, OpenAI, Lenovo, AWS, IBM, and Cisco—employers can provide more paid internships, project-based learning, or clear competency frameworks tied to hiring. For students, especially working adults, those connections are often what turn training into a job. For faculty, they are how instruction stays grounded in real practice. Treating community colleges as strategic partners in building the applied AI workforce means investing not just in tools and content, but in sustained, work-based learning opportunities.
What’s emerging is a clearer picture of what the country needs to do next. Community colleges are not a peripheral part of the AI economy; they are the institutions most capable of preparing the people who will actually work with these systems every day. That role requires treating applied AI training as national workforce infrastructure: investing in faculty at scale, modernizing credential pathways, supporting working students, and aligning industry participation with how hiring is changing. The demand is already here. The question is whether we will build the capacity to meet it.
Reyes, among many of our alumni, has a job that didn’t exist a couple of years ago. Multiply her by thousands, across cybersecurity, healthcare, logistics, manufacturing, and beyond, and you have a workforce ready for what’s actually coming in the transformation AI promises to make to the economy. Community colleges, backed by the right partners and the right policy, can make that happen.
Antonio Delgado Fornaguera is the vice president of innovation and technology partnerships at Miami Dade College and is the founder of the National Applied AI Consortium, an NSF-funded initiative linking more than 350 community colleges with major tech employers and public agencies to scale applied AI training.