Join/Renew
AI is creating new job opportunities for college graduates, and higher education and employers can adjust the ways they operate and interact to ensure students are prepared for these roles.
“AI is turning entry-level jobs into hybrid roles requiring technical literacy, adaptability, and ethics. Hence, universities must embed AI across disciplines—not just as one course—and employers should recruit for skills and portfolios—not just credentials,” says Subodha Kumar, Ph.D., professor of statistics, operations, and data science and founding director of the Center for Business Analytics and Disruptive Technologies in Temple University’s Fox School of Business.
Dr. Kumar identifies several entry-level jobs AI is creating for new college graduates, such as:
- Prompt engineer—designing, refining, and testing prompts or instructions given to large language models (LLMs) or generative AI systems to get desired output in an optimized manner. As more tools, such as ChatGPT and Claude, are used in companies, the quality of interaction with them matters because prompt design affects cost, performance, and results.
- AI ethics/governance/risk/compliance—ensuring AI use aligns with legal, ethical, and societal standards; developing policies; doing impact assessments; auditing. As AI systems have greater influence—on people, on fairness, and on privacy—organizations are under pressure to use them responsibly. Also, regulation and accountability are emerging.
- AI/machine learning (ML) support roles/quality assurance/testing—testing AI systems; reviewing outputs for errors; debugging; overseeing model behavior in edge cases; ensuring models are safe and fair. Models often go wrong in weird ways; having humans in the loop is important. As AI is deployed in products, companies need people to ensure reliability, mitigate bias, and oversee performance.
- AI/data-driven analyst/insights roles—using AI/ML-assisted tools to analyze business or domain data; generating insights; aiding decision making; working with dashboards; sometimes building simple models. Companies want to make smarter decisions; AI helps, but people are needed to interpret and act on insights. These roles help bridge business and tech.
According to Dr. Kumar, these jobs are being created by a range of industries, including:
- Technology/software/IT services—building AI tools, platforms, cloud services, machine learning, model deployment, agent systems, and software with AI features.
- Healthcare and life sciences—diagnostics, e.g., image and signal processing, drug discovery, patient monitoring, personalized medicine, health records analysis, and predictive health analytics.
- Banking, financial services, and insurance (BFSI)—fraud detection, credit scoring, risk modelling, automated trading, personalization of services, chatbots, customer support automation.
- Manufacturing/automotive/industrial—predictive maintenance, robotics/automation, quality control, supply chain optimization, robotics, and Internet of Things (IOT) and AI.
- Retail/e-commerce/consumer goods—personalization (recommendation engines), customer analytics, inventory forecasting, supply-chain optimizations, chatbots, and content generation/marketing.
- Professional services/consulting—advising other firms on integrating AI: risk, compliance, strategy; legal or ethical counsel; AI governance; auditing of AI systems; and implementation of AI tools.
- Education/EdTech—AI tutoring, personalization of learning, automating grading/content creation, course recommendation, assessment tools.
- Government/public sector/regulation—policy on AI; regulation/oversight; public service applications (health, agriculture, disaster, transportation), citizen-facing services, and safety/privacy.
“Colleges and universities can adjust to employer need to fill these positions and best prepare students for successful entry into the workforce by modernizing curriculum to teach AI basics and data literacy across all majors,” he suggests.
“They can add applied modules, such as prompt engineering, AI ethics, and domain-specific AI, and focus on project-based learning.”
Dr. Kumar offers other recommendations, including providing practical exposure by leveraging industry partnerships for real problem statements; internships and apprenticeships in AI support, QA, annotation, and compliance; and assistance with hackathons and AI labs.
He also suggests colleges and universities strengthen students’ soft skills, such as communication, which will be helpful as they translate AI outputs for nonexperts, and critical thinking and error detection.
“They can upgrade career services by mapping emerging AI career tracks and helping students build AI portfolios and train for AI-relevant interviews and resumes,” Dr. Kumar adds.
“Colleges and universities can also upskill faculty in AI tools and applications, encourage cross-disciplinary teaching, and align programs with global standards and local industry needs.”
From the employer perspective, organizations can account for these AI-created positions first by leveraging their campus partnerships and shaping curricula to ensure graduates meet industry demand. For example, BFSI firms can influence finance and AI programs and healthcare firms can influence bioinformatics.
“Doing so allows employers to directly recruit AI-literate graduates without retraining costs and employers can also tap into universities as low-cost innovation hubs,” Dr. Kumar notes.
“For regulatory and ethics collaboration, employers can partner with law/policy schools to tackle responsible AI challenges.”
To address these new positions, some employers are adjusting the way they conduct internships and/or recruiting. For example, internships may now include:
- AI-focused projects—Interns are working on hands-on AI tasks—prompt testing, annotating datasets, training dashboards, and auditing AI outputs to name a few—rather than shadowing.
- Microinternships/short-term projects—Companies are offering six- to eight-week AI “sprints” during which students solve a defined AI problem, such as data cleanup or chatbot evaluation.
- Remote/hybrid setups—These are beneficial since much AI work is digital, remote internships give organizations access to global talent.
- Cross-disciplinary exposure—Interns aren’t limited to computer science teams; finance, marketing, healthcare, and policy students are taking part in AI projects too.
- Ethics and compliance work—Some internships now focus on reviewing model bias, documenting AI use, or drafting governance policies.
“In terms of recruiting and hiring, employers care more about projects, portfolios, and tool fluency than just GPA or degree prestige. Students showing prompt-engineering examples, GitHub repos, or case studies stand out,” Dr. Kumar says.
Other changes in recruiting students for AI positions include:
- Hackathons and AI challenges—Companies are recruiting from AI hackathons, datathons, and campus competitions instead of only traditional job fairs.
- Partnership recruiting—Firms are co-creating AI courses with universities and recruit directly from those classes.
- Earlier engagement—Employers are connecting with students in their first or second year of college through workshops, seminars, and AI clubs to build a pipeline.
- Virtual recruiting—Online portfolios, AI project demos, and remote assessments are increasingly replacing in-person interviews.
“Universities should make AI a core literacy, and employers should give young hires room to use it responsibly and creatively,” Dr. Kumar says.
“Success will come from preparing people to work with AI, not around AI.”
