Would Students Want to Be Graduating From High School or University Right Now?

With summer approaching, it’s the time for college and high school graduations. Around the world, more young people than ever are reaching this transition point: global high school completion has reached roughly 60%, and enrollment in colleges, universities, and other postsecondary institutions has climbed to a record 264 million students. 

Tens of millions will graduate from higher ed institutions this year. China alone expects about 12.7 million college graduates in 2026, while U.S. institutions are projected to give out more than 4.2 million associate, bachelor’s, master’s, and doctoral degrees in the 2024–25 academic year.

The landscape of post-secondary and next-stage education currently facing adolescents and their parents likely differs significantly from the patterns seen five or ten years ago. 

Post-COVID, the world has changed with the emergence of AI. There are factors that today’s graduates may encounter that might make them question their decisions about life and careers, demanding a level of constant, unprecedented change.

The key question is this: How can students prepare to become vital members of the workforce, or of the next university population, in this world? 

Generative AI and agentic AI are challenging traditional work assumptions. IDC believes that there will be 1 billion agents delivering 217 billion instructions per day in less than three years. Set against today’s human workforce of about 3.7 billion people, these 1 billion agents working 24/7/365 could be the rough equivalent of adding almost 30% to the global workforce. 

A 2025 study reported that more than half of U.S. teens had used AI chatbots to search for information (57%) or get help with schoolwork (54%), while one in 10 said chatbots helped them complete all or most of their schoolwork. Separately, RAND found that high school students’ use of AI for homework rose from 48% in May 2025 to 62% in December 2025, underscoring how quickly AI is becoming part of everyday student work.

GenAI and agentic AI are changing the game to the point that college or high school graduates may need to find new ways to add value in the working world. 

In that environment, adaptive learning and a growth mindset may become prerequisites for success. The ability to learn and adjust constantly can carry more weight than the historical value of simply accumulating knowledge. The rise of the agentic enterprise is one important signal of how quickly this shift is taking hold. 

Globally, EDB research shows that 13% of the world’s largest enterprises have already deployed at least 10 distinct agentic workforces, and they are seeing 5x the ROI of their peers. This sample represents a broad universe of 17,000 organizations leading the AI and data sovereignty revolution. These leaders may hire tens of thousands of graduates every year, yet it remains uncertain whether those graduates' jobs will be agent-free.

One of the key skills is increasingly adaptive learning. The ability to reason, debate, and act could become a critical real-world skill. Knowledge alone may not be enough. Repetitive tasks can be turned into agents at speed.

New graduates may have to learn to work with agents from day one, especially inside the world’s leading enterprises deploying AI intelligence at scale. 

As every enterprise moves closer to its goal of becoming its own AI and data platform in the next 780 days, this revolution will move fast. The 13% of organizations already operating this way are setting a pattern that others are likely to follow over the next few years.

Michael Gale, Chief Marketing Officer of EnterpriseDB, cautions that if any child is about to enter university this fall, they should consider what the workplace may look like by graduation. In four years, many employers could have agentic workforces embedded across nearly every function.

Many organizations aim to utilize their own AI, data, and agents to build a unique competitive advantage, a goal that is often supported by sovereign infrastructure.

Graduates need to think about themselves in a similar way: as their own adaptive, AI, and data platforms of skills, judgment, and knowledge. Young people entering the workforce can benefit by navigating their own capabilities, their companies’ knowledge bases, and the AI tools around them to deliver value where, when, and how it is needed.

Relying on one skill, one discipline, or one silo may not be enough. As the patterns of work keep changing, graduates may need to generate new forms of value across functions, teams, and technologies.

“The new working norm is not just knowing more. It is knowing how to connect your skills, your judgment, your company’s knowledge, and the AI systems around you to solve problems faster and better. That is what graduates need to prepare for now.”

— Michael Gale, Chief Marketing Officer, EnterpriseDB (EDB) 

In the United States, it’s estimated that STEM workers account for 39.2% ($10.8 trillion) of the national GDP. More broadly, studies on high-income economies consistently show that tertiary education is one of the drivers of productivity and long-term GDP growth. This might necessitate a complete rethink of how the most educated population is equipped to thrive in this new world.

What was once a difficult transition from college into the working world could become a challenge if new graduates cannot find their way into meaningful work. Asking interns to do repetitive tasks as a learning foundation may be the exact opposite of what is needed for success. Asking them to problem-solve using knowledge bases, data, AI, and agents will be the key.

The leaders of the future may need to be AI fluent, not just AI aware, using agentic-first approaches in automating mundane tasks, and, most importantly, focusing on agile problem resolution. That means learning to work across organizational needs, not inside narrow silos.

Gale says that this is one reason why today’s most successful enterprises are increasingly sovereign by design: bringing their data and AI together, and enabling broad ecosystems of teams to learn, experiment, and put AI into mainstream production.