By Javid Huseynov, Ph.D., Associate Professor of Professional Practice in Applied Analytics
Over the past 25 years, I’ve developed AI systems for organizations across a wide range of industries, including Fortune 500 companies like IBM, Disney, and Amazon. Throughout this journey, I’ve seen AI reshape not only business operations but also the very fabric of how we live and work. Today, AI is no longer a futuristic concept but core infrastructure that drives decision-making and innovation in nearly every sector. As its impact grows, I bring my professional experience into the classroom, so that students learn not only to understand AI, but to apply it confidently, ethically, and effectively.
Yet this educational mission arises from a pressing reality: access to AI literacy, the skills required to work with these technologies, remains uneven. A gap is widening between those who build AI and those who must use it, creating a divide reminiscent of the 1990s revolution in computer literacy. Nations that ensured universal computer access became knowledge economies, while others fell behind. Today, a similar dynamic is unfolding with AI. But the stakes are exponentially higher, and the window to act is rapidly closing.
The Real Challenge: A Skills Gap, Not a Robot Uprising
Success with AI depends less on cutting-edge tools and more on people who can apply them with skill and insight. Many still view AI as either science fiction or a direct threat to their jobs. While these concerns are understandable, they often miss the fundamental issue: it’s not that AI will replace workers, but that workers without AI skills will be replaced by those who have them. Agentic AI systems capable of planning, acting, and adapting with limited human input are already transforming industries. Navigating this new environment requires more than theoretical knowledge. It demands fluency in algorithms, user-centered design, and cloud computing, all grounded in an ethical framework to assess AI’s broader societal implications. These are the capabilities essential for our students to thrive.
The Global Landscape and Our Role
The urgency of this mission is reflected globally. This year, China and the EU committed $138 billion and €200 billion, respectively, to AI infrastructure and education, highlighting the link between competitiveness and broad AI fluency. The U.S. has generally relied on fragmented private-sector efforts, but in April 2025, President Trump signed an executive order establishing the White House Task Force on AI Education. While this initiative lays vital groundwork, significant gaps in implementation remain. For example, according to a recent RAND survey, only about half of school districts had trained teachers on generative AI tools like ChatGPT as of fall 2024. Another quarter planned to begin training in the 2024–25 academic year, but rural and low-income districts often lack the resources to participate.
Higher education institutions can help bridge that divide. Universities like Columbia can partner with K–12 districts to develop and share teacher training programs, adapt curriculum materials, and run outreach initiatives in underserved communities. Simultaneously, we must continue embedding AI literacy into our own curricula. This will prepare future educators, researchers, and industry leaders with the technical know-how, ethical grounding, and critical thinking skills necessary to foster AI fluency nationwide and sustain American competitiveness.
Cultivating a Future of AI Fluency
As AI continues to redefine the global economy, universal AI literacy is no longer optional; it is essential. By drawing on industry academic partnerships and proven pedagogical approaches, we can ensure students master not only algorithms and cloud platforms but also the ethical principles guiding responsible AI. Through hands-on projects, crossdisciplinary collaboration, and inclusive outreach, higher education can bridge the emerging AI divide. Ultimately, our success will be measured by a workforce capable of innovating with confidence, safeguarding societal values, and sustaining national competitiveness. Together, we have an opportunity to cultivate an AI fluency that empowers individuals and strengthens our collective future.
About the Program
Columbia University’s Master of Science in Applied Analytics prepares students with the practical data and leadership skills to succeed. The program combines in-depth knowledge of data analytics with the leadership, management, and communication principles and tactics necessary to impact decision-making across industries and organizational functions.
The spring 2026 application deadline for the M.S. in Applied Analytics program is November 1. Learn more about the program here. The program is available full-time and part-time, online and on-campus.
