AI Fluency: The Essential Skill for Work and Growth

What AI Fluency Means—and Why Employers Value It AI fluency is not the ability to build AI models. It is the practical ability to use artificial intelligence in daily work, integrate it into workflows, judge its outputs critically and increasingly create with it. As AI becomes a common language of work, fluency means knowing not just how to use a tool, but how to apply it thoughtfully. Employers’ demand is rising quickly. Lightcast, which supplies job-postings data to Stanford’s AI Index, found that the share of US job postings mentioning AI skills rose 60% in the first half of 2026. Growth was rapid in human resources, customer support, hospitality and finance—not only technology roles. There is a reported salary premium, too. An earlier Lightcast analysis of 1.3 billion job ads found that postings requiring AI skills offered salaries 28% higher, or nearly $18,000 more per year. More than half of those postings were outside IT. That combination of broadening demand and higher pay signals that AI fluency is becoming a valuable skill across the labor market. Why People and Leadership Are the Bottleneck AI use at work is growing faster than workplace guidance. Gallup reports that 40% of US employees use AI at work, nearly double the share from two years ago. While 44% say their organization has begun integrating AI, only 22% say leadership has communicated a clear plan. The result is a gap between people using the technology and having direction on how to use it. That gap matters because AI’s biggest gains are expected to come from redesigning how work gets done, not simply automating tasks one by one. Workers, managers and leaders must make practical judgments: what to delegate to an AI tool, what to verify, what to escalate and where human judgment should remain involved. AI fluency is therefore not just knowing how to use a tool; it means being prepared to make those decisions critically in daily work. Leadership plans are closely tied to that preparedness. Gallup finds employees whose leaders have communicated a clear AI plan are three times as likely to feel very prepared to work with AI. Clear direction can help people understand how the technology fits into their work—and where their own judgment remains essential. Build Fluency Through Practice Without Losing Apprenticeships AI fluency is not a certification people complete once. As models and uses change, workers need continuing, hands-on learning built into their real work. The habit matters more than mastering a particular tool: people learn by using AI, evaluating its outputs and seeing where human judgment belongs. Organizations can make that practice practical. Lighthouse teams can model new ways of working, while peer coaches help adoption spread from colleague to colleague. Leaders have a central role: coach teams, review AI-generated work and redesign workflows—not simply announce that a tool is available. The gains depend on deciding what AI should handle, what people must verify and where judgment must remain in the loop. Redesign also must account for how people develop expertise. Stanford’s Digital Economy Lab finds employment of workers ages 22 to 25 in the most AI-exposed occupations is 19% below where it would have been if it had kept pace with less-exposed peers, driven mainly by slower hiring. Lightcast counts roughly one junior posting for every four senior ones, compared with one for every two and a half in 2022. If AI absorbs routine tasks that once helped young professionals build judgment, employers must deliberately preserve entry-level work as an apprenticeship. Efficiency and learning can be in tension; organizations must choose to protect opportunities to learn. Who Must Build AI Fluency—and New York’s Opportunity No single institution can build an AI-fluent workforce alone. Employers have a central role because they decide how work is organized; they must make practice part of the workday and ensure workers can develop fluency on the job. Educators—including schools, community colleges and credentialing bodies—and government must help learning pathways keep pace as skills and work change. The scale of the task is clear: the World Economic Forum projects that 59 of every 100 workers will need training by 2030. New York has a foundation to build on. Its Empire AI consortium, backed by more than $500 million, connects 10 institutions, including SUNY, CUNY, Columbia University, Cornell, NYU and Mount Sinai. Combined with the region’s capital markets and diverse workforce, this offers an opportunity to pursue a bold goal: make AI fluency universal across the region, not a perk reserved for engineers and executives. That requires employers to name someone accountable, protect time for practice and preserve entry-level apprenticeships that build judgment.