Arizona State University is expanding its portfolio of artificial intelligence programs at a moment when more than one-third of entry-level positions now list AI skills as a requirement. That figure has nearly tripled since fall 2025, according to a spring 2026 survey of employers by the National Association of Colleges and Employers (NACE).
The latest addition is a doctoral degree in artificial intelligence offered through the School of Computing and Augmented Intelligence. Announced in December 2025, the program opens applications for fall 2026 with a priority deadline of Jan. 15. It trains students from varied backgrounds to conduct original research while building expertise in statistical learning, embodied AI, cognition, perception, reasoning and decision-making.
Ross Maciejewski, director of the school, called ASU “a top-tier place to get your next generation of artificial intelligence training.” He pointed to the university’s existing strength. “We’ve got world-class scientists winning research awards,” he said in The Arizona State Press. AI, he added, has long been the school’s “bread and butter.”
But the push goes far beyond the new Ph.D. Earlier, in 2024, the W.P. Carey School of Business introduced a bachelor’s degree in artificial intelligence in business. The program combines technical training with business acumen. Students learn to deploy machine learning ethically inside organizations. Success, officials stressed, demands both coding ability and strategic insight.
“Successful AI strategies require not only technical skills but also business skills and knowledge to manage and implement AI within an organization,” Michele Pfund, senior associate dean of undergraduate programs at W.P. Carey, said in a release covered by BestColleges. The launch aligned with ASU’s earlier partnership with OpenAI, making it the first university to collaborate at an institutional level with the ChatGPT creator.
Daniel Mazzola, clinical professor in the information systems department, captured the urgency. “The rate of change is certainly amazing,” he observed. The central question for educators becomes how to equip students to handle that pace.
These moves reflect a broader scramble. Global AI talent demand outstrips supply by more than three to one, with over 1.6 million open positions and only about 518,000 qualified candidates, a 2026 analysis found (SecondTalent). In the U.S., postings mentioning AI or related terms jumped 130 percent even as overall hiring remained soft. IT roles show AI skills in 78 percent of listings. Seven of the fastest-growing tech jobs tie directly to artificial intelligence.
Entry-level work has shifted especially fast. The NACE survey revealed that 28 percent of employers actively seek early-career hires who can use AI tools. Nearly 60 percent assign AI-related projects to interns. Discussions inside companies focus less on replacing people and more on how the technology changes tasks, ethics and productivity. Over two-thirds of respondents said AI integration happens within existing jobs. Only 11 percent talked about eliminating positions. More than half reported that AI had not reduced the volume of entry-level work.
Yet the talent gap persists. Occupations requiring AI fluency grew sevenfold between 2023 and 2025, reaching roughly seven million workers, per McKinsey data cited in industry reports. Generative AI postings quadrupled in the same period. Projections suggest further tripling by the close of 2026. Employers want more than prompt engineering. They seek professionals who understand model deployment, data pipelines, ethical guardrails and business impact.
ASU’s approach tries to meet that need at multiple levels. The master’s in artificial intelligence in business, available online, targets working professionals. Ten courses blend strategy, governance and a capstone project. The new Ph.D. aims at research leaders. Undergraduates in the business school gain practical exposure without a pure computer-science prerequisite. And foundational tracks in computer science, mathematics and engineering feed into specialized AI study.
Siddharth Srivastava, graduate chair for the doctoral program, highlighted the curriculum’s design. It brings together interdisciplinary students and adds depth across key subfields. The goal is reliable, resilient, safe and effective AI systems. Graduates emerge ready for roles as AI engineers, researchers, machine learning specialists, natural language processing experts or robotics investigators, according to ASU’s degree pages.
Critics might ask whether universities can move quickly enough. Technology evolves faster than curricula. Partnerships like the one with OpenAI offer licenses, tools and real-world testing grounds. ASU collected 175 faculty proposals in the first round of its AI Innovation Challenge and funded 105 of them. A second round opened to students. The effort produced 863 unlimited GPT-4 licenses.
Still, questions remain about outcomes. Will these graduates command the salaries that justify the investment? Influencer salaries sometimes rival nurses, as one recent Yahoo Finance report noted in a different context, but AI engineers already outpace many fields. Median pay for machine learning engineers exceeds $150,000 in many markets. Shortages drive premiums.
And. The pressure is mounting. PwC’s 2026 Global AI Jobs Barometer examined over a billion job ads across six continents. It found AI-exposed junior roles seven times more likely to demand leadership and strategic thinking than less-exposed ones. The career ladder is compressing. New tasks emphasize judgment, empathy and creativity. Those qualities become more valuable as AI handles routine work.
ASU officials believe their scale and innovation culture position them well. The university has topped innovation rankings for more than a decade. Its online programs reach hundreds of thousands of students. That reach lets it test new models fast.
But execution matters. Programs must deliver hands-on projects, not just theory. Internships that assign real AI tasks will differentiate graduates. Partnerships with employers who articulate exact skill needs become essential. Ethics cannot be an afterthought. The NACE data shows companies already discuss ethical implications at the organizational level.
One recent personal account illustrated the non-linear path. A graduate with an economics degree from ASU transitioned into AI engineering by deliberately acquiring technical skills post-graduation. “When I tell people I studied Economics at Arizona State University and now work as an AI Engineer building chatbots, I usually get a confused look,” he wrote on DEV Community. The analytical training helped. Yet deliberate upskilling proved necessary.
Universities that blend domain knowledge with technical fluency may hold an edge. Business students who understand both balance sheets and neural networks. Engineers who grasp regulatory constraints and user trust. Researchers capable of advancing safe systems.
The new doctoral program builds on years of investment. ASU’s Ira A. Fulton Schools of Engineering house strong AI faculty. The university’s design aspirations emphasize values and principled innovation. One student project even created an AI bot grounded in that framework.
So the pieces exist. The question is whether they coalesce into a coherent advantage. Demand shows no sign of slowing. IMF research from 2026 notes that AI skills now appear in nearly 5 percent of U.S. job postings, up from under 1 percent before 2015. IT competencies lead the surge, followed by data analysis and business skills.
Employers face a compressed timeline. They cannot wait for traditional four-year pipelines. Online options, stackable certificates and accelerated paths gain appeal. ASU’s mix of in-person doctoral training, online master’s and undergraduate business degrees tries to cover those bases.
Challenges persist. Not every student arrives with the same foundation. Interdisciplinary cohorts require careful scaffolding. Faculty must stay current in a field that reinvents itself quarterly. And measuring success goes beyond enrollment numbers. Placement rates, starting salaries, research output and employer feedback will tell the real story.
Yet the bet looks rational. When entry-level AI requirements triple in six months, universities that hesitate risk falling behind. ASU has chosen acceleration. Its programs now span the spectrum from undergraduate business applications to doctoral research. The university is betting that breadth, speed and industry alignment will produce graduates who meet the moment.
Whether that bet pays off will unfold over the next several hiring cycles. For now, the data supports urgency. Talent shortages are acute. Skill demands are rising. And companies are assigning AI work to interns before many students even graduate. In that environment, bold curriculum moves stop looking experimental. They start to look necessary.
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