Skills Shift: What People Need to Learn Now That AI Is Here

Lutheran University’s Vlad Vaiman, Ph.D. offers this commentary on the skills shift and what people need to learn now that AI is here. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Conversations around artificial intelligence have largely centered on one question: Which jobs will AI replace?

Optimists envision a productivity revolution in which machines handle routine work, freeing humans to focus on more meaningful activities. Pessimists warn of widespread displacement and shrinking career opportunities. Both perspectives contain elements of truth, but neither fully captures what is actually happening inside organizations.

AI’s most significant impact is not the elimination of work but its reconfiguration. Tasks are being redistributed, roles are being redesigned, and the skills that create value are changing. As a result, the most important question for workers, educators, and employers is no longer whether AI will take jobs, but what people need to learn to remain valuable in an AI-enabled economy.

Recent evidence suggests that the labor market transformation underway is more nuanced than many headlines imply. A national survey conducted by the Strada Institute for the Future of Work found that employers are currently more likely to increase entry-level hiring because of AI than decrease it. In fact, nearly three times as many employers expect AI to increase entry-level hiring in 2026 rather than reduce it.

These findings challenge the popular narrative that AI will simply eliminate the first rung of the career ladder. Instead, many organizations are discovering that AI can create new opportunities for growth, productivity, and innovation that require additional human talent rather than less.

Understanding this trend requires distinguishing between tasks and jobs. Much of the public discussion about automation assumes that if a machine can perform a task, the corresponding job becomes unnecessary. In reality, jobs are complex combinations of activities, relationships, judgment, accountability, and institutional knowledge. While AI can automate specific tasks, it rarely replaces the broader context in which those tasks occur. Instead, what often happens is that some responsibilities are delegated to technology.

AI systems, for example, can now generate substantial amounts of functional code, dramatically reducing the time required for certain programming activities. Yet software developers are not disappearing. Their work is evolving toward higher-value activities, such as evaluating outputs, identifying errors, integrating systems, understanding user needs, and aligning technological solutions with business objectives. The nature of the role may change, but not the need for skilled professionals.

This pattern is increasingly visible across industries. The Strada survey found that more than 40% of employers believe AI has increased analytical and judgment-based responsibilities within entry-level jobs, while a similar proportion report reductions in routine administrative work. In effect, AI is automating many of the repetitive tasks that once occupied junior employees, enabling them to focus on more complex responsibilities earlier in their careers. While this trend creates opportunities, it also raises expectations. Employers increasingly expect new hires to contribute at a higher level from the beginning.

Consequently, the skills that matter most in the labor market are also shifting. Interestingly, the survey found that employers continue to place greater value on critical thinking, communication, collaboration, and workplace readiness than on AI literacy itself. In fact, AI literacy ranked lowest among the skills evaluated.

This finding reflects an important reality: While AI tools can be learned relatively quickly, the ability to think critically, communicate effectively, collaborate with others, and exercise sound judgment remains difficult to automate. As AI systems become more capable, these distinctly human capabilities become increasingly valuable. Organizations do not simply need employees who know how to operate AI tools. They need individuals who can interpret outputs, identify limitations, evaluate competing alternatives, navigate ambiguity, and make decisions in situations where no clear answer exists.

The workers who thrive in the coming decade won’t be those who resist AI or who simply use it. It will be those who combine AI capabilities with human judgment. Rather than competing against AI, successful professionals will learn how to amplify their effectiveness through AI while simultaneously deepening the uniquely human capabilities that machines struggle to replicate.

Critical thinking becomes more important because AI-generated answers must be evaluated; they cannot be accepted unilaterally. Communication becomes more important because organizations need individuals who can translate insights into action and align stakeholders around shared goals. Collaboration becomes more important because work increasingly occurs across teams that include both humans and intelligent technologies. Adaptability becomes more important because the tools themselves continue to evolve rapidly.

The implications for education are equally significant. For decades, universities have focused primarily on helping students acquire disciplinary knowledge. While subject-matter expertise remains important, employers increasingly seek evidence that graduates can apply knowledge in practical settings. Multiple reports reveal that work experience is the strongest predictor of employability for entry-level candidates. Employers consistently prefer candidates with internships, project-based learning experiences, apprenticeships, or relevant work histories over candidates with exceptional academic performance but little practical experience.

This finding suggests that experiential learning should no longer be viewed as a supplemental component of education. It is becoming central to workforce preparation. Students need opportunities to solve real-world problems, work with organizations, collaborate in teams, and gain experience using emerging technologies in practical contexts. Universities that successfully integrate internships, consulting projects, industry partnerships, and applied learning into their curricula will be better positioned to prepare graduates for an AI-enabled labor market.

Organizations face their own challenges. Historically, many entry-level positions served as developmental pathways where employees acquired foundational knowledge and professional judgment. If AI increasingly performs routine work, some traditional learning opportunities may disappear. Organizations will need to become more intentional about talent development, creating new mechanisms for employees to gain experience, develop expertise, and prepare for future leadership roles.

Ultimately, the future of work is not a contest between humans and machines. It is a process of adaptation, where the relative value of different skills is changing. Technical knowledge and AI literacy will certainly matter, but they are unlikely to be sufficient on their own. The capabilities that will distinguish successful individuals are those who have always been associated with effective leadership and professional performance: critical thinking, communication, collaboration, judgment, adaptability, and the ability to learn continuously.

The AI era does not eliminate the importance of human capability — it elevates it. As routine work becomes increasingly automated, the skills that remain uniquely human move from being desirable attributes to becoming essential sources of value. Those who learn to combine these capabilities with the power of AI will not simply adapt to the future of work; they will help shape it.